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Frontiers in antiviral therapy and immunotherapy https://doi.org/10.1002/cti2.1115 SHA: facbfdfa7189ca9ff83dc30e5d241ab22e962dbf Authors: Heaton, Steven M Date: 2020 DOI: 10.1002/cti2.1115 License: cc-by Abstract: nan Text: Globally, recent decades have witnessed a growing disjunction, a 'Valley of Death' 1,2 no less, between broadening strides in fundamental biomedical research and their incommensurate reach into the clinic. Plumbing work on research funding and development pipelines through recent changes in the structure of government funding, 2 new public and private joint ventures and specialist undergraduate and postgraduate courses now aim to incorporate pathways to translation at the earliest stages. Reflecting this shift, the number of biomedical research publications targeting 'translational' concepts has increased exponentially, up 1800% between 2003 and 2014 3 and continuing to rise rapidly up to the present day. Fuelled by the availability of new research technologies, as well as changing disease, cost and other pressing issues of our time, further growth in this exciting space will undoubtedly continue. Despite recent advances in the therapeutic control of immune function and viral infection, current therapies are often challenging to develop, expensive to deploy and readily select for resistance-conferring mutants. Shaped by the hostvirus immunological 'arms race' and tempered in the forge of deep time, the biodiversity of our world is increasingly being harnessed for new biotechnologies and therapeutics. Simultaneously, a shift towards host-oriented antiviral therapies is currently underway. In this Clinical & Translational Immunology Special Feature, I illustrate a strategic vision integrating these themes to create new, effective, economical and robust antiviral therapies and immunotherapies, with both the realities and the opportunities afforded to researchers working in our changing world squarely in mind. Opening this CTI Special Feature, I outline ways these issues may be solved by creatively leveraging the so-called 'strengths' of viruses. Viral RNA polymerisation and reverse transcription enable resistance to treatment by conferring extraordinary genetic diversity. However, these exact processes ultimately restrict viral infectivity by strongly limiting virus genome sizes and their incorporation of new information. I coin this evolutionary dilemma the 'information economy paradox'. Many viruses attempt to resolve this by manipulating multifunctional or multitasking host cell proteins (MMHPs), thereby maximising host subversion and viral infectivity at minimal informational cost. 4 I argue this exposes an 'Achilles Heel' that may be safely targeted via host-oriented therapies to impose devastating informational and fitness barriers on escape mutant selection. Furthermore, since MMHPs are often conserved targets within and between virus families, MMHP-targeting therapies may exhibit both robust and broadspectrum antiviral efficacy. Achieving this through drug repurposing will break the vicious cycle of escalating therapeutic development costs and trivial escape mutant selection, both quickly and in multiple places. I also discuss alternative posttranslational and RNA-based antiviral approaches, designer vaccines, immunotherapy and the emerging field of neo-virology. 4 I anticipate international efforts in these areas over the coming decade will enable the tapping of useful new biological functions and processes, methods for controlling infection, and the deployment of symbiotic or subclinical viruses in new therapies and biotechnologies that are so crucially needed. Upon infection, pathogens stimulate expression of numerous host inflammatory factors that support recruitment and activation of immune cells. On the flip side, this same process also causes immunopathology when prolonged or deregulated. 5 In their contribution to this Special Feature, Yoshinaga and Takeuchi review endogenous RNA-binding proteins (RBPs) that post-transcriptionally control expression of crucial inflammatory factors in various tissues and their potential therapeutic applications. 6 These RBPs include tristetraprolin and AUF1, which promote degradation of AU-rich element (ARE)-containing mRNA; members of the Roquin and Regnase families, which respectively promote or effect degradation of mRNAs harbouring stem-loop structures; and the increasingly apparent role of the RNA methylation machinery in controlling inflammatory mRNA stability. These activities take place in various subcellular compartments and are differentially regulated during infection. In this way, mRNA-destabilising RBPs constitute a 'brake' on the immune system, which may ultimately be toggled therapeutically. I anticipate continued efforts in this area will lead to new methods of regaining control over inflammation in autoimmunity, selectively enhancing immunity in immunotherapy, and modulating RNA synthesis and virus replication during infection. Another mRNA under post-transcriptional regulation by Regnase-1 and Roquin is Furin, which encodes a conserved proprotein convertase crucial in human health and disease. Furin, along with other PCSK family members, is widely implicated in immune regulation, cancer and the entry, maturation or release of a broad array of evolutionarily diverse viruses including human papillomavirus (HPV), influenza (IAV), Ebola (EboV), dengue (DenV) and human immunodeficiency virus (HIV). Here, Braun and Sauter review the roles of furin in these processes, as well as the history and future of furin-targeting therapeutics. 7 They also discuss their recent work revealing how two IFN-cinducible factors exhibit broad-spectrum inhibition of IAV, measles (MV), zika (ZikV) and HIV by suppressing furin activity. 8 Over the coming decade, I expect to see an ever-finer spatiotemporal resolution of host-oriented therapies to achieve safe, effective and broad-spectrum yet costeffective therapies for clinical use. The increasing abundance of affordable, sensitive, high-throughput genome sequencing technologies has led to a recent boom in metagenomics and the cataloguing of the microbiome of our world. The MinION nanopore sequencer is one of the latest innovations in this space, enabling direct sequencing in a miniature form factor with only minimal sample preparation and a consumer-grade laptop computer. Nakagawa and colleagues here report on their latest experiments using this system, further improving its performance for use in resource-poor contexts for meningitis diagnoses. 9 While direct sequencing of viral genomic RNA is challenging, this system was recently used to directly sequence an RNA virus genome (IAV) for the first time. 10 I anticipate further improvements in the performance of such devices over the coming decade will transform virus surveillance efforts, the importance of which was underscored by the recent EboV and novel coronavirus (nCoV / COVID-19) outbreaks, enabling rapid deployment of antiviral treatments that take resistance-conferring mutations into account. Decades of basic immunology research have provided a near-complete picture of the main armaments in the human antiviral arsenal. Nevertheless, this focus on mammalian defences and pathologies has sidelined examination of the types and roles of viruses and antiviral defences that exist throughout our biosphere. One case in point is the CRISPR/Cas antiviral immune system of prokaryotes, which is now repurposed as a revolutionary gene-editing biotechnology in plants and animals. 11 Another is the ancient lineage of nucleocytosolic large DNA viruses (NCLDVs), which are emerging human pathogens that possess enormous genomes of up to several megabases in size encoding hundreds of proteins with unique and unknown functions. 12 Moreover, hundreds of human-and avian-infective viruses such as IAV strain H5N1 are known, but recent efforts indicate the true number may be in the millions and many harbour zoonotic potential. 13 It is increasingly clear that host-virus interactions have generated truly vast yet poorly understood and untapped biodiversity. Closing this Special Feature, Watanabe and Kawaoka elaborate on neo-virology, an emerging field engaged in cataloguing and characterising this biodiversity through a global consortium. 14 I predict these efforts will unlock a vast wealth of currently unexplored biodiversity, leading to biotechnologies and treatments that leverage the host-virus interactions developed throughout evolution. When biomedical innovations fall into the 'Valley of Death', patients who are therefore not reached all too often fall with them. Being entrusted with the resources and expectation to conceive, deliver and communicate dividends to society is both cherished and eagerly pursued at every stage of our careers. Nevertheless, the road to research translation is winding and is built on a foundation of basic research. Supporting industry-academia collaboration and nurturing talent and skills in the Indo-Pacific region are two of the four pillars of the National Innovation and Science Agenda. 2 These frame Australia's Medical Research and Innovation Priorities, which include antimicrobial resistance, global health and health security, drug repurposing and translational research infrastructure, 15 capturing many of the key elements of this CTI Special Feature. Establishing durable international relationships that integrate diverse expertise is essential to delivering these outcomes. To this end, NHMRC has recently taken steps under the International Engagement Strategy 16 to increase cooperation with its counterparts overseas. These include the Japan Agency for Medical Research and Development (AMED), tasked with translating the biomedical research output of that country. Given the reciprocal efforts at accelerating bilateral engagement currently underway, 17 the prospects for new areas of international cooperation and mobility have never been more exciting nor urgent. With the above in mind, all contributions to this CTI Special Feature I have selected from research presented by fellow invitees to the 2018 Awaji International Forum on Infection and Immunity (AIFII) and 2017 Consortium of Biological Sciences (ConBio) conferences in Japan. Both Australia and Japan have strong traditions in immunology and related disciplines, and I predict that the quantity, quality and importance of our bilateral cooperation will accelerate rapidly over the short to medium term. By expanding and cooperatively leveraging our respective research strengths, our efforts may yet solve the many pressing disease, cost and other sustainability issues of our time.

What has CRISPR/Cas antiviral immune system of prokaryotes been repurposed as?

Respiratory Viral Infections in Exacerbation of Chronic Airway Inflammatory Diseases: Novel Mechanisms and Insights From the Upper Airway Epithelium https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7052386/ SHA: 45a566c71056ba4faab425b4f7e9edee6320e4a4 Authors: Tan, Kai Sen; Lim, Rachel Liyu; Liu, Jing; Ong, Hsiao Hui; Tan, Vivian Jiayi; Lim, Hui Fang; Chung, Kian Fan; Adcock, Ian M.; Chow, Vincent T.; Wang, De Yun Date: 2020-02-25 DOI: 10.3389/fcell.2020.00099 License: cc-by Abstract: Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations (GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018) . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway (such as chronic rhinosinusitis, CRS) and lower airway (such as asthma and chronic obstructive pulmonary disease, COPD) which greatly affect the patients' quality of life (Calus et al., 2012; Bao et al., 2015) . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness (Xepapadaki and Papadopoulos, 2010) . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease (Hashimoto et al., 2008; Viniol and Vogelmeier, 2018) . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway (Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018) . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively (Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019) . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations (Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019) . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases (Rowan et al., 2015; Tan et al., 2017) . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection (Kutter et al., 2018) . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity (Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017) . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the "United Airway" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s (Pattemore et al., 1992) ; with bacterial infections previously considered as the likely culprit for acute exacerbation (Stevens, 1953; Message and Johnston, 2002) . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s (Message and Johnston, 2002) . Rhinovirus (RV) and respiratory syncytial virus (RSV) are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases (Jartti and Gern, 2017) . Other viruses such as parainfluenza virus (PIV), influenza virus (IFV) and adenovirus (AdV) have also been implicated in acute exacerbations but to a much lesser extent (Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019) . More recently, other viruses including bocavirus (BoV), human metapneumovirus (HMPV), certain coronavirus (CoV) strains, a specific enterovirus (EV) strain EV-D68, human cytomegalovirus (hCMV) and herpes simplex virus (HSV) have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway (Mallia and Johnston, 2006; Britto et al., 2017) . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche (Busse et al., 2010) . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection (Vareille et al., 2011; Braciale et al., 2012) . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms (Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019) . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole (Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019) . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I (IFNα/β) and type III (IFNλ) interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α (MIP-1α) and monocyte chemotactic protein 1 (MCP-1) (Wark and Gibson, 2006; Matsukura et al., 2013) . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells (APCs) that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon (IFNγ), IL-2, IL-4, IL-5, IL-9, and IL-12 (Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012) . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells (Wark and Gibson, 2006; Braciale et al., 2012) . The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp (CRSwNP), viral infections such as RV and RSV promote a Type 2-biased immune response (Becker, 2006; Jackson et al., 2014; Jurak et al., 2018) . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP (Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015) . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp (CRSsNP) are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 (Cukic et al., 2012; Brightling and Greening, 2019) . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases (MMPs) released from infiltrating neutrophils (Linden et al., 2019) . Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases (Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019) . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin (TSLP), and their interaction with group 2 innate lymphoid cells (ILC2) has also recently been identified (Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019) . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier (Gabryelska et al., 2019; Roan et al., 2019) . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated (Scanlon and McKenzie, 2012; Li and Hendriks, 2013) . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway (Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019) . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation (Camelo et al., 2017) . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation (Silver et al., 2016) . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals (Yan et al., 2016; Tan et al., 2018a) ; despite augmenting a type 2 exacerbation in chronically inflamed airways (Jurak et al., 2018) . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | www.frontiersin.org mechanisms have been implicated in acute exacerbations during and after viral infection (Murray et al., 2006) . Murray et al. (2006) has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance (Kim et al., 2008; Stolz et al., 2019) in particular the late onset of a bacterial infection (Singanayagam et al., 2018 (Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the "united airway" hypothesis (Kurai et al., 2013) . On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease (Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019) . Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance (Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b) . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells (Becker, 2006; McKendry et al., 2016) . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV (Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019) . Additionally, there are also evidence of reduced type I (IFNβ) and III (IFNλ) interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium (Contoli et al., 2006; Hwang et al., 2019; Wark, 2019) . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation (Wood et al., 2011; Essaidi-Laziosi et al., 2018) . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer (Imperiale and Jiang, 2015) , further contributing to chronic activation of inflammation when they infect the airway (Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017) . With that note, human papilloma virus (HPV), a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies (de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015) . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection (Chi et al., 2011; Ford et al., 2013; Papi et al., 2013) . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance (Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b) . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV (Yan et al., 2016; Guibas et al., 2018) and certain CoV (including the recently emerged COVID-19 virus) (Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020) , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection (Yan et al., 2016; Tan et al., 2019) . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium (Ampomah et al., 2018; Tan et al., 2019) . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway (Guibas et al., 2018) . Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections (Vasileiou et al., 2017; Zheng et al., 2018) ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M (OSM) which causes tight junction opening (Pothoven et al., 2015; Tian et al., 2018) . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases (Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018) . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 (ANGPTL4) and bactericidal/permeabilityincreasing fold-containing family member A1 (BPIFA1) are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018) . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment (Diver et al., 2019) . In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present (Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019) . In addition, viral infection may disrupt biofilm colonies in the upper airway (e.g., Streptococcus pneumoniae) microbiome to be release into the lower airway and worsening the inflammation (Marks et al., 2013; Chao et al., 2014) . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth (Siegel et al., 2014; Mallia et al., 2018) . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms (Singanayagam et al., 2018 (Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019) . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles (Teo et al., 2018) . These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases (Wark et al., 2013; Singanayagam et al., 2018) . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome (Bakken et al., 2011) . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism (with literature evidence) Oxidative stress ROS production (RV, RSV, IFV, HSV) As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation (see text for abbreviations). that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection (Griggs et al., 2017) . Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown (Peng et al., , 2019 Qiu et al., 2018) . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 (DNAH5) and multiciliate differentiation And DNA synthesis associated cell cycle protein (MCIDAS) (Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model (Zhu et al., 2020) . Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells (Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a) . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation (Zhu et al., 2009) . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs (miRNAs) are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases (Tan et al., 2014) . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation (Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018) . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases (Wardzynska et al., 2020) . Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids (Kim et al., 2017) . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections (Hsu et al., 2016 (Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations (Moheimani et al., 2018) . Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections (Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018) . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes (Cardenas et al., 2019; Gomez, 2019) . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations (McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019) . In addition, Spalluto et al. (2017) also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases (Spalluto et al., 2017) . Finally, viral infection can result in enhanced production of reactive oxygen species (ROS), oxidative stress and mitochondrial dysfunction in the airway epithelium (Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018) . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway (Barnes, 2017; van der Vliet et al., 2018) . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018) . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway (Tiwari et al., 2002) . A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing (RNA-seq) or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway (Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a) . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation (Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019) . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition (Ito et al., 2019; Li and Di Santo, 2019) . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment.

What do the acute exacerbations cause?

Respiratory Viral Infections in Exacerbation of Chronic Airway Inflammatory Diseases: Novel Mechanisms and Insights From the Upper Airway Epithelium https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7052386/ SHA: 45a566c71056ba4faab425b4f7e9edee6320e4a4 Authors: Tan, Kai Sen; Lim, Rachel Liyu; Liu, Jing; Ong, Hsiao Hui; Tan, Vivian Jiayi; Lim, Hui Fang; Chung, Kian Fan; Adcock, Ian M.; Chow, Vincent T.; Wang, De Yun Date: 2020-02-25 DOI: 10.3389/fcell.2020.00099 License: cc-by Abstract: Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations (GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018) . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway (such as chronic rhinosinusitis, CRS) and lower airway (such as asthma and chronic obstructive pulmonary disease, COPD) which greatly affect the patients' quality of life (Calus et al., 2012; Bao et al., 2015) . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness (Xepapadaki and Papadopoulos, 2010) . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease (Hashimoto et al., 2008; Viniol and Vogelmeier, 2018) . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway (Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018) . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively (Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019) . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations (Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019) . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases (Rowan et al., 2015; Tan et al., 2017) . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection (Kutter et al., 2018) . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity (Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017) . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the "United Airway" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s (Pattemore et al., 1992) ; with bacterial infections previously considered as the likely culprit for acute exacerbation (Stevens, 1953; Message and Johnston, 2002) . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s (Message and Johnston, 2002) . Rhinovirus (RV) and respiratory syncytial virus (RSV) are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases (Jartti and Gern, 2017) . Other viruses such as parainfluenza virus (PIV), influenza virus (IFV) and adenovirus (AdV) have also been implicated in acute exacerbations but to a much lesser extent (Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019) . More recently, other viruses including bocavirus (BoV), human metapneumovirus (HMPV), certain coronavirus (CoV) strains, a specific enterovirus (EV) strain EV-D68, human cytomegalovirus (hCMV) and herpes simplex virus (HSV) have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway (Mallia and Johnston, 2006; Britto et al., 2017) . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche (Busse et al., 2010) . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection (Vareille et al., 2011; Braciale et al., 2012) . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms (Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019) . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole (Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019) . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I (IFNα/β) and type III (IFNλ) interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α (MIP-1α) and monocyte chemotactic protein 1 (MCP-1) (Wark and Gibson, 2006; Matsukura et al., 2013) . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells (APCs) that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon (IFNγ), IL-2, IL-4, IL-5, IL-9, and IL-12 (Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012) . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells (Wark and Gibson, 2006; Braciale et al., 2012) . The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp (CRSwNP), viral infections such as RV and RSV promote a Type 2-biased immune response (Becker, 2006; Jackson et al., 2014; Jurak et al., 2018) . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP (Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015) . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp (CRSsNP) are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 (Cukic et al., 2012; Brightling and Greening, 2019) . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases (MMPs) released from infiltrating neutrophils (Linden et al., 2019) . Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases (Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019) . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin (TSLP), and their interaction with group 2 innate lymphoid cells (ILC2) has also recently been identified (Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019) . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier (Gabryelska et al., 2019; Roan et al., 2019) . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated (Scanlon and McKenzie, 2012; Li and Hendriks, 2013) . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway (Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019) . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation (Camelo et al., 2017) . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation (Silver et al., 2016) . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals (Yan et al., 2016; Tan et al., 2018a) ; despite augmenting a type 2 exacerbation in chronically inflamed airways (Jurak et al., 2018) . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | www.frontiersin.org mechanisms have been implicated in acute exacerbations during and after viral infection (Murray et al., 2006) . Murray et al. (2006) has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance (Kim et al., 2008; Stolz et al., 2019) in particular the late onset of a bacterial infection (Singanayagam et al., 2018 (Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the "united airway" hypothesis (Kurai et al., 2013) . On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease (Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019) . Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance (Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b) . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells (Becker, 2006; McKendry et al., 2016) . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV (Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019) . Additionally, there are also evidence of reduced type I (IFNβ) and III (IFNλ) interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium (Contoli et al., 2006; Hwang et al., 2019; Wark, 2019) . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation (Wood et al., 2011; Essaidi-Laziosi et al., 2018) . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer (Imperiale and Jiang, 2015) , further contributing to chronic activation of inflammation when they infect the airway (Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017) . With that note, human papilloma virus (HPV), a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies (de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015) . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection (Chi et al., 2011; Ford et al., 2013; Papi et al., 2013) . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance (Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b) . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV (Yan et al., 2016; Guibas et al., 2018) and certain CoV (including the recently emerged COVID-19 virus) (Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020) , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection (Yan et al., 2016; Tan et al., 2019) . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium (Ampomah et al., 2018; Tan et al., 2019) . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway (Guibas et al., 2018) . Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections (Vasileiou et al., 2017; Zheng et al., 2018) ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M (OSM) which causes tight junction opening (Pothoven et al., 2015; Tian et al., 2018) . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases (Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018) . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 (ANGPTL4) and bactericidal/permeabilityincreasing fold-containing family member A1 (BPIFA1) are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018) . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment (Diver et al., 2019) . In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present (Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019) . In addition, viral infection may disrupt biofilm colonies in the upper airway (e.g., Streptococcus pneumoniae) microbiome to be release into the lower airway and worsening the inflammation (Marks et al., 2013; Chao et al., 2014) . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth (Siegel et al., 2014; Mallia et al., 2018) . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms (Singanayagam et al., 2018 (Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019) . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles (Teo et al., 2018) . These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases (Wark et al., 2013; Singanayagam et al., 2018) . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome (Bakken et al., 2011) . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism (with literature evidence) Oxidative stress ROS production (RV, RSV, IFV, HSV) As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation (see text for abbreviations). that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection (Griggs et al., 2017) . Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown (Peng et al., , 2019 Qiu et al., 2018) . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 (DNAH5) and multiciliate differentiation And DNA synthesis associated cell cycle protein (MCIDAS) (Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model (Zhu et al., 2020) . Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells (Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a) . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation (Zhu et al., 2009) . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs (miRNAs) are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases (Tan et al., 2014) . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation (Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018) . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases (Wardzynska et al., 2020) . Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids (Kim et al., 2017) . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections (Hsu et al., 2016 (Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations (Moheimani et al., 2018) . Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections (Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018) . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes (Cardenas et al., 2019; Gomez, 2019) . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations (McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019) . In addition, Spalluto et al. (2017) also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases (Spalluto et al., 2017) . Finally, viral infection can result in enhanced production of reactive oxygen species (ROS), oxidative stress and mitochondrial dysfunction in the airway epithelium (Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018) . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway (Barnes, 2017; van der Vliet et al., 2018) . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018) . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway (Tiwari et al., 2002) . A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing (RNA-seq) or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway (Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a) . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation (Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019) . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition (Ito et al., 2019; Li and Di Santo, 2019) . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment.

What are IFV infection shown to do?

Evidence for the Convergence Model: The Emergence of Highly Pathogenic Avian Influenza (H5N1) in Viet Nam https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4580613/ SHA: ee5b43d20a640664510cb7a540caaae4a8e19933 Authors: Saksena, Sumeet; Fox, Jefferson; Epprecht, Michael; Tran, Chinh C.; Nong, Duong H.; Spencer, James H.; Nguyen, Lam; Finucane, Melissa L.; Tran, Vien D.; Wilcox, Bruce A. Date: 2015-09-23 DOI: 10.1371/journal.pone.0138138 License: cc-by Abstract: Building on a series of ground breaking reviews that first defined and drew attention to emerging infectious diseases (EID), the ‘convergence model’ was proposed to explain the multifactorial causality of disease emergence. The model broadly hypothesizes disease emergence is driven by the co-incidence of genetic, physical environmental, ecological, and social factors. We developed and tested a model of the emergence of highly pathogenic avian influenza (HPAI) H5N1 based on suspected convergence factors that are mainly associated with land-use change. Building on previous geospatial statistical studies that identified natural and human risk factors associated with urbanization, we added new factors to test whether causal mechanisms and pathogenic landscapes could be more specifically identified. Our findings suggest that urbanization spatially combines risk factors to produce particular types of peri-urban landscapes with significantly higher HPAI H5N1 emergence risk. The work highlights that peri-urban areas of Viet Nam have higher levels of chicken densities, duck and geese flock size diversities, and fraction of land under rice or aquaculture than rural and urban areas. We also found that land-use diversity, a surrogate measure for potential mixing of host populations and other factors that likely influence viral transmission, significantly improves the model’s predictability. Similarly, landscapes where intensive and extensive forms of poultry production overlap were found at greater risk. These results support the convergence hypothesis in general and demonstrate the potential to improve EID prevention and control by combing geospatial monitoring of these factors along with pathogen surveillance programs. Text: Two decades after the Institute of Medicine's seminal report [1] recognized novel and reemerging diseases as a new category of microbial threats, the perpetual and unexpected nature of the emergence of infectious diseases remains a challenge in spite of significant clinical and biomedical research advances [2] . Highly Pathogenic Avian Influenza (HPAI) (subtype H5N1) is the most significant newly emerging pandemic disease since HIV/AIDS. Its eruption in Southeast Asia in 2003-4 and subsequent spread globally to more than 60 countries fits the complex systems definition of "surprise" [3] . In this same year that IOM had published its final report on microbial threats which highlighted H5N1's successful containment in Hong Kong in 1997 [4] , massive outbreaks occurred in Southeast Asia where it remains endemic, along with Egypt's Nile Delta. Since 2003, HPAI H5N1 has killed millions of poultry in countries throughout Asia, Europe, and Africa, and 402 humans have died from it in sixteen countries according to WHO data as of January 2015. The threat of a pandemic resulting in millions of human cases worldwide remains a possibility [5] . Lederberg et al. [1] first pointed to the multiplicity of factors driving disease emergence, which later were elaborated and described in terms of 'the convergence model' [6] . The model proposes emergence events are precipitated by the intensifying of biological, environmental, ecological, and socioeconomic drivers. Microbial "adaptation and change," along with "changing ecosystems" and "economic development and land use" form major themes. Joshua Lederberg, the major intellectual force behind the studies summed-up saying "Ecological instabilities arise from the ways we alter the physical and biological environment, the microbial and animal tenants (humans included) of these environments, and our interactions (including hygienic and therapeutic interventions) with the parasites" [6] . Combining such disparate factors and associated concepts from biomedicine, ecology, and social sciences in a single framework remains elusive. One approach suggested has been to employ social-ecological systems theory that attempts to capture the behavior of so-called 'coupled natural-human systems', including the inevitable unexpected appearance of new diseases, themselves one of the "emerging properties" of complex adaptive systems (CAS) [7, 8] . The convergence model can be so adapted by incorporating the dynamics of urban, agricultural, and natural ecosystem transformations proposed with this framework. These associated multifaceted interactions including feedbacks that affect ecological communities, hosts and pathogen populations, are the proximate drivers of disease emergence. The initial HPAI H5N1 outbreaks in Vietnam represent an ideal opportunity to adapt and test a CAS-convergence model. Emergence risk should be highest in the most rapidly transforming urban areas, peri-urban zones where mixes of urban-rural, modern-traditional land uses and poultry husbandry coincide most intensely. Specifically we hypothesized a positive association between the presence of HPAI outbreaks in poultry at the commune level and: 1) peri-urban areas, as defined by Saksena et al. [9] , 2) land-use diversity, and 3) co-location of intensive and extensive systems of poultry. We used the presence or absence at the commune level of HPAI H5N1 outbreaks in poultry as the dependent variable. Vietnam experienced its first HPAI H5N1 outbreak in late 2003, since then, there have been five waves and sporadic outbreaks recorded over the years [10, 11] . We chose to study the first wave (Wave 1) that ended in February 2004 and the second wave (Wave 2) that occurred between December 2004 and April 2005. We used data from the Viet Nam 2006 Agricultural Census to develop an urbanicity classification that used data collected at a single point in time (2006) but across space (10,820 communes) to infer processes of change (urbanization, land-use diversification, and poultry intensification) [9] . The 58 provinces in Vietnam (not counting the 5 urban provinces that are governed centrally) are divided into rural districts, provincial towns, and provincial cities. Rural districts are further divided into communes (rural areas) and towns, and provincial towns and cities are divided into wards (urban subdistricts) and communes. A commune in Viet Nam is thus the third level administrative subdivision, consisting of villages/hamlets. For the purpose of simplicity we will henceforth use the term "commune" to refer to the smallest administrative unit whether it is a commune, town, or ward. We included risk factors documented in previous work. We also aimed to understand the differences, if any, in risk dynamics at different scales; comparing risks at the national scale to those at two sub-national agro-ecological zones. For this purpose we chose to study the Red River and Mekong River deltas, well known hot spots of the disease. Hence we conducted two sets of analyses (waves 1 and 2) for three places (nation, Red River Delta, and Mekong Delta) producing a total of 6 wave-place analyses. Data on outbreaks were obtained from the publicly available database of Viet Nam's Department of Animal Health. Given the highly complex dynamics of the epidemics and in keeping with recent methodological trends, we used multiple modeling approaches-parametric and non-parametric-with a focus on spatial analysis. We used both 'place' oriented models that can take into account variations in factors such as policies and administration as well as 'space' oriented models that recognize the importance of physical proximity in natural phenomenon [12] . Very few empirical studies have attempted to determine whether urbanization is related to EID outbreaks or whether urbanization is associated primarily with other factors related to EID outbreaks. One immediate problem researchers face is defining what is rural, urban, and transitional (i.e., peri-urban). Some studies have used official administrative definitions of urban and rural areas, but this approach is limited in its bluntness [13] . Other studies prioritized human population density as a satisfactory surrogate [11, [14] [15] [16] [17] [18] [19] [20] , but this approach ignores the important fact that density is not a risk factor if it is accompanied by sufficient infrastructure to handle the population. Spencer [21] examined urbanization as a non-linear characteristic, using household-level variables such as water and sanitation services. He found evidence that increased diversity in water supply sources and sanitation infrastructure were associated with higher incidences of HPAI. These studies employed a limited definition of urbanization that lacked a well-defined characterization of peri-urbanization. Still other studies have mapped the relative urban nature of a place, a broad concept that is often referred to as 'urbanicity' [22] [23] [24] [25] . While these studies show differences in the rural/ urban nature of communities across space and time, they have been limited to small-to medium-scale observational studies; and they have failed to distinguish between different levels of "ruralness". Perhaps the best known model of peri-urbanization is McGee's concept of desakota (Indonesian for "village-town") [26] . McGee identified six characteristics of desakota regions: 1) a large population of smallholder cultivators; 2) an increase in non-agricultural activities; 3) extreme fluidity and mobility of population; 4) a mixture of land uses, agriculture, cottage industries, suburban development; 5) increased participation of the female labor force; and 6) "grey-zones", where informal and illegal activities group [26] . Saksena et al. [9] built on McGee's desakota concepts and data from the 2006 Viet Nam Agricultural Census to establish an urbanicity classification. That study identified and mapped the 10,820 communes, the smallest administrative unit for which data are collected, as being rural, peri-urban, urban, or urban core. This project used the Saksena classification to assess associations between urbanicity classes, other risks factors, and HPAI outbreaks. Researchers have estimated that almost 75% of zoonotic diseases are associated with landcover and land-use changes (LCLUC) [27, 28] . LCLUC such as peri-urbanization and agricultural diversification frequently result in more diverse and fragmented landscapes (number of land covers or land uses per unit of land). The importance of landscape pattern, including diversity and associated processes, which equate to host species' habitat size and distribution, and thus pathogen transmission dynamics is axiomatic though the specific mechanisms depend on the disease [29, 30] . Landscape fragmentation produces ecotones, defined as abrupt edges or transitions zones between different ecological systems, thought to facilitate disease emergence by increasing the intensity and frequency of contact between host species [31] Furthermore, fragmentation of natural habitat tends to interrupt and degrade natural processes, including interspecies interactions that regulate densities of otherwise opportunistic species that may serve as competent hosts [32] , although it is not clear if reduced species diversity necessarily increases pathogen transmission [33] . Rarely has research connected land-use diversification to final health endpoints in humans or livestock; this study attempts to link land-use diversity with HPAI H5N1 outbreaks. Human populations in the rapidly urbanizing cities of the developing world require access to vegetables, fruits, meat, etc. typically produced elsewhere. As theorized by von Thünen in 1826 [34] , much of this demand is met by farms near cities [35] , many in areas undergoing processes of peri-urbanization [26] . Due to the globalization of poultry trade, large-scale chicken farms raising thousands of birds have expanded rapidly in Southeast Asia and compete with existing small backyard farmers [36] . Large, enterprise-scale (15,000-100,000 birds) operations are still rare in Viet Nam (only 33 communes have such a facility). On the other hand, domestic and multinational companies frequently contract farmers to raise between 2,000 and 15,000 birds. Recent studies have examined the relative role of extensive (backyard) systems and intensive systems [15, [17] [18] [19] 37] . In much of Asia there is often a mix of commercial and backyard farming at any one location [36] . Experts have suggested that from a biosecurity perspective the co-location of extensive and intensive systems is a potential risk factor [38] . Intensive systems allow for virus evolution (e.g. Low Pathogenic Avian Influenza to HPAI) and transformation, while extensive systems allow for environmental persistence and circulation [39] . Previous studies of chicken populations as a risk factor have distinguished between production systems-native chickens, backyard chickens; flock density; commercial chickens, broilers and layers density, etc. [15, [17] [18] [19] 37] . In isolation, however, none of these number and/or density based poultry metrics adequately measures the extent of co-location of intensive and extensive systems in any given place. Intensive and extensive systems in Viet Nam have their own fairly well defined flock sizes. A diversity index of the relative number of intensive and extensive systems of poultry-raising can better estimate the effect of such co-location; this study attempts to link a livestock diversity index with the presence or absence of HPAI H5N1 outbreaks at the commune level. This study investigated for the 10,820 communes of Viet Nam a wide suite of socio-economic, agricultural, climatic and ecological variables relevant to poultry management and the transmission and persistence of the HPAI virus. Many of these variables were identified based on earlier studies of HPAI (as reviewed in Gilbert and Pfeiffer [40] ). Three novel variables were included based on hypotheses generated by this project. All variables were measured or aggregated to the commune level. The novel variables were: • Degree of urbanization: We used the urbanicity classification developed by Saksena et al. [9] to define the urban character of each commune. The classification framework is based on four characteristics: 1) percentage of households whose main income is from agriculture, aquaculture and forestry, 2) percentage of households with modern forms of toilets, 3) percentage of land under agriculture, aquaculture and forestry and 4) the Normalized Differentiated Vegetation Index (NDVI). The three-way classification enabled testing for non-linear and non-monotonous responses. • Land-use diversity: We measured land-use diversity using the Gini-Simpson Diversity Index [41] . The Gini-Simpson Diversity Index is given by 1-λ, where λ equals the probability that two entities taken at random from the dataset of interest represent the same type. In situations with only one class (complete homogeneity) the Gini-Simpson index would have a value equal to zero. Such diversity indices have been used to measure land-use diversity [42] . We used the following five land-use classes: annual crops, perennial crops, forests, aquaculture and built-up land (including miscellaneous uses) for which data were collected in the 2006 Agricultural Census. The area under the last class was calculated as the difference between the total area and the sum of the first four classes. The following variables are listed according to their role in disease introduction, transmission and persistence, though some of these factors may have multiple roles. • Human population related transmission. Human population density [11, 14-16, 18, 19, 44, 45] . • Poultry trade and market. Towns and cities were assumed to be active trading places [10, 18, 37, 44, 46] . So, the distance to the nearest town/city was used as indicator of poultry trade. Trade is facilitated by access to transportation infrastructure [37, 47, 48] . So, the distance to the nearest a) national highway and b) provincial highway was used as indicator of transportation infrastructure. • Disease introduction and amplification. The densities of chicken were calculated based on commune area [15, 19, 37, 49] . • Intermediate hosts. Duck and geese densities were calculated using total commune area [11, 19, 49] . As previous studies have shown a link between scavenging in rice fields by ducks and outbreaks, we also calculated duck density using only the area under rice. • Agro-ecological and environmental risk factors. Previous studies have shown that the extent of rice cultivation is a risk factor, mainly due its association with free ranging ducks acting as scavengers [10] . We used percentage of land under rice cultivation as a measure of extent. Rice cropping intensity is also a known risk factor [11, 17, 37] . We used the mean number of rice crops per year as a measure of intensity. The extent of aquaculture is a known risk factor [10] , possibly because water bodies offer routes for transmission and persistence of the virus. The percentage of land under aquaculture was used as a metric. Proximity to water bodies increases the risk of outbreaks [47, [50] [51] [52] , possibly by increasing the chance of contact between wild water birds and domestic poultry. We measured the distance between the commune and the nearest: a) lake and b) river. Climatic variables-annual mean temperature and annual precipitation-have been associated with significant changes in risk [48, 53] . Elevation, which is associated with types of land cover and agriculture, has been shown to be a significant risk factor in Vietnam [10] . Compound Topographical Index (CTI, also known as Topographical Wetness Index) is a measure of the tendency for water to pool. Studies in Thailand and elsewhere [54] have shown that the extent of surface water is a strong risk factor, possibly due to the role of water in long-range transmission and persistence of the virus. In the absence of reliable and inexpensive data on the extent of surface water we used CTI as a proxy. CTI has been used in Ecological Niche Models (ENM) of HPAI H5N1 [55, 56] . However, given the nature of ENM studies, the effect of CTI as a risk factor has been unknown so far. CTI has been used as a risk factor in the study of other infectious and non-infectious diseases [57] . Some studies have shown that at local scales, the slope of the terrain (a component of CTI) was significantly correlated with reservoir species dominance [58] . CTI is a function of both the slope and the upstream contributing area per unit width orthogonal to the flow direction. CTI is computed as follows: CTI = ln (A s / (tan (β)) where; A s = Area Value calculated as ((flow accumulation + 1) Ã (pixel area in m 2 )) and β is the slope expressed in radians [59] . Though previous studies have indicated that Normalized Difference Vegetation Index (NDVI) is a risk factor [10, 20, 55, 60, 61], we did not include it explicitly in our models, as the urban classification index we used included NDVI [9] . We obtained commune level data on HPAI H5N1 outbreaks from the publicly available database of the Department of Animal Health [10] . Viet Nam experienced its first major epidemic waves between December 2003 and February 2006 [10] . We chose to study the first wave (Wave 1) that ended in February 2004 and the second wave (Wave 2) that occurred between December 2004 and April 2005. In Wave 1, 21% of the communes and in Wave 2, 6% of the communes experienced outbreaks. We used data from the 1999 Population Census of Viet Nam to estimate human population per commune. We relied on data from two Agriculture Censuses of Viet Nam. This survey is conducted every five years covering all rural households and those peri-urban households that own farms. Thus about three-fourths of all of the country's households are included. The contents of the survey include number of households in major production activities, population, labor classified by sex, age, qualification, employment and major income source; agriculture, forestry and aquaculture land used by households classified by source, type, cultivation area for by crop type; and farming equipment by purpose. Commune level surveys include information on rural infrastructure, namely electricity, transportation, medical stations, schools; fresh water source, communication, markets, etc. Detailed economic data are collected for large farms. We used the 2006 Agriculture Census for most variables because the first three epidemic waves occurred between the Agricultural Censuses of 2001 and 2006 but were closer in time to the 2006 census [10] . However, for data on poultry numbers we used the 2001 Agriculture Census data set because between 1991 and 2003 the poultry population grew at an average rate of 7% annually. However, in 2004, after the first wave of the H5N1 epidemic, the poultry population fell 15%. Only by mid-2008 did the poultry population return close to pre-epidemic levels. Thus, we considered the poultry population data from the 2001 census to be more representative. We aggregated census household data to the commune level. A three-way classification of the rural-to-urban transition was based on a related study [9] . Raster data on annual mean temperature and precipitation were obtained from the World-Clim database and converted to commune level data. The bioclimatic variables were compiled from the monthly temperature and precipitation values and interpolated to surfaces at 90m spatial resolution [62] . This public database provides data on the average climatic conditions of the period 1950-2000. Elevation was generated from SRTM 90 meter Digital Elevation Models (DEM) acquired from the Consortium for Spatial Information (CGIAR-CSI). Compound Topographical Index (CTI) data were generated using the Geomorphometry and Gradient Metrics Toolbox for Arc-GIS 10.1. Prior to risk factor analysis we cleaned the data by identifying illogical values for all variables and then either assigning a missing value to them or adjusting the values. Illogical values occurred mainly (less than 1% of the cases) for land-related variables such as percentage of commune land under a particular type of land use. Next we tested each variable for normality using the BestFit software (Palisade Corporation). Most of the variables were found to follow a log-normal distribution and a log-transform was used on them. We then examined the bi-variate correlations between all the risk factors (or their log-transform, as the case may be). Correlations were analyzed separately for each place. Certain risk factors were then eliminated from consideration when |r| ! 0.5 (r is the Pearson correlation coefficient). When two risk factors were highly correlated, we chose to include the one which had not been adequately studied explicitly in previously published risk models. Notably, we excluded a) elevation (correlated with human population density, chicken density, duck density, percentage land under paddy, annual temperature and compound topographical index), b) human population density (correlated with elevation and CTI), c) chicken density (only at national level, correlated with CTI), d) duck and goose density (correlated with elevation, chicken density, percentage land under paddy, land use diversity index and CTI), e) annual temperature (correlated with elevation and CTI) and f) cropping intensity (correlated with percentage land under paddy). Considering the importance of spatial autocorrelation in such epidemics, we used two modeling approaches: 1) multi-level Generalized Linear Mixed Model (GLMM) and 2) Boosted Regression trees (BRT) [63, 64] with an autoregressive term [65] . GLMM is a 'place' oriented approach that is well suited to analyzing the effect of administrative groupings, while BRT is a 'space' oriented approach that accounts for the effects of physical proximity. We began by deriving an autoregressive term by averaging the presence/absence among a set of neighbors defined by the limit of autocorrelation, weighted by the inverse of the Euclidean distance [65] . The limit of the autocorrelation of the response variable was obtained from the range of the spatial correlogram ρ (h) [66] . To determine which predictor variables to include in the two models, we conducted logistic regression modeling separately for each of them one by one but included the autoregressive term each time. We finally included only those variables whose coefficient had a significance value p 0.2 (in at least one wave-place combination) and we noted the sign of the coefficient. This choice of p value for screening risk factors is common in similar studies [15, 18, 45, 67] . We used a two-level GLMM (communes nested under districts) to take account of random effects for an area influenced by its neighbors, and thus, we studied the effect of spatial autocorrelation. We used robust standard errors for tests of fixed effects. Boosted regression trees, also known as stochastic gradient boosting, was performed to predict the probability of HPAI H5N1 occurrence and determine the relative influence of each risk factor to the HPAI H5N1 occurrence. This method was developed recently and applied widely for distribution prediction in various fields of ecology [63, 64] . It is widely used for species distribution modeling where only the sites of occurrence of the species are known [68] . The method has been applied in numerous studies for predicting the distribution of HPAI H5N1 disease [16, 51, [69] [70] [71] . BRT utilizes regression trees and boosting algorithms to fit several models and combines them for improving prediction by performing iterative loop throughout the model [63, 64] . The advantage of BRT is that it applies stochastic processes that include probabilistic components to improve predictive performance. We used regression trees to select relevant predictor variables and boosting to improve accuracy in a single tree. The sequential process allows trees to be fitted iteratively through a forward stage-wise procedure in the boosting model. Two important parameters specified in the BRT model are learning rate (lr) and tree complexity (tc) to determine the number of trees for optimal prediction [63, 64] . In our model we used 10 sets of training and test points for cross-validation, a tree complexity of 5, a learning rate of 0.01, and a bag fraction of 0.5. Other advantages of BRT include its insensitivity to co-linearity and non-linear responses. However, for the sake of consistency with the GLMM method, we chose to eliminate predictors that were highly correlated with other predictors and to make log-transforms where needed. In the GLMM models we used p 0.05 to identify significant risk factors. The predictive performances of the models were assessed by the area under the curve (AUC) of the receiver operation characteristic (ROC) curve. AUC is a measure of the overall fit of the model that varies from 0.5 (chance event) to 1.0 (perfect fit) [72] . A comparison of AUC with other accuracy metrics concluded that it is the most robust measure of model performance because it remained constant over a wide range of prevalence rates [73] . We used the corrected Akaike Information Criteria (AICc) to compare each GLMM model with and without its respective suite of fixed predictors. We used SPSS version 21 (IBM Corp., New York, 2012) for GLMM and R version 3.1.0 (The R Foundation for Statistical Computing, 2014) for the BRT. For calculating the spatial correlogram we used the spdep package of R. The fourteen predictor variables we modeled (see tables) were all found to be significantly associated with HPAI H5N1 outbreaks (p 0.2) in at least one wave-place combination based on univariate analysis (but including the autoregressive term) ( Table 1) . Land-use diversity, chicken density, poultry flock size diversity and distance to national highway were found to have significant associations across five of the six wave-place combinations. power of the GLMM models, as measured by the AUC, is very good with AUC values ranging from 0.802 to 0.952 (Tables 2-7 ). The predictive power of the national models was higher than that of the delta models. The predictive power of the BRT models is good, with AUCs ranging from 0.737 to 0.914. The BRT models also had a better predictive power at the national level than at the delta level. These values are higher than those reported for Wave 1 (AUC = 0.69) and Wave 2 (AUC = 0.77) by Gilbert et al. [11] . Both Gilbert et al. [11] and this study found that at the national level the predictive performance for Wave 2 was higher than that for Wave 1. Wave 2 mainly affected the Mekong River Delta. Previous studies indicated the duck density was an important predictor [11] ; our results, however, indicated that the diversity of duck flock size was a more important predictor than duck density. Both the GLMM and BRT models found annual precipitation to be a significant factor. The GLMM model indicated a negative association; similar to what was found by studies in China [51] and in the Red River Delta [53] . A global study of human cases also found occurrence to be higher under drier conditions [74] . Generally, the role of precipitation was found to be far more significant in the deltas than for the country as a whole. The unadjusted Relative Risk (RR) of peri-urban areas in comparison with non-peri-urban areas was 1.41 and 1.60 for Waves 1 and 2, respectively. In terms of urbanicity, we found that chicken density, percentage of land under rice, percentage of land under aquaculture, flock size diversity for duck and geese, and the Compound Topographical Index (CTI) to be highest in peri-urban areas (Fig 1a-1e) . We also found that land-use diversity was higher in rural areas, but peri-urban areas had diversity levels only marginally lower (Fig 1f) . The urbanicity variable alone, however, was not found to be significantly associated with HPAI H5N1 in any place according to the GLMM model except for the urban level in Red River Delta for Wave 2 and in the Mekong River Delta for Wave 1. The BRT model ranked urbanicity as one of the least influential variables. Land-use diversity was found to be significantly associated with HPAI H5N1 in both waves for Viet Nam according to the GLMM model, but at the delta level the association was significant only for Wave 2 in the Mekong River Delta. The BRT model indicated that land-use diversity highly influenced HPAI H5N1 at the national level in Wave 2. For the remaining waveplace combinations land-use diversity had middle to below-middle rank of influence. Both the GLMM and BRT models indicated that the diversity of chicken flock-size had a strong association with HPAI H5N1 for both waves at the national level. This was generally found to be true at the delta levels with some exceptions. The diversity of duck and goose flock size was also significantly associated with HPAI H5N1 in all places, but the associations were much stronger in Wave 2 than in Wave 1. The GLMM model indicated that the CTI had a very strong association with HPAI H5N1 at the national level in both waves although this was not true in the two deltas. The CTI is a steady state wetness index commonly used to quantify topographic control on hydrological processes. Accumulation numbers in flat areas, like deltas, are very large; hence the CTI was not a relevant variable in the GLMM model in these areas. The BRT model however indicated that CTI had middle to low influence in all waves and places. We found very high spatial clustering effects as indicated by the fact that in all waves and places the BRT model found the spatial autocorrelation term to have the highest rank of influence. As expected, the relative influence of the autocorrelation term at the national level was higher (60-78%) than at the delta levels (14-35%). In the GLMM models we found the Akaike Information Criterion (AIC) using the entire set of 14 variables to be much lower than the AICs of a GLMM model without fixed effects. This indicated that though clustering effects were significant, our theory driven predictor variables improved model performance. A limitation of using surveillance methods for the dependent variable (poultry outbreaks) is that the data may have reporting/detection biases [11] . Under-reporting/detection in rural areas as compared to peri-urban areas is possible. We believe that the urbanicity and the shortest distance to nearest town risk factors serve as rough proxies for reporting/detection efficiency. Previous studies have tended to use human population density as a proxy for this purpose. In our study we found a strong association between human population density and urbanicity. But we acknowledge that a categorical variable such as urbanicity may provide less sensitivity than a continuous variable such as human population density in this specific context. This study explored the validity of a general model for disease emergence that combined the IOM 'convergence model' [6] and the social-ecological systems model [7, 8] , for investigating the specific case of HPAI in Vietnam. We sought to test the hypotheses that measures of urbanization, land-use diversification, and poultry intensification are correlated with outbreaks in poultry. Our results generally support the hypothesis that social-ecological system transformations are associated with H5NI outbreaks in poultry. The results presented here highlight three main findings: 1) when relevant risk factors are taken into account, urbanization is generally not a significant independent risk factor; but in peri-urban landscapes emergence factors converge, including higher levels of chicken densities, duck and geese flock size diversities, and fraction of land under rice or aquaculture; 2) high land-use diversity landscapes, a variable not previously considered in spatial studies of HPAI H5N1, are at significantly greater risk for HPAI H5N1 outbreaks; as are 3) landscapes where intensive and extensive forms of poultry production are co-located. Only one other study has explicitly examined urbanicity in the context of HPAI H5N1. Loth et al. [17] found peri-urban areas in Indonesia were significantly associated with HPAI H5N1 cases, even based on multivariate models. Our study, however, attempted both to associate HPAI H5N1 with degree of urbanicity and to determine the features of peri-urban areas that place them at risk. When those features (i.e., chicken densities, duck and geese flock size diversities, and the fraction of land under rice or aquaculture) are included in multivariate models, the role of the urbanization variable per se diminishes. We found in the main river deltas in Viet Nam (Red River and Mekong), urbanization had no significant association with HPAI H5N1. This may be due to the fact that the deltas are more homogenous, in terms of urbanization, than the country as a whole. This is the first study to examine land-use diversity as a risk factor for HPAI H5N1. Measured by the Gini-Simpson Diversity Index of the five land-use classes on which data were collected in the 2006 Viet Nam Agricultural Census, and the presence or absence of HPAI outbreaks at the commune level, our results indicate a strong association between land-use diversity and HPAI H5N1 at the national level and in the Mekong River Delta. This metric captures both the variety of habitats and of the complexity of geospatial patterning likely associated with transmission intensity. Our results are similar to what has been observed by studies of other EIDs using fragmentation metrics (e.g. [75] [76] [77] . This is one of the few studies, however, to link landscape fragmentation to an EID disease in poultry and not just to the vector and/or hosts of the EID. Previous studies have focused on poultry production factors such as type of species, size of flocks, and extent of commercialization (e.g. [15, [17] [18] [19] . This study expands on those findings by providing evidence that when intensive and extensive systems of chicken and/or duck and geese production co-exist in the same commune, the commune experiences higher risk of disease outbreak. Future studies need to examine the biological causal mechanisms in this context. We suggest that national census data (particularly agricultural censuses) compiled at local levels of administration provide valuable information that are not available from remotely sensed data (such as poultry densities) or require a large amount of labor to map at national to larger scales (land-use diversity). Mapping land-use classes at the national scale for local administrative units (i.e., the 10,820 communes in Viet Nam) is not an insignificant task. Future studies, however, could examine the correlation between a census-based metric with metrics derived from remote sensing used to measure proportional abundance of each landcover type within a landscape [78] . Vietnam is relatively advanced in making digital national population and agricultural census data available in a format that can be linked to administrative boundaries. While other nations are beginning to develop similar capacities, in the short term the application of this method to other countries may be limited. Ultimately, both census and remotely sensed data can be used independently to map the urban transition and diversity of land use; these tools, however, may provide their greatest insights when used together. Another important contribution of this study was the discovery of the importance of CTI. So far CTI had been used only in ecological niche modeling studies of HPAI H5N1; the specific role and direction of influence of CTI had has so far been unknown. Our study, the first to use CTI as a risk factor, found it had a large positive influence on HPAI H5N1 risk at the national level. Previous studies have highlighted the role of surface water extent in the persistence and transmission of the HPAI H5N1 virus. These studies measured surface water extent as area covered by water, magnitude of seasonal flooding, distance to the nearest body of water, or other variables that are often difficult to map using remotely sensed data, especially for large area studies. CTI on the other hand has the potential to serve as an excellent surrogate which can easily be measured in a GIS database. The national and regional (delta) models differed quite considerably, both in terms of performance and significant risk factors. In the deltas we commonly found only chicken density, duck flock size diversity and annual precipitation to be significant. This suggests dynamics of risk at the commune level are strongly dependent on the spatial range of analysis, consistent with another study in the Mekong Delta [61] . Though that study's model initially included three dozen commonly known risk factors, the significant risk factors were limited to poultry flock density, proportion households with electricity, re-scaled NDVI median May-October, buffalo density and sweet potato yield. Another study in the Red River Delta [79] found that in addition to the typical poultry density metrics, only the presence of poultry traders was significant. We speculate that for smaller regions, especially for known hot-spots, the relevant risk factors are those that reflect short-range, short-term driving forces such as poultry trading, presence of live bird markets and wet markets etc. Improving model performance for smaller regions would require highly refined and nuanced metrics for poultry trading, road infrastructure, water bodies, etc.-data that are typically not available through census surveys. The differences between the national and regional models suggest that our results can inform planners making decisions at different hierarchical levels of jurisdiction: national, region and local. Our study has the potential to inform the design of future research related to the epidemiology of other EIDs in Viet Nam and elsewhere. For example, we speculate that in Southeast Asia, Japanese encephalitis, the transmission of which is associated with rice cultivation and flood irrigation [80] , may also show a strong association with peri-urbanization. In some areas of Asia these ecological conditions occur near, or occasionally within, urban centers. Likewise, Hantaan virus, the cause of Korean hemorrhagic fever, is associated with the field mouse Apodemus agrarius and rice harvesting in fields where the rodents are present [80] . Our work has demonstrated that the percentage of land under rice in peri-urban areas and rural areas is similar. Hence diseases associated with rice production are likely to peak in peri-urban areas given other risk factors such as land-use diversity, CTI, and distance to infrastructure. Our poultry flock-size diversity findings may also be relevant to understanding the dynamics of other poultry related infections such as Newcastle disease. Finally, these results suggest the validity of a general model of zoonotic disease emergence that integrates IOM's convergence model with the subsequently proposed social-ecological systems and EID framework. Thus, convergence represents the coalescence in time and space of processes associated with land-cover and land-use changes. Project results question whether the urban/rural land-use dichotomy is useful when large areas and parts of the population are caught between the two. Planners need better tools for mapping the rural-urban transition, and for understanding how the specific nature of peri-urban environments creates elevated health risk that require adaptation of existing planning, land use, and development practices.

How does land use fragmentation increase the risk of flu-like diseases?

Community-acquired pneumonia in children — a changing spectrum of disease https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5608782/ SHA: eecb946b106a94f26a79a964f0160e8e16f79f42 Authors: le Roux, David M.; Zar, Heather J. Date: 2017-09-21 DOI: 10.1007/s00247-017-3827-8 License: cc-by Abstract: Pneumonia remains the leading cause of death in children outside the neonatal period, despite advances in prevention and management. Over the last 20 years, there has been a substantial decrease in the incidence of childhood pneumonia and pneumonia-associated mortality. New conjugate vaccines against Haemophilus influenzae type b and Streptococcus pneumoniae have contributed to decreases in radiologic, clinical and complicated pneumonia cases and have reduced hospitalization and mortality. The importance of co-infections with multiple pathogens and the predominance of viral-associated disease are emerging. Better access to effective preventative and management strategies is needed in low- and middle-income countries, while new strategies are needed to address the residual burden of disease once these have been implemented. Text: Pneumonia has been the leading cause of death in children younger than 5 years for decades. Although there have been substantial decreases in overall child mortality and in pneumonia-specific mortality, pneumonia remains the major single cause of death in children outside the neonatal period, causing approximately 900,000 of the estimated 6.3 million child deaths in 2013 [1] . Substantial advances have occurred in the understanding of risk factors and etiology of pneumonia, in development of standardized case definitions, and in prevention with the production of improved vaccines and in treatment. Such advances have led to changes in the epidemiology, etiology and mortality from childhood pneumonia. However in many areas access to these interventions remains sub-optimal, with large inequities between and within countries and regions. In this paper we review the impact of recent preventative and management advances in pneumonia epidemiology, etiology, radiologic presentation and outcome in children. The overall burden of childhood pneumonia has been reduced substantially over the last decade, despite an increase in the global childhood population from 605 million in 2000 to 664 million in 2015 [2] . Recent data suggest that there has been a 25% decrease in the incidence of pneumonia, from 0.29 episodes per child year in low-and middle-income countries in 2000, to 0.22 episodes per child year in 2010 [3] . This is substantiated by a 58% decrease in pneumonia-associated disability-adjusted life years between 1990 and 2013, from 186 million to 78 million as estimated in the Global Burden of Disease study [1] . Pneumonia deaths decreased from 1.8 million in 2000 to 900,000 in 2013 [1] . These data do not reflect the full impact of increasingly widespread use of pneumococcal conjugate vaccine in low-and middle-income countries because the incidence of pneumonia and number of deaths are likely to decrease still further as a result of this widespread intervention [4] . Notwithstanding this progress, there remains a disproportionate burden of disease in low-and middle-income countries, where more than 90% of pneumonia cases and deaths occur. The incidence in high-income countries is estimated at 0.015 episodes per child year, compared to 0.22 episodes per child year in low-and middle-income countries [3] . On average, 1 in 66 children in high-income countries is affected by pneumonia per year, compared to 1 in 5 children in low-and middle-income countries. Even within low-and middleincome countries there are regional inequities and challenges with access to health care services: up to 81% of severe pneumonia deaths occur outside a hospital [5] . In addition to a higher incidence of pneumonia, the case fatality rate is estimated to be almost 10-fold higher in low-and middle-income countries as compared to high-income countries [3, 5] . Childhood pneumonia can also lead to significant morbidity and chronic disease. Early life pneumonia can impair longterm lung health by decreasing lung function [6] . Severe or recurrent pneumonia can have a worse effect on lung function; increasing evidence suggests that chronic obstructive pulmonary disease might be related to early childhood pneumonia [7, 8] . A meta-analysis of the risk of long-term outcomes after childhood pneumonia categorized chronic respiratory sequelae into major (restrictive lung disease, obstructive lung disease, bronchiectasis) and minor (chronic bronchitis, asthma, abnormal pulmonary function) groups [9] . The risk of developing at least one of the major sequelae was estimated as 6% after an ambulatory pneumonia event and 14% after an episode of hospitalized pneumonia. Because respiratory diseases affect almost 1 billion people globally and are a major cause of mortality and morbidity [10] , childhood pneumonia might contribute to substantial morbidity across the life course. Chest radiologic changes have been considered the gold standard for defining a pneumonia event [11] because clinical findings can be subjective and clinical definitions of pneumonia can be nonspecific. In 2005, to aid in defining outcomes of pneumococcal vaccine studies, the World Health Organization's (WHO) standardized chest radiograph description defined a group of children who were considered most likely to have pneumococcal pneumonia [12] . The term "end-point consolidation" was described as a dense or fluffy opacity that occupies a portion or whole of a lobe, or the entire lung. "Other infiltrate" included linear and patchy densities, peribronchial thickening, minor patchy infiltrates that are not of sufficient magnitude to constitute primary end-point consolidation, and small areas of atelectasis that in children can be difficult to distinguish from consolidation. "Primary end-point pneumonia" included either end-point consolidation or a pleural effusion associated with a pulmonary parenchymal infiltrate (including "other" infiltrate). Widespread use of pneumococcal conjugate vaccination and Haemophilus influenzae type B conjugate vaccination has decreased the incidence of radiologic pneumonia. In a review of four randomized controlled trials and two case-control studies of Haemophilus influenzae type B conjugate vaccination in high-burden communities, the vaccination was associated with an 18% decrease in radiologic pneumonia [13] . Introduction of pneumococcal conjugate vaccination was associated with a 26% decrease in radiologic pneumonia in California between 1995 and 1998 [14] . In vaccine efficacy trials in low-and middle-income countries, pneumococcal conjugate vaccination reduced radiologic pneumonia by 37% in the Gambia [15] , 25% in South Africa [16] and 26% in the Philippines [17] . The WHO radiologic case definition was not intended to distinguish bacterial from viral etiology but rather to define a sub-set of pneumonia cases in which pneumococcal infection was considered more likely and to provide a set of standardized definitions through which researchers could achieve broad agreement in reporting chest radiographs. However, despite widespread field utilization, there are concerns regarding inter-observer repeatability. There has been good consensus for the description of lobar consolidation but significant disagreement on the description of patchy and perihilar infiltrates [18, 19] . In addition, many children with clinically severe lung disease do not have primary end-point pneumonia: in one pre-pneumococcal conjugate vaccination study, only 34% of children hospitalized with pneumonia had primary end-point pneumonia [20] . A revised case definition of "presumed bacterial pneumonia" has been introduced, and this definition includes pneumonia cases with WHO-defined alveolar consolidation, as well as those with other abnormal chest radiograph infiltrates and a serum C-reactive protein of at least 40 mg/L [21, 22] . This definition has been shown to have greater sensitivity than the original WHO radiologic definition of primary end-point pneumonia for detecting the burden of pneumonia prevented by pneumococcal conjugate vaccination [23] . Using the revised definition, the 10-valent pneumococcal conjugate vaccine (pneumococcal conjugate vaccination-10), had a vaccine efficacy of 22% in preventing presumed bacterial pneumonia in young children in South America [22] , and pneumococcal conjugate vaccination-13 had a vaccine efficacy of 39% in preventing presumed bacterial pneumonia in children older than 16 weeks who were not infected with human immunodeficiency virus (HIV) in South Africa [21] . Thus there is convincing evidence that pneumococcal conjugate vaccination decreases the incidence of radiologic pneumonia; however there is no evidence to suggest that pneumococcal conjugate vaccination modifies the radiologic appearance of pneumococcal pneumonia. Empyema is a rare complication of pneumonia. An increased incidence of empyema in children was noted in some high-income countries following pneumococcal conjugate vaccination-7 introduction, and this was attributed to pneumococcal serotypes not included in pneumococcal conjugate vaccination-7, especially 3 and 19A [24] . In the United States, evidence from a national hospital database suggests that the incidence of empyema increased 1.9-fold between 1996 and 2008 [25] . In Australia, the incidence rate ratio increased by 1.4 times when comparing the pre-pneumococcal conjugate vaccination-7 period (1998 to 2004) to the post-pneumococcal conjugate vaccination-7 period (2005 to 2010) [26] . In Scotland, incidence of empyema in children rose from 6.5 per million between 1981 and 1998, to 66 per million in 2005 [27] . These trends have been reversed since the introduction of pneumococcal conjugate vaccination-13. Data from the United States suggest that empyema decreased by 50% in children younger than 5 years [28] ; similarly, data from the United Kingdom and Scotland showed substantial reduction in pediatric empyema following pneumococcal conjugate vaccination-13 introduction [29, 30] . Several national guidelines from high-income countries, as well as the WHO recommendations for low-and middleincome countries, recommend that chest radiography should not be routinely performed in children with ambulatory pneumonia [31] [32] [33] . Indications for chest radiography include hospitalization, severe hypoxemia or respiratory distress, failed initial antibiotic therapy, or suspicion for other diseases (tuberculosis, inhaled foreign body) or complications. However, point-of-care lung ultrasound is emerging as a promising modality for diagnosing childhood pneumonia [34] . In addition to the effect on radiologic pneumonia, pneumococcal conjugate vaccination reduces the risk of hospitalization from viral-associated pneumonia, probably by reducing bacterial-viral co-infections resulting in severe disease and hospitalization [35] . An analysis of ecological and observational studies of pneumonia incidence in different age groups soon after introduction of pneumococcal conjugate vaccination-7 in Canada, Italy, Australia, Poland and the United States showed decreases in all-cause pneumonia hospitalizations ranging from 15% to 65% [36] . In the United States after pneumococcal conjugate vaccination-13 replaced pneumococcal conjugate vaccination-7, there was a further 17% decrease in hospitalizations for pneumonia among children eligible for the vaccination, and a further 12% decrease among unvaccinated adults [28] . A systematic review of etiology studies prior to availability of new conjugate vaccines confirmed S. pneumoniae and H. influenzae type B as the most important bacterial causes of pneumonia, with Staphylococcus aureus and Klebsiella pneumoniae associated with some severe cases. Respiratory syncytial virus was the leading viral cause, identified in 15-40% of pneumonia cases, followed by influenza A and B, parainfluenza, human metapneumovirus and adenovirus [37] . More recent meta-analyses of etiology data suggest a changing pathogen profile, with increasing recognition that clinical pneumonia is caused by the sequential or concurrent interaction of more than one organism. Severe disease in particular is often caused by multiple pathogens. With high coverage of pneumococcal conjugate vaccination and Haemophilus influenzae type B conjugate vaccination, viral pathogens increasingly predominate [38] . In recent case-control studies, at least one virus was detected in 87% of clinical pneumonia cases in South Africa [39] , while viruses were detected in 81% of radiologic pneumonia cases in Sweden [40] . In a large multi-center study in the United States, viral pathogens were detected in 73% of children hospitalized with radiologic pneumonia, while bacteria were detected in only 15% of cases [41] . A meta-analysis of 23 case-control studies of viral etiology in radiologically confirmed pneumonia in children, completed up to 2014, reported good evidence of causal attribution for respiratory syncytial virus, influenza, metapneumovirus and parainfluenza virus [42] . However there was no consistent evidence that many other commonly described viruses, including rhinovirus, adenovirus, bocavirus and coronavirus, were more commonly isolated from cases than from controls. Further attribution of bacterial etiology is difficult because it is often not possible to distinguish colonizing from pathogenic bacteria when they are isolated from nasal specimens [43] . Another etiology is pertussis. In the last decade there has also been a resurgence in pertussis cases, especially in highincome countries [44] . Because pertussis immunity after acellular pertussis vaccination is less long-lasting than immunity after wild-type infection or whole-cell vaccination, many women of child-bearing age have waning pertussis antibody levels. Their infants might therefore be born with low transplacental anti-pertussis immunoglobulin G levels, making them susceptible to pertussis infection before completion of the primary vaccination series [45] . In 2014, more than 40,000 pertussis cases were reported to the Centers for Disease Control and Prevention in the United States; in some states, population-based incidence rates are higher than at any time in the last 70 years [44] . In contrast, most low-and middleincome countries use whole-cell pertussis vaccines and the numbers of pertussis cases in those countries were stable or decreasing until 2015 [46] . However recent evidence from South Africa (where the acellular vaccine is used) shows an appreciable incidence of pertussis among infants presenting with acute pneumonia: 2% of clinical pneumonia cases among infants enrolled in a birth cohort were caused by pertussis [39] , and 3.7% of infants and young children presenting to a tertiary academic hospital had evidence of pertussis infection [47] . Similarly, childhood tuberculosis is a major cause of morbidity and mortality in many low-and middle-income countries, and Mycobacterium tuberculosis has increasingly been recognized as a pathogen in acute pneumonia in children living in high tuberculosis-prevalence settings. Postmortem studies of children dying from acute respiratory illness have commonly reported M. tuberculosis [48, 49] . A recent systematic review of tuberculosis as a comorbidity of childhood pneumonia reported culture-confirmed disease in about 8% of cases [50] . Because intrathoracic tuberculosis disease is only culture-confirmed in a minority of cases, the true burden could be even higher; tuberculosis could therefore be an important contributor to childhood pneumonia incidence and mortality in high-prevalence areas. Childhood pneumonia and clinically severe disease result from a complex interaction of host and environmental risk factors [37] . Because of the effectiveness of pneumococcal conjugate vaccination and Haemophilus influenzae type B conjugate vaccination for prevention of radiologic and clinical pneumonia, incomplete or inadequate vaccination must be considered as a major preventable risk factor for childhood pneumonia. Other risk factors include low birth weight, which is associated with 3.2 times increased odds of severe pneumonia in low-and middle-income countries, and 1.8 times increased odds in high-income countries [51] . Similarly, lack of exclusive breastfeeding for the first 4 months of life increases odds of severe pneumonia by 2.7 times in low-and middle-income countries and 1.3 times in highincome countries. Markers of undernutrition are strong risk factors for pneumonia in low-and middle-income countries only, with highly significant odds ratios for underweight for age (4.5), stunting (2.6) and wasting (2.8) . Household crowding has uniform risk, with odds ratios between 1.9 and 2.3 in both low-and middle-income countries and high-income countries. Indoor air pollution from use of solid or biomass fuels increases odds of pneumonia by 1.6 times; lack of measles vaccination by the end of the first year of age increases odds of pneumonia by 1.8 times [51] . It is estimated that the prevalence of these critical risk factors in low-and middle-income countries decreased by 25% between 2000 and 2010, contributing to reductions in pneumonia incidence and mortality in low-and middle-income countries, even in countries where conjugate vaccines have not been available [3] . The single strongest risk factor for pneumonia is HIV infection, which is especially prevalent in children in sub-Saharan Africa. HIV-infected children have 6 times increased odds of developing severe pneumonia or of death compared to HIV-uninfected children [52] . Since the effective prevention of mother-to-child transmission of HIV, there is a growing population of HIV-exposed children who are uninfected; their excess risk of pneumonia, compared to HIV unexposed children, has been described as 1.3-to 3.4-fold higher [53] [54] [55] [56] [57] . The pneumococcal conjugate vaccination and Haemophilus influenzae type B conjugate vaccination have been effective tools to decrease pneumonia incidence, severity and mortality [58, 59] . However, equitable coverage and access to vaccines remains sub-optimal. By the end of 2015, Haemophilus influenzae type B conjugate vaccination had been introduced in 73 countries, with global coverage estimated at 68%. However, inequities are still apparent among regions: in the Americas coverage is estimated at 90%, while in the Western Pacific it is only 25%. By 2015, pneumococcal conjugate vaccination had been introduced into 54 countries, with global coverage of 35% for three doses of pneumococcal conjugate vaccination for infant populations [60] . To address this issue, the WHO's Global Vaccine Access Plan initiative was launched to make life-saving vaccines more equitably available. In addition to securing guarantees for financing of vaccines, the program objectives include building political will in low-and middle-income countries to commit to immunization as a priority, social marketing to individuals and communities, strengthening health systems and promoting relevant local research and development innovations [61] . Maternal vaccination to prevent disease in the youngest infants has been shown to be effective for tetanus, influenza and pertussis [62] . Influenza vaccination during pregnancy is safe, provides reasonable maternal protection against influenza, and also protects infants for a limited period from confirmed influenza infection (vaccine efficacy 63% in Bangladesh [63] and 50.4% in South Africa [64] ). However as antibody levels drop sharply after birth, infant protection does not persist much beyond 8 weeks [65] . Recently respiratory syncytial virus vaccination in pregnancy has been shown to be safe and immunogenic, and a phase-3 clinical trial of efficacy at preventing respiratory syncytial virus disease in infants is under way [66] . Within a decade, respiratory syncytial virus in infancy might be vaccine-preventable, with further decreases in pneumonia incidence, morbidity and mortality [67] . Improved access to health care, better nutrition and improved living conditions might contribute to further decreases in childhood pneumonia burden. The WHO Integrated Global Action Plan for diarrhea and pneumonia highlights many opportunities to protect, prevent and treat children [68] . Breastfeeding rates can be improved by programs that combine education and counseling interventions in homes, communities and health facilities, and by promotion of baby-friendly hospitals [69] . Improved home ventilation, cleaner cooking fuels and reduction in exposure to cigarette smoke are essential interventions to reduce the incidence and severity of pneumonia [70, 71] . Prevention of pediatric HIV is possible by providing interventions to prevent mother-to-child transmission [72] . Early infant HIV testing and early initiation of antiretroviral therapy and cotrimoxazole prophylaxis can substantially reduce the incidence of community-acquired pneumonia among HIV-infected children [73] . Community-based interventions reduce pneumonia mortality and have the indirect effect of improved-careseeking behavior [58] . If these cost-effective interventions were scaled up, it is estimated that 67% of pneumonia deaths in lowand middle-income countries could be prevented by 2025 [58] . Case management of pneumonia is a strategy by which severity of disease is classified as severe or non-severe. All children receive early, appropriate oral antibiotics, and severe cases are referred for parenteral antibiotics. When implemented in highburden areas before the availability of conjugate vaccines, case management as part of Integrated Management of Childhood Illness was associated with a 27% decrease in overall child mortality, and 42% decrease in pneumonia-specific mortality [74] . However the predominance of viral causes of pneumonia and low case fatality have prompted concern about overuse of antibiotics. Several randomized controlled trials comparing oral antibiotics to placebo for non-severe pneumonia have been performed [75] [76] [77] and others are ongoing [78] . In two studies, performed in Denmark and in India, outcomes of antibiotic and placebo treatments were equivalent [76, 77] . In the third study, in Pakistan, there was a non-significant 24% vs. 20% rate of failure in the placebo group, which was deemed to be non-equivalent to the antibiotic group [75] . Furthermore, because WHO-classified non-severe pneumonia and bronchiolitis might be considered within a spectrum of lower respiratory disease, many children with clinical pneumonia could actually have viral bronchiolitis, for which antibiotics are not beneficial [79] . This has been reflected in British [33] and Spanish [31] national pneumonia guidelines, which do not recommend routine antibiotic treatment for children younger than 2 years with evidence of pneumococcal conjugate vaccination who present with non-severe pneumonia. The United States' national guidelines recommend withholding antibiotics in children up to age 5 years presenting with non-severe pneumonia [32] . However, given the high mortality from pneumonia in low-and middle-income countries, the lack of easy access to care, and the high prevalence of risk factors for severe disease, revised World Health Organization pneumonia guidelines still recommend antibiotic treatment for all children who meet the WHO pneumonia case definitions [80] . Use of supplemental oxygen is life-saving, but this is not universally available in low-and middle-income countries; it is estimated that use of supplemental oxygen systems could reduce mortality of children with hypoxic pneumonia by 20% [81] . Identifying systems capacity to increase availability of oxygen in health facilities, and identifying barriers to further implementation are among the top 15 priorities for future childhood pneumonia research [82] . However, up to 81% of pneumonia deaths in 2010 occurred outside health facilities [5] , so there are major challenges with access to health services and health-seeking behavior of vulnerable populations. Identifying and changing the barriers to accessing health care is an important area with the potential to impact the survival and health of the most vulnerable children [82] . Much progress has been made in decreasing deaths caused by childhood pneumonia. Improved socioeconomic status and vaccinations, primarily the conjugate vaccines (against Haemophilus influenzae and pneumococcus), have led to substantial reductions in the incidence and severity of childhood pneumonia. Stronger strategies to prevent and manage HIV have reduced HIV-associated pneumonia deaths. However, despite the substantial changes in incidence, etiology and radiology globally, there remain inequities in access to care and availability of effective interventions, especially in low-and middle-income countries. Effective interventions need to be more widely available and new interventions developed for the residual burden of childhood pneumonia.

What is the effect of childhood tuberculosis in childhood pneumonia?

Severe Acute Respiratory Syndrome Coronavirus Viroporin 3a Activates the NLRP3 Inflammasome https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6361828/ SHA: f02d0c1e8b0109648e578662dc250abe349a033c Authors: Chen, I-Yin; Moriyama, Miyu; Chang, Ming-Fu; Ichinohe, Takeshi Date: 2019-01-29 DOI: 10.3389/fmicb.2019.00050 License: cc-by Abstract: Nod-like receptor family, pyrin domain-containing 3 (NLRP3) regulates the secretion of proinflammatory cytokines interleukin 1 beta (IL-1β) and IL-18. We previously showed that influenza virus M2 or encephalomyocarditis virus (EMCV) 2B proteins stimulate IL-1β secretion following activation of the NLRP3 inflammasome. However, the mechanism by which severe acute respiratory syndrome coronavirus (SARS-CoV) activates the NLRP3 inflammasome remains unknown. Here, we provide direct evidence that SARS-CoV 3a protein activates the NLRP3 inflammasome in lipopolysaccharide-primed macrophages. SARS-CoV 3a was sufficient to cause the NLRP3 inflammasome activation. The ion channel activity of the 3a protein was essential for 3a-mediated IL-1β secretion. While cells uninfected or infected with a lentivirus expressing a 3a protein defective in ion channel activity expressed NLRP3 uniformly throughout the cytoplasm, NLRP3 was redistributed to the perinuclear space in cells infected with a lentivirus expressing the 3a protein. K(+) efflux and mitochondrial reactive oxygen species were important for SARS-CoV 3a-induced NLRP3 inflammasome activation. These results highlight the importance of viroporins, transmembrane pore-forming viral proteins, in virus-induced NLRP3 inflammasome activation. Text: Severe acute respiratory syndrome coronavirus (SARS-CoV), a member of the genus Betacoronavirus within the family Coronaviridae, is an enveloped virus with a single-stranded positive-sense RNA genome of approximately 30 kb in length. The 5 two-thirds of the genome encodes large polyprotein precursors, open reading frame (ORF) 1 and ORF1b, which are proteolytically cleaved to generate 16 non-structural proteins (Tan et al., 2005) . The 3 one-third of the genome encodes four structural proteins, spike (S), envelope (E), matrix (M) and nucleocapsid (N), and non-structural proteins, along with a set of accessory proteins (3a, 3b, 6, 7a, 7b, 8a, 8b, and 9b) (Perlman and Dandekar, 2005; Tan et al., 2005) . SARS-CoV is the etiological agent of SARS (Drosten et al., 2003; Fouchier et al., 2003; Ksiazek et al., 2003; Kuiken et al., 2003; Peiris et al., 2003) . At least 8,098 laboratory-confirmed cases of human infection, with a fatality rate of 9.6%, were reported to the World Health Organization from November 2002 to July 2003. High levels of proinflammatory cytokines, including tumor necrosis factor (TNF)-α, interleukin (IL)-1β, and IL-6, were detected in autopsy tissues from SARS patients (He et al., 2006) . Although dysregulation of inflammatory cytokines may be involved in lung injury and the pathogenesis of SARS-CoV, the underlying molecular mechanisms are not fully understood. The innate immune systems utilizes pattern recognition receptors (PRRs) to detect pathogen-associated molecular patterns (Medzhitov, 2001; Kawai and Akira, 2010) . Recognition of virus infection plays an important role in limiting virus replication at the early stages of infection. Nod-like receptor family, pyrin domain-containing 3 (NLRP3) is activated by a wide variety of stimuli, including virus infection (Bauernfeind et al., 2011) . Four models describing activation of the NLRP3 inflammasome have been proposed thus far (Hornung and Latz, 2010; Schroder et al., 2010; Tschopp and Schroder, 2010) . First, the disturbances in intracellular ionic concentrations, including K + efflux and Ca 2+ influx, play an important role (Fernandes-Alnemri et al., 2007; Petrilli et al., 2007; Arlehamn et al., 2010; Ichinohe et al., 2010; Ito et al., 2012; Murakami et al., 2012; Munoz-Planillo et al., 2013) . Second, cathepsin B and L, which are specific lysosomal cysteine proteases, are though to play a role after phagocytosis of cholesterol crystals (Duewell et al., 2010) , fibrillar peptide amyloid-beta , silica crystals, and aluminum salts . Third is the release of reactive oxygen species (ROS) or mitochondrial DNA from damaged mitochondria (Zhou et al., , 2011 Nakahira et al., 2011; Shimada et al., 2012) . Finally, viral RNA or RNA cleavage products generated by RNase L activate the NLRP3 inflammasome via the DExD/H-box helicase, DHX33 (Allen et al., 2009; Mitoma et al., 2013; Chen et al., 2014; Chakrabarti et al., 2015) . Upon activation, the NLRP3 is recruited to the mitochondria via association with mitochondrial antiviral signaling (MAVS) or mitofusin 2 expressed on the outer mitochondrial membrane Subramanian et al., 2013) ; these molecules then recruit the apoptosis-associated speck-like protein containing a caspase recruitment domain (ASC) and pro-caspase-1 to form the NLRP3 inflammasome. This event activates the downstream molecule, caspase-1, which catalyzes the proteolytic processing of pro-IL-1β and pro-IL-18 into their active forms and stimulates their secretion (Kayagaki et al., 2015; Shi et al., 2015) . It is increasingly evident that NLRP3 detects RNA viruses by sensing the cellular damage or distress induced by viroporins (Ichinohe et al., 2010; Ito et al., 2012; Triantafilou et al., 2013; Nieto-Torres et al., 2015) , transmembrane pore-forming proteins, encoded by certain RNA viruses; these proteins alter membrane permeability to ions by forming membrane channels (Tan et al., 2005; Chen and Ichinohe, 2015) . A recent study shows that the SARS-CoV E protein, which comprise only 76 amino acids, forms Ca 2+ -permeable ion channels and activates the NLRP3 inflammasome (Nieto-Torres et al., 2015) . Although the E and 3a proteins of SARS-CoV, which comprise 274 amino acids and contain three transmembrane domains (Zeng et al., 2004; Lu et al., 2006) , are thought to act as Na + /K + and K + channels, respectively (Wilson et al., 2004; Lu et al., 2006; Torres et al., 2007; Parthasarathy et al., 2008; Pervushin et al., 2009; Wang et al., 2011) , the role of the 3a protein in activating the NLRP3 inflammasome remains unknown. Here, we examined the role of the 3a protein in activating the NLRP3 inflammasome. Six-week-old female C57BL/6 mice were purchased from The Jackson Laboratory. All animal experiments were approved by the Animal Committees of the Institute of Medical Science (The University of Tokyo). Bone marrow-derived macrophages (BMMs) were prepared as described previously (Ichinohe et al., 2009) . In brief, bone marrow was obtained from the tibia and femur by flushing with Dulbecco's modified Eagle's medium (DMEM; Nacalai Tesque). Bone marrow cells were cultured for 5 days in DMEM supplemented with 30% L929 cell supernatant containing macrophage colony-stimulating factor, 10% heat-inactivated fetal bovine serum (FBS), and L-glutamine (2 mM) at 37 • C/5% CO 2 . HEK293FT cells (a human embryonic kidney cell line) and HeLa cells (a human epithelial carcinoma cell line) were maintained in DMEM supplemented with 10% FBS, penicillin (100 units/ml), and streptomycin (100 µg/ml) (Nacalai Tesque). MDCK cells (Madin-Darby canine kidney cells) and HT-1080 cells (a human fibrosarcoma cell line) were grown in Eagle's minimal essential medium (E-MEM; Nacalai Tesque) supplemented with 10% FBS, penicillin (100 units/ml), and streptomycin (100 µg/ml) (Nacalai Tesque). Influenza A virus strain A/PR8 (H1N1) was grown at 35 • C for 2 days in the allantoic cavities of 10-day-old fertile chicken eggs (Ichinohe et al., 2009) . The viral titer was quantified in a standard plaque assay using MDCK cells (Pang et al., 2013) . Plasmids cDNAs encoding the E and M proteins of SARS-CoV Frankfurt 1 strain (Matsuyama et al., 2005) were obtained by reverse transcription and PCR of total RNA extracted from SARS-CoVinfected Vero cells, followed by PCR amplification using specific primers. pcDNA3.1D-3a-V5His was provided by Ming-Fu Chang (National Taiwan University College of Medicine, Taipei, Taiwan). To generate the plasmids pLenti6-E-V5His, pLenti6-3a-V5His, and pLenti-M-V5His, cDNA fragments of E, 3a, and M were amplified from pcDNA3.1D-E-V5His, pcDNA3.1D-3a-V5His, and pcDNA3.1D-M-V5His using specific primer sets and then ligated into pLenti6-TOPO vectors (Invitrogen). To generate plasmids pCA7-flag-E, pCA7-flag-3a, and pCA7flag-M, pCA7-HA-E, pCA7-HA-3a, and pCA7-HA-M, cDNA fragments of E, 3a, and M were amplified from pcDNA3.1D-E-V5His, pcDNA3.1D-3a-V5His, and pcDNA3.1D-M-V5His using specific primer sets, digested with EcoR I and Not I, and subcloned into the EcoR I-Not I sites of the pCA7-flag-ASC plasmid or pCA7-HA-M2 plasmid, respectively (Ito et al., 2012) . To construct plasmids expressing the E mutant V25F, the mutated E fragments were amplified by inverse PCR with wildtype E-containing plasmids and specific primer sets. The PCR products were cleaved by Dpn I, ligated in a ligase-and T4 kinase-containing reaction and then transformed into DH5α competent cells (TOYOBO). To construct plasmids expressing the 3a mutant 3a-CS, fragments were amplified from wildtype 3a-containing plasmids using 3a-specific primer sets and transformed as described above. HEK293FT cells were seeded in 24-well cluster plates and transfected with 1 µg pLenti6-E/3a/M-V5His, pLenti-GFP (green fluorescent protein), or pLenti-M2 using polyethylenimine (PEI) Max. At 24 h post-transfection, the cells were lysed with RIPA buffer (50 mM Tris-HCl, 1% NP-40, 0.05% sodium dodecyl sulfate (SDS), 150 mM NaCl and 1 mM EDTA). And the lysates were subjected to SDS-polyacrylamide gel electrophoresis (PAGE) followed by electroblotting onto polyvinylidene difluoride (PVDF) membranes. The membranes were incubated over night with mouse anti-V5-tag (R960-25, Invitrogen), mouse anti-influenza A virus M2 (14C2, Abcam), mouse anti-GFP (GF200, Nacalai Tesque), or rabbit antitubulin (DM1A, Santa Cruz) antibodies, followed by horseradish peroxide-conjugated anti-mouse IgG (Jackson Immuno Research Laboratories) or anti-rabbit IgG (Invitrogen). After washing 3 times with washing buffer (0.05% Tween-20/PBS), the membranes were exposed using Chemi-Lumi One Super (Nacalai Tesque), and the chemiluminescent signals were captured by an ImageQuant LAS-4000 mini apparatus (GE Healthcare). To generate lentiviruses expressing V5-tagged SARS-CoV E, 3a, and M proteins, the full-length cDNA encoding each viral protein was cloned into the pLenti6.3/V5-TOPO vector (Invitrogen) using the following primers: SARS-CoV E forward, 5 -caccatgtactcattcgtttcgga-3 , and reverse, 5 -gaccagaagatcaggaactc-3 ; SARS-CoV 3a forward, 5caccatggatttgtttatgagatt-3 , and reverse, 5 -caaaggcacgctagtagtcg-3 ; SARS-CoV M forward, 5 -caccatggcagacaacggtactat-3 , and reverse, 5 -ctgtactagcaaagcaatat-3 . Sub-confluent monolayers of HEK293FT cells seeded in a collagen-coated dish (10 cm in diameter) were transfected with 3 µg of pLenti6.3/V5-TOPO vector expressing each viral protein or EGFP together with ViraPower Packaging Mix (Invitrogen) using Lipofectamine 2000 (Invitrogen). The supernatants containing lentiviruses were harvested and filtered through a 0.45 µm filter (Millipore) at 72-96 h post-transfection (Ito et al., 2012) . The lentiviral titer was then quantified using HT-1080 cells as described previously . Bone marrow-derived macrophages were plated at a density of 8 × 10 5 in 24-well plate and infected with A/PR8 influenza virus or lentivirus at a multiplicity of infection (MOI) of 5 or 0.2 for 1 h, respectively. Then, BMMs were stimulated with 1 µg/ml of LPS and cultured for additional 23 h in complete media. Supernatants were collected at 24 h post-infection and centrifuged to remove cell debris. The amount of IL-1β in the supernatants was measured in an enzyme-linked immunosorbent assay (ELISA) using paired antibodies (eBioscience) (Ichinohe et al., 2010 . To clarify the cellular localization of the wild-type and mutant 3a proteins of SARS-CoV, HeLa cells were cultured on coverslips and transfected with 1 µg of pCA7-flag-3a or pCD7-flag-3a-CS together with 0.5 µg of ER-mCherry or DsRed-Golgi (Ito et al., 2012) . At 24 h post-transfection, cells were fixed with 4% paraformaldehyde and permeabilized with 1% Triton X-100/PBS. After washing with PBS and blocking with 4% BSA/PBS, the cells were incubated with a mouse anti-flag antibody (M2, Sigma) followed by incubation with Alexa Fluor 488-conjugated goat anti-mouse IgG (H+L) (Life Technologies). To observe the cellular distribution of NLRP3 in the E-or 3a-expressing cells, HeLa cells were cultured on coverslips and transfected with 1 µg of pCA7-HA-E, pCA7-HA-EV25F, pCA7-HA-3a, pCA7-HA-3a-CS, or pCA7 control vector together with 0.5 µg of pCA7-NLRP3. At 24 h post-transfection, cells were fixed and permeabilized with 4% paraformaldehyde and 1% Triton X-100/PBS. After washing and blocking, the cells were incubated with rabbit anti-HA (561, MBL) and mouse anti-NLRP3 (Cryo-2; AdipoGen) antibodies, followed by Alexa Fluor 488-conjugated goat anti-rabbit IgG (H+L) and Alexa Fluor 568-conjugated goat anti-mouse IgG (H+L) (Life Technologies). Fluorescent signals were observed by confocal microscopy (A1R + , Nikon). Statistical significance was tested using a two-tailed Student's t-test. P-values < 0.05 were considered statistically significant. We previously demonstrated that the influenza virus M2 protein (a proton-selective ion channel), its H37G mutant (which has lost its proton selectivity and enables the transport of other cations such as Na + and K + ), and the EMCV 2B protein (a Ca 2+ channel) stimulates NLRP3 inflammasome-mediated IL-1β secretion (Ichinohe et al., 2010; Ito et al., 2012) . In addition, the SARS-CoV E protein acts as a Ca 2+ -permeable ion channels that activates the NLRP3 inflammasome (Nieto- Torres et al., 2015) . The fact that 3a protein of SARS-CoV acts as viroporin prompted us to examine whether it also triggers inflammasome activation. Thus, we first generated lentivirus plasmids expressing V5-tagged proteins and confirmed their expression in HEK293FT cells by immunoblot analysis (Figures 1A-C) . We next transduced lipopolysaccharide (LPS)-primed BMMs with the lentiviruses expressing the SARS-CoV E, 3a, M, influenza virus M2, or EMCV 2B proteins. Consistent with previous reports (Ichinohe et al., Figure 1D) . Similarly, the lentiviruses expressing the SARS-CoV E or 3a proteins stimulated IL-1β release from LPS-primed BMMs ( Figure 1D) . Furthermore, IL-1β secretion from LPSprimed BMMs co-infected with E-and 3a-expressing lentiviruses was significantly higher than that from SARS-CoV E-expressing lentivirus-infected cells ( Figure 1E) . These data indicated that the expression of SARS-CoV viroporin 3a is sufficient to stimulate IL-1β secretion by LPS-primed BMMs. Previous studies demonstrated that the N-terminal 40 amino acids of the SARS-CoV E protein are important for ion channel formation, and that mutations N15A and V25F [located in the transmembrane domain (from amino acid residues 7-38)] prevent ion conductivity (Wilson et al., 2004; Torres et al., 2007; Verdia-Baguena et al., 2012) . In addition, the SARS-CoV 3a protein contains a cysteine-rich domain (amino acid residues 127-133) that is involved in the formation of a homodimer to generate the ion channel (Lu et al., 2006; Chan et al., 2009) . Thus, mutation of the cysteine-rich domain blocks the ion conductivity by the 3a protein (Chan et al., 2009) . To this end, we substituted amino acids Cys-127, Cys-130, and Cys-133 within the cysteine-rich domain of the SARS-CoV 3a protein with serine to generate a lentivirus expressing the ion channel activity-loss mutant, 3a-CS (Chan et al., 2009; Figure 2A) . To test whether the ion channel activity of the SARS-CoV 3a protein is required to stimulate secretion of IL-1β, we transduced LPSprimed BMMs with lentiviruses expressing the SARS-CoV E, V25F, 3a, 3a-CS, or M proteins. Consistent with a previous report (Nieto -Torres et al., 2015) , we found that the V25F mutant lentivirus failed to stimulate IL-1β release from BMMs ( Figure 2B) . Notably, the 3a-CS mutant completely abrogated IL-1β secretion (Figure 2B) , suggesting that the ion channel activity of the 3a protein is required for SARS-CoV 3a-induced IL-1β secretion. FIGURE 4 | NLRP3 inflammasome activation by SARS-CoV 3a. HeLa cells were transfected with the expression plasmid encoding NLRP3 and that encoding HA-tagged SARS-CoV 3a, 3a-CS, E, or V25F, and by with a confocal microscope. Scale bars, 10 µm. Data are representative of at least three independent experiments. Next, we determined the subcellular localization of the SARS-CoV 3a protein using confocal microscopy. When the SARS-CoV Cell-free supernatants were collected at 24 h (lentiviruses) or 6 h (ATP) post-infection or stimulation, and analyzed for IL-1β by ELISA. Data are representative of at least three independent experiments, and indicate the mean ± SD; * * P < 0.01 and * * * P < 0.001. 3a protein was expressed in HeLa cells, we observed two main distribution patterns. Consistent with previous reports (Yu et al., 2004; Yuan et al., 2005) , the 3a protein localized to the Golgi apparatus ( Figure 3A ). In addition, the 3a proteins concentrated in spot structures, which mainly localized to the endoplasmic reticulum (ER) (Figure 3B ). By contrast, the 3a-CS mutant was concentrated in the Golgi apparatus rather than in the ER and did not form spot structures (Figures 3A,B) . We next examined the intracellular localization of NLRP3. Activation of the NLRP3 inflammasome led to a redistribution from the cytosol to the perinuclear space, a process considered as a hallmark of NLRP3 activation (Zhou et al., 2011; Ito et al., 2012; Johnson et al., 2013; Moriyama et al., 2016) . Although cells expressing the ion channel activity-loss mutants 3a-CS or V25F uniformly expressed NLRP3 throughout the cytoplasm, it was redistributed to the perinuclear region in SARS-CoV 3a-or E-expressing cells (Figure 4) . Together, these data provide evidence that the ion channel activity of the SARS-CoV 3a protein is essential for triggering the NLRP3 inflammasome. Both K + Efflux and ROS Production Are Involved in the IL-1β Release Induced by the SARS-CoV 3a Protein Finally, we investigated the mechanism by which SARS-CoV 3a triggers NLRP3 inflammasome activation. A previous study showed that the 3a protein of SARS-CoV acts as a K + channel (Lu et al., 2006) . In addition, K + efflux is a well-known activator of the NLRP3 inflammasome (Mariathasan et al., 2006; Petrilli et al., 2007) . These observations prompted us to examine whether K + efflux is required for 3a-mediated IL-1β secretion. To this end, BMMs in K + -rich medium were infected with influenza A virus or lentiviruses expressing the SARS-CoV E or 3a proteins. In agreement with a previous result (Ichinohe et al., 2010) , we found that IL-1β secretion caused by influenza virus was completely blocked when the extracellular K + concentration was increased to 130 mM ( Figure 5A) . The inhibitory effect of the K + -rich medium was also observed when cells were stimulated with lentiviruses expressing the SARS-CoV E or 3a proteins ( Figure 5B ). Since mitochondrial ROS are important for NLRP3 inflammasome activation (Nakahira et al., 2011; Zhou et al., 2011) , we next stimulated BMMs with extracellular ATP or lentiviruses expressing the SARS-CoV E or 3a proteins in the presence or absence of the antioxidant, Mito-TEMPO, a scavenger that is specific for mitochondrial ROS Trnka et al., 2009) . As reported previously (Nakahira et al., 2011; Ito et al., 2012) , treatment of BMMs with Mito-TEMPO completely blocked IL-1β secretion in response to ATP ( Figure 6A) . Similarly, IL-1β release induced by the SARS-CoV E and 3a proteins was significantly inhibited by Mito-TEMPO ( Figure 6B) . These observations indicate that the SARS-CoV 3a protein disrupts intracellular ionic concentrations and causes mitochondrial damages, thereby activating the NLRP3 inflammasome. In summary, we found that the ion channel activity of SARS-CoV 3a protein is essential for activation of the NLRP3 inflammasome. In addition, both K + efflux and mitochondrial ROS production are required for SARS-CoV 3a-mediated IL-1β secretion. Thus far, several models have been proposed to explain NLRP3 inflammasome activation by RNA viruses. First, viral RNA or RNA cleavage products generated by RNase L activate the NLRP3 inflammasome via the DExD/H-box helicase, DHX33 (Allen et al., 2009; Mitoma et al., 2013; Chen et al., 2014; Chakrabarti et al., 2015) . Second, viroporins encoded by RNA viruses activates the NLRP3 inflammasome (Ichinohe et al., 2010; Ito et al., 2012; Triantafilou et al., 2013; Nieto-Torres et al., 2015) . In the case of influenza virus, the proton-selective M2 ion channel in the acidic trans-Golgi network activates the NLRP3 inflammasome (Ichinohe et al., 2010) . Interestingly, an M2 mutant in which histidine was substituted with glycine at position 37 (H37G), causing loss of proton selectivity, enables transport of other cations (i.e., Na + and K + ), thereby leading to enhanced secretion of IL-1β from LPS-primed BMMs and dendritic cells when compared with the wild-type M2 protein. In addition, the 2B proteins of EMCV, poliovirus, enterovirus 71 (EV71), and human rhinovirus (a member of the Picornaviridae family) triggers NLRP3 inflammasome activation by inducing Ca 2+ flux from the ER and Golgi compartments (Ito et al., 2012; Triantafilou et al., 2013) . Furthermore, hepatitis C virus stimulates NLRP3 inflammasome-mediated IL-1β production though its p7 viroporin (Negash et al., 2013; Farag et al., 2017) . Third, a recent study has demonstrated that the 3D protein of EV71 directly interacts with NLRP3 to facilitate the assembly of NLRP3 inflammasome complex (Wang et al., 2017) . In the case of SARS-CoV, the viroporin E forms forms Ca 2+permeable ion channels and activates the NLRP3 inflammasome (Nieto-Torres et al., 2015) . In addition, another viroporin 3a was found to induce NLRP3 inflammasome activation (Yue et al., 2018) . Although alanine substitution at Cys-133, which is required for dimer or tetramer formation (Lu et al., 2006) , still allows activation of the NLRP3 inflammasome by interacting with caspase-1 (Yue et al., 2018) , the ion channel activity-loss mutant 3a-CS (Cys-to-Ser substitution at positions Cys-127, Cys-130, and Cys-133) (Chan et al., 2009 ) completely abrogated IL-1β secretion from LPS-primed BMMs, suggesting that the 3a protein of SARS-CoV has the ability to induce the NLRP3 inflammasome activation by multiple mechanisms. Previous studies show that the 3a protein of SARS-CoV is localized to the plasma membrane (Minakshi and Padhan, 2014) and acts as a K + channel (Lu et al., 2006) , thereby (presumably) stimulating the K + efflux at the plasma membrane. Indeed, we found that IL-1β secretion caused by the 3a protein was significantly inhibited when the extracellular K + concentration increased to 130 mM. Although it remains unclear whether another viroporin 8a of SARS-CoV (Castano-Rodriguez et al., 2018) activates the NLRP3 inflammasome, these data highlights the importance of viroporins in SARS-CoV-induced NLRP3 inflammasome activation. A better understanding of the mechanism that governs the NLRP3 inflammasome will facilitate the development of more effective interventions for the treatment of infectious diseases and increase our understanding of viral pathogenesis.

Where does the NLRP3 inflammasome activate after a SARS-CoV infection?

1918 Influenza: the Mother of All Pandemics Jeffery K. Taubenberger" and David M. Morens1- The “Spanish" influenza pandemic of 1918—1919, which caused :50 million deaths worldwide, remains an ominous warning to public health. Many questions about its origins, its unusual epidemiologic features, and the basis of its pathogenicity remain unanswered. The public health implications of the pandemic therefore remain in doubt even as we now grapple with the feared emergence of a pandemic caused by H5N1 or other virus. However, new information about the 1918 virus is emerging, for example, sequencing of the entire genome from archival autopsy tis- sues. But, the viral genome alone is unlikely to provide answers to some critical questions. Understanding the 1918 pandemic and its implications for future pandemics requires careful experimentation and in-depth historical analysis. ”Curiouser and curiouser/ ” criedAlice Lewis Carroll, Alice’s Adventures in Wonderland, 1865 An estimated one third of the world’s population (or z500 million persons) were infected and had clinical- ly apparent illnesses (1,2) during the 191871919 influenza pandemic. The disease was exceptionally severe. Case- fatality rates were >2.5%, compared to <0.1% in other influenza pandemics (3,4). Total deaths were estimated at z50 million (577) and were arguably as high as 100 mil- lion (7). The impact of this pandemic was not limited to 191871919. All influenza A pandemics since that time, and indeed almost all cases of influenza A worldwide (except- ing human infections from avian Viruses such as H5N1 and H7N7), have been caused by descendants of the 1918 Virus, including “drifted” H1N1 Viruses and reassorted H2N2 and H3N2 Viruses. The latter are composed of key genes from the 1918 Virus, updated by subsequently-incor— porated avian influenza genes that code for novel surface *Armed Forces Institute of Pathology, Rockville, Maryland, USA; and TNational Institutes of Health, Bethesda, Maryland, USA proteins, making the 1918 Virus indeed the “mother” of all pandemics. In 1918, the cause of human influenza and its links to avian and swine influenza were unknown. Despite clinical and epidemiologic similarities to influenza pandemics of 1889, 1847, and even earlier, many questioned whether such an explosively fatal disease could be influenza at all. That question did not begin to be resolved until the 1930s, when closely related influenza Viruses (now known to be H1N1 Viruses) were isolated, first from pigs and shortly thereafter from humans. Seroepidemiologic studies soon linked both of these viruses to the 1918 pandemic (8). Subsequent research indicates that descendants of the 1918 Virus still persists enzootically in pigs. They probably also circulated continuously in humans, undergoing gradual antigenic drift and causing annual epidemics, until the 1950s. With the appearance of a new H2N2 pandemic strain in 1957 (“Asian flu”), the direct H1N1 Viral descen- dants 0f the 1918 pandemic strain disappeared from human circulation entirely, although the related lineage persisted enzootically in pigs. But in 1977, human H1N1 Viruses suddenly “reemerged” from a laboratory freezer (9). They continue to circulate endemically and epidemically. Thus in 2006, 2 major descendant lineages of the 1918 H1N1 Virus, as well as 2 additional reassortant lineages, persist naturally: a human epidemic/endemic H1N1 line- age, a porcine enzootic H1N1 lineage (so-called classic swine flu), and the reassorted human H3N2 Virus lineage, which like the human H1N1 Virus, has led to a porcine H3N2 lineage. None of these Viral descendants, however, approaches the pathogenicity of the 1918 parent Virus. Apparently, the porcine H1N1 and H3N2 lineages uncom- monly infect humans, and the human H1N1 and H3N2 lin- eages have both been associated with substantially lower rates ofillness and death than the virus of 1918. In fact, cur- rent H1N1 death rates are even lower than those for H3N2 lineage strains (prevalent from 1968 until the present). H1N1 Viruses descended from the 1918 strain, as well as H3N2 Viruses, have now been cocirculating worldwide for 29 years and show little evidence of imminent extinction. Trying To Understand What Happened By the early 1990s, 75 years of research had failed to answer a most basic question about the 1918 pandemic: why was it so fatal? No Virus from 1918 had been isolated, but all of its apparent descendants caused substantially milder human disease. Moreover, examination of mortality data from the 1920s suggests that within a few years after 1918, influenza epidemics had settled into a pattern of annual epidemicity associated with strain drifting and sub- stantially lowered death rates. Did some critical Viral genet- ic event produce a 1918 Virus of remarkable pathogenicity and then another critical genetic event occur soon after the 1918 pandemic to produce an attenuated H1N1 Virus? In 1995, a scientific team identified archival influenza autopsy materials collected in the autumn of 1918 and began the slow process of sequencing small Viral RNA fragments to determine the genomic structure of the causative influenza Virus (10). These efforts have now determined the complete genomic sequence of 1 Virus and partial sequences from 4 others. The primary data from the above studies (11717) and a number of reviews covering different aspects of the 1918 pandemic have recently been published ([8720) and confirm that the 1918 Virus is the likely ancestor of all 4 of the human and swine H1N1 and H3N2 lineages, as well as the “extinct” H2N2 lineage. No known mutations correlated with high pathogenicity in other human or animal influenza Viruses have been found in the 1918 genome, but ongoing studies to map Virulence factors are yielding interesting results. The 1918 sequence data, however, leave unanswered questions about the ori- gin of the Virus (19) and about the epidemiology of the pandemic. When and Where Did the 1918 Influenza Pandemic Arise? Before and after 1918, most influenza pandemics developed in Asia and spread from there to the rest of the world. Confounding definite assignment of a geographic point of origin, the 1918 pandemic spread more or less simultaneously in 3 distinct waves during an z12-month period in 191871919, in Europe, Asia, and North America (the first wave was best described in the United States in March 1918). Historical and epidemiologic data are inade- quate to identify the geographic origin of the Virus (21), and recent phylogenetic analysis of the 1918 Viral genome does not place the Virus in any geographic context ([9). Although in 1918 influenza was not a nationally reportable disease and diagnostic criteria for influenza and pneumonia were vague, death rates from influenza and pneumonia in the United States had risen sharply in 1915 and 1916 because of a major respiratory disease epidemic beginning in December 1915 (22). Death rates then dipped slightly in 1917. The first pandemic influenza wave appeared in the spring of 1918, followed in rapid succes- sion by much more fatal second and third waves in the fall and winter of 191871919, respectively (Figure 1). Is it pos- sible that a poorly-adapted H1N1 Virus was already begin- ning to spread in 1915, causing some serious illnesses but not yet sufficiently fit to initiate a pandemic? Data consis- tent with this possibility were reported at the time from European military camps (23), but a counter argument is that if a strain with a new hemagglutinin (HA) was caus- ing enough illness to affect the US national death rates from pneumonia and influenza, it should have caused a pandemic sooner, and when it eventually did, in 1918, many people should have been immune or at least partial- ly immunoprotected. “Herald” events in 1915, 1916, and possibly even in early 1918, if they occurred, would be dif- ficult to identify. The 1918 influenza pandemic had another unique fea- ture, the simultaneous (or nearly simultaneous) infection of humans and swine. The Virus of the 1918 pandemic like- ly expressed an antigenically novel subtype to which most humans and swine were immunologically naive in 1918 (12,20). Recently published sequence and phylogenetic analyses suggest that the genes encoding the HA and neu- raminidase (NA) surface proteins of the 1918 Virus were derived from an avianlike influenza Virus shortly before the start of the pandemic and that the precursor Virus had not circulated widely in humans or swine in the few decades before (12,15, 24). More recent analyses of the other gene segments of the Virus also support this conclu- sion. Regression analyses of human and swine influenza sequences obtained from 1930 to the present place the ini- tial circulation of the 1918 precursor Virus in humans at approximately 191571918 (20). Thus, the precursor was probably not circulating widely in humans until shortly before 1918, nor did it appear to have jumped directly from any species of bird studied to date (19). In summary, its origin remains puzzling. Were the 3 Waves in 1918—1 919 Caused by the Same Virus? If So, How and Why? Historical records since the 16th century suggest that new influenza pandemics may appear at any time of year, not necessarily in the familiar annual winter patterns of interpandemic years, presumably because newly shifted influenza Viruses behave differently when they find a uni- versal or highly susceptible human population. Thereafter, confronted by the selection pressures of population immu- nity, these pandemic Viruses begin to drift genetically and eventually settle into a pattern of annual epidemic recur- rences caused by the drifted Virus variants. Figure 1. Three pandemic waves: weekly combined influenza and pneumonia mortality, United Kingdom, 1918—1919 (21). In the 1918-1919 pandemic, a first or spring wave began in March 1918 and spread unevenly through the United States, Europe, and possibly Asia over the next 6 months (Figure 1). Illness rates were high, but death rates in most locales were not appreciably above normal. A sec- ond or fall wave spread globally from September to November 1918 and was highly fatal. In many nations, a third wave occurred in early 1919 (21). Clinical similari- ties led contemporary observers to conclude initially that they were observing the same disease in the successive waves. The milder forms of illness in all 3 waves were identical and typical of influenza seen in the 1889 pandem- ic and in prior interpandemic years. In retrospect, even the rapid progressions from uncomplicated influenza infec- tions to fatal pneumonia, a hallmark of the 191871919 fall and winter waves, had been noted in the relatively few severe spring wave cases. The differences between the waves thus seemed to be primarily in the much higher fre- quency of complicated, severe, and fatal cases in the last 2 waves. But 3 extensive pandemic waves of influenza within 1 year, occurring in rapid succession, with only the briefest of quiescent intervals between them, was unprecedented. The occurrence, and to some extent the severity, of recur- rent annual outbreaks, are driven by Viral antigenic drift, with an antigenic variant Virus emerging to become domi- nant approximately every 2 to 3 years. Without such drift, circulating human influenza Viruses would presumably disappear once herd immunity had reached a critical threshold at which further Virus spread was sufficiently limited. The timing and spacing of influenza epidemics in interpandemic years have been subjects of speculation for decades. Factors believed to be responsible include partial herd immunity limiting Virus spread in all but the most favorable circumstances, which include lower environ- mental temperatures and human nasal temperatures (bene- ficial to thermolabile Viruses such as influenza), optimal humidity, increased crowding indoors, and imperfect ven- tilation due to closed windows and suboptimal airflow. However, such factors cannot explain the 3 pandemic waves of 1918-1919, which occurred in the spring-sum- mer, summer—fall, and winter (of the Northern Hemisphere), respectively. The first 2 waves occurred at a time of year normally unfavorable to influenza Virus spread. The second wave caused simultaneous outbreaks in the Northern and Southern Hemispheres from September to November. Furthermore, the interwave peri- ods were so brief as to be almost undetectable in some locales. Reconciling epidemiologically the steep drop in cases in the first and second waves with the sharp rises in cases of the second and third waves is difficult. Assuming even transient postinfection immunity, how could suscep- tible persons be too few to sustain transmission at 1 point, and yet enough to start a new explosive pandemic wave a few weeks later? Could the Virus have mutated profoundly and almost simultaneously around the world, in the short periods between the successive waves? Acquiring Viral drift sufficient to produce new influenza strains capable of escaping population immunity is believed to take years of global circulation, not weeks of local circulation. And hav- ing occurred, such mutated Viruses normally take months to spread around the world. At the beginning of other “off season” influenza pan- demics, successive distinct waves within a year have not been reported. The 1889 pandemic, for example, began in the late spring of 1889 and took several months to spread throughout the world, peaking in northern Europe and the United States late in 1889 or early in 1890. The second recurrence peaked in late spring 1891 (more than a year after the first pandemic appearance) and the third in early 1892 (21 ). As was true for the 1918 pandemic, the second 1891 recurrence produced of the most deaths. The 3 recur- rences in 1889-1892, however, were spread over >3 years, in contrast to 191871919, when the sequential waves seen in individual countries were typically compressed into z879 months. What gave the 1918 Virus the unprecedented ability to generate rapidly successive pandemic waves is unclear. Because the only 1918 pandemic Virus samples we have yet identified are from second-wave patients ([6), nothing can yet be said about whether the first (spring) wave, or for that matter, the third wave, represented circulation of the same Virus or variants of it. Data from 1918 suggest that persons infected in the second wave may have been pro- tected from influenza in the third wave. But the few data bearing on protection during the second and third waves after infection in the first wave are inconclusive and do lit- tle to resolve the question of whether the first wave was caused by the same Virus or whether major genetic evolu- tionary events were occurring even as the pandemic exploded and progressed. Only influenza RNAipositive human samples from before 1918, and from all 3 waves, can answer this question. What Was the Animal Host Origin of the Pandemic Virus? Viral sequence data now suggest that the entire 1918 Virus was novel to humans in, or shortly before, 1918, and that it thus was not a reassortant Virus produced from old existing strains that acquired 1 or more new genes, such as those causing the 1957 and 1968 pandemics. On the con- trary, the 1918 Virus appears to be an avianlike influenza Virus derived in toto from an unknown source (17,19), as its 8 genome segments are substantially different from contemporary avian influenza genes. Influenza Virus gene sequences from a number offixed specimens ofwild birds collected circa 1918 show little difference from avian Viruses isolated today, indicating that avian Viruses likely undergo little antigenic change in their natural hosts even over long periods (24,25). For example, the 1918 nucleoprotein (NP) gene sequence is similar to that ofviruses found in wild birds at the amino acid level but very divergent at the nucleotide level, which suggests considerable evolutionary distance between the sources of the 1918 NP and of currently sequenced NP genes in wild bird strains (13,19). One way of looking at the evolutionary distance of genes is to com- pare ratios of synonymous to nonsynonymous nucleotide substitutions. A synonymous substitution represents a silent change, a nucleotide change in a codon that does not result in an amino acid replacement. A nonsynonymous substitution is a nucleotide change in a codon that results in an amino acid replacement. Generally, a Viral gene sub- jected to immunologic drift pressure or adapting to a new host exhibits a greater percentage of nonsynonymous mutations, while a Virus under little selective pressure accumulates mainly synonymous changes. Since little or no selection pressure is exerted on synonymous changes, they are thought to reflect evolutionary distance. Because the 1918 gene segments have more synony- mous changes from known sequences of wild bird strains than expected, they are unlikely to have emerged directly from an avian influenza Virus similar to those that have been sequenced so far. This is especially apparent when one examines the differences at 4-fold degenerate codons, the subset of synonymous changes in which, at the third codon position, any of the 4 possible nucleotides can be substituted without changing the resulting amino acid. At the same time, the 1918 sequences have too few amino acid difierences from those of wild-bird strains to have spent many years adapting only in a human or swine intermedi- ate host. One possible explanation is that these unusual gene segments were acquired from a reservoir of influenza Virus that has not yet been identified or sampled. All of these findings beg the question: where did the 1918 Virus come from? In contrast to the genetic makeup of the 1918 pandem- ic Virus, the novel gene segments of the reassorted 1957 and 1968 pandemic Viruses all originated in Eurasian avian Viruses (26); both human Viruses arose by the same mech- anismireassortment of a Eurasian wild waterfowl strain with the previously circulating human H1N1 strain. Proving the hypothesis that the Virus responsible for the 1918 pandemic had a markedly different origin requires samples of human influenza strains circulating before 1918 and samples of influenza strains in the wild that more closely resemble the 1918 sequences. What Was the Biological Basis for 1918 Pandemic Virus Pathogenicity? Sequence analysis alone does not ofier clues to the pathogenicity of the 1918 Virus. A series of experiments are under way to model Virulence in Vitro and in animal models by using Viral constructs containing 1918 genes produced by reverse genetics. Influenza Virus infection requires binding of the HA protein to sialic acid receptors on host cell surface. The HA receptor-binding site configuration is different for those influenza Viruses adapted to infect birds and those adapted to infect humans. Influenza Virus strains adapted to birds preferentially bind sialic acid receptors with 01 (273) linked sugars (27729). Human-adapted influenza Viruses are thought to preferentially bind receptors with 01 (2%) link- ages. The switch from this avian receptor configuration requires of the Virus only 1 amino acid change (30), and the HAs of all 5 sequenced 1918 Viruses have this change, which suggests that it could be a critical step in human host adaptation. A second change that greatly augments Virus binding to the human receptor may also occur, but only 3 of5 1918 HA sequences have it (16). This means that at least 2 H1N1 receptor-binding vari- ants cocirculated in 1918: 1 with high—affinity binding to the human receptor and 1 with mixed-affinity binding to both avian and human receptors. No geographic or chrono- logic indication eXists to suggest that one of these variants was the precursor of the other, nor are there consistent dif- ferences between the case histories or histopathologic fea- tures of the 5 patients infected with them. Whether the Viruses were equally transmissible in 1918, whether they had identical patterns of replication in the respiratory tree, and whether one or both also circulated in the first and third pandemic waves, are unknown. In a series of in Vivo experiments, recombinant influen- za Viruses containing between 1 and 5 gene segments of the 1918 Virus have been produced. Those constructs bearing the 1918 HA and NA are all highly pathogenic in mice (31). Furthermore, expression microarray analysis performed on whole lung tissue of mice infected with the 1918 HA/NA recombinant showed increased upregulation of genes involved in apoptosis, tissue injury, and oxidative damage (32). These findings are unexpected because the Viruses with the 1918 genes had not been adapted to mice; control experiments in which mice were infected with modern human Viruses showed little disease and limited Viral replication. The lungs of animals infected with the 1918 HA/NA construct showed bronchial and alveolar epithelial necrosis and a marked inflammatory infiltrate, which suggests that the 1918 HA (and possibly the NA) contain Virulence factors for mice. The Viral genotypic basis of this pathogenicity is not yet mapped. Whether pathogenicity in mice effectively models pathogenicity in humans is unclear. The potential role of the other 1918 pro- teins, singularly and in combination, is also unknown. Experiments to map further the genetic basis of Virulence of the 1918 Virus in various animal models are planned. These experiments may help define the Viral component to the unusual pathogenicity of the 1918 Virus but cannot address whether specific host factors in 1918 accounted for unique influenza mortality patterns. Why Did the 1918 Virus Kill So Many Healthy Young Ad ults? The curve of influenza deaths by age at death has histor- ically, for at least 150 years, been U-shaped (Figure 2), exhibiting mortality peaks in the very young and the very old, with a comparatively low frequency of deaths at all ages in between. In contrast, age-specific death rates in the 1918 pandemic exhibited a distinct pattern that has not been documented before or since: a “W—shaped” curve, similar to the familiar U-shaped curve but with the addition of a third (middle) distinct peak of deaths in young adults z20410 years of age. Influenza and pneumonia death rates for those 1534 years of age in 191871919, for example, were 20 times higher than in previous years (35). Overall, near- ly half of the influenza—related deaths in the 1918 pandem- ic were in young adults 20410 years of age, a phenomenon unique to that pandemic year. The 1918 pandemic is also unique among influenza pandemics in that absolute risk of influenza death was higher in those <65 years of age than in those >65; persons <65 years of age accounted for >99% of all excess influenza—related deaths in 191871919. In com- parison, the <65-year age group accounted for 36% of all excess influenza—related deaths in the 1957 H2N2 pandem- ic and 48% in the 1968 H3N2 pandemic (33). A sharper perspective emerges when 1918 age-specific influenza morbidity rates (21) are used to adj ust the W- shaped mortality curve (Figure 3, panels, A, B, and C [35,37]). Persons 65 years of age in 1918 had a dispro- portionately high influenza incidence (Figure 3, panel A). But even after adjusting age-specific deaths by age-specif— ic clinical attack rates (Figure 3, panel B), a W—shaped curve with a case-fatality peak in young adults remains and is significantly different from U-shaped age-specific case- fatality curves typically seen in other influenza years, e.g., 192871929 (Figure 3, panel C). Also, in 1918 those 5 to 14 years of age accounted for a disproportionate number of influenza cases, but had a much lower death rate from influenza and pneumonia than other age groups. To explain this pattern, we must look beyond properties of the Virus to host and environmental factors, possibly including immunopathology (e.g., antibody-dependent infection enhancement associated with prior Virus exposures [38]) and exposure to risk cofactors such as coinfecting agents, medications, and environmental agents. One theory that may partially explain these findings is that the 1918 Virus had an intrinsically high Virulence, tem- pered only in those patients who had been born before 1889, e.g., because of exposure to a then-circulating Virus capable of providing partial immunoprotection against the 1918 Virus strain only in persons old enough (>35 years) to have been infected during that prior era (35). But this the- ory would present an additional paradox: an obscure pre- cursor Virus that left no detectable trace today would have had to have appeared and disappeared before 1889 and then reappeared more than 3 decades later. Epidemiologic data on rates of clinical influenza by age, collected between 1900 and 1918, provide good evi- dence for the emergence of an antigenically novel influen- za Virus in 1918 (21). Jordan showed that from 1900 to 1917, the 5- to 15-year age group accounted for 11% of total influenza cases, while the >65-year age group accounted for 6 % of influenza cases. But in 1918, cases in Figure 2. “U-” and “W—” shaped combined influenza and pneumo- nia mortality, by age at death, per 100,000 persons in each age group, United States, 1911—1918. Influenza- and pneumonia- specific death rates are plotted for the interpandemic years 1911—1917 (dashed line) and for the pandemic year 1918 (solid line) (33,34). Incidence male per 1 .nao persunslage group Mortality per 1.000 persunslige group + Case—fataiity rale 1918—1919 Case fatalily par 100 persons ill wilh P&I pel age group Figure 3. Influenza plus pneumonia (P&l) (combined) age-specific incidence rates per 1,000 persons per age group (panel A), death rates per 1,000 persons, ill and well combined (panel B), and case-fatality rates (panel C, solid line), US Public Health Service house-to-house surveys, 8 states, 1918 (36). A more typical curve of age-specific influenza case-fatality (panel C, dotted line) is taken from US Public Health Service surveys during 1928—1929 (37). the 5 to 15-year-old group jumped to 25% of influenza cases (compatible with exposure to an antigenically novel Virus strain), while the >65-year age group only accounted for 0.6% of the influenza cases, findings consistent with previously acquired protective immunity caused by an identical or closely related Viral protein to which older per- sons had once been exposed. Mortality data are in accord. In 1918, persons >75 years had lower influenza and pneumonia case-fatality rates than they had during the prepandemic period of 191171917. At the other end of the age spectrum (Figure 2), a high proportion of deaths in infancy and early childhood in 1918 mimics the age pat- tern, if not the mortality rate, of other influenza pandemics. Could a 1918-like Pandemic Appear Again? If So, What Could We Do About It? In its disease course and pathologic features, the 1918 pandemic was different in degree, but not in kind, from previous and subsequent pandemics. Despite the extraordi- nary number of global deaths, most influenza cases in 1918 (>95% in most locales in industrialized nations) were mild and essentially indistinguishable from influenza cases today. Furthermore, laboratory experiments with recombi- nant influenza Viruses containing genes from the 1918 Virus suggest that the 1918 and 1918-like Viruses would be as sensitive as other typical Virus strains to the Food and Drug Administrationiapproved antiinfluenza drugs riman- tadine and oseltamivir. However, some characteristics of the 1918 pandemic appear unique: most notably, death rates were 5 7 20 times higher than expected. Clinically and pathologically, these high death rates appear to be the result of several factors, including a higher proportion of severe and complicated infections of the respiratory tract, rather than involvement of organ systems outside the normal range of the influenza Virus. Also, the deaths were concentrated in an unusually young age group. Finally, in 1918, 3 separate recurrences of influenza followed each other with unusual rapidity, resulting in 3 explosive pandemic waves within a year’s time (Figure 1). Each of these unique characteristics may reflect genetic features of the 1918 Virus, but understand- ing them will also require examination of host and envi- ronmental factors. Until we can ascertain which of these factors gave rise to the mortality patterns observed and learn more about the formation of the pandemic, predictions are only educated guesses. We can only conclude that since it happened once, analogous conditions could lead to an equally devastating pandemic. Like the 1918 Virus, H5N1 is an avian Virus (39), though a distantly related one. The evolutionary path that led to pandemic emergence in 1918 is entirely unknown, but it appears to be different in many respects from the cur- rent situation with H5N1. There are no historical data, either in 1918 or in any other pandemic, for establishing that a pandemic “precursor” Virus caused a highly patho- genic outbreak in domestic poultry, and no highly patho- genic avian influenza (HPAI) Virus, including H5N1 and a number of others, has ever been known to cause a major human epidemic, let alone a pandemic. While data bearing on influenza Virus human cell adaptation (e.g., receptor binding) are beginning to be understood at the molecular level, the basis for Viral adaptation to efficient human-to- human spread, the chief prerequisite for pandemic emer- gence, is unknown for any influenza Virus. The 1918 Virus acquired this trait, but we do not know how, and we cur- rently have no way of knowing whether H5N1 Viruses are now in a parallel process of acquiring human-to-human transmissibility. Despite an explosion of data on the 1918 Virus during the past decade, we are not much closer to understanding pandemic emergence in 2006 than we were in understanding the risk of H1N1 “swine flu” emergence in 1976. Even with modern antiviral and antibacterial drugs, vaccines, and prevention knowledge, the return of a pan- demic Virus equivalent in pathogenicity to the Virus of 1918 would likely kill >100 million people worldwide. A pandemic Virus with the (alleged) pathogenic potential of some recent H5N1 outbreaks could cause substantially more deaths. Whether because of Viral, host or environmental fac- tors, the 1918 Virus causing the first or ‘spring’ wave was not associated with the exceptional pathogenicity of the second (fall) and third (winter) waves. Identification of an influenza RNA-positive case from the first wave could point to a genetic basis for Virulence by allowing differ- ences in Viral sequences to be highlighted. Identification of pre-1918 human influenza RNA samples would help us understand the timing of emergence of the 1918 Virus. Surveillance and genomic sequencing of large numbers of animal influenza Viruses will help us understand the genet- ic basis of host adaptation and the extent of the natural reservoir of influenza Viruses. Understanding influenza pandemics in general requires understanding the 1918 pan- demic in all its historical, epidemiologic, and biologic aspects. Dr Taubenberger is chair of the Department of Molecular Pathology at the Armed Forces Institute of Pathology, Rockville, Maryland. His research interests include the molecular patho- physiology and evolution of influenza Viruses. Dr Morens is an epidemiologist with a long-standing inter- est in emerging infectious diseases, Virology, tropical medicine, and medical history. Since 1999, he has worked at the National Institute of Allergy and Infectious Diseases. References 1. Frost WH. Statistics of influenza morbidity. Public Health Rep. 19203558497. 2. Bumet F, Clark E. Influenza: a survey ofthe last 50 years in the light of modern work on the Virus of epidemic influenza. Melbourne: MacMillan; 1942. 3. Marks G, Beatty WK. Epidemics. New York: Scribners, 1976. 4. Rosenau MJ, Last JM. Maxcy-Rosenau preventative medicine and public health. New York: Appleton-Century-Crofts; 1980. 5. Crosby A. America’s forgotten pandemic. Cambridge (UK): Cambridge University Press;1989. 6. Patterson KD, Pyle GF. The geography and mortality of the 1918 influenza pandemic. Bull Hist Med. 1991;65:4–21. 7. Johnson NPAS, Mueller J. Updating the accounts: global mortality of the 1918–1920 “Spanish” influenza pandemic. 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Proc Natl Acad Sci U S A 1999;96:1651–6. 13. Reid AH, Fanning TG, Janczewski TA, Lourens RM, and Taubenberger JK. Novel origin of the 1918 pandemic influenza virus nucleoprotein gene segment. J Virol. 2004;78:12462–70. 14. Reid AH, Fanning TG, Janczewski TA, McCall S, Taubenberger JK. Characterization of the 1918 “Spanish” influenza virus matrix gene segment. J Virol. 2002;76:10717–23. 15. Reid AH, Fanning TG, Janczewski TA, Taubenberger JK. Characterization of the 1918 “Spanish” influenza virus neuraminidase gene. Proc Natl Acad Sci U S A 2000;97:6785–90. 16. Reid AH, Janczewski TA, Lourens RM, Elliot AJ, Daniels RS, Berry CL, et al. 1918 influenza pandemic caused by highly conserved viruses with two receptor-binding variants. Emerg Infect Dis. 2003;9:1249–53. 17. Taubenberger JK, Reid AH, Lourens RM, Wang R, Jin G, Fanning TG. Characterization of the 1918 influenza virus polymerase genes. Nature. 2005;437:889–93. 18. Reid AH, Taubenberger JK. The 1918 flu and other influenza pandemics: “over there” and back again. Lab Invest. 1999;79:95–101. 19. Reid AH, Taubenberger JK, Fanning TG. Evidence of an absence: the genetic origins of the 1918 pandemic influenza virus. Nat Rev Microbiol. 2004;2:909–14. 20. Taubenberger JK, Reid AH, Fanning TG. The 1918 influenza virus: a killer comes into view. Virology. 2000;274:241–5. 21. Jordan E. Epidemic influenza: a survey. Chicago: American Medical Association, 1927. 22. Capps J, Moody A. The recent epidemic of grip. JAMA. 1916;67:1349–50. 33. Oxford JS, Sefton A, Jackson R, Innes W, Daniels RS, Johnson NP. World War I may have allowed the emergence of “Spanish” influenza. Lancet Infect Dis. 2002;2:111–4. 24. Fanning TG, Slemons RD, Reid AH, Janczewski TA, Dean J, Taubenberger JK. 1917 avian influenza virus sequences suggest that the 1918 pandemic virus did not acquire its hemagglutinin directly from birds. J Virol. 2002;76:7860–2. 25. 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Grove RD, Hetzel AM. Vital statistics rates in the United States: 1940–1960. Washington: US Government Printing Office, 1968. 34. Linder FE, Grove RD. Vital statistics rates in the United States: 1900–1940. Washington: US Government Printing Office, 1943. 35. Simonsen L, Clarke MJ, Schonberger LB, Arden NH, Cox NJ, Fukuda K. Pandemic versus epidemic influenza mortality: a pattern of changing age distribution. J Infect Dis 1998;178:53–60. 36. Frost WH. The epidemiology of influenza. Public Health Rep. 1919;34:1823–61. 37. Collins SD. Age and sex incidence of influenza and pneumonia morbidity and mortality in the epidemic of 1928-1929 with comparative data for the epidemic of 1918–1919. Public Health Rep. 1931;46:1909–37. 38. Majde JA. Influenza: Learn from the past. ASM News. 1996;62:514. 39. Peiris JS, Yu WC, Leung CW, Cheung CY, Ng WF, Nicholls JM, et al. Re-emergence of fatal human influenza A subtype H5N1 disease. Lancet. 2004;363:617–9. Address for correspondence: Jeffery K. Taubenberger, Department of Molecular Pathology, Armed Forces Institute of Pathology, 1413 Research Blvd, Bldg 101, Rm 1057, Rockville, MD 20850-3125, USA; fax. 301-295-9507; email: [email protected] The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the Centers for Disease Control and Prevention or the institutions with which the authors are affiliated.

What descendant lineages of the swine flu (Spanish Influenza) virus were identified in 2006?

CDC Summary 21 MAR 2020, https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/summary.html This is a rapidly evolving situation and CDC will provide updated information and guidance as it becomes available. Updated March 21, 2020 CDC is responding to a pandemic of respiratory disease spreading from person-to-person caused by a novel (new) coronavirus. The disease has been named “coronavirus disease 2019” (abbreviated “COVID-19”). This situation poses a serious public health risk. The federal government is working closely with state, local, tribal, and territorial partners, as well as public health partners, to respond to this situation. COVID-19 can cause mild to severe illness; most severe illness occurs in older adults. Situation in U.S. Different parts of the country are seeing different levels of COVID-19 activity. The United States nationally is in the initiation phase of the pandemic. States in which community spread is occurring are in the acceleration phase. The duration and severity of each pandemic phase can vary depending on the characteristics of the virus and the public health response. CDC and state and local public health laboratories are testing for the virus that causes COVID-19. View CDC’s Public Health Laboratory Testing map. All 50 states have reported cases of COVID-19 to CDC. U.S. COVID-19 cases include: Imported cases in travelers Cases among close contacts of a known case Community-acquired cases where the source of the infection is unknown. Twenty-seven U.S. states are reporting some community spread of COVID-19. View latest case counts, deaths, and a map of states with reported cases. CDC Recommends Everyone can do their part to help us respond to this emerging public health threat: On March 16, the White House announced a program called “15 Days to Slow the Spread,”pdf iconexternal icon which is a nationwide effort to slow the spread of COVID-19 through the implementation of social distancing at all levels of society. Older people and people with severe chronic conditions should take special precautions because they are at higher risk of developing serious COVID-19 illness. If you are a healthcare provider, use your judgment to determine if a patient has signs and symptoms compatible with COVID-19 and whether the patient should be tested. Factors to consider in addition to clinical symptoms may include: Does the patient have recent travel from an affected area? Has the patient been in close contact with someone with COVID-19 or with patients with pneumonia of unknown cause? Does the patient reside in an area where there has been community spread of COVID-19? If you are a healthcare provider or a public health responder caring for a COVID-19 patient, please take care of yourself and follow recommended infection control procedures. People who get a fever or cough should consider whether they might have COVID-19, depending on where they live, their travel history or other exposures. More than half of the U.S. is seeing some level of community spread of COVID-19. Testing for COVID-19 may be accessed through medical providers or public health departments, but there is no treatment for this virus. Most people have mild illness and are able to recover at home without medical care. For people who are ill with COVID-19, but are not sick enough to be hospitalized, please follow CDC guidance on how to reduce the risk of spreading your illness to others. People who are mildly ill with COVID-19 are able to isolate at home during their illness. If you have been in China or another affected area or have been exposed to someone sick with COVID-19 in the last 14 days, you will face some limitations on your movement and activity. Please follow instructions during this time. Your cooperation is integral to the ongoing public health response to try to slow spread of this virus. COVID-19 Emergence COVID-19 is caused by a coronavirus. Coronaviruses are a large family of viruses that are common in people and many different species of animals, including camels, cattle, cats, and bats. Rarely, animal coronaviruses can infect people and then spread between people such as with MERS-CoV, SARS-CoV, and now with this new virus (named SARS-CoV-2). The SARS-CoV-2 virus is a betacoronavirus, like MERS-CoV and SARS-CoV. All three of these viruses have their origins in bats. The sequences from U.S. patients are similar to the one that China initially posted, suggesting a likely single, recent emergence of this virus from an animal reservoir. Early on, many of the patients at the epicenter of the outbreak in Wuhan, Hubei Province, China had some link to a large seafood and live animal market, suggesting animal-to-person spread. Later, a growing number of patients reportedly did not have exposure to animal markets, indicating person-to-person spread. Person-to-person spread was subsequently reported outside Hubei and in countries outside China, including in the United States. Some international destinations now have ongoing community spread with the virus that causes COVID-19, as do some parts of the United States. Community spread means some people have been infected and it is not known how or where they became exposed. Learn more about the spread of this newly emerged coronavirus. Severity The complete clinical picture with regard to COVID-19 is not fully known. Reported illnesses have ranged from very mild (including some with no reported symptoms) to severe, including illness resulting in death. While information so far suggests that most COVID-19 illness is mild, a reportexternal icon out of China suggests serious illness occurs in 16% of cases. Older people and people of all ages with severe chronic medical conditions — like heart disease, lung disease and diabetes, for example — seem to be at higher risk of developing serious COVID-19 illness. A CDC Morbidity & Mortality Weekly Report that looked at severity of disease among COVID-19 cases in the United States by age group found that 80% of deaths were among adults 65 years and older with the highest percentage of severe outcomes occurring in people 85 years and older. Learn more about the symptoms associated with COVID-19. COVID-19 Pandemic A pandemic is a global outbreak of disease. Pandemics happen when a new virus emerges to infect people and can spread between people sustainably. Because there is little to no pre-existing immunity against the new virus, it spreads worldwide. The virus that causes COVID-19 is infecting people and spreading easily from person-to-person. Cases have been detected in most countries worldwide and community spread is being detected in a growing number of countries. On March 11, the COVID-19 outbreak was characterized as a pandemic by the WHOexternal icon. This is the first pandemic known to be caused by the emergence of a new coronavirus. In the past century, there have been four pandemics caused by the emergence of novel influenza viruses. As a result, most research and guidance around pandemics is specific to influenza, but the same premises can be applied to the current COVID-19 pandemic. Pandemics of respiratory disease follow a certain progression outlined in a “Pandemic Intervals Framework.” Pandemics begin with an investigation phase, followed by recognition, initiation, and acceleration phases. The peak of illnesses occurs at the end of the acceleration phase, which is followed by a deceleration phase, during which there is a decrease in illnesses. Different countries can be in different phases of the pandemic at any point in time and different parts of the same country can also be in different phases of a pandemic. There are ongoing investigations to learn more. This is a rapidly evolving situation and information will be updated as it becomes available. Risk Assessment Risk depends on characteristics of the virus, including how well it spreads between people; the severity of resulting illness; and the medical or other measures available to control the impact of the virus (for example, vaccines or medications that can treat the illness) and the relative success of these. In the absence of vaccine or treatment medications, nonpharmaceutical interventions become the most important response strategy. These are community interventions that can reduce the impact of disease. The risk from COVID-19 to Americans can be broken down into risk of exposure versus risk of serious illness and death. Risk of exposure: The immediate risk of being exposed to this virus is still low for most Americans, but as the outbreak expands, that risk will increase. Cases of COVID-19 and instances of community spread are being reported in a growing number of states. People in places where ongoing community spread of the virus that causes COVID-19 has been reported are at elevated risk of exposure, with the level of risk dependent on the location. Healthcare workers caring for patients with COVID-19 are at elevated risk of exposure. Close contacts of persons with COVID-19 also are at elevated risk of exposure. Travelers returning from affected international locations where community spread is occurring also are at elevated risk of exposure, with level of risk dependent on where they traveled. Risk of Severe Illness: Early information out of China, where COVID-19 first started, shows that some people are at higher risk of getting very sick from this illness. This includes: Older adults, with risk increasing by age. People who have serious chronic medical conditions like: Heart disease Diabetes Lung disease CDC has developed guidance to help in the risk assessment and management of people with potential exposures to COVID-19. What May Happen More cases of COVID-19 are likely to be identified in the United States in the coming days, including more instances of community spread. CDC expects that widespread transmission of COVID-19 in the United States will occur. In the coming months, most of the U.S. population will be exposed to this virus. Widespread transmission of COVID-19 could translate into large numbers of people needing medical care at the same time. Schools, childcare centers, and workplaces, may experience more absenteeism. Mass gatherings may be sparsely attended or postponed. Public health and healthcare systems may become overloaded, with elevated rates of hospitalizations and deaths. Other critical infrastructure, such as law enforcement, emergency medical services, and sectors of the transportation industry may also be affected. Healthcare providers and hospitals may be overwhelmed. At this time, there is no vaccine to protect against COVID-19 and no medications approved to treat it. Nonpharmaceutical interventions will be the most important response strategy to try to delay the spread of the virus and reduce the impact of disease. CDC Response Global efforts at this time are focused concurrently on lessening the spread and impact of this virus. The federal government is working closely with state, local, tribal, and territorial partners, as well as public health partners, to respond to this public health threat. Highlights of CDC’s Response CDC established a COVID-19 Incident Management System on January 7, 2020. On January 21, CDC activated its Emergency Operations Center to better provide ongoing support to the COVID-19 response. The U.S. government has taken unprecedented steps with respect to travel in response to the growing public health threat posed by this new coronavirus: Foreign nationals who have been in China, Iran, the United Kingdom, Ireland and any one of the 26 European countries in the Schengen Area within the past 14 days cannot enter the United States. U.S. citizens, residents, and their immediate family members who have been any one of those countries within in the past 14 days can enter the United States, but they are subject to health monitoring and possible quarantine for up to 14 days. People at higher risk of serious COVID-19 illness avoid cruise travel and non-essential air travel. CDC has issued additional specific travel guidance related to COVID-19. CDC has issued clinical guidance, including: Clinical Guidance for Management of Patients with Confirmed Coronavirus Disease (COVID-19). Infection Prevention and Control Recommendations for Patients, including guidance on the use of personal protective equipment (PPE) during a shortage. CDC also has issued guidance for other settings, including: Preparing for COVID-19: Long-term Care Facilities, Nursing Homes Discontinuation of Home Isolation for Persons with COVID-19 CDC has deployed multidisciplinary teams to support state health departments in case identification, contact tracing, clinical management, and public communications. CDC has worked with federal partners to support the safe return of Americans overseas who have been affected by COVID-19. An important part of CDC’s role during a public health emergency is to develop a test for the pathogen and equip state and local public health labs with testing capacity. CDC developed an rRT-PCR test to diagnose COVID-19. As of the evening of March 17, 89 state and local public health labs in 50 states, the District of Columbia, Guam, and Puerto Rico have successfully verified and are currently using CDC COVID-19 diagnostic tests. Commercial manufacturers are now producing their own tests. CDC has grown the COVID-19 virus in cell culture, which is necessary for further studies, including for additional genetic characterization. The cell-grown virus was sent to NIH’s BEI Resources Repositoryexternal icon for use by the broad scientific community. CDC also is developing a serology test for COVID-19. Other Available Resources The following resources are available with information on COVID-19 World Health Organization, Coronavirusexternal icon

What are the phases of a pandemic?

Pretreatment Hepatitis C Virus NS5A/NS5B Resistance-Associated Substitutions in Genotype 1 Uruguayan Infected Patients https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6112080/ SHA: f01ad3545245b4f884b48aa2b69c9deb942c3e77 Authors: Aldunate, Fabián; Echeverría, Natalia; Chiodi, Daniela; López, Pablo; Sánchez-Cicerón, Adriana; Fajardo, Alvaro; Soñora, Martín; Cristina, Juan; Hernández, Nelia; Moreno, Pilar Date: 2018-08-14 DOI: 10.1155/2018/2514901 License: cc-by Abstract: Hepatitis C Virus (HCV) infection treatment has dramatically changed with the advent of direct-acting antiviral agents (DAAs). However, the efficacy of DAAs can be attenuated by the presence of resistance-associated substitutions (RASs) before and after treatment. Indeed, RASs detected in DAA treatment-naïve HCV-infected patients could be useful for clinical management and outcome prediction. Although the frequency of naturally occurring HCV NS5A and NS5B RASs has been addressed in many countries, there are only a few reports on their prevalence in the South American region. The aim of this study was to investigate the presence of RASs to NS5A and NS5B inhibitors in a DAA treatment naïve cohort of Uruguayan patients infected with chronic hepatitis C and compare them with reports from other South American countries. Here, we found that naturally occurring substitutions conferring resistance to NS5A and NS5B inhibitors were present in 8% and 19.2%, respectively, of treatment-naïve HCV genotype 1 infected patients. Importantly, the baseline substitutions in NS5A and NS5B herein identified differ from the studies previously reported in Brazil. Furthermore, Uruguayan strains subtype 1a clustered within all major world clades, showing that HCV variants currently circulating in this country are characterized by a remarkable genetic diversity. Text: Hepatitis C Virus (HCV) infection treatment has dramatically improved thanks to the introduction of direct-acting antiviral agents (DAAs). These antivirals have significantly increased response rates (up to 98%) and greatly reduced treatment duration [1] . Currently available DAAs are classified into four categories given their molecular targets in the HCV replication cycle: (1) NS3/4A protease inhibitors (PIs) bind to the active site of the NS3/4A protease; (2) NS5A inhibitors interact with domain 1 of the NS5A dimer, although the exact mechanism of NS5A inhibition remains to be fully elucidated; (3) nucleos(t)ide analog NS5B polymerase inhibitors are incorporated into the nascent RNA chain resulting in chain termination by compromising the binding of the incoming nucleotide; (4) nonnucleoside NS5B polymerase inhibitors interact with either the thumb 1, thumb 2, palm 1, or palm 2 domain of NS5B and inhibit polymerase activity by allosteric mechanisms [2] [3] [4] . However, the extreme mutation and high replication rates of HCV, together with the immune system pressure, lead to a remarkable genetic variability that can compromise the high response rates to DAAs due to the preexistence of resistanceassociated substitutions (RASs) [5, 6] . Each drug or class of DAA is characterized by specific resistance profiles. The likelihood that a DAA will select for and allow outgrowth of viral populations carrying RASs depends on the DAA's genetic barrier to resistance (the number and type of mutations needed to generate an amino acid substitution that confers resistance), the viral fitness (replicative capacity) of the resistant variant, and viral genotypes and subtypes [7, 8] . The prevalence of RASs in treatment-naïve patients has been broadly reported worldwide [9] [10] [11] [12] [13] [14] [15] [16] . However, apart from Brazil and Argentina, this issue has not been fully addressed in other South American countries yet [9, [17] [18] [19] . The lack of information in relation to preexisting baseline RASs, added to the high cost of these new drugs, are the major limiting factors for the broad implementation of these new therapies in Uruguay as well as in other Latin American countries (low-or lower-middle income) [20] . In this study, we explored the presence of resistance variants to NS5A and NS5B inhibitors in a DAA treatment naïve cohort of Uruguayan patients chronically infected with hepatitis C. Here, we aimed to contribute to the knowledge of the circulation of HCV resistant variants in the South American region. Samples. Serum samples were obtained from 31 patients with serological markers for HCV, which were recruited between 2015 and 2017 at the Gastroenterology Clinic from Hospital de Clínicas, Montevideo, Uruguay. HCV infection was confirmed by Abbott realtime HCV (Abbott Molecular Inc., Des Plaines, USA). Patients selected for this study were both chronically infected with HCV genotype 1 and DAA treatment-naïve at the time of blood extraction. Written informed consent was obtained from all patients. The studies have been performed according to the World Medical Association Declaration of Helsinki and approved by the appropriate institutional board (Hospital de Clínicas ethical committee). 2.2. RNA Extraction, cDNA Synthesis, and NS5A and NS5B Amplification. Viral RNA was extracted from 140 μl of serum using the QIAamp Viral RNA mini kit (QIAgen, Hilden, Germany) according to the manufacturer's protocol. The viral RNA was heated at 65°C for 5 min and used as a template for a reverse transcription reaction. The reverse transcription reaction mixture contained 5 μl of the RNA template, 1 μl of random hexamer 100 ng/μl (Invitrogen Life Technologies, Carlsbad, CA, USA), 1 μl of dNTP mix (10 mM each), 4 μl of 5X first-strand buffer, 2 μl of 0.1 M DTT, 1 μl of SuperScript II reverse transcriptase (200 U/μl) (Invitrogen Life Technologies, Carlsbad, CA, USA), and 1 μl (40 U/μl) RNaseOUT (Invitrogen Life Technologies, Carlsbad, CA, USA). The reverse transcription was performed at 42°C for 50 min, and then the reverse transcriptase enzyme was inactivated at 70°C for 15 min. PCR amplification of NS5A and NS5B genome regions was performed using primers and conditions previously described [10] . Amplicons were purified using the Illustra GFX PCR DNA and Gel Band Purification Kit (GE Healthcare Life Science, Buckinghamshire, UK) according to the manufacturer's protocol. 2.3. NS5A and NS5B Sequencing. The purified product was then sequenced using the same sets of primers used for PCR amplification. Bidirectional Sanger sequencing was performed by Macrogen Korea (http://www.macrogen.com). 2.4. NS5A and NS5B Genotype Determination. HCV NS5A and NS5B consensus sequences obtained from Uruguayan patients were aligned with sequences from HCV representing all genotypes and main subtypes isolated in different geographic regions of the world. These sequences were obtained from Los Alamos HCV sequence database and from the NIAID Virus Pathogen Database and Analysis Resource (ViPR) [21, 22] . For strains included in these studies, see Supplementary Material Table S1 . Sequences were aligned using the CLUSTAL W software [23] . Once aligned, the best evolutionary model that described our sequence data was assessed using ModelGenerator program [24] . Using the GTR + G + I model (General time reversible + gamma + invariant sites), maximum likelihood phylogenetic trees were constructed for both NS5A and NS5B using the MEGA 5.0 software [25] . For NS5A, 953 nucleotides (positions 6367 to 7319, relative to HCV 1a reference strain, H77 NC_004102) were included in the phylogenetic analysis, whereas for NS5B, only 361 nucleotides corresponding to the Okamoto region (positions 8265 to 8625, relative to strain H77 NC_004102) were included. As a measure of the robustness of each node, we employed the bootstrapping method (1000 pseudoreplicates). For NS5A 1a Uruguayan sequences (n = 20), a second alignment and maximum likelihood phylogenetic tree was generated in order to analyze HCV evolutionary relationships between Uruguayan, Brazilian, and worldwide strains. For non-Uruguayan strains included in this analysis, see Supplementary Material Table S2. 2.5. NS5A and NS5B Sequence Analysis. In order to properly identify substitution changes in NS5A and NS5B regions from HCV strains circulating in Uruguayan patients, we generated world consensus sequences for 1a and 1b subtypes using a wide range of NS5A and NS5B sequences from HCV strains isolated worldwide. For this purpose, NS5A gene sequences corresponding to subtypes 1a (n = 160) and 1b (n = 88) were retrieved from Los Alamos HCV sequence database and from the NIAID ViPR [21, 22] . Likewise, datasets of 150 and 124 NS5B sequences were generated for subtypes 1a and 1b, respectively. Using Seqman program, implemented in DNAStar 5.01 package (DNASTAR, Madison, USA), a world consensus nucleotide sequences were generated for each gene and subtype. Each Uruguayan sequence was subsequently aligned to the corresponding reference sequences, and then in silico translated. The amino acid sequences obtained were compared in order to explore the presence of RASs as well as the presence of polymorphisms at a RAS position (RAPs) in Uruguayan HCV strains. RAPs are defined as any change from reference sequence for a specific genotype at a position associated with NS5A resistance [26] . To study the genetic variability of NS5A and NS5B regions of HCV strains circulating in Uruguayan patients, sequences of these regions (accession numbers MH070029-MH070090) were aligned with corresponding sequences from 59 HCV strains isolated elsewhere, representing all genotypes and main subtypes (for strains included in these analyses, see Supplementary Material Table S1 ). Therefore, maximum likelihood phylogenetic trees were constructed. The results of these studies are shown in Figure 1 All strains in the phylogenies were assigned according to their genotype, and each cluster was supported by very high bootstrap values for both analyzed regions. Strains isolated from Uruguayan patients (n = 31) were assigned to genotype 1, 20 of which corresponded to subtype 1a and 11 to subtype 1b. The results of NS5A (Figure 1 (a)) and NS5B (Figure 1 Genotype 1b phylogenetic analyses were concordant for both genomic regions in all 31 sequences, suggesting no recombination events between these regions. To further analyze the evolutionary relationships between the Uruguayan strains and those circulating in Brazil and elsewhere, a second maximum likelihood phylogenetic tree of HCV-1a sequences of NS5A partial region was built ( Figure 2 ). As was previously described, two distinct 1a clades (clades 1 and 2) were observed. Brazilian sequences clustered in a large group of related sequences inside clade 1 [9] . Whereas NS5A Uruguayan strains (in red) did not cluster in a particular clade, rather, they grouped dispersedly within all major world clades. With the purpose of studying the amino acid (AA) substitutions along the NS5A protein, Uruguayan HCV AA sequences were aligned with NS5A world consensus sequences (residues 23 to 354 relative to NS5A protein sequence). AA substitutions at positions previously found to be potentially associated with resistance to NS5A inhibitors, as well as polymorphisms at a RAS position, were identified. These results are summarized in Table 1 . RASs to NS5A inhibitors (L31M and L31V) were identified in 2 strains out of 25 (8%) fully sequenced samples. RAPs were found in 3 strains (subtype 1a): 2 exhibited the substitution H58P and 1 the substitution K24Q. Although these substitutions were not reported as resistant, some changes at these positions were previously described as RASs in subtype 1a, namely H58D and K24R [27, 28] . Finally, substitution E62D was found in one subtype 1a strain. This change is considered as a secondary substitution because, although it does not confer resistance by itself, when combined with a known RAS it does. In fact, it confers a higher level of resistance than the one achieved by the RAS alone [26] . In addition, several polymorphisms that have not been previously reported to be associated with a resistant phenotype were also detected (see Supplementary Material Table S3 ). In order to study substitutions along NS5B protein, Uruguayan HCV AA sequences were aligned to the NS5B world consensus sequences. Almost full-length AA sequences were obtained in 26 out of 31 analyzed strains. 23 sequences span residues 36 to 539 whereas the remaining 3 span residues 36 to 557 of NS5B protein. This issue limited our studies, since many of the described RASs are observed as of residue 553. Importantly, RASs to NS5B inhibitors ( Table 2) were observed in 5 strains out of 26 sequenced samples (19.2%). C451R was found in two isolates while A421V was found in only one. In 2 of the 3 strains for which we were able to obtain longer sequences, RASs S556G (subtype 1a) and Q556R (subtype 1b) were observed. Finally, we found two RAPs: A421V (in 2 subtype 1b strains) and A553G (in 1 subtype 1a strain). Although A421V has been associated with resistance to beclabuvir (BCV) in patients infected with HCV subtype 1a, this resistant phenotype has not been proven in strains subtype 1b [29] . In position 553, the substitution reported as resistant was A553T [8] . As was the case for NS5A, different polymorphisms not previously associated with a resistant phenotype were also detected in NS5B (see Supplementary Material Table S4 ). The advent of DAAs therapies constitutes one of the major breakthroughs in HCV infected patients management. However, these new treatment options are far from being universally available, in particular for HCV infected patients relying on Latin American public healthcare systems. The main limiting factors for worldwide access to DAAs in our region concern the high cost, the inadequate management of public healthcare systems, the limited access of low-income or uninsured populations to healthcare providers, and the lack of accurate epidemiological information [20, [30] [31] [32] . In Uruguay, these therapies became recently available, and although some have been approved for their use by the public health authorities (Viekira pak and sofosbuvir/ledipasvir therapies), they are not currently financially covered, except in specific cases. Despite the high rates of viral response achieved with DAA-based treatments, still 1 to10% of the patients fails to eliminate infection, and in these cases, baseline and emergent resistance variants turn out to be key factors contributing to treatment failure [5, 17, 33] . Unfortunately, we are currently unable to properly assess the number of HCV infected people in Uruguay and even more to figure out the frequency and type of RASs circulating. These facts could compromise the effectiveness of these new therapies in our country. We have previously reported that naturally occurring substitutions conferring resistance to NS3 inhibitors exist in a significant proportion of Uruguayan patients infected with HCV genotype 1, and we showed that this frequency seemed to be higher than in other South American countries (Brazil and Argentina) [34] . The present study describes the prevalence of baseline NS5A and NS5B RASs in HCV genotype 1 infected DAA-naïve patients in a Uruguayan cohort. The presence of substitutions conferring resistance to NS5A inhibitors has been widely reported both in therapynaïve and in relapser patients from Europe [10, 33, [35] [36] [37] [38] , USA [37, 39, 40] , and Asia [41] [42] [43] . However, NS5A sequences from South America are poorly analyzed yet [9, 44] . Recent studies have revealed that the mean prevalence of NS5A genotype 1 baseline RASs to different inhibitors ranges from 6% to 16% using population sequencing or deep sequencing [27, 37, 45, 46] . Importantly, the prevalence and type of baseline NS5A RASs varies slightly by geographic regions. For instance, L31M was found in 2.2% of genotype 1a infected patients in Europe, in 4.1% of those in Oceania, and strikingly in no patient from the USA [27] . For this reason, we believe that there is a need to contribute data from our region, for which we still do not have enough information, apart from Brazil [9, 44] . The results of this study indicate the presence of DAA NS5A RASs in 2 HCV strains (8% of the patients enrolled in this study), with baseline RASs detected at position 31 (see Table 1 ). L31M substitution confers resistance to daclatasvir (DCV), ledipasvir (LDV), and elbasvir (EBV) in both 1a and 1b subtypes [5, 6, 8, 28, 47, 48] , whereas substitution L31V does it to DCV in subtypes 1a and 1b, to LDV in subtype 1b, and to EBV in subtype 1a [5, 6, 28] . Given that both L31V and L31M are clinically relevant RASs, their detection at baseline may influence the choice of first-line treatment regimens [28] . The substitutions H58P and K24Q found in two patients are considered as resistance-associated polymorphisms (RAPs). The RASs characterized at these positions were H58D and K24G/N/R [5, 6, 27, 28, 49, 50] . The substitution H58P was found as a baseline RAP in relapsers to LDV (HARVONI prescription, https://www.gilead.com/-/ media/files/pdfs/medicines/liver-disease/harvoni/harvoni_pi. pdf?la=en). However, it is sometimes regarded as a RAS [10, 51] , despite conferring only 1.2 fold change in resistance in in vitro studies using the 1a replicon system [39] . We did not find M28T/V, Q30R/H, or Y93H substitutions as there were previously reported in Brazil and worldwide [9, 27, 44] . The amino acid substitution E62H was found in one Uruguayan patient. Although this change does not confer resistance by itself but in combination with Q30R, it generates a high resistance level to DCV [52] . The presence of baseline NS5A RASs impacts treatment outcome in some patient groups by affecting SVR rates. The detection of NS5A preexistent RASs may play a relevant role in the choice of first-line treatment regimens or in the simplification/shortening of recommended regimens, in order to bring SVR rates close to the highest achievable [27, 38, 41, 53] , in particular in countries such as Uruguay, where only two different DAA-containing treatment regimens are approved for their use. Regarding NS5B gene, global analysis (with the exception of South America [17, 19] ) revealed that NS5B DAA resistance substitutions are infrequent [14] . Our study showed the presence of NS5B inhibitors RASs in 5 out of 26 analyzed HCV infected Uruguayan patients naïve to treatment (19.2%). Substitutions found in this work were A421V and S556G associated in subtype 1a with resistance to BCV and dasabuvir (DSV), respectively [8, 28, 29, 54, 55] , and Q556R associated with resistance to DSV both in genotype 1a and 1b [12, 28] . Substitution C451R, observed in two Uruguayan patients, was reported previously in patients who failed to clear the infection after treatment with OBV/PTV/r + DSV ± RBV. In these cases, it appeared in combination with G558R (Trial Coral I-Cohort 2: http:// www.hcv-trials.com/showStudy.asp?Study=86). RAPs in positions 421 and 553 (A421V in two subtype 1b isolates and A553G in one subtype 1b isolate) were also found. Although A421V has been associated with resistance to BCV in patients with subtype 1a, this phenotype has not been proven in strains of subtype 1b [29] . In position 553, the substitutions reported as resistant are A553T in subtype 1a [8] and A553V in subtype 1b [54] , conferring resistance to DSV. In contrast to our results, Noble and coworkers (2016) reported the presence of V321A, A421G, M414V, Y448H, L159F, and C316N in Brazilian isolates [17] , yet none of these mutations were found in this study, probably due to the diversity found between Uruguayan and Brazilian strains ( Figure 2 ). Nevertheless, substitution A421V was found in Brazil [17] , Argentina [19] , and Uruguay. The RAS S282T was detected neither in Brazilian reports nor in this current work (Uruguay) [17, 18, 56] . Our findings further confirm and complement previous studies which evidenced a low prevalence of this substitution in vivo, probably due to its low replicative fitness [14, 18, 57] . Despite our results, it is worth mentioning that the presence of baseline NS5B RASs conferring resistance to nucleotide or nonnucleoside NS5B inhibitors has not been shown to have any impact on virologic responses thus far [53, 58] . These results show both diversity in the baseline polymorphisms found in different Latin American countries and in the evolutionary relationships of Uruguayan isolates ( Figure 2 ). This fact could be linked not only to the isolates' geographic region and viral intrinsic characteristics but also to the genetic background of the host. It is worth mentioning that we live in a vast continent inhabited by populations with different genotypic characteristics that might, depending on the situation, require different approaches to treatment. Indeed, we have recently found that allele and genotype frequencies at IL28B locus of Uruguayan individuals closely resemble those of an admixed population rather than a uniformly European-descendant one [59] . Altogether, we believe that it could be important to carry out studies throughout the South American region in order to establish the prevalence of RASs in NS5A and NS5B in different countries. In fact, this will aid in understanding that not every treatment regimen might be adequate for every patient and country. The data we presented here might guide not only physicians in making therapeutic decisions but also public health authorities in approving more diverse treatment combinations. These treatment formulations would cover most of the circulating strains in our region, a region with an extremely diverse genetic background population. To our knowledge, the present study revealed for the first time the presence of RASs in the NS5A and NS5B regions of HCV genotype 1 Uruguayan strains from patients who have not been previously treated with DAAs and is one of the few South American countries to report on this matter. It is currently unclear if preexisting viral variants with reduced susceptibility to DAAs are clinically relevant for the prediction of virologic treatment failure. However, individualized DAA therapy based on baseline resistance analysis may be beneficial for optimizing treatment efficacy in patients with HCV genotype 1 infection and risk factors for treatment failure. Therefore, the potential role of baseline resistance testing remains an area of critical research and clinical questions. The data used to support the findings of this study are included within the article. The authors declare that they have no conflicts of interest. Fabián Aldunate and Natalia Echeverría contributed equally to this work. Supplementary Material Table S1 : hepatitis C Virus NS5A and NS5B sequences used as representatives of each genotype to perform the phylogenetic analysis. Their corresponding genotype, country of isolation, and GenBank accession number are indicated. Supplementary Material Table S2 : hepatitis C Virus NS5A subtype 1a sequences used to reveal evolutionary relationships between Uruguayan strains and others isolated elsewhere. Their corresponding country of isolation and GenBank accession number are indicated. Supplementary Material Table S3 : amino acid substitutions in NS5A protein not previously associated with resistance to NS5A inhibitors. Supplementary Material Table S4 : amino acid substitutions in NS5B protein not previously associated with resistance to polymerase inhibitors. (Supplementary Materials)

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Potential Rapid Diagnostics, Vaccine and Therapeutics for 2019 Novel Coronavirus (2019-nCoV): A Systematic Review https://doi.org/10.3390/jcm9030623 SHA: 9b0c87f808b1b66f2937d7a7acb524a756b6113b Authors: Pang, Junxiong; Wang, Min Xian; Ang, Ian Yi Han; Tan, Sharon Hui Xuan; Lewis, Ruth Frances; Chen, Jacinta I. Pei; Gutierrez, Ramona A.; Gwee, Sylvia Xiao Wei; Chua, Pearleen Ee Yong; Yang, Qian; Ng, Xian Yi; Yap, Rowena K. S.; Tan, Hao Yi; Teo, Yik Ying; Tan, Chorh Chuan; Cook, Alex R.; Yap, Jason Chin-Huat; Hsu, Li Yang Date: 2020 DOI: 10.3390/jcm9030623 License: cc-by Abstract: Rapid diagnostics, vaccines and therapeutics are important interventions for the management of the 2019 novel coronavirus (2019-nCoV) outbreak. It is timely to systematically review the potential of these interventions, including those for Middle East respiratory syndrome-Coronavirus (MERS-CoV) and severe acute respiratory syndrome (SARS)-CoV, to guide policymakers globally on their prioritization of resources for research and development. A systematic search was carried out in three major electronic databases (PubMed, Embase and Cochrane Library) to identify published studies in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Supplementary strategies through Google Search and personal communications were used. A total of 27 studies fulfilled the criteria for review. Several laboratory protocols for confirmation of suspected 2019-nCoV cases using real-time reverse transcription polymerase chain reaction (RT-PCR) have been published. A commercial RT-PCR kit developed by the Beijing Genomic Institute is currently widely used in China and likely in Asia. However, serological assays as well as point-of-care testing kits have not been developed but are likely in the near future. Several vaccine candidates are in the pipeline. The likely earliest Phase 1 vaccine trial is a synthetic DNA-based candidate. A number of novel compounds as well as therapeutics licensed for other conditions appear to have in vitro efficacy against the 2019-nCoV. Some are being tested in clinical trials against MERS-CoV and SARS-CoV, while others have been listed for clinical trials against 2019-nCoV. However, there are currently no effective specific antivirals or drug combinations supported by high-level evidence. Text: Since mid-December 2019 and as of early February 2020, the 2019 novel coronavirus (2019-nCoV) originating from Wuhan (Hubei Province, China) has infected over 25,000 laboratory-confirmed cases across 28 countries with about 500 deaths (a case-fatality rate of about 2%). More than 90% of the cases and deaths were in China [1] . Based on the initial reported surge of cases in Wuhan, the majority were males with a median age of 55 years and linked to the Huanan Seafood Wholesale Market [2] . Most of the reported cases had similar symptoms at the onset of illness such as fever, cough, and myalgia or fatigue. Most cases developed pneumonia and some severe and even fatal respiratory diseases such as acute respiratory distress syndrome [3] . The 2019 novel coronavirus (2019-nCoV), a betacoronavirus, forms a clade within the subgenus sarbecovirus of the Orthocoronavirinae subfamily [4] . The severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) are also betacoronaviruses that are zoonotic in origin and have been linked to potential fatal illness during the outbreaks in 2003 and 2012, respectively [5, 6] . Based on current evidence, pathogenicity for 2019-nCoV is about 3%, which is significantly lower than SARS-CoV (10%) and MERS-CoV (40%) [7] . However, 2019-nCoV has potentially higher transmissibility (R0: 1.4-5.5) than both SARS-CoV (R0: [2] [3] [4] [5] and MERS-CoV (R0: <1) [7] . With the possible expansion of 2019-nCoV globally [8] and the declaration of the 2019-nCoV outbreak as a Public Health Emergency of International Concern by the World Health Organization, there is an urgent need for rapid diagnostics, vaccines and therapeutics to detect, prevent and contain 2019-nCoV promptly. There is however currently a lack of understanding of what is available in the early phase of 2019-nCoV outbreak. The systematic review describes and assesses the potential rapid diagnostics, vaccines and therapeutics for 2019-nCoV, based in part on the developments for MERS-CoV and SARS-CoV. A systematic search was carried out in three major electronic databases (PubMed, Embase and Cochrane Library) to identify published studies examining the diagnosis, therapeutic drugs and vaccines for Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS) and the 2019 novel coronavirus (2019-nCoV), in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. There were two independent reviewers each focusing on SARS, MERS, and 2019-nCoV, respectively. A third independent reviewer was engaged to resolve any conflicting article of interest. We used the key words "SARS", "coronavirus", "MERS", "2019 Novel coronavirus", "Wuhan virus" to identify the diseases in the search strategy. The systematic searches for diagnosis, therapeutic drugs and vaccines were carried out independently and the key words "drug", "therapy", "vaccine", "diagnosis", "point of care testing" and "rapid diagnostic test" were used in conjunction with the disease key words for the respective searches. Examples of search strings can be found in Table S1 . We searched for randomized controlled trials (RCTs) and validation trials (for diagnostics test) published in English, that measured (a) the sensitivity and/or specificity of a rapid diagnostic test or a point-of-care testing kit, (b) the impact of drug therapy or (c) vaccine efficacy against either of these diseases with no date restriction applied. For the 2019-nCoV, we searched for all in vitro, animal, or human studies published in English between 1 December 2019 and 6 February 2020, on the same outcomes of interest. In addition, we reviewed the references of retrieved articles in order to identify additional studies or reports not retrieved by the initial searches. Studies that examined the mechanisms of diagnostic tests, drug therapy or vaccine efficacy against SARS, MERS and 2019-nCoV were excluded. A Google search for 2019-nCoV diagnostics (as of 6 February 2020; Table S2 ) yielded five webpage links from government and international bodies with official information and guidelines (WHO, Europe CDC, US CDC, US FDA), three webpage links on diagnostic protocols and scientific commentaries, and five webpage links on market news and press releases. Six protocols for diagnostics using reverse transcriptase polymerase chain reaction (RT-PCR) from six countries were published on WHO's website [9] . Google search for 2019-nCoV vaccines yielded 19 relevant articles. With the emergence of 2019-nCoV, real time RT-PCR remains the primary means for diagnosing the new virus strain among the many diagnostic platforms available ( [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] ; Table S3 ). Among the 16 diagnostics studies selected, one study discussed the use of RT-PCR in diagnosing patients with 2019-nCoV [11] ( Table 1 ). The period and type of specimen collected for RT-PCR play an important role in the diagnosis of 2019-nCoV. It was found that the respiratory specimens were positive for the virus while serum was negative in the early period. It has also suggested that in the early days of illness, patients have high levels of virus despite the mild symptoms. Apart from the commonly used RT-PCR in diagnosing MERS-CoV, four studies identified various diagnostic methods such as reverse transcription loop-mediated isothermal amplification (RT-LAMP), RT-insulated isothermal PCR (RT-iiPCR) and a one-step rRT-PCR assay based on specific TaqMan probes. RT-LAMP has similar sensitivity as real time RT-PCR. It is also highly specific and is used to detect MERS-CoV. It is comparable to the usual diagnostic tests and is rapid, simple and convenient. Likewise, RT-iiPCR and a one-step rRT-PCR assay have also shown similar sensitivity and high specificity for MER-CoV. Lastly, one study focused on the validation of the six commercial real RT-PCR kits, with high accuracy. Although real time RT-PCR is a primary method for diagnosing MERS-CoV, high levels of PCR inhibition may hinder PCR sensitivity (Table 1) . There are eleven studies that focus on SARS-CoV diagnostic testing (Table 1) . These papers described diagnostic methods to detect the virus with the majority of them using molecular testing for diagnosis. Comparison between the molecular test (i.e RT-PCR) and serological test (i.e., ELISA) showed that the molecular test has better sensitivity and specificity. Hence, enhancements to the current molecular test were conducted to improve the diagnosis. Studies looked at using nested PCR to include a pre-amplification step or incorporating N gene as an additional sensitive molecular marker to improve on the sensitivity (Table 1 ). In addition, there are seven potential rapid diagnostic kits (as of 24 January 2020; Table 2 ) available on the market for 2019-nCoV. Six of these are only for research purposes. Only one kit from Beijing Genome Institute (BGI) is approved for use in the clinical setting for rapid diagnosis. Most of the kits are for RT-PCR. There were two kits (BGI, China and Veredus, Singapore) with the capability to detect multiple pathogens using sequencing and microarray technologies, respectively. The limit of detection of the enhanced realtime PCR method was 10 2 -fold higher than the standard real-time PCR assay and 10 7fold higher than conventional PCR methods In the clinical aspect, the enhanced realtime PCR method was able to detect 6 cases of SARS-CoV positive samples that were not confirmed by any other assay [25] • The real time PCR has a threshold sensitivity of 10 genome equivalents per reaction and it has a good reproducibility with the inter-assay coefficients of variation of 1.73 to 2.72%. • 13 specimens from 6 patients were positive with viral load range from 362 to 36,240,000 genome equivalents/mL. The real-time RT-PCR reaction was more sensitive than the nested PCR reaction, as the detection limit for the nested PCR reaction was about 10 3 genome equivalents in the standard cDNA control. [34] Real-time reverse-transcription PCR (rRT-PCR); RNA-dependent RNA polymerase (RdRp); open reading frame 1a (ORF1a); Loop-mediated isothermal amplification (LAMP); enzyme-linked immunosorbent assay (ELISA); immunofluorescent assay (IFA); immunochromatographic test (ICT); nasopharyngeal aspirate (NPA). With the emergence of 2019-nCoV, there are about 15 potential vaccine candidates in the pipeline globally (Table 3 ), in which a wide range of technology (such as messenger RNA, DNA-based, nanoparticle, synthetic and modified virus-like particle) was applied. It will likely take about a year for most candidates to start phase 1 clinical trials except for those funded by Coalition for Epidemic Preparedness Innovations (CEPI). However, the kit developed by the BGI have passed emergency approval procedure of the National Medical Products Administration, and are currently used in clinical and surveillance centers of China [40] . Of the total of 570 unique studies on 2019-nCoV, SARS CoV or MERS-CoV vaccines screened, only four were eventually included in the review. Most studies on SARS and MERS vaccines were excluded as they were performed in cell or animal models ( Figure 1 ). The four studies included in this review were Phase I clinical trials on SARS or MERS vaccines (Table 4 ) [44] [45] [46] [47] . There were no studies of any population type (cell, animal, human) on the 2019-nCoV at the point of screening. The published clinical trials were mostly done in United States except for one on the SARS vaccine done in China [44] . All vaccine candidates for SARS and MERS were reported to be safe, well-tolerated and able to trigger the relevant and appropriate immune responses in the participants. In addition, we highlight six ongoing Phase I clinical trials identified in the ClinicalTrials.gov register ( [48, 49] ); Table S4 ) [50] [51] [52] . These trials are all testing the safety and immunogenicity of their respective MERS-CoV vaccine candidates but were excluded as there are no results published yet. The trials are projected to complete in December 2020 (two studies in Russia [50, 51] ) and December 2021 (in Germany [52] ). Existing literature search did not return any results on completed 2019-nCoV trials at the time of writing. Among 23 trials found from the systematic review (Table 5) , there are nine clinical trials registered under the clinical trials registry (ClinicalTrials.gov) for 2019-nCoV therapeutics [53] [54] [55] [56] [57] [58] [59] [60] [61] . Of which five studies on hydroxychloroquine, lopinavir plus ritonavir and arbidol, mesenchymal stem cells, traditional Chinese medicine and glucocorticoid therapy usage have commenced recruitment. The remaining four studies encompass investigation of antivirals, interferon atomization, darunavir and cobicistat, arbidol, and remdesivir usage for 2019-nCoV patients (Table 5) . Seroconversion measured by S1-ELISA occurred in 86% and 94% participants after 2 and 3 doses, respectively, and was maintained in 79% participants up to study end at week 60. Neutralising antibodies were detected in 50% participants at one or more time points during the study, but only 3% maintained neutralisation activity to end of study. T-cell responses were detected in 71% and 76% participants after 2 and 3 doses, respectively. There were no differences in immune responses between dose groups after 6 weeks and vaccine-induced humoral and cellular responses were respectively detected in 77% and 64% participants at week 60. [47] Molecules developed by the university scientists inhibit two coronavirus enzymes and prevent its replication. The discovered drug targets are said to be more than 95% similar to enzyme targets found on the SARS virus. Researchers note that identified drugs may not be available to address the ongoing outbreak but they hope to make it accessible for future outbreaks. [85] Besides the six completed randomized controlled trials (RCT) selected from the systematic review (Table 6) , there is only one ongoing randomized controlled trial targeted at SARS therapeutics [92] . The studies found from ClinicalTrials.gov have not been updated since 2013. While many prospective and retrospective cohort studies conducted during the epidemic centered on usage of ribavirin with lopinavir/ritonavir or ribavirin only, there has yet to be well-designed clinical trials investigating their usage. Three completed randomized controlled trials were conducted during the SARS epidemic-3 in China, 1 in Taiwan and 2 in Hong Kong [93] [94] [95] [96] [97] . The studies respectively investigated antibiotic usage involving 190 participants, combination of western and Chinese treatment vs. Chinese treatment in 123 participants, integrative Chinese and Western treatment in 49 patients, usage of a specific Chinese medicine in four participants and early use of corticosteroid in 16 participants. Another notable study was an open non-randomized study investigating ribavirin/lopinavir/ritonavir usage in 152 participants [98] . One randomized controlled trial investigating integrative western and Chinese treatment during the SARS epidemic was excluded as it was a Chinese article [94] . There is only one ongoing randomized controlled trial targeted at MERS therapeutics [99] . It investigates the usage of Lopinavir/Ritonavir and Interferon Beta 1B. Likewise, many prospective and retrospective cohort studies conducted during the epidemic centered on usage of ribavirin with lopinavir/ritonavir/ribavirin, interferon, and convalescent plasma usage. To date, only one trial has been completed. One phase 1 clinical trial investigating the safety and tolerability of a fully human polyclonal IgG immunoglobulin (SAB-301) was found in available literature [46] . The trial conducted in the United States in 2017 demonstrated SAB-301 to be safe and well-tolerated at single doses. Another trial on MERS therapeutics was found on ClinicalTrials.gov-a phase 2/3 trial in the United States evaluating the safety, tolerability, pharmacokinetics (PK), and immunogenicity on coadministered MERS-CoV antibodies REGN3048 & REGN3051 [100]. Rapid diagnostics plays an important role in disease and outbreak management. The fast and accurate diagnosis of a specific viral infection enables prompt and accurate public health surveillance, prevention and control measures. Local transmission and clusters can be prevented or delayed by isolation of laboratory-confirmed cases and their close contacts quarantined and monitored at home. Rapid diagnostic also facilitates other specific public health interventions such as closure of high-risk facilities and areas associated with the confirmed cases for prompt infection control and environmental decontamination [11, 101] . Laboratory diagnosis can be performed by: (a) detecting the genetic material of the virus, (b) detecting the antibodies that neutralize the viral particles of interest, (c) detecting the viral epitopes of interest with antibodies (serological testing), or (d) culture and isolation of viable virus particles. The key limitations of genetic material detection are the lack of knowledge of the presence of viable virus, the potential cross-reactivity with non-specific genetic regions and the short timeframe for accurate detection during the acute infection phase. The key limitations of serological testing is the need to collect paired serum samples (in the acute and convalescent phases) from cases under investigation for confirmation to eliminate potential cross-reactivity from non-specific antibodies from past exposure and/or infection by other coronaviruses. The limitation of virus culture and isolation is the long duration and the highly specialized skills required of the technicians to process the samples. All patients recovered. Significantly shorted time from the disease onset to the symptom improvement in treatment (5.10 ± 2.83 days) compared to control group (7.62 ± 2.27 days) (p < 0.05) No significant difference in blood routine improvement, pulmonary chest shadow in chest film improvement and corticosteroid usgae between the 2 groups. However, particularly in the respect of improving clinical symptoms, elevating quality of life, promoting immune function recovery, promoting absorption of pulmonary inflammation, reducing the dosage of cortisteroid and shortening the therapeutic course, treatment with integrative chinese and western medicine treatment had obvious superiority compared with using control treatment alone. Single infusions of SAB-301 up to 50 mg/kg appear to be safe and well-tolerated in healthy participants. [46] Where the biological samples are taken from also play a role in the sensitivity of these tests. For SARS-CoV and MERS-CoV, specimens collected from the lower respiratory tract such as sputum and tracheal aspirates have higher and more prolonged levels of viral RNA because of the tropism of the virus. MERS-CoV viral loads are also higher for severe cases and have longer viral shedding compared to mild cases. Although upper respiratory tract specimens such as nasopharyngeal or oropharyngeal swabs can be used, they have potentially lower viral loads and may have higher risk of false-negatives among the mild MERS and SARS cases [102, 103] , and likely among the 2019-nCoV cases. The existing practices in detecting genetic material of coronaviruses such as SARS-CoV and MERS-CoV include (a) reverse transcription-polymerase chain reaction (RT-PCR), (b) real-time RT-PCR (rRT-PCR), (c) reverse transcription loop-mediated isothermal amplification (RT-LAMP) and (d) real-time RT-LAMP [104] . Nucleic amplification tests (NAAT) are usually preferred as in the case of MERS-CoV diagnosis as it has the highest sensitivity at the earliest time point in the acute phase of infection [102] . Chinese health authorities have recently posted the full genome of 2019-nCoV in the GenBank and in GISAID portal to facilitate in the detection of the virus [11] . Several laboratory assays have been developed to detect the novel coronavirus in Wuhan, as highlighted in WHO's interim guidance on nCoV laboratory testing of suspected cases. These include protocols from other countries such as Thailand, Japan and China [105] . The first validated diagnostic test was designed in Germany. Corman et al. had initially designed a candidate diagnostic RT-PCR assay based on the SARS or SARS-related coronavirus as it was suggested that circulating virus was SARS-like. Upon the release of the sequence, assays were selected based on the match against 2019-nCoV upon inspection of the sequence alignment. Two assays were used for the RNA dependent RNA polymerase (RdRP) gene and E gene where E gene assay acts as the first-line screening tool and RdRp gene assay as the confirmatory testing. All assays were highly sensitive and specific in that they did not cross-react with other coronavirus and also human clinical samples that contained respiratory viruses [11] . The Hong Kong University used two monoplex assays which were reactive with coronaviruses under the subgenus Sarbecovirus (consisting of 2019-nCoV, SARS-CoV and SARS-like coronavirus). Viral RNA extracted from SARS-CoV can be used as the positive control for the suggested protocol assuming that SARS has been eradicated. It is proposed that the N gene RT-PCR can be used as a screening assay while the Orf1b assay acts as a confirmatory test. However, this protocol has only been evaluated with a panel of controls with the only positive control SARS-CoV RNA. Synthetic oligonucleotide positive control or 2019-nCoV have yet to be tested [106] . The US CDC shared the protocol on the real time RT-PCR assay for the detection of the 2019-nCoV with the primers and probes designed for the universal detection of SARS-like coronavirus and the specific detection of 2019-nCoV. However, the protocol has not been validated on other platforms or chemistries apart from the protocol described. There are some limitations for the assay. Analysts engaged have to be trained and familiar with the testing procedure and result interpretation. False negative results may occur due to insufficient organisms in the specimen resulting from improper collection, transportation or handling. Also, RNA viruses may show substantial genetic variability. This could result in mismatch between the primer and probes with the target sequence which can diminish the assay performance or result in false negative results [107] . Point-of-care test kit can potentially minimize these limitations, which should be highly prioritized for research and development in the next few months. Serological testing such as ELISA, IIFT and neutralization tests are effective in determining the extent of infection, including estimating asymptomatic and attack rate. Compared to the detection of viral genome through molecular methods, serological testing detects antibodies and antigens. There would be a lag period as antibodies specifically targeting the virus would normally appear between 14 and 28 days after the illness onset [108] . Furthermore, studies suggest that low antibody titers in the second week or delayed antibody production could be associated with mortality with a high viral load. Hence, serological diagnoses are likely used when nucleic amplification tests (NAAT) are not available or accessible [102] . Vaccines can prevent and protect against infection and disease occurrence when exposed to the specific pathogen of interest, especially in vulnerable populations who are more prone to severe outcomes. In the context of the current 2019-nCoV outbreak, vaccines will help control and reduce disease transmission by creating herd immunity in addition to protecting healthy individuals from infection. This decreases the effective R0 value of the disease. Nonetheless, there are social, clinical and economic hurdles for vaccine and vaccination programmes, including (a) the willingness of the public to undergo vaccination with a novel vaccine, (b) the side effects and severe adverse reactions of vaccination, (c) the potential difference and/or low efficacy of the vaccine in populations different from the clinical trials' populations and (d) the accessibility of the vaccines to a given population (including the cost and availability of the vaccine). Vaccines against the 2019-nCoV are currently in development and none are in testing (at the time of writing). On 23 January 2020, the Coalition for Epidemic Preparedness Innovations (CEPI) announced that they will fund vaccine development programmes with Inovio, The University of Queensland and Moderna, Inc respectively, with the aim to test the experimental vaccines clinically in 16 weeks (By June 2020). The vaccine candidates will be developed by the DNA, recombinant and mRNA vaccine platforms from these organizations [109] . Based on the most recent MERS-CoV outbreak, there are already a number of vaccine candidates being developed but most are still in the preclinical testing stage. The vaccines in development include viral vector-based vaccine, DNA vaccine, subunit vaccine, virus-like particles (VLPs)-based vaccine, inactivated whole-virus (IWV) vaccine and live attenuated vaccine. The latest findings for these vaccines arebased on the review by Yong et al. (2019) in August 2019 [110] . As of the date of reporting, there is only one published clinical study on the MERS-CoV vaccine by GeneOne Life Science & Inovio Pharmaceuticals [47] . There was one SARS vaccine trial conducted by the US National Institute of Allergy and Infectious Diseases. Both Phase I clinical trials reported positive results, but only one has announced plans to proceed to Phase 2 trial [111] . Due to the close genetic relatedness of SARS-CoV (79%) with 2019-nCoV [112] , there may be potential cross-protective effect of using a safe SARS-CoV vaccine while awaiting the 2019-nCoV vaccine. However, this would require small scale phase-by-phase implementation and close monitoring of vaccinees before any large scale implementation. Apart from the timely diagnosis of cases, the achievement of favorable clinical outcomes depends on the timely treatment administered. ACE2 has been reported to be the same cell entry receptor used by 2019-nCoV to infect humans as SARS-CoV [113] . Hence, clinical similarity between the two viruses is expected, particularly in severe cases. In addition, most of those who have died from MERS-CoV, SARS-CoV and 2019-nCoV were advance in age and had underlying health conditions such as hypertension, diabetes or cardiovascular disease that compromised their immune systems [114] . Coronaviruses have error-prone RNA-dependent RNA polymerases (RdRP), which result in frequent mutations and recombination events. This results in quasispecies diversity that is closely associated with adaptive evolution and the capacity to enhance viral-cell entry to cause disease over time in a specific population at-risk [115] . Since ACE2 is abundantly present in humans in the epithelia of the lung and small intestine, coronaviruses are likely to infect the upper respiratory and gastrointestinal tract and this may influence the type of therapeutics against 2019-nCoV, similarly to SAR-CoV. However, in the years following two major coronavirus outbreaks SARS-CoV in 2003 and MERS-CoV in 2012, there remains no consensus on the optimal therapy for either disease [116, 117] . Well-designed clinical trials that provide the gold standard for assessing the therapeutic measures are scarce. No coronavirus protease inhibitors have successfully completed a preclinical development program despite large efforts exploring SARS-CoV inhibitors. The bulk of potential therapeutic strategies remain in the experimental phase, with only a handful crossing the in vitro hurdle. Stronger efforts are required in the research for treatment options for major coronaviruses given their pandemic potential. Effective treatment options are essential to maximize the restoration of affected populations to good health following infections. Clinical trials have commenced in China to identify effective treatments for 2019-nCoV based on the treatment evidence from SARS and MERS. There is currently no effective specific antiviral with high-level evidence; any specific antiviral therapy should be provided in the context of a clinical study/trial. Few treatments have shown real curative action against SARS and MERS and the literature generally describes isolated cases or small case series. Many interferons from the three classes have been tested for their antiviral activities against SARS-CoV both in vitro and in animal models. Interferon β has consistently been shown to be the most active, followed by interferon α. The use of corticosteroids with interferon alfacon-1 (synthetic interferon α) appeared to have improved oxygenation and faster resolution of chest radiograph abnormalities in observational studies with untreated controls. Interferon has been used in multiple observational studies to treat SARS-CoV and MERS-CoV patients [116, 117] . Interferons, with or without ribavirin, and lopinavir/ritonavir are most likely to be beneficial and are being trialed in China for 2019-nCoV. This drug treatment appears to be the most advanced. Timing of treatment is likely an important factor in effectiveness. A combination of ribavirin and lopinavir/ritonavir was used as a post-exposure prophylaxis in health care workers and may have reduced the risk of infection. Ribavirin alone is unlikely to have substantial antiviral activities at clinically used dosages. Hence, ribavirin with or without corticosteroids and with lopinavir and ritonavir are among the combinations employed. This was the most common agent reported in the available literature. Its efficacy has been assessed in observational studies, retrospective case series, retrospective cohort study, a prospective observational study, a prospective cohort study and randomized controlled trial ranging from seven to 229 participants [117] . Lopinavir/ritonavir (Kaletra) was the earliest protease inhibitor combination introduced for the treatment of SARS-CoV. Its efficacy was documented in several studies, causing notably lower incidence of adverse outcomes than with ribavirin alone. Combined usage with ribavirin was also associated with lower incidence of acute respiratory distress syndrome, nosocomial infection and death, amongst other favorable outcomes. Recent in vitro studies have shown another HIV protease inhibitor, nelfinavir, to have antiviral capacity against SARS-CoV, although it has yet to show favorable outcomes in animal studies [118] . Remdesivir (Gilead Sciences, GS-5734) nucleoside analogue in vitro and in vivo data support GS-5734 development as a potential pan-coronavirus antiviral based on results against several coronaviruses (CoVs), including highly pathogenic CoVs and potentially emergent BatCoVs. The use of remdesivir may be a good candidate as an investigational treatment. Improved mortality following receipt of convalescent plasma in various doses was consistently reported in several observational studies involving cases with severe acute respiratory infections (SARIs) of viral etiology. A significant reduction in the pooled odds of mortality following treatment of 0.25 compared to placebo or no therapy was observed [119] . Studies were however at moderate to high risk of bias given their small sample sizes, allocation of treatment based on the physician's discretion, and the availability of plasma. Factors like concomitant treatment may have also confounded the results. Associations between convalescent plasma and hospital length of stay, viral antibody levels, and viral load respectively were similarly inconsistent across available literature. Convalescent plasma, while promising, is likely not yet feasible, given the limited pool of potential donors and issues of scalability. Monoclonal antibody treatment is progressing. SARS-CoV enters host cells through the binding of their spike (S) protein to angiotensin converting enzyme 2 (ACE2) and CD209L [118] . Human monoclonal antibodies to the S protein have been shown to significantly reduce the severity of lung pathology in non-human primates following MERS-CoV infection [120] . Such neutralizing antibodies can be elicited by active or passive immunization using vaccines or convalescent plasma respectively. While such neutralizing antibodies can theoretically be harvested from individuals immunized with vaccines, there is uncertainty over the achievement of therapeutic levels of antibodies. Other therapeutic agents have also been reported. A known antimalarial agent, chloroquine, elicits antiviral effects against multiple viruses including HIV type 1, hepatitis B and HCoV-229E. Chloroquine is also immunomodulatory, capable of suppressing the production and release of factors which mediate the inflammatory complications of viral diseases (tumor necrosis factor and interleukin 6) [121] . It is postulated that chloroquine works by altering ACE2 glycosylation and endosomal pH. Its anti-inflammatory properties may be beneficial for the treatment of SARS. Niclosamide as a known drug used in antihelminthic treatment. The efficacy of niclosamide as an inhibitor of virus replication was proven in several assays. In both immunoblot analysis and immunofluorescence assays, niclosamide treatment was observed to completely inhibit viral antigen synthesis. Reduction of virus yield in infected cells was dose dependent. Niclosamide likely does not interfere in the early stages of virus attachment and entry into cells, nor does it function as a protease inhibitor. Mechanisms of niclosamide activity warrant further investigation [122] . Glycyrrhizin also reportedly inhibits virus adsorption and penetration in the early steps of virus replication. Glycyrrhizin was a significantly potent inhibitor with a low selectivity index when tested against several pathogenic flaviviruses. While preliminary results suggest production of nitrous oxide (which inhibits virus replication) through induction of nitrous oxide synthase, the mechanism of Glycyrrhizin against SARS-CoV remains unclear. The compound also has relatively lower toxicity compared to protease inhibitors like ribavirin [123] . Inhibitory activity was also detected in baicalin [124] , extracted from another herb used in the treatment of SARS in China and Hong Kong. Findings on these compounds are limited to in vitro studies [121] [122] [123] [124] . Due to the rapidly evolving situation of the 2019-nCoV, there will be potential limitations to the systematic review. The systematic review is likely to have publication bias as some developments have yet to be reported while for other developments there is no intention to report publicly (or in scientific platforms) due to confidentiality concerns. However, this may be limited to only a few developments for review as publicity does help in branding to some extent for the company and/or the funder. Furthermore, due to the rapid need to share the status of these developments, there may be reporting bias in some details provided by authors of the scientific articles or commentary articles in traditional media. Lastly, while it is not viable for any form of quality assessment and metaanalysis of the selected articles due to the limited data provided and the heterogeneous style of reporting by different articles, this paper has provided a comprehensive overview of the potential developments of these pharmaceutical interventions during the early phase of the outbreak. This systematic review would be useful for cross-check when the quality assessment and meta-analysis of these developments are performed as a follow-up study. Rapid diagnostics, vaccines and therapeutics are key pharmaceutical interventions to limit transmission of respiratory infectious diseases. Many potential developments on these pharmaceutical interventions for 2019-nCoV are ongoing in the containment phase of this outbreak, potentially due to better pandemic preparedness than before. However, lessons from MERS-CoV and SARS-CoV have shown that the journeys for these developments can still be challenging moving ahead. Supplementary Materials: The following are available online at www.mdpi.com/xxx/s1, Table S1 : Example of full search strategy in Pubmed, Table S2 : Google Search: 2019-nCoV diagnostics, Table S3 : Summary of diagnostic assays developed for 2019-nCoV, Table S4

What are roles of the period and type of specimens?

Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus (SARS-CoV-Z) and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown (11th March), although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March [95% credible interval 21,000-120,000]. Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London (2020), doi: https://doi.org/10.25561/77731 1 Introduction Following the emergence of a novel coronavirus (SARS-CoV-Z) in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing (such as banning large gatherings and advising individuals not to socialize outside their households), border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase (dependent on how much greater than 1 the reproduction number is) until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups (e.g. contacts of cases). Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds (Bayesian credible intervals) of the total populations infected (attack rates), case detection probabilities, and the reproduction number over time (Rt). We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards (Figure 1). Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced (Figure 2- 12). Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected (Figure 2) than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 [1.9-15.2] million people have been infected as of March 28th, giving an attack rate of 9.8% [3.2%-25%] of the population (Table 1). Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% (7.0 [18-19] million people) have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 [240,000-1,500,000] people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected (mean [95% credible intervall) Austria 1.1% [0.36%-3.1%] Belgium 3.7% [1.3%-9.7%] Denmark 1.1% [0.40%-3.1%] France 3.0% [1.1%-7.4%] Germany 0.72% [0.28%-1.8%] Italy 9.8% [3.2%-26%] Norway 0.41% [0.09%-1.2%] Spain 15% [3.7%-41%] Sweden 3.1% [0.85%-8.4%] Switzerland 3.2% [1.3%-7.6%] United Kingdom 2.7% [1.2%-5.4%] 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 [3.01-4.66], which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers (Appendix 8.4.2, Appendix 8.4.6). The initial reproduction numbers are also uncertain due to (a) importation being the dominant source of new infections early in the epidemic, rather than local transmission (b) possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths (e.g. Italy, Spain), suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 [0.14-2.14] for Norway to a posterior mean of2.64 [1.40-4.18] for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 (posterior probability of being less than 1.0 is 44% on average across the countries). We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic (e.g. Germany, UK, Norway). Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well (Bayesian goodness of fit tests). We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths (Appendix 8.3). The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect (Figure 1, Figure 4). However, when doing a sensitivity analysis (Appendix 8.4.3) with uninformative prior distributions (where interventions can increase deaths) we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval (CI), light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model (assuming no intervention had taken place). Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented (and R, = R0 i.e. the initial reproduction number estimated before interventions). Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed (Figure 3). Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C). By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 [21,000-120,000] deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 [13,000- 84,000] and 16,000 [5,400-35,000] deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 [73-1,000] deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions (up to 28 March) and forecasts (after) for (a) Italy and (b) Spain from our model with interventions (blue) and from the no interventions counterfactual model (pink); credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% (no effect on transmissibility) (ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 (red) or was subsequent to other interventions (green). Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% [l.9%-ll%] with considerable variation between countries (Table 1). Our estimates imply that the populations in Europe are not close to herd immunity ("50-75% if R0 is 2-4). Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths (Italy, Spain). lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified (see Appendix 8.4 for sensitivity analysis). While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear (on the logarithmic scale) reduction in deaths (see Figure 10). The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher (38,000 deaths averted) than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions (e.g. Italy, Spain) strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths (e.g. Germany, UK). We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, (see the sensitivity analysis reported in Appendix 8.4.3) and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC (European Centre of Disease Control), where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing (for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely). In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed (ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards). Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals (e.g. UK) or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police (e.g. France). The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples (Norway/Denmark) have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 (details in Appendix 8.1 and 8.2). Replication code is available at https://github.com/|mperia|CollegeLondon/covid19model/releases/tag/vl.0 We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution (time from infection to the onset of symptoms), an onset-to-death distribution (time from the onset of symptoms to death), and the population-averaged infection fatality ratio (adjusted for the age structure and contact patterns of each country, see Appendix). Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution (the average time from infection of one person to the time at which they infect another) and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths (and get stronger as time progresses). To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust (Appendix 8.4). 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E(Dam) that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths (1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr (probability of death given infection)9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates (the unadjusted ifr, referred to here as ifr’) in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period (infection to onset of symptoms or infection- to-onset) distribution and the time between onset of symptoms and death (onset-to-death). The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ (Gamma(5.1,0.86) + Gamma(18.8,0.45)) Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution (mean 23.9 days). Right, survival probability of infected individuals per day given the infection fatality ratio (1%) and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g(T), (the time between when a person gets infected and when they subsequently infect another other people), which we choose to be Gamma distributed: g ~ Gamma (6.50.62). The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg(T)dT fors = 2,3, and 91 = fT=Og(T)dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures (k=l), self—isolating if ill (k=2), banning of public events (k=3), any government intervention in place (k=4), implementing a partial or complete lockdown (k=5) and encouraging social distancing and isolation (k=6). We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” (k=4) indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp(— 212:1 O(Rheum)- The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0(1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma(. 5,1). The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal(2.4, IKI) with K ~ Normal(0,0.5), Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential(T), where T~Exponential(0.03). These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo (HMC) sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed (see below). 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data (non-cumulative) and fit our model. We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo (MCMC) chains have converged to the equilibrium distribution (the correct posterior distribution). Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval (SI) distributions (means between 5 and 8 days). We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval (SI) distribution means between 5 and 8 days. We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below (compare with the main analysis in Figure 4). In this series of univariate analyses, we find (Figure 15) that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others (such as the UK). To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g(r) with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = (1 + %) .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 (red) vs R(FO) (black) Figure 18: Our estimated R0 (black) versus theoretically derived Ru(red) from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions (up to 28 March) and forecasts (after) for all countries except Italy and Spain from our model with interventions (blue) and from the no interventions counterfactual model (pink); credible intervals are shown one week into the future. DOI: https://doi.org/10.25561/77731 Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities (primary ordered schools also shut on 16th).26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states (kantons).54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV2). Science (2020) doi:10.1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11(2019) 3. Worldometers.info. Hong Kong: coronavirus cases. https://www.wo rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4. Ferguson, N. et al. 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What is an alternative way to estimate the course of an epidemic?

Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus (SARS-CoV-Z) and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown (11th March), although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March [95% credible interval 21,000-120,000]. Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London (2020), doi: https://doi.org/10.25561/77731 1 Introduction Following the emergence of a novel coronavirus (SARS-CoV-Z) in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing (such as banning large gatherings and advising individuals not to socialize outside their households), border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase (dependent on how much greater than 1 the reproduction number is) until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups (e.g. contacts of cases). Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds (Bayesian credible intervals) of the total populations infected (attack rates), case detection probabilities, and the reproduction number over time (Rt). We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards (Figure 1). Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced (Figure 2- 12). Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected (Figure 2) than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 [1.9-15.2] million people have been infected as of March 28th, giving an attack rate of 9.8% [3.2%-25%] of the population (Table 1). Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% (7.0 [18-19] million people) have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 [240,000-1,500,000] people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected (mean [95% credible intervall) Austria 1.1% [0.36%-3.1%] Belgium 3.7% [1.3%-9.7%] Denmark 1.1% [0.40%-3.1%] France 3.0% [1.1%-7.4%] Germany 0.72% [0.28%-1.8%] Italy 9.8% [3.2%-26%] Norway 0.41% [0.09%-1.2%] Spain 15% [3.7%-41%] Sweden 3.1% [0.85%-8.4%] Switzerland 3.2% [1.3%-7.6%] United Kingdom 2.7% [1.2%-5.4%] 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 [3.01-4.66], which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers (Appendix 8.4.2, Appendix 8.4.6). The initial reproduction numbers are also uncertain due to (a) importation being the dominant source of new infections early in the epidemic, rather than local transmission (b) possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths (e.g. Italy, Spain), suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 [0.14-2.14] for Norway to a posterior mean of2.64 [1.40-4.18] for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 (posterior probability of being less than 1.0 is 44% on average across the countries). We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic (e.g. Germany, UK, Norway). Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well (Bayesian goodness of fit tests). We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths (Appendix 8.3). The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect (Figure 1, Figure 4). However, when doing a sensitivity analysis (Appendix 8.4.3) with uninformative prior distributions (where interventions can increase deaths) we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval (CI), light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model (assuming no intervention had taken place). Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented (and R, = R0 i.e. the initial reproduction number estimated before interventions). Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed (Figure 3). Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C). By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 [21,000-120,000] deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 [13,000- 84,000] and 16,000 [5,400-35,000] deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 [73-1,000] deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions (up to 28 March) and forecasts (after) for (a) Italy and (b) Spain from our model with interventions (blue) and from the no interventions counterfactual model (pink); credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% (no effect on transmissibility) (ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 (red) or was subsequent to other interventions (green). Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% [l.9%-ll%] with considerable variation between countries (Table 1). Our estimates imply that the populations in Europe are not close to herd immunity ("50-75% if R0 is 2-4). Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths (Italy, Spain). lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified (see Appendix 8.4 for sensitivity analysis). While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear (on the logarithmic scale) reduction in deaths (see Figure 10). The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher (38,000 deaths averted) than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions (e.g. Italy, Spain) strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths (e.g. Germany, UK). We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, (see the sensitivity analysis reported in Appendix 8.4.3) and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC (European Centre of Disease Control), where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing (for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely). In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed (ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards). Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals (e.g. UK) or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police (e.g. France). The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples (Norway/Denmark) have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 (details in Appendix 8.1 and 8.2). Replication code is available at https://github.com/|mperia|CollegeLondon/covid19model/releases/tag/vl.0 We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution (time from infection to the onset of symptoms), an onset-to-death distribution (time from the onset of symptoms to death), and the population-averaged infection fatality ratio (adjusted for the age structure and contact patterns of each country, see Appendix). Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution (the average time from infection of one person to the time at which they infect another) and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths (and get stronger as time progresses). To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust (Appendix 8.4). 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E(Dam) that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths (1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr (probability of death given infection)9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates (the unadjusted ifr, referred to here as ifr’) in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period (infection to onset of symptoms or infection- to-onset) distribution and the time between onset of symptoms and death (onset-to-death). The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ (Gamma(5.1,0.86) + Gamma(18.8,0.45)) Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution (mean 23.9 days). Right, survival probability of infected individuals per day given the infection fatality ratio (1%) and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g(T), (the time between when a person gets infected and when they subsequently infect another other people), which we choose to be Gamma distributed: g ~ Gamma (6.50.62). The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg(T)dT fors = 2,3, and 91 = fT=Og(T)dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures (k=l), self—isolating if ill (k=2), banning of public events (k=3), any government intervention in place (k=4), implementing a partial or complete lockdown (k=5) and encouraging social distancing and isolation (k=6). We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” (k=4) indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp(— 212:1 O(Rheum)- The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0(1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma(. 5,1). The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal(2.4, IKI) with K ~ Normal(0,0.5), Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential(T), where T~Exponential(0.03). These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo (HMC) sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed (see below). 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data (non-cumulative) and fit our model. We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo (MCMC) chains have converged to the equilibrium distribution (the correct posterior distribution). Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval (SI) distributions (means between 5 and 8 days). We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval (SI) distribution means between 5 and 8 days. We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below (compare with the main analysis in Figure 4). In this series of univariate analyses, we find (Figure 15) that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others (such as the UK). To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g(r) with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = (1 + %) .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 (red) vs R(FO) (black) Figure 18: Our estimated R0 (black) versus theoretically derived Ru(red) from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions (up to 28 March) and forecasts (after) for all countries except Italy and Spain from our model with interventions (blue) and from the no interventions counterfactual model (pink); credible intervals are shown one week into the future. DOI: https://doi.org/10.25561/77731 Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities (primary ordered schools also shut on 16th).26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states (kantons).54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV2). Science (2020) doi:10.1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11(2019) 3. Worldometers.info. Hong Kong: coronavirus cases. https://www.wo rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4. Ferguson, N. et al. 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Why is it hard to know the true incidence of infections number?

Interactome analysis of the lymphocytic choriomeningitis virus nucleoprotein in infected cells reveals ATPase Na(+)/K(+) transporting subunit Alpha 1 and prohibitin as host-cell factors involved in the life cycle of mammarenaviruses https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5834214/ SHA: efbd0dfc426da5dd25ce29411d6fa37571623773 Authors: Iwasaki, Masaharu; Minder, Petra; Caì, Yíngyún; Kuhn, Jens H.; Yates, John R.; Torbett, Bruce E.; de la Torre, Juan C. Date: 2018-02-20 DOI: 10.1371/journal.ppat.1006892 License: cc0 Abstract: Several mammalian arenaviruses (mammarenaviruses) cause hemorrhagic fevers in humans and pose serious public health concerns in their endemic regions. Additionally, mounting evidence indicates that the worldwide-distributed, prototypic mammarenavirus, lymphocytic choriomeningitis virus (LCMV), is a neglected human pathogen of clinical significance. Concerns about human-pathogenic mammarenaviruses are exacerbated by of the lack of licensed vaccines, and current anti-mammarenavirus therapy is limited to off-label use of ribavirin that is only partially effective. Detailed understanding of virus/host-cell interactions may facilitate the development of novel anti-mammarenavirus strategies by targeting components of the host-cell machinery that are required for efficient virus multiplication. Here we document the generation of a recombinant LCMV encoding a nucleoprotein (NP) containing an affinity tag (rLCMV/Strep-NP) and its use to capture the NP-interactome in infected cells. Our proteomic approach combined with genetics and pharmacological validation assays identified ATPase Na(+)/K(+) transporting subunit alpha 1 (ATP1A1) and prohibitin (PHB) as pro-viral factors. Cell-based assays revealed that ATP1A1 and PHB are involved in different steps of the virus life cycle. Accordingly, we observed a synergistic inhibitory effect on LCMV multiplication with a combination of ATP1A1 and PHB inhibitors. We show that ATP1A1 inhibitors suppress multiplication of Lassa virus and Candid#1, a live-attenuated vaccine strain of Junín virus, suggesting that the requirement of ATP1A1 in virus multiplication is conserved among genetically distantly related mammarenaviruses. Our findings suggest that clinically approved inhibitors of ATP1A1, like digoxin, could be repurposed to treat infections by mammarenaviruses pathogenic for humans. Text: Introduction Mammarenaviruses (Arenaviridae: Mammarenavirus) cause chronic infections of rodents worldwide [1] . Invasion of human dwellings by infected rodents can result in human infections through mucosal exposure to aerosols or by direct contact of abraded skin with infectious material. Several mammarenaviruses cause viral hemorrhagic fevers (VHFs) in humans and pose important public health problems in their endemic areas [2] [3] [4] [5] [6] . Mammarenaviruses are classified into two main groups, Old World (OW) and New World (NW) [1] . The OW Lassa virus (LASV), causative agent of Lassa fever (LF), is the most significant OW mammarenaviral pathogen. LASV is estimated to infect several hundred thousand individuals annually in Western Africa, resulting in a high number of LF cases associated with high morbidity and lethality. Moreover, LASV endemic regions are expanding [7] , and the association of the recently identified mammarenavirus Lujo virus with a VHF outbreak in Southern Africa [8, 9] has raised concerns about the emergence of novel VHF-causing mammarenaviruses. The most significant NW mammarenavirus is Junín virus (JUNV), which causes Argentinian hemorrhagic fever [10] . The worldwide-distributed OW mammarenavirus lymphocytic choriomeningitis virus (LCMV) is a neglected human pathogen of clinical significance especially in congenital infections [11] [12] [13] [14] [15] . Moreover, LCMV poses a particular threat to immunocompromised individuals, as has been illustrated by fatal cases of LCMV infection associated with organ transplants [16, 17] . However, LCMV research can be safely performed at BSL-2 containment, rather than the BSL-4 containment necessary for live LASV or JUNV research [18] . No US Food and Drug Administration (FDA)-licensed vaccines are available for the treatment of arenavirus infections, although a live attenuated vaccine strain of JUNV, Candid#1, is licensed in Argentina. Likewise, current anti-mammarenavirus therapy is limited to an offlabel use of the nucleoside analogue ribavirin that is only partially effective and can cause significant side effects [19] [20] [21] . Development of effective anti-mammarenavirus drugs has been hampered by the lack of detailed understanding of virus/host-cell interactions required for mammarenavirus multiplication that could represent amenable targets for antiviral therapy. the potential problem that overexpression of a single viral gene product may potentiate PPI interactions that are not relevant during the course of a natural virus infection. To overcome this issue, we designed a recombinant LCMV (rLCMV) encoding a tandem [WSHPQFEK (GGGS) 3 WSHPQFEK] Strep-tag fused to the amino-terminus of NP (rLCMV/Strep-NP) (Fig 1A and 1B) . To facilitate the identification of specific PPI between NP and host cell proteins, we used our mammarenavirus tri-segmented (r3) platform [30] to design an r3LCMV expressing a C-terminus Strep-tag version of enhanced green fluorescent protein (r3LCMV/eGFP-Strep) that we used as a negative control (Fig 1A and 1B) . We rescued rLCMV/Strep-NP and r3LCMV/eGFP-Strep and confirmed the expression of strep-tagged NP and eGFP in rLCMV/ Strep-NP-and r3LCMV/eGFP-Strep-infected cells, respectively (Fig 1C) . Next, we examined the growth properties of rLCMV/Strep-NP and r3LCMV/eGFP-Strep in three different cells lines from hamsters, humans, and nonhuman primates (BHK-21, A549, and Vero E6 cells, respectively) (Fig 1D) . The fitness of rLCMV/Strep-NP and r3LCMV/eGFP-Strep was modestly decreased compared to that observed with wild-type (WT) Armstrong (rLCMV ARM) and Clone 13 (rLCMV Cl-13) strains of LCMV. However, both rLCMV/Strep-NP and r3LCMV/eGFP-Strep had WT-like growth kinetics and reached high titers. As with WT LCMV, infection with rLCMV/Strep-NP prevented production of bioactive IFN-I by cells in response to Sendai virus (SeV) infection as determined using an IFN bioassay based on protection against the cytopathic effect (CPE) induced by infection with vesicular stomatitis virus (VSV) (Fig 1E) . Vero cells treated for 16 h with tissue cultured supernatants (TCS) from A549 cells infected first with WT LCMV or rLCMV/Strep, followed by 24 h infection with SeV, remained fully susceptible to VSV-induced CPE. In contrast, Vero cells treated with TCS from A549 cells infected with rLCMV/NP(D382A), a mutant unable to prevent induction of IFN-I [30] , and subsequently with SeV, were protected against VSV induced CPE. We selected the human A549 cell line because lung epithelial cells are one of the initial cell targets of humans following inhalation of mammarenavirions. We infected A549 cells (multiplicity of infection [MOI] = 0.1) with either rLCMV/Strep-NP or r3LCMV/eGFP-Strep (Fig 2A) . At 48 h post-inoculation (pi), we prepared total cell lysates for pull-down (PD) assays using a sepharose resin coated with strep-tactin. Aliquots of the protein complexes present in the PD samples were fractionated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) (Fig 2B) followed by SYPRO Ruby protein gel staining. We compared the pattern of stained protein bands detected between rLCMV/Strep-NP-and r3LCMV/eGFP-Strepinfected samples and confirmed the presence of Strep-NP and eGFP-Strep in pertinent samples (Fig 2B) . Protein complexes in the rest of eluates from the PD samples were concentrated by trichloroacetic acid (TCA) precipitation and subjected to trypsin digestion (Fig 2A) . Digested peptides were subjected to liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis using a hybrid mass spectrometer consisting of linear quadrupole ion dual cell trap (LTQ) Velos and an Orbitrap analyser. We classified host-cell proteins identified by LC-MS/MS analysis from two independent biological replicates into two groups: 1) proteins found only in Strep-NP PD samples with at least five spectral counts ( Table 1) , and 2) proteins found in both Strep-NP and eGFP-Strep PD samples with five or higher spectral counts in Strep-NP samples and at least 2-fold higher spectral counts in Strep-NP PD compared to eGFP PD samples ( Table 2) . Filtering the data using these criteria resulted in identification of 139 candidate host-cell proteins as NP-interacting partners. Among 53 proteins found present in both NP-and eGFP-PD samples, 36 had spectral counts in the NP-PD sample that were less than two-fold higher than their corresponding spectral counts in the eGFP-PD sample (Fig 2C and S1 Table) . Based on the criteria we described above, we considered these proteins to be highly unlikely specific NPinteractors with an involvement in the LCMV life cycle, and therefore we did not consider these hits for further analysis. However, we acknowledge that we cannot formally rule out that some of these hits could still play a role on the life cycle of LCMV. The protein annotation through evolutionary relationship (PANTHER) protein family classification (Biological Process) of the NP-interacting protein candidates revealed that a large number of proteins were involved in metabolic and cellular processes (Fig 2D) . We also analyzed the molecular functions of the NP-interacting protein candidates according to the PAN-THER protein profile classification (Fig 2E) , which revealed diversified biochemical functions enriched for nucleic acid-binding proteins and chaperones. To initially assess pro-or anti-viral roles of NP-interacting host-cell proteins identified by LC-MS/MS, we examined the effect of small interfering RNA (siRNA)-mediated knockdown (kd) of each of the corresponding genes on multiplication of rLCMV expressing reporter gene ZsGreen (ZsG) in A549 cells (Fig 3A) . The siRNAs we used were from the genome-wide ON TARGET-Plus (OTP) Human siRNA library (18,301 genes, 4 siRNAs/gene). OTPs are latest generation of siRNAs and offer significant advantages over previous generations. Off-target effects are primarily driven by antisense strand microRNA (miR)-like seed activity. In OTPs, the sense strand is modified to favor antisense strand uptake whereas the antisense strand seed region is modified to drastically reduce seed-related off-targeting [33] . In addition, OTPs are designed on the foundation of the SMARTselection algorithm (Dharmacon), widely considered to be the best algorithm for rational siRNA design strategy. Numerous host-cell factors showed an anti-LCMV effect (increased ZsG expression by kd of the genes), including microtubule-associated protein 1B (MAP1B) [ [38] , dengue virus 2 (DENV-2) [39] , and chikungunya virus (CHIKV) [40] . To begin assessing the potential biological implications of the identified NP-host cell protein interactions, we selected ATP1A1 and PHB given the availability of reagents, existing knowledge about their roles in cell physiology, and evidence of participation in multiplication of other viruses. We confirmed that cells transfected with siRNA specific to ATP1A1 and PHB exhibited the predicted reduced levels in ATP1A1 and PHB protein expression (Fig 3B) . Likewise, we examined whether siRNA-mediated reduced expression levels of ZsGreen correlated with reduced LCMV progeny titers. For this, we transfected A549 cells with siRNA targeting Expression of Strep-tagged proteins. A549 cells seeded (5.0 x 10 5 cells/well) in a 6-well plate and cultured overnight were infected (MOI = 0.1) with the indicated rLCMVs. At 48 h pi, total cell lysates were prepared, and protein expression was analyzed by western blotting. (D) Growth properties of rLCMV expressing Strep-tagged proteins. BHK-21 (1.75 x 10 5 cells/well), A549 (1.25 x 10 5 cells/well), or Vero E6 (1.25 x 10 5 cells/well) cells seeded in 24-well plates and cultured overnight were infected (MOI = 0.01) with the indicated rLCMVs. At the indicated times pi, TCSs were collected and virus titers determined by IFFA. Results represent means ± SD of the results of three independent experiments. (E) Lack of induction of IFN-I in cells infected with rLCMV/Strep-NP. A549 cells were infected (MOI = 0.1) with the indicated rLCMV or mock-infected, and 36 h later infected with SeV (MOI = 1). At 24 h pi with SeV, TCS were collected and used, following virus inactivation by U.V., to treat Vero E6 cells for 16 h, followed by infection with rVSV (MOI = 0.1) [rVSV(+)] or mockinfection [rVSV (-) ]. At 24 h pi with rVSV, cells were fixed with 4% PFA and stained with crystal violet to assess rVSV-induced cytopathic effect. We used as control rLCMV/NP(D382A) that contains mutation D382A within NP, which has been shown to abrogate the NP's ability to counteract the induction of IFN-I production. https://doi.org/10.1371/journal.ppat.1006892.g001 ATP1A1 or with NC siRNA 72 h prior to infection with rLCMV/ZsG. We found that siRNAmediated kd of ATP1A1 dramatically inhibited ZsGreen expression (Fig 3Ci) , which was associated with a significant reduction of infectious LCMV progeny (Fig 3Cii) . Our attempts to see interaction between NP and ATP1A1 or NP and PHB by co-immunoprecipitation were unsuccessful. Several possibilities could account for this, including interactions of low affinity or high on/off rate or both. Another consideration is that only a minor fraction of NP might be engaged in the interaction with a given host cell protein, and therefore, detection of these interactions would require highly sensitive methods such as LC-MS/MS. To overcome this problem we used confocal microscopy to examine the co-localization of NP with ATP1A1 and PHB in LCMV-infected cells. Weighted co-localization coefficients (CC), determined by taking into consideration the brightness of each channel signal, were significantly higher than non-weighted CC, indicating the presence of brighter pixels in the co-localized regions compared to the non-co-localized regions (Fig 4) . The cardiac glycoside ouabain is an inhibitor of ATP1A1 that has been used to treat congestive heart failure in European countries [41] . The PHB inhibitor rocaglamide is a flavagline from an Aglaia tree used in traditional Chinese medicine [42] that has potent anticancer activity [43] . To examine whether pharmacological inhibition of ATP1A1 or PHB inhibited LCMV multiplication, we pretreated human (A549 and HEK 293T), nonhuman primate (Vero E6), and rodent (murine L929 and hamster BHK-21) cells with ouabain or rocaglamide and infected them with rLCMV/eGFP (S1 Fig). Ouabain treatment resulted in a strong dosedependent inhibition of eGFP expression in infected human-and nonhuman primate cells, but did not affect eGFP expression intensity in infected rodent cells (S1A Fig) . This finding is consistent with rodents expressing an ATP1A1 allele that is resistant to ouabain inhibition [44] . Likewise, we observed a dose-dependent rocaglamide inhibition of eGFP expression in all cell lines infected with rLCMV/eGFP (S1B Fig) . Consistent with these findings, production of infectious LCMV progeny was reduced by treatment with either ouabain or rocaglamide ( Fig 5A) within a concentration range that had minimal impact on cell viability (Fig 5B) . To examine the correlation between efficacy and cytotoxicity of these compounds, we determined their therapeutic index (TI = CC 50 /IC 50 (Fig 5Bi) ; whereas rocaglamide had TIs of >105 (CC 50 > 1000 nM, IC 50 = 9.51 nM) and 10.3 (CC 50 = 100 nM, IC 50 = 9.75 nM) in A549 and Vero E6 cells, respectively (Fig 5Bii) . Moreover, the ATP1A1 antagonist inhibitor, bufalin, also exhibited robust anti-LCMV activity with TIs of 8.92 (CC 50 Heat shock protein HSP 90-alpha HSP90AA1 P07900 8 10 9 Endoplasmin HSP90B1 P14625 9 9 9 Large neutral amino acids transporter small subunit 1 SLC7A5 Q01650 6 12 9 Keratin, type II cuticular Hb1 KRT81 Q14533 10 8 9 Putative helicase MOV-10 MOV10 Q9HCE1 8 10 9 Microtubule-associated protein 1B MAP1B P46821 6 11 8.5 Spectrin beta chain, rocaglamide (100 nM) (Fig 5C) , further supporting a specific anti-LCMV activity of ouabain and rocaglamide that was not due to reduced cell viability. To gain insights about the mechanisms by which ouabain and rocaglamide exert their anti-LCMV activity, we examined effects of these compounds on distinct steps of the LCMV life cycle. First, we asked whether ouabain and rocaglamide affected cell entry of LCMV. We conducted time-of-addition experiments in which we treated cells with ouabain or rocaglamide prior to virus inoculation (-1.5 h), at the time of inoculation (0 h), or 1.5 h pi (+1.5 h) (Fig 6A) . In some samples, we used ammonium chloride starting at 4 h pi to block multiple rounds of virus infection. The timing of compound addition did not significantly change the number of eGFP-positive cells, indicating that neither ouabain nor rocaglamide inhibited cell entry of LCMV. The number of eGFP + cells in ouabain-treated cells was reduced at all time-of-addition points compared to vehicle dimethyl sulfoxide (DMSO)-treated cells, but was similar to that observed in ammonium chloride-treated cells. Thus, ouabain did not inhibit LCMV RNA replication and gene expression, but rather a late step of the LCMV life cycle. In contrast, rocaglamide treatment resulted in a negligible number of eGFP + cells, indicating that rocaglamide inhibited virus RNA replication and gene transcription. To further investigate the effect of ouabain and rocaglamide on virus RNA synthesis, we infected A549 cells with a recombinant single-cycle infectious LCMV expressing eGFP (rLCMVΔGPC/eGFP) and treated cells with either ouabain or rocaglamide. Seventy-two hours later, we determined percent normalized eGFP expression in infected cells (Fig 6B) . Consistent with our results from the time-of-addition experiment, ouabain did not affect reporter eGFP expression. However, rocaglamide reduced eGFP expression, confirming inhibitory effect of rocaglamide on virus RNA synthesis. We also examined the effect of ouabain and rocaglamide on the late step of the arenavirus life cycle, Z-mediated budding. For this experiment, we transfected cells with Z-Strep-and Z-FLAG (DYKDDDDK epitope)-expressing plasmids from LCMV and LASV, respectively. At 24 h post-transfection, we removed the tissue culture supernatant (TCS) and washed extensively transfected cells to eliminate already budded Z. We cultured the transfected cells in the presence or absence of ouabain or rocaglamide. At 24 h, we determined by WB levels of Z protein in both whole cell lysates and associated with virus-like particles (VLPs) collected from TCS. Treatment with rocaglamide, but not with ouabain, caused a reduction in both LCMV and LASV Z budding efficiency (Fig 6C and 6D) . The reproducibility of these findings was confirmed based on results from four independent experiments (Fig 6E) . We also examined whether ouabain could interfere with a step of assembly of infectious progeny that was not captured by the Z budding assay through two additional experiments. The first experiment involved the use of a newly generated single-cycle infectious recombinant LCMV expressing the reporter gene ZsGreen (scrLCMV/ZsG-P2A-NP) to infect (MOI = 0.1) A549 cells (1 st infection). These cells were subsequently transfected with a plasmid expressing LCMV GPC. After 24 h, we used TCS to infect a fresh cell monolayer (2 nd infection) and identified infected cells based on ZsGreen expression. To assess the effect of ouabain on de novo assembly of infectious progeny we determined normalized ratios (2 nd /1 st infection) of ZsGreen + cells (Fig 6F) . The second experiment involved infection (MOI = 0.1) of cells with WT LCMV, and 48 h later we washed infected cells three times to remove the extracellular infectious progeny produced during the first 48 h of infection. Then, fresh media containing ouabain or DMSO vehicle control were added, and 24 h later we determined virus titers in TCS (Fig 6G) . Results from both experiments consistently showed that ouabain did not inhibit assembly de novo of extracellular infectious virus. Combination therapy can significantly alleviate the problem posed by the rapid emergence of drug-resistant variants commonly observed during monotherapy strategies to control RNA virus infections. Since ouabain and rocaglamide inhibited different steps of the LCMV life cycle, we examined whether their use in combination results in a synergistic anti-LCMV effect. For this experiment, we infected A549 cells with rLCMV/eGFP and treated them with ouabain and rocaglamide using different concentrations and combinations. At 48 h pi, we determined percent eGFP expression (Fig 7) . Combination treatment with ouabain and rocaglamide resulted in synergistic anti-LCMV activity that was enhanced under conditions using higher concentrations of ouabain and lower concentrations of rocaglamide. We next asked whether the ATP1A1 and PHB host-cell factors contributed also to multiplication of viral hemorrhagic fever-causing LASV. We treated A549 cells with ouabain, bufalin, or rocaglamide and inoculated the treated cells with recombinant LASV expressing eGFP (rLASV/eGFP). eGFP expression was examined 48 h later. Similar to rLCMV infection, LASV multiplication was restricted in ouabain-, bufalin-, or rocaglamide-treated cells at concentrations minimally impacting cell viability, although their IC 50 values were slightly higher than those found with the LCMV infection system (Fig 5B and S2 Fig) as ouabain had IC 50 of 9.34 nM, bufalin had IC 50 of 1.66 nM and rocaglamide had IC 50 of 37.0 nM (Fig 8) . We also tested the effect of compounds targeting ATP1A1 and PHB on multiplication of JUNV. Consistent with our results obtained with LCMV and LASV, ouabain, bufalin, and rocaglamide strongly suppressed JUNV multiplication (S3 Fig). These findings indicate that ATP1A1 and PHB function as pro-viral factors of a range of mammarenaviruses. We identified ATP1A1 and PHB as novel host-cell proteins that contribute to efficient multiplication of mammarenaviruses. Our approach using a recombinant LCMV expressing NP with an affinity tag facilitated defining the NP interactome in the context of LCMV infection. Recently, using an NP-specific monoclonal antibody (mAb) to precipitate NP and associated cellular protein partners in a mammarenavirus NP interactome, King et al. identified 348 host proteins that associated with LCMV NP [45] . We found 99 common proteins between our filtered LCMV NP interactome of 171 proteins and the LCMV NP interactome documented by King et al. Differences in both experimental conditions and analysis methodologies used to generate the LCMV NP interactome documented by King et al. and ours likely h post-transfection, cells were washed with fresh media to eliminate Z-mediated production of VLPs in the absence of compound treatment, and cultured for another 24 h in fresh media in the presence of ouabain or Roc-A at the indicated concentrations. VLPs present in TCS were collected by ultracentrifugation, and cell lysates were prepared. Z protein expression in VLPs and cell lysates were determined by western blots using antibodies to Strep-tag (C) and FLAG-tag (D). Budding efficiency for each sample was estimated by dividing the signal intensity of the Z protein associated with VLPs by that of Z detected in the cell lysate. Numbers on the bottom of panel C correspond to LCMV Z budding efficiencies determined in a representative experiment. Results shown in panel E correspond to the average and SD from four independent experiments including the one shown in panel D. The mean budding efficiency of DMSO treatedsamples was set to 100%. Data represent mean ± SD of four independent experiments. (F) Effect of ouabain on incorporation of viral glycoprotein into virions. 293T cells seeded (4.0 x 10 5 cells/well) in a 12-well plate and cultured overnight were infected (MOI = 0.1) with scrLCMV/ZsG (1 st infection) for 2 h and subsequently transfected with 0.5 μg of pC-GPC. At 24 h pi, cells were washed with fresh medium to eliminate infectious virus particle produced in the absence of compound treatment, and cultured for another 24 h in fresh media in the presence of ouabain at 40 nM (OUA). At 48 h pi, TCS was collected and used to infect fresh monolayer of BHK-21 cells (2 nd infection) seeded (4.0 x 10 5 cells/well) in a 12-well plate 1 day before the infection, and 293T cell lysate was prepared. 24 h later, BHK-21 cell lysate was prepared. ZsGreen signal intensity was measured by a fluorescent plate reader. GP-incorporation efficiency was estimated by dividing ZsGreen signal intensity in BHK-21 cell lysate (2 nd ) by that in 293T cell lysate (1 st ). The mean GP-incorporation efficiency of DMSO treated samples was set to 100%. Data represent means ± SD from three independent experiments. contributed to the observed differences between data sets. Despite progress in the implementation of global proteomics-based screens to identify virus-host protein-protein interactions, overlap between datasets for the same viral system is usually limited. However, the substantial overlap of 99 of the 171 NP-interacting proteins from both studies supports the overall reliability of both systems. We used results of the eGFP-Strep interactome, determined in r3LCMV/eGFP-Strep-infected cells, as a control to filter out non-specific NP interactions, which may have resulted in a higher degree of stringency than in the study by King et al for selection of NP-interacting candidates. The combined information provided by the NP interactome reported by King et al. and the one we present in this work, will facilitate future studies to further characterize the functional and biological implications of NP-host cell interacting proteins. All tested mammarenavirus NPs, with exception of the NP from TCRV, blocked IRF-3-dependent IFN-I induction [25, 46] . The anti-IFN activity of NP was mapped to its C-terminal part and linked to the 3'-5' exonuclease domain present with the NP C-terminal part [30] . Inhibitor-B kinase ε (IKKε) was identified as an NP-binding protein using plasmid-mediated overexpression in transfected cells [47] , and the NP-IKKε binding affinity correlated with NP's ability to inhibit IFN-I induction [47] . We, as well as the work by King et al. [45] , did not detect this NP-IKKε interaction. This discrepancy may have been caused by very low expression of IKKε in LCMV-infected cells, which prevented detection of IKKε by LC-MS/MS. Alternatively, NP-IKKε interaction could possibility be temporarily regulated and take place at early times pi, but could be mostly absent at 48 h pi, the time at which we prepared the cell lysates for our proteomics studies. Future studies comparing the NP interactome at different times during infection will contribute to a better understanding of the dynamics of NP/hostcell protein interactions. Na + /K + -ATPase is a well-characterized membrane ion transporter and is composed of two functional subunits (α and β) and one regulatory γ subunit [48] . ATP1A1 represents one of four α subunits [49, 50] . Recent evidence has suggested that the Na + /K + -ATPase is involved in multiple cell signaling pathways that are independent of its ion-pumping function [51] . Cardiac glycoside inhibitors of the Na + /K + -ATPase (NKA), so-called cardiotonic steroids (CST; e.g., ouabain, bufalin), have been shown to inhibit multiplication of different viruses including Ebola virus [35], coronaviruses [36], herpes simplex virus 1 [52, 53] , CHIKV [54] , human immunodeficiency virus 1 (HIV-1) [55] , adenovirus [56] and porcine reproductive and respiratory syndrome virus 1 [57] . Different mechanisms are likely to contribute to the antiviral activity of CSTs, including altered cell functions modulated by the signaling activity of Na + /K + -ATPase [58] . Thus, a low concentration of ouabain induces a conformational change in ATP1A1 that results in activation and release of proto-oncogene tyrosine protein kinase, Src, from ATP1A1, followed by activation of as yet unknown downstream signaling that inhibits, for instance, cell entry of murine hepatitis virus (MHV) [59] . However, our results indicated that ouabain did not interfere with LCMV cell entry. In addition, treatment with the Src inhibitor 4-amino-5-(4-methylphenyl)-7-(t-butyl)pyrazolo [3,4-d] pyrimidine (PP1) did not counteract the anti-LCMV activity of ouabain (S4 Fig). Nevertheless, ATP1A1-mediated Src signaling could plausibly contribute to the inhibitory effect of ouabain on JUNV multiplication as similarly to that observed with MHV. Moreover, cell entry of JUNV occurs also by clathrin-mediated endocytosis [60] , a process affected by Src signaling. Ouabain has been clinically used in several European countries for the management of congestive heart failure, whereas bufalin has been tested in clinical trials for cancer treatments [61] , and the CST digoxin has been FDA-approved since 1997 to treat heart failure and atrial fibrillation. Hence, opportunities for the repurposing CSTs have potential as therapeutics to treat infections caused by viral hemorrhagic fever-causing arenaviruses. The PHB inhibitor, rocaglamide, appeared to interfere with LCMV RNA synthesis and budding, but did not affect LCMV cell entry. In contrast, PHB was reported to be a cell entry receptor for DENV-2 [39] and CHIKV [40] . On the other hand, PHB did not act as a virus cell entry receptor for HCV. Rather, PHB contributed to HCV cell entry through binding to cellular RAF (c-Raf; proto-oncogene serine/threonine-protein kinase) and subsequent Harvey rat sarcoma proto-oncogene (HRas) activation that induces a signal transduction pathway required for epidermal growth factor receptor (EGFR)-mediated HCV cell entry [37] . In addition, siRNA-mediated kd of PHB decreased production of H5N1 FLUAV [38] . These findings indicate that PHB is involved in different steps of the life cycle of a variety of viruses, and thereby an attractive target for the development of broad-spectrum antiviral drugs. Rocaglate is a group of natural compounds, which includes rocaglamide, that inhibits protein synthesis by targeting the ATP-dependent DEAD-box RNA helicase eukaryotic initiation factor 4A (eIF4A) and exerts anti-tumor activity [62, 63] . The rocaglate compound, silvestrol, inhibits Ebola virus multiplication likely by interfering with the role of eIF4A in viral protein translation [64] . While we focused on two host proteins, ATP1A1 and PHB, in this study, our proteomics approach also identified several NP-interacting host-cell proteins whose kd expression via siRNA resulted in increased LCMV multiplication. These proteins, which included MAP1B, might have anti-LCMV activity. MAP1B has been shown to bind to nonstructural proteins 1 (NS1) and 2 (NS2) of human respiratory syncytial virus (HRSV) [34] . NS1 and NS2 of HRSV antagonizes host IFN-I response by reducing the levels of TNF receptor associated factor (TRAF3), IKKε (NS1), and STAT2 (NS2) [65] . NS2-MAP1B interaction interfered with HRSV NS2's ability to reduce levels of STAT2, whereas the role of NS1-MAP1B interaction remains to be determined [34] . Examining the role of NP-MAP1B interaction in modulating NP's ability to inhibit induction of type I IFN is of interest. We identified among the NP-interacting host cell proteins the RNA helicase Moloney leukemia virus 10 (MOV10), which has been reported to be an antiviral factor for FLUAV [66] , retroviruses [67] [68] [69] [70] [71] , and DENV-2 [72] . We did not observe increased LCMV multiplication in cells subjected to siRNA-mediated kd of MOV10, a finding that would question an anti-LCMV activity of MOV10. However, we consider that LCMV has already optimal multiplication in A549 cells and further increases may occur only under rather unique conditions. MOV10 was shown to enhance IRF-3-mediated IFN-I induction following SeV infection through a tank binding kinase 1 (TBK1)-independent and IKKε-dependent manner. This finding was further supported by demonstrating MOV10-IKKε interaction by co-immunoprecipitation studies [73] . We documented that the anti-IFN activity of mammarenavirus NP correlated with its ability to interact with IKKε [47] . Whether NP-MOV10 interaction prevents MOV10 from enhancing IRF-3-mediated IFN-I induction remains to be determined. Several members of the mammalian chaperonin-containing T-complex (CCT) were identified as prominent hits in our NP interactome. The mammalian CCT is critical for folding of many proteins with important functions in diverse cellular processes [74] , and may protect complex protein topologies within its central cavity during biosynthesis and folding [75] . The contribution of CCT members to NP assembly into a nucleocapsid structure could account for their presence in the NP, but not eGFP, interactome. Interestingly, members of the CCT have been implicated in multiplication of different viruses including rabies virus [76, 77] , HCV [78] and FLUAV [79] . However, the role of these CCT proteins in virus multiplication remains unknown and may involve functions other than acting as molecular chaperones. Previous studies documented the presence of several components of the of eIF4F, including 4A, within viral replication-transcription complexes (RTC) detected in cells infected with LCMV [80] and TCRV [81] . These findings, together with the detection of a number of ribosomal proteins in the NP interactome, suggest that translation of viral mRNAs may take place within RTC. However, rocaglamide interference with the activity of eIF4A within the viral RTC might contribute to its anti-LCMV activity. In this work, we documented the generation of rLCMV/Strep-NP and its use to define the NP-interactome in infected cells. We presented evidence that ATP1A1 and PHB contribute to efficient multiplication of mammarenaviruses using genetics and pharmacological inhibition of the genes. Consistent with our findings, bioinformatic analysis revealed that the protein network associated with ATP1A1 and PHB involves host cell proteins with functions in biological processes that have been implicated in virus multiplication (S5 Fig). The overall experimental approach described here can facilitate the identification of biologically relevant NP-interacting host-cell proteins. Future studies elucidating the roles of pro-and antiviral host-cell factors identified in this study in mammarenavirus multiplication will advance our understanding of the multiple functions of NP and uncover novel cellular targets for the development of antimammarenaviral drugs. In addition, by identifying proviral host-cell factors, drugs that are already approved can be repurposing as therapeutics to combat human pathogenic mammarenavirus infections. Baby hamster kidney BHK-21 (American Type Culture Collection, ATCC, CCL-10), house mouse L929 (ATCC CCL-1), grivet Vero E6 (ATCC CRL-1586), human A549 (ATCC CCL-185), and human HEK 293T (ATCC CRL-3216) cells were grown in Dulbecco's modified Eagle's medium (Thermo Fisher Scientific, Waltham, MA) containing 10% heat-inactivated fetal bovine serum, 2 mM of L-glutamine, 100 mg/ml of streptomycin, and 100 U/ml of penicillin at 37˚C and 5% CO 2 . WT recombinant LCMVs, Armstrong (rLCMV ARM) and clone-13 (rLCMV Cl-13) strains, were generated as described [30, 82, 83] . Generation of rLCMV/NP(D382A) and SeV, strain Cantell, was described [30, 82, 83 ]. An rLCMV lacking GPC and expressing eGFP (rLCMVΔGPC/eGFP) was generated by reverse genetics using procedures previously described [84] . rLCMV/Strep-NP and r3LCMV/eGFP-Strep were generated by reverse genetics using similar procedures to generate WT rLCMV and tri-segmented LCMV (r3LCMV) expressing eGFP [30] . For the generation of these novel rLCMVs, we created pol1S Cl-13 plasmids that directed Pol1-mediated intracellular synthesis of recombinant LCMV S genome RNA species coding for Strep-tagged NP or eGFP, respectively (Fig 1A and 1B) . The rLCMV expressing eGFP (rLCMV/eGFP) was generated as described [85] , and the rLCMV expressing ZsGreen (rLCMV/ZsG) instead of eGFP was generated by reverse genetics using similar procedures to generate rLCMV/eGFP. Generation of rLASV expressing eGFP (rLASV/eGFP) will be described elsewhere. A tri-segmented recombinant live-attenuated Candid #1 strain of JUNV expressing eGFP (r3JUNV/eGFP) was generated as described [86] . For the generation of a novel single cycle rLCMV expressing ZsGreen (scrLCMV/ZsG-P2A-NP), a pol1S plasmid was created by omitting GPC open reading frame (ORF) from pol1S plasmid used for the generation of rLCMV/ZsG. scrLCMV/ZsG-P2A-NP was rescued by reverse genetics using similar procedures to generate rLCMVΔGPC/eGFP [84] . LCMV titers were determined by immunofocus forming assay (IFFA) as described [87] . Briefly, 10-fold serial virus dilutions were used to infect Vero E6 cell monolayers in a 96-well plate, and at 20 h pi, cells were fixed with 4% paraformaldehyde (PFA) in phosphate-buffered saline (PBS). After cell permeabilization by treatment with dilution buffer (DB) (0.3% Triton X-100 in PBS-containing 3% bovine serum albumin [BSA]), cells were stained with a rat mAb to NP (VL-4, Bio X Cell, West Lebanon, NH) conjugated with Alexa Fluor 488 (VL-4-AF488, Protein Labeling Kit, Life Technologies, Carlsbad, CA). VSV titers were determined by a plaque assay. Total cell lysates were prepared in PD lysis buffer (+) (250 mM of NaCl, 50 mM of Tris-HCl [pH = 7.5], 0.5% TritonX-100, 10% glycerol, 1 mM of MgCl 2 , 1 μM of CaCl 2 , 1 μM of ZnCl 2 ) and clarified by centrifugation at 21,130 x g at 4˚C for 10 min. Clarified lysates were mixed at a 1:1 ratio with loading buffer (100 mM of Tris [pH 6.8], 20% 2-mercaptoethanol, 4% SDS, 0.2% bromophenol blue, 20% glycerol) and boiled for 5 min. Proteins samples were fractionated by SDS-PAGE using 4-20% gradient polyacrylamide gels (Mini-PROTEAN TGX gels 4-20%, Bio-Rad, Hercules, CA), and proteins were transferred by electroblotting onto polyvinylidene difluoride membranes (Immobilin Transfer Membranes, Millipore, Billerica, MA). To detect Strep-tagged proteins, membranes were reacted with mouse monoclonal antibodies to Strep (QIAGEN, Germantown, MD), eGFP (Takara Bio USA, Mountain View, CA), GP 2 (We33/ 36), ATP1A1 (TehrmoFisher Scientific, Rockford, IL), PHB (Abcam, Cambridge, MA) or rabbit polyclonal antibodies to α-tubulin (Cell Signaling Technologies, Danvers, MA) or glyceraldehyde-3-phosphate dehydrogenase (GAPDH, Millipore), respectively, followed by incubation with appropriate horseradish peroxidase-conjugated anti-mouse or anti-rabbit immunoglobulin G (IgG) antibodies (Jackson ImmunoResearch Laboratories, West Grove, PA). SuperSignal West Pico or Femto chemiluminescent substrate (Thermo Fisher Scientific) was used to elicit chemiluminescent signals that were visualized using ImageQuant LAS 4000 Imager (GE Healthcare Bio-Sciences, Pittsburgh, PA). Pull down of strep-tagged proteins from infected cell lysate. A549 cells prepared in six 15-cm dishes (approximately 1.0 x 10 8 cells in total) were infected with either rLCMV/Strep-NP or r3LCMV/eGFP at an MOI of 0.1. At 48 h pi, cells were washed three times with ice-cold PBS, scraped into fresh ice-cold PBS, and centrifuged at 400 x g at 4˚C for 10 min. Supernatant was removed, and cells were lysed with 12 ml of PD lysis buffer (+) supplemented with halt protease and phosphatase inhibitor cocktail (Thermo Fisher Scientific) and 5 μg/ml of deoxyribonuclease I (Worthington Biochemical Corporation, Lakewood, NJ). Lysate was clarified by centrifugation at 3,900 x g at 4˚C for 30 min to remove cell debris. Clarified cell lysate was then incubated with strep-tactin sepharose resin (QIAGEN) at 4˚C. After 2 h of incubation, the resin was washed three times with PD lysis buffer (+) and once with PD lysis buffer without TritonX-100 (PD lysis buffer [-] ). After the centrifugation at 1,600 x g and 4˚C for 5 min, the last wash buffer was removed, and protein complexes associated with the resin were eluted into 2 ml of PD lysis buffer (-) containing 2.5 mM of desthiobiotin. The eluate was then subjected to TCA precipitation followed by trypsin digestion. Multidimensional protein identification technology microcolumn. A MudPIT microcolumn was prepared by first creating a Kasil frit at one end of an un-deactivated 250-μm outside diameter (OD) capillary tube (interior diameter of 360 μm)(Agilent Technologies, Inc., Santa Clara, CA). The Kasil frit was prepared by briefly dipping a 20-30-cm capillary tube in 300 μl of Kasil 1624 potassium silicate well-mixed solution (PQ Corporation, Malvern, PA) and 100 μl of formamide, curing at 100˚C for 4 h, and cutting the frit to a length of %2 mm. Strong cation exchange particles (SCX Luna, 5-μm diameter, 125 Å pores, Phenomenex, Torrance, CA) were packed in-house from particle slurries in methanol to 2.5 cm. Reversed phase particles (2 cm, C18 Aqua, 3-μm diameter, 125 Å pores, Phenomenex) were then successively packed onto the capillary tube using the same method as SCX loading. MudPIT analysis. An analytical reversed-phase liquid chromatography column was generated by pulling a 100-μm (interior diameter (ID) of 360 μm) OD capillary tube (Polymicro Technologies, Phoenix, AZ) to 5-μm ID tip. Reversed-phase particles (Luna C18, 3-μm diameter, 125 Å pores, Phenomenex) were packed directly into the pulled column at 5.5 mPa until 15 cm long. The column was further packed, washed, and equilibrated at 10 mPa with buffer B (80% acetonitrile, 0.1% formic acid) followed by buffer A (5% acetonitrile and 0.1% formic acid). MudPIT and analytical columns were assembled using a zero-dead volume union (Upchurch Scientific, Oak Harbor, WA). LC-MS/MS analysis was performed with an Agilent high-pressure LC pump (Agilent) and linear quadrupole ion dual cell trap Orbitrap Velos (Thermo) using an in-house built electrospray stage. Electrospray was performed directly from the analytical column by applying the electrospray ionization (ESI) voltage at a tee (150 μm ID, Upchurch Scientific) directly downstream of a 1:1,000 split flow to reduce the flow rate to 300 nl/min through the columns. MudPIT experiments (10-step) were performed in which each step corresponds to 0, 10, 20, 40, 50, 60, 70, 80, 90 , and 100% buffer C (500 mM of ammonium acetate, 0.1% formic acid, and 5% acetonitrile) and was run for 3 min at the beginning of a 110-min gradient. Data analysis. Protein and peptide identification were performed with Integrated Proteomics Pipeline-IP2 (Integrated Proteomics Applications, San Diego, CA. http://www. integratedproteomics.com/) using ProLuCID and DTASelect2 algorithms. DTASelect parameters were-p 2 -y 1-trypstat-pfp .01 -extra-pI-DB-dm-in. Spectrum raw files were extracted into ms2 files from raw files using open source RawExtract 1.9.9 (Scripps Research Institute, La Jolla, CA; http://fields.scripps.edu/downloads.php), and the tandem mass spectra were searched against a human protein database (UniprotKB). To accurately estimate peptide probabilities and false discovery rates, we used a decoy database containing the reversed sequences of all the proteins appended to the target database. Tandem mass spectra were matched to sequences using the ProLuCID algorithm with a 600-ppm peptide mass tolerance. ProLuCID searches were done on an Intel Xeon cluster processor running under the Linux operating system. The search space included half and fully tryptic peptide candidates that fell within the mass tolerance window with no miscleavage constraint. Carbamidomethylation (+57.02146 Da) of cysteine was considered as a static modification. siRNA screening A549 cells (1,000 cells/well) in a 384-well plate were reverse transfected with 0.5 pmol of siRNA pool (S2 Table) targeting each gene using 0.1 μl of Lipofectamine RNAiMAX (Thermo Fisher Scientific) (final siRNA concentration was 10 nM), followed by incubation at 37˚C and 5% CO 2 . At 72 h post-transfection, cells were infected (MOI = 0.05) with rLCMV/ZsG. siRNA target host-cell proteins were selected based on availability of validated siRNA sequences. The siRNAs we used to examine the effects on LCMV multiplication of knockdown expression of NP-interacting host cell protein candidate hits corresponded to the Genome-wide ON TAR-GET-Plus (OTP) Human siRNA library (18,301 genes, 4 siRNAs/gene; Dharmacon, Lafayette, CO). A549 cells (3.0 x 10 4 cells/well) were reverse transfected in a 24-well plate with 6 pmol of siRNA pools targeting each gene using 1 μl of Lipofectamine RNAiMAX (final siRNA concentration is 10 nM). At 72 h post-transfection, total cell lysate was prepared in modified lysis A buffer (25 mM Tris-HCl [pH = 8.0], 50 mM NaCl, 1%Triton X-100, 1.25% sodium deoxycholate) and clarified by centrifugation at 21,130 x g at 4˚C for 10 min. The total protein concentration of clarified cell lysate was measured by Pierce BCA Protein Assay Kit (Thermo Fisher Scientific). The same amount of protein from each sample was subjected to SDS-PAGE, and the protein expression of siRNA-targeted genes was analyzed by western blots. Cells infected with eGFP-or ZsGreen-expressing rLCMV were fixed with 4% PFA in PBS. After cell permeabilization by treatment with DB, cells were stained with 4',6-diamidino-2-phenylindole (DAPI). Green fluorescence (eGFP or ZsGreen) and DAPI signals were measured by a fluorescent plate reader (Synergy H4 Hybrid Multi-Mode Microplate Reader, Bio-Tek, Winooski, VT). Mock-and virus-infected cells were fixed with 4% PFA. After cell permeabilization and blocking by treatment with DB containing 1% normal goat serum, cells were incubated with primary mouse anti ATP1A1 or PHB antibody followed by secondary anti-mouse IgG antibody conjugated with Alexa Fluore 568 (anti-mouse IgG-AF568). Subsequently, cells were stained with VL-4-AF488. In some samples, primary antibody against ATP1A1 or PHB was omitted to determine background fluorescence. To visualize nuclei, DAPI Fluoromount-G (SouthernBiotech, Birmingham, AL) was used to mount coverslips on a slide glass. Stained cells were observed under a confocal microscope (LSM 710, Zeiss) and data analyzed by ZEN software (Zeiss). Co-localization analysis was performed on a pixel by pixel basis using Zen software (Zeiss). Eight green (NP-positive) cells were marked and every pixel in the marked area was plotted in the scatter diagram based on its intensity level from each channel. Thresholds for green and red channels were determined using mock-infected cells stained with VL-4-AF488 (anti-NP) and anti-mouse IgG antibody conjugated with Alexa Fluor 568, without using anti-ATP1A1 or -PHB antibodies. Each pixel was assigned a value of 1. Co-localization coefficients (CC) (or non-weighted CC) were determined by dividing the sum of both green-and red-positive pixels by the sum of green positive pixels. This calculation was repeated for eight individual cells. To assess the specificity of co-localization, we determined weighted CC by taking into consideration the brightness of each channel signal. Comparison of non-weighted and weighted CC allowed us to determine whether brighter pixels were present in the co-localized regions compared to the non-co-localized regions. p values were determined by a two-tailed paired t test using GraphPad Prism software. A549 or Vero E6 cells seeded (2.0 x 10 4 cells/well) in a 96-well plate and cultured overnight were treated with 3-fold serial compound dilutions at 37˚C and 5% CO 2 for 2 h, followed by infection with rLCMV/eGFP (MOI = 0.01). Compounds were present to study endpoint. At 48 h pi, cells were fixed with 4% PFA in PBS, and eGFP expression was examined by a fluorescent plate reader (Synergy H4 Hybrid Multi-Mode Microplate Reader, BioTek). Mean values obtained with DMSO-treated and rLCMV/eGFP-infected cells were set to 100%. The IC 50 concentrations were determined using GraphPad Prism. A549 or Vero E6 cells seeded in a 96-well plate (2.0 x 10 4 cells/well) and cultured overnight were treated with 3-fold serial compound dilutions and cultured at 37˚C and 5% CO 2 for 48 h. Then, CellTiter 96 AQ ueous one solution reagent (Promega, Madison, WI) was added. Thereafter, the assay was performed according to the manufacturer's recommendations, and the absorbance (490 nm) was obtained using an enzyme-linked immunosorbent assay (ELISA) reader (SPECTRA max plus 384; Molecular Devices, Sunnyvale, CA). Mean values obtained with DMSO-treated cells were set to 100%. The CC 50 concentrations were determined using GraphPad Prism. A plasmid expressing C-terminus Strep-tagged Z protein (pC-LCMV-Z-Strep) was generated using similar procedure to generate a plasmid expressing C-terminus FLAG-tagged LASV Z protein (pC-LASV-Z-FLAG), and the budding assay was performed as previously described [88] . Cells (HEK 293T) in a 12-well plate were transfected with 0.5 μg of empty pCAGGS vector or pC-LCMV-Z-Strep or pC-LASV-Z-FLAG using Lipofectamine 2000. At 5 h post-transfection, media were replaced with fresh media and incubated at 37˚C and 5% CO 2 for 19 h. Then the cells were three times washed with fresh medium. After the removal of the last wash medium, cells were cultured in fresh medium containing ouabain (30 or 40 nM) or rocaglamide (50 or 100 nM) or equivalent concentration of DMSO, and 24 h later, virion-like particle (VLP)-containing TCS and cells were collected. Total cell lysate was prepared by lysing the cells with lysis buffer (1% NP-40, 50 mM of Tris-HCl [pH 8.0], 62.5 mM NaCl, 0.4% sodium deoxycholate). After clarification of TCS from cell debris by centrifugation at 400 x g and 4˚C for 10 min, VLPs were collected by ultracentrifugation at 100,000 x g and 4˚C for 30 min through a 20% sucrose cushion. VLPs were resuspended in PBS, and Z expression in total cell lysate and TCS (containing VLPs) were analyzed by western blots. A549 cells infected with rLCMV/eGFP were harvested using Accutase cell detachment solution (Innovative Cell Technologies, San Diego, CA) and fixed with 4% PFA in PBS. eGFP expression was examined by flow cytometry using a BD LSR II (Becton Dickson), and data were analyzed with FlowJo (Tree Star, Inc., Ashland, OR). 293T cells seeded (4.0 x 10 5 cells/well) in a 12-well plate and cultured overnight were infected with scrLCMV/ZsG-P2A-NP for 2 h and subsequently transfected with 0.5 μg of pC-GPC. At 24 h pi, cells were three times washed with fresh media to eliminate infectious virus particle produced in the absence of compound treatment, and cultured for another 24 h in fresh media in the presence of 40 nM of ouabain or vehicle control (DMSO). At 48 h pi, TCS was collected and used to infect fresh monolayers of BHK-21 cells seeded (4.0 x 10 5 cells/well) in a 12-well plate 1 day before the infection, and 293T cell lysate was prepared. 24 h later, BHK-21 cell lysate was prepared. Total cell lysate was prepared in cell lysis buffer (150 mM of NaCl, 50 mM of Tris-HCl [pH = 7.5], 0.5% [NP-40], 1 mM of EDTA] and clarified by centrifugation at 21,130 x g at 4˚C for 10 min. ZsGreen signal intensity in clarified cell lysate was measured by a fluorescent plate reader (Synergy H4 Hybrid Multi-Mode Microplate Reader, BioTek). A549 cells seeded (2 x 10 5 cells/well) in a 96-well plate and cultured overnight were treated with combinations of different concentrations of ouabain and rocaglamide for 2 h and then infected (MOI = 0.01) with rLCMV/eGFP. Compounds were present in the culture medium throughout the experiment. At 48 h pi, cells were fixed, permeabilized by treatment with DB, and stained with DAPI. eGFP and DAPI signals were measured by a fluorescent plate reader (Synergy H4 Hybrid Multi-Mode Microplate Reader, BioTek). eGFP readouts were normalized by DAPI readouts, and normalized data were used to analyze synergistic effect of the two compounds by the MacSynergy II program [89] . Data were analyzed for p values by a two-tailed unpaired t test using GraphPad Prism software.

How are Mammarenaviruses spread from rodents to humans?

High Burden of Non-Influenza Viruses in Influenza-Like Illness in the Early Weeks of H1N1v Epidemic in France https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3157400/ SHA: f4c1afe385e9e31eb5678e15a3c280ba97326554 Authors: Schnepf, Nathalie; Resche-Rigon, Matthieu; Chaillon, Antoine; Scemla, Anne; Gras, Guillaume; Semoun, Oren; Taboulet, Pierre; Molina, Jean-Michel; Simon, François; Goudeau, Alain; LeGoff, Jérôme Date: 2011-08-17 DOI: 10.1371/journal.pone.0023514 License: cc-by Abstract: BACKGROUND: Influenza-like illness (ILI) may be caused by a variety of pathogens. Clinical observations are of little help to recognise myxovirus infection and implement appropriate prevention measures. The limited use of molecular tools underestimates the role of other common pathogens. OBJECTIVES: During the early weeks of the 2009–2010 flu pandemic, a clinical and virological survey was conducted in adult and paediatric patients with ILI referred to two French University hospitals in Paris and Tours. Aims were to investigate the different pathogens involved in ILI and describe the associated symptoms. METHODS: H1N1v pandemic influenza diagnosis was performed with real time RT-PCR assay. Other viral aetiologies were investigated by the molecular multiplex assay RespiFinder19®. Clinical data were collected prospectively by physicians using a standard questionnaire. RESULTS: From week 35 to 44, endonasal swabs were collected in 413 patients. Overall, 68 samples (16.5%) were positive for H1N1v. In 13 of them, other respiratory pathogens were also detected. Among H1N1v negative samples, 213 (61.9%) were positive for various respiratory agents, 190 in single infections and 23 in mixed infections. The most prevalent viruses in H1N1v negative single infections were rhinovirus (62.6%), followed by parainfluenza viruses (24.2%) and adenovirus (5.3%). 70.6% of H1N1v cases were identified in patients under 40 years and none after 65 years. There was no difference between clinical symptoms observed in patients infected with H1N1v or with other pathogens. CONCLUSION: Our results highlight the high frequency of non-influenza viruses involved in ILI during the pre-epidemic period of a flu alert and the lack of specific clinical signs associated with influenza infections. Rapid diagnostic screening of a large panel of respiratory pathogens may be critical to define and survey the epidemic situation and to provide critical information for patient management. Text: In order to monitor the spread of influenza and alert health handlers, several epidemiological tools have been developed. In France, a network of 1300 general practitioners, ''Réseau Sentinelles'', working throughout the country, provides real-time clinical data used to evaluate regional and national influenza spreading [1, 2] . The criteria used by this network to define clinical influenza-like illness (ILI) are the occurrence of a sudden fever above 39uC with myalgia and respiratory signs. In general no formal viral diagnosis is carried out. The Groupes Régionaux d'Observation de la Grippe (GROG) is a second French network that surveys the emergence and the spread of the influenza viruses [3, 4] . This network is based on clinical surveillance of acute respiratory infections and laboratory analysis of nasal specimens collected from adults and children by volunteer general practitioners and pediatricians. According to the sentinel network's criteria, French health authorities proclaimed that flu epidemic level was reached during the second week of September 2009 (week 37) [5, 6] . On the contrary, data provided by the GROG showed only sporadic H1N1v activity until the last week of October (week 44) [6, 7] . Thus, it became rapidly obvious that a variety of viruses were circulating in the community and that an overestimation of myxovirus infection was at stake [8, 9, 10, 11] . As a better knowledge of the epidemic status was a key feature for national healthcare organization, hospital preparedness, patient management and disease control, unambiguous viral diagnosis appeared critical. In France, data on viral aetiologies associated with ILI were at best sporadic and correlations with clinical symptoms were often lacking. Extensive molecular assays to screening for respiratory viruses were not available countrywide for routine diagnosis. Therefore the epidemiological pattern of respiratory pathogens with overlapping seasonality was poorly known. The aim of the present study was to investigate respiratory pathogens involved in ILI during the early weeks of the 2009-2010 H1N1v diffusion in France (weeks 35 through 44) and describe the associated symptoms in paediatric and adult populations. This study was a non-interventional study with no addition to usual proceedures. Biological material and clinical data were obtained only for standard viral diagnostic following physicians' prescriptions (no specific sampling, no modification of the sampling protocol, no supplementary question in the national standardized questionnaire). Data analyses were carried out using an anonymized database. According to the French Health Public Law (CSP Art L 1121-1.1), such protocol does not require approval of an ethics committee and is exempted from informed consent application. In the two academic hospitals, Saint-Louis hospital (SLS) in Paris and Tours hospital (TRS), influenza-like illness (ILI) was defined as a patient suffering from at least one general symptom (fever above 38uC, asthenia, myalgia, shivers or headache) and one respiratory symptom (cough, dyspnoea, rhinitis or pharyngitis), in agreement with the guidelines from the French Institut de Veille Sanitaire (InVS), a governmental institution responsible for surveillance and alert in all domains of public health [12] . Criteria for severe clinical presentation were temperature below 35uC or above 39uC despite antipyretic, cardiac frequency above 120/min, respiratory frequency above 30/min, respiratory distress, systolic arterial pressure below 90 mmHg or altered consciousness. Predisposing factors of critical illness were children younger than one year old, pregnant women, diabetes, chronic pre-existing disease (such as respiratory, cardiovascular, neurologic, renal, hepatic or hematologic diseases) and immunosuppression (associated with HIV infection, organ or hematopoietic stem cells transplantation, receipt of chemotherapy or corticosteroids) [13, 14] . A cluster of suspected influenza infections was defined as at least three possible cases in a week in a closed community (household, school,…) [15] . In the two institutions, the prescription of H1N1v molecular testing was recommended for patients with ILI and with either a severe clinical presentation, an underlying risk factor of complications or a condition which was not improving under antiviral treatment. Investigation of grouped suspected cases was also recommended. From week 35 (last week of August) to 44 (last week of October), 413 endonasal swabs were collected in 3 ml of Universal Transport Medium (Copan Diagnostics Inc, Murrieta, CA) from adults and children seen in emergency rooms for suspected ILI (Table 1 ) and sent to SLS and TRS laboratories for H1N1v detection. The two microbiology laboratories participated in the reference laboratories network for the detection of pandemic influenza H1N1v. Clinical data were collected at the time of medical attention and reported by clinicians on a national standardized questionnaire provided by InVS [1, 12] . This questionnaire included the presence or absence of the main general and respiratory symptoms associated with ILI (fever, asthenia, myalgia, shivers, headache, cough, rhinitis, pharyngitis, sudden onset) [12] . Total nucleic acid was extracted from 400 mL of Universal Transport Medium using the EasyMag System (Biomérieux, Marcy l'Etoile, France) in SLS or the EZ1 Advanced XL (Qiagen, Courtaboeuf, France) in TRS, according to the manufacturers' instructions (elution volume: 100 mL in SLS or 90 mL in TRS). Before extraction, 5 ml of an Internal Amplification Control (IAC) which contained an encephalomyocarditis virus (EMC) RNA transcript was added into the sample. Pandemic H1N1v infection was diagnosed by real-time reverse transcription-PCR (RT-PCR) assay on a 7500 Real Time PCR System (Applied Biosystems, Foster City, CA) according to the protocol of the Centers for Disease Control (CDC) [16] . Other respiratory infections were investigated by a multiplex molecular assay based on the Multiplex Ligation-dependent Probe-Amplification (MLPA) technology (RespiFinder19H, Pathofinder, Maastricht, The Netherlands) that allows the detection and differentiation of 14 respiratory viruses, including influenza virus A (InfA), influenza virus B (InfB), rhinovirus (RHV), parainfluenza viruses 1 to 4 (PIV-1 to PIV-4), human metapneumovirus (hMPV), adenovirus (ADV), respiratory syncytial virus A (RSVA), respiratory syncytial virus B (RSVB) and human coronaviruses 229E, OC43 and NL63 (Cor-229E, Cor-OC43, Cor-NL63) [17] . The test allows also the detection of H5N1 influenza A virus and of four bacteria: Chlamydophila pneumoniae (CP), Mycoplasma pneumoniae (MP), Legionella pneumophila (LP) and Bordetella pertussis (BP). The amplified MLPA products were analyzed on an ABI 3100 genetic analyzer (Applied Biosystems, Foster City, CA). Fragment sizing analysis was performed with the GeneMarker software (SoftGenetics, LLC, State College, PA). Further testing for H1N1v was carried out with Simplexa TM Influenza A H1N1 (2009) (Focus Diagnostics, Cypress, California) when the CDC real time RT-PCR assay was negative for H1N1 and the RespiFinder19H assay was positive for Influenza A. If this latter assay was negative, H3N2 typing was performed as previously described [18] . Data from our study are summarized as frequencies and percentages for categorical variables. Quantitative variables are presented as medians, 25th and 75th percentiles. To compare those variables according to the viral infection status, Fisher tests By using CDC reference assay, H1N1v was detected in 66 samples out of 413 (16.6%), more frequently in SLS (38 samples) than in TRS (28 samples) (p,10 24 ). Overall, weekly percentage of H1N1v positive endonasal swabs remained under 10% until week 41 and increase significantly after (P Trend ,0.0001) ( Figure 1 ). Rate of H1N1v detection reached 30% in SLS at week 42 and in TRS at week 44. Overall, this rate was in agreement with results provided by the GROG network, showing an earlier start of H1N1v epidemic in Paris area [7, 19] . All 413 nucleic acid extracts were analyzed using the RespiFinder19H assay ( Figure 2 ). Sixty six patients tested H1N1v positive with CDC real time RT-PCR assay were confirmed with the multiplex assay. Thirteen were also co-infected by one or two other respiratory pathogens (multiple infections) ( Figure 2 ). Three of the 347 H1N1v negative samples could not be studied with the multiplex assay because they contained RT-PCR inhibitors (no amplification of the internal control). Two hundred and fifteen (62.5%) of the remaining 344 H1N1v negative samples were found positive for at least one respiratory pathogen ( Figure 2 ). Two hundred and twelve were positive for non influenza pathogens (189 single infections and 23 mixed infections with two, three or four viruses) and three additional single infections by influenza A were identified in SLS, including two by pandemic H1N1v and one by seasonal H3N2, as determined after molecular typing (data not shown). Overall, 68 patients (16.5%) were then positive for H1N1v, one for H3N2 and 212 for non influenza pathogens. There were 245 single infections (55 with H1N1v and 190 with other respiratory pathogens) and 36 mixed infections (13 with H1N1v and 23 without H1N1v) ( Figure 2 ). Among H1N1v negative single infections, the most prevalent viruses were rhinovirus (62.6%, 119 patients), followed by parainfluenza viruses 1 to 4 (24.2%, 46 patients), adenovirus (5.3%, 10 patients), human coronavirus 229E, OC43 and NL63 (3.2%, 6 patients) and respiratory syncytial virus A and B (2.6%, 5 patients) (Figure 2 ). In addition, RespiFinder19H assay identified three patients with bacterial infection, two with Mycoplasma pneumoniae (one 25 years old female in SLS and one 39 years old female in TRS) and one with Bordetella pertussis (one 60 years old male in SLS). No single infection by influenza B, hMPV, Chlamydophila pneumoniae or Legionella pneumophila was identified ( Figure 2 To analyze if viral co-infections occurred more frequently for some viruses, we carried out a two by two comparisons, that showed a higher proportion of co-infection only for ADV (p = 0.05). Non-influenza respiratory viruses presented a different epidemic profile compared to H1N1v. Overall, in both hospitals, weekly rate of non-H1N1v respiratory viruses whether alone or involved in co-infection increased between week 37 and 39 (from 51.4% to 81.3%) and then consistently decreased ( Figure 3 ). RHV infections that represented nearly half of non-H1N1v viral infections (141 out of 213, 66.2%) were a significant contributing factor. In both hospitals, emergence of H1N1v cases was associated with a rapid decline of RHV rate of infection from 50-60% down to less than 20% with a one to two weeks gap between SLS and TRS. Data on age ( In both institutions, 85.5% (106/124) children younger than 15 years of age were infected by at least one respiratory pathogen ( Table 2 ). H1N1v infected patients were not significantly younger than H1N1v non infected patients (27 years old vs. 25 years old, p = 0.80) (Figure 4) . However, 70.6% (48/68) of H1N1v cases were identified in patients under 40 years old (22 in SLS and 26 in TRS) and no case was observed in patients older than 65 years ( Table 2) . PIV infection occurred in very young patients (median (Figure 4) . Consequently, PIV and ADV were more frequently detected in the younger population of TRS versus SLS (p,10 24 and p,10 23 respectively). In contrast, although individuals with RHV infection were slightly younger than individuals without (median age = 24 vs. 29 for patients without RHV, p = 0.05) (Figure 4) , influenza-like illness associated with RHV was more frequent in SLS than in TRS (p = 0.012). Finally, patients with viral multiple infection were significantly younger than those with single infection (median, IDR: 4, 2-18.5 vs. 25, 6-43) and rates of mixed infection At the time of medical attention, 383 (92.7%) standardized clinical questionnaires were collected out of 413 patients. Four of them could not be exploited because they were too incomplete. A review of the 379 workable questionnaires showed that 90.8% (344/379) of the patients included in this study fulfilled the criteria of ILI as defined above, and 52.5% had either a severe clinical presentation or an underlying risk factor of complications (45.9%, 174/379), or were in a suspected cluster of grouped cases (6.6%, 25/379). Overall, most patients have fever (93.9%) and cough (86.1%) ( Table 3) . Other classical clinical signs associated with ILI such as asthenia, myalgia, shivers, headache, rhinitis or pharyngitis were less frequent. A sudden onset was also described in 59.2% of cases. Only 32.5% of the patients had a temperature above 39uC; the age of these patients ranged from zero to 86 years, with a median age of 32 years and a mean age of 34 years (data not shown). In H1N1v infected patients (including single and multiple infections), the main symptoms were also fever (98.2%) and cough (89.5%) ( We then compared clinical characteristics between patients positive for H1N1v, patients positive for other respiratory pathogens and negative for H1N1v and patients without any detection of respiratory pathogens (as detected with RespiFin-der19H) ( Table 3 ). There was no difference between the three groups except for fever, cough, pharyngitis. However for these latter symptoms, the comparison between patients positive for H1N1v and those positive for other respiratory pathogens or between patients positive for H1N1v and those without any detection of respiratory pathogens, showed no difference except for pharyngitis, which was less frequent in patients positive for H1N1v than in patients positive for other respiratory pathogens ( Table 3) . As RHV was the most frequent aetiology in ILI, we also compared clinical symptoms observed in patients with a single infection by RHV or by H1N1v (data not shown). There was no difference except that rhinitis and pharyngitis were significantly more frequent in RHV infection (62.7% vs. 34.1% [p = 0.006] and 39.0% vs. 10.0% [p = 0.001], respectively). Viral multiple infection (including samples with H1N1v) was not associated with a different clinical presentation. Fever and cough were observed in over 90% of the patients (90.6% and 90.3%, respectively), but only 33.3% of these patients had a temperature above 39uC, which was not different from patients with single viral infection (28.6%). Our results highlight the high frequency of non-influenza viruses involved in acute respiratory infections during the epidemic period of a flu alert as defined by the Réseau Sentinelles according to ILI definition (a sudden fever above 39uC accompanied by myalgia and respiratory signs). These data extent previous observations in Europe reporting high prevalence of RHV infections before seasonal influenza [4, 20] or in 2009, before H1N1v pandemic influenza [1, 8, 9, 11, 21] . We confirm that RHV represent the most frequent aetiology of acute respiratory Table 2 . Age of patients with respiratory samples positive for H1N1v, positive for other respiratory pathogens or negative. infections both in adult and paediatric populations and may represent more than 50% of cases. We show that other viral infections than influenza and RHV may represent up to 30% of aetiologies. We observed differences between the two hospitals, with a higher frequency of parainfluenza and ADV infections in Tours in contrast with a higher frequency of RHV in Paris, likely explained by the higher proportion of paediatric samples collected in Tours. However, despite the distance between the two institutions (about 250 km) and differences between the two populations, both presented similar patterns of high frequency of non-influenza viruses in acute respiratory infections before the flu epidemic wave and a decline when influenza reached epidemic levels. In the two cities, high frequencies of RHV were seen at the same level with a likely different evolution speed, with sudden increase and decrease in SLS and more progressive variation in TRS. In both institutions, there was a decrease in the proportion and number of RHV diagnoses roughly in parallel with the increase of influenza diagnoses. Indeed, H1N1v exceeds 20% of positive detection's rate only when RHV dropped under 40%. These data are thus consistent with negative interaction of the two epidemics at the population level. It was previously hypothesised that RHV epidemic could interfere with the spread of pandemic influenza [20, 21, 22] . Few in vitro data support this hypothesis. It has been reported that interferon and other cytokines production by RHV infected cells induced a refractory state to virus infection These data include the three patients whose respiratory samples could not be studied with the multiplex assay because of RT-PCR inhibitors. of neighbouring cells [23] . Further work is needed to confirm in vitro and in vivo such negative interactions and if viral interference are really translated to a population level. Analysis of rhinovirus and influenza epidemics in previous years should also help to determine if similar interferences were observed with seasonal influenza and to elaborate modelling and prediction of the spread of influenza according to respiratory viruses' circulation. Systematic extensive screening of respiratory viruses at a national level should be implemented for this purpose. Very few RSV infections were observed in contrast to usual epidemiology which was characterized the last four past years by a start of epidemics in weeks 44-45 [1] . It has been confirmed by other laboratories and the French InVS that the 2009-10 RSV epidemic was delayed and had a lower impact compared with the previous winter season [1, 24] . Delayed and reduced RSV spread may be due to viral interference between RSV and influenza. Another possible explanation is better prevention behaviour about respiratory infections as recommended by a national campaign including recommendations for hands washing after sneezing and the use of mask [1] . Influenza infections were mainly detected in patient under 40 years old and no case was found in patients older than 65. These results corroborate previous data suggesting that past seasonal H1N1 infections or vaccination may give partial crossed protection [10, 13, 25] . We have previously shown that the neutralizing titers against pandemic H1N1v virus correlate significantly with neutralizing titers against a seasonal H1N1 virus, and that the H1N1v pandemic influenza virus neutralizing titer was significantly higher in subjects who had recently been inoculated by a seasonal trivalent influenza vaccine [26] . Viral co-infections were predominantly seen in paediatric patients, as previously described [4, 27, 28, 29] , both in influenza and non-influenza cases at a similar rate. No evidence of more pronounced respiratory impact was seen in these patients. Our results showed the lack of specific clinical signs associated with proven H1N1v infections. Clinical characteristics did not differ between influenza infections or other viral infections. In particular, the proportion of patients with fever above 39uC was not higher in H1N1v positive patients. In addition, the patients without any evidence of respiratory viral infections did not have different symptoms. These patients may have been infected with other virus not included in the multiplex assay (human Bocavirus, coronavirus HKU1) [9, 10, 11] or were seen too late at the time of viral shedding was cleared [30] . However, to determine how specific the symptoms are for influenza would require to assess also the distribution of respiratory pathogens (H1N1v and other respiratory viruses) and related symptoms in patients presented at the emergency departments in SLS and TRS with respiratory syndromes, but not tested for H1N1v. In addition, despite some underlying conditions that were associated with complications not previously observed in seasonal influenza, most illnesses caused by the H1N1v virus were acute and self-limited [13, 31] . The higher proportion of non influenza viruses reported in ILI in 2009 was thus most likely a consequence of more frequent visits to a doctor for respiratory tract infections than usually observed for fear of the flu pandemic. The general lack of difference in symptoms in the particular context of H1N1v pandemic has therefore to be considered with caution and does not rule out that more significant differences may arise in future influenza epidemics with other influenza viruses. Our data confirm that it may be virtually impossible to recognize symptoms heralding H1N1v infections and virological data should be helpful along with clinical reports to monitor influenza epidemic [10] . Molecular multiplex detection has recently emerged as a potent diagnostic tool to determine acute respiratory infections' aetiologies [11, 32, 33] . These data show that sensitive molecular multiplex detection of respiratory viruses is feasible and efficient for the detection of virus involved in acute respiratory infections and provides insights into their epidemic profile. Our results confirm the performance of RespiFinder19H assay to detecting respiratory viruses in the general population as recently shown in transplant patients with ILI [34] . RespiFinder19H confirmed all H1N1 infections detected by the CDC reference assay and was able to identify two additional H1N1 cases suggesting a high sensitivity of this multiplex assay to detect influenza A infections. In conclusion, our results highlight that successive and mixed outbreaks of respiratory viral infections may affect influenza epidemiology and can lead to misinterpret the early development of a flu epidemic. Rapid diagnostic screening of a large panel of respiratory pathogens may be critical to define and survey the epidemic situation and to provide critical information for patient management.

What network of physicians provides real-time clinical data on the spread of influenza in France?

Role of S-Palmitoylation on IFITM5 for the Interaction with FKBP11 in Osteoblast Cells https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3776769/ Tsukamoto, Takashi; Li, Xianglan; Morita, Hiromi; Minowa, Takashi; Aizawa, Tomoyasu; Hanagata, Nobutaka; Demura, Makoto 2013-09-18 DOI:10.1371/journal.pone.0075831 License:cc-by Abstract: Recently, one of the interferon-induced transmembrane (IFITM) family proteins, IFITM3, has become an important target for the activity against influenza A (H1N1) virus infection. In this protein, a post-translational modification by fatty acids covalently attached to cysteine, termed S-palmitoylation, plays a crucial role for the antiviral activity. IFITM3 possesses three cysteine residues for the S-palmitoylation in the first transmembrane (TM1) domain and in the cytoplasmic (CP) loop. Because these cysteines are well conserved in the mammalian IFITM family proteins, the S-palmitoylation on these cysteines is significant for their functions. IFITM5 is another IFITM family protein and interacts with the FK506-binding protein 11 (FKBP11) to form a higher-order complex in osteoblast cells, which induces the expression of immunologically relevant genes. In this study, we investigated the role played by S-palmitoylation of IFITM5 in its interaction with FKBP11 in the cells, because this interaction is a key process for the gene expression. Our investigations using an established reporter, 17-octadecynoic acid (17-ODYA), and an inhibitor for the S-palmitoylation, 2-bromopalmitic acid (2BP), revealed that IFITM5 was S-palmitoylated in addition to IFITM3. Specifically, we found that cysteine residues in the TM1 domain and in the CP loop were S-palmitoylated in IFITM5. Then, we revealed by immunoprecipitation and western blot analyses that the interaction of IFITM5 with FKBP11 was inhibited in the presence of 2BP. The mutant lacking the S-palmitoylation site in the TM1 domain lost the interaction with FKBP11. These results indicate that the S-palmitoylation on IFITM5 promotes the interaction with FKBP11. Finally, we investigated bone nodule formation in osteoblast cells in the presence of 2BP, because IFITM5 was originally identified as a bone formation factor. The experiment resulted in a morphological aberration of the bone nodule. This also indicated that the S-palmitoylation contributes to bone formation. Text: The interferon-induced transmembrane (IFITM) protein family (also known as the Fragilis family in mice) is a part of the dispanin family [1] and is composed of double-transmembrane α-helices connected by a cytoplasmic (CP) loop and extracellular (EC) amino-and carboxyl-terminal polypeptide sequences (Figure 1-A) . The IFITM proteins are evolutionarily conserved in vertebrates [2] . Recent genomic research has revealed that there are 5 IFITM members in humans (IFITM1, 2, 3, 5 and 10) and 7 members in mice (IFITM1, 2, 3, 5, 6, 7, and 10). These proteins play roles in diverse biological processes, such as germ cell maturation during gastrulation (IFITM1-3) [3] [4] [5] , cell-to-cell adhesion (IFITM1) [6] [7] [8] , antiviral activity (IFITM1-3) [9] [10] [11] [12] [13] [14] [15] [16] [17] , and bone formation (IFITM5) [18] [19] [20] [21] [22] , although the detailed functions of IFITM6, 7, and 10 are unknown at present. In particular, IFITM3 has been a target of intensive studies on its activity against influenza A (H1N1) virus infection and internalization [9] [10] [11] [12] [13] [14] . In 2010, Dr. Yount and co-workers reported that the antiviral activity of IFITM3 is dependent on S-palmitoylation on the protein [10] . The S-palmitoylation [23] is a post-translational modification on proteins by C 16 saturated-fatty acids (palmitic acids) covalently attached to certain cysteine residues via a thioester linkage (Figure 1-B) . The modification is reversibly catalyzed by protein acyltransferases and acylprotein thioesterases, and confers unique properties to the protein, such as membrane binding and targeting, immunoreactivity, Amino-acid sequence alignment of IFITM5, IFITM1, IFITM2, and IFITM3 derived from mice. The conserved residues are highlighted in black. The three conserved cysteines are highlighted in red and numbered based on the sequence of IFITM5 (top) and IFITM3 (bottom). The residues unique in IFITM5 are highlighted in gray. The first and the second transmembrane domains, the extracellular sequences, and the cytoplasmic loop are indicated by arrows and denoted as TM1 and TM2, EC, and the CP loop, respectively. The TM domains were predicted by SOSUI. The aspartates at the C-terminal region in IFITM5 are shown in blue. B) The schematic illustration of the protein S-palmitoylation. The C 16 -palmitic acid is attached to cysteine via a thioester linkage. The palmitoylation and depalmitoylation are catalyzed by protein acyltransferases and acylprotein thioesterases, respectively. In this study, hydroxylamine, NH 2 OH, was used to reduce the thioester linkage. C) The amino acid sequence identity (similarity) among IFITM5, IFITM1, IFITM2, and IFITM3 is summarized. doi: 10.1371/journal.pone.0075831.g001 and protein-protein interaction. The authors revealed that IFITM3 is S-palmitoylated on three membrane proximal cysteines, Cys71 and Cys72 in the first transmembrane (TM1) domain, and Cys105 in the CP loop (Figure 1-A) [10] . In addition, IFITM3 lacking the S-palmitoylation is not clustered in the cell membrane and significantly diminishes the antiviral activity. Moreover, the cysteines in IFITM2, Cys70, Cys71, and Cys104 are also palmitoylated in the same manner, which affects the intracellular localization [24] . A resent study has revealed that murine IFITM1 has four cysteine residues (Cys49, Cys50, Cys83, and Cys103) for the S-palmitoylation, which is required for the antiviral activity and the protein stability [25] . The other IFITM family members also possess these cysteines (Figure 1-A) , and thus the role of the Spalmitoylation on the cysteines should be significant for the functions of IFITM proteins. Here, we focused on IFITM5, which is also known as bonerestricted IFITM-like (BRIL) protein [18] . Among the IFITM family proteins, IFITM5 is unique. (i) Expression of IFITM5: Unlike the other IFITM family proteins, the expression of IFITM5 is not induced by interferons because the region upstream of the ifitm5 gene lacks the interferon regulatory elements [26] . Furthermore, the expression of IFITM5 is mostly restricted to osteoblast cells [18, 19, 27] , while the other IFITM proteins are expressed ubiquitously (ii). Amino-acid sequence similarity: The amino acid sequence of IFITM5 is relatively dissimilar to IFITM1-3 proteins (~ 65% similarity), while IFITM1-3 proteins share ~ 85% similarity with each other (Figure 1 -C). In addition, IFITM5 has an aspartate-rich domain in the C-terminal region, which could be involved in calcium binding (Figure 1 -A) [26] . (iii) Role of IFITM5 in bone formation: The expression of IFITM5 is associated with mineralization during the bone formation process in osteoblast cells [18] [19] [20] [21] . Previous studies have confirmed the expression of IFITM5 in bone tissues in mice, rats, humans and tammar wallabies [2] . The ifitm5-gene knockout mice have smaller bones [19] . Moreover, the knockdown of the ifitm5 gene by small hairpin RNA induces a decrease in bone nodule formation, whereas overexpression of the gene in UMR106 cells has been shown to increase calcium uptake and bone nodule formation [18] . (iv) Role of IFITM5 for immune activity: Recent studies have revealed that IFITM5 interacts with the FK506-binding protein 11 (FKBP11) to form IFITM5-FKBP11-CD81-the prostaglandin F2 receptor negative regulator (FPRP) complex [28] . When the complex is formed, the expressions of 5 interferon-induced genes are induced, including bone marrow stromal cell antigen 2 (Bst2), interferon inducible protein 1 (Irgm), interferoninduced protein with tetratricopeptide repeats 3 (Ifit3), b(2)microglobulin (B2m), and MHC class I antigen gene. Consequently, these results indicate that IFITM5 is involved not only in the bone formation but also in the immune system activity. In this study, we investigated the S-palmitoylation of IFITM5 and its role in the interaction with FKBP11 in mouse osteoblast cells. Cells transfected by a plasmid DNA encoding mouse IFITM5 were grown in the presence of an established chemical reporter, 17-octadecynoic acid (17-ODYA) [29, 30] , or an inhibitor for the S-palmitoylation, 2-bromopalmitic acid (2BP) [31] . The biochemical assays using these compounds revealed that the wild-type IFITM5 is S-palmitoylated. To identify the Spalmitoylation site in IFITM5, we prepared cysteine-substituted mutants, IFITM5-C86A, -C52A/C53A, and -C52A/53A/86A (Cys-less). The chemical reporter assay suggested that at least two out of three cysteines in IFITM5 are S-palmitoylated. The interaction of IFITM5 with FKBP11 was examined by immunoprecipitation assay, resulting in the loss of the interaction in the presence of 2BP. The same result was obtained in the two mutants, C52A/C53A and Cys-less. These results suggested that the S-palmitoylation on Cys52 and/or Cys53 in the TM1 domain of IFITM5 is necessary for the interaction with FKBP11. On the other hand, Cys86 in the CP loop of IFITM5 was S-palmitoylated but not involved in the interaction. Because this interaction is important for the immunologically relevant gene expression, it was indicated that the role of the S-palmitoylation is to promote the interaction of IFITM5 with FKBP11 and to regulate the immune activity in the osteoblast cells. The possible interaction mechanism and the effect of the S-palmitoylation on the bone nodule formation will be discussed. For mammalian cell expression, plasmid vectors of wild-type IFITM5 (IFITM5-WT) and FLAG-fused FKBP11 (FKBP11-FLAG) were constructed by inserting the cloned genes into a pBApo-CMV Neo expression vector (Takara Bio, Shiga, Japan). The details of the recombinant DNA constructs were the same as described previously [19] . The genes of IFITM5 mutants (IFITM5-C86A, -C52A/53A, and -C52A/C53A/C86A (Cys-less)) were prepared using a QuikChange site-directed mutagenesis kit (Stratagene, La Jolla, CA). The plasmid vectors of FLAG-fused IFITM5-WT, -C52A/53A, and Cys-less were constructed by inserting the cloned genes into the pBApo-CMV Neo expression vector. For E. coli cell expression, the plasmid vector of IFITM5-WT was constructed by inserting the cloned gene into a pET22b (Novagen, Madison, WI) expression vector. The forward primer 5'-GGAATTCCATATGGACACTTCATATCCCCGTG-3' and the reverse primer 5'-CCGCTCGAGGTTATAGTCCTCCTCATCAAACTTGG-3' were used to amplify the gene encoding the entire IFITM5 from the plasmid vector for mammalian cell expression described above. The underlined letters denote an NdeI and an XhoI cleavage site, respectively. The plasmids of IFITM5 mutants were prepared using a QuikChange site-directed mutagenesis kit. The sense and anti-sense primers used were 5'-GGCAGTATGGCTCCAAAGCCAAGGCGTACAACATCCTGG CTGC-3' and 5'-GCAGCCAGGATGTTGTACGCCTTGGCTTTGGAGCCATACT GCC-3' for IFITM5-C86A; and 5'-GCACGATGTACCTGAATCTGGCGGCGCTTGGATTCCTGG CGC-3' and 5'-GCGCCAGGAATCCAAGCGCCGCCAGATTCAGGTACATCG TGC-3' for IFITM5-C52A/C53A, respectively (Sigma-Aldrich, St. Louis, MO). Osteoblast-like MC3T3 cells were provided by the RIKEN, Cell Bank (RCB 1126). The procedures for cell culture, transfection, and protein expression were the same as reported previously. When necessary, 2-bromopalmitic acid (2BP; Wako, Osaka, Japan) and 17-octadecynoic acid (17-ODYA; Sigma-Aldrich) were dissolved in 99.5% dimethyl sulfoxide (DMSO; Wako) and added to differentiation medium at concentrations of 100 μM and 50 μM in less than 0.1% DMSO, respectively [30, 31] . Wild-type and mutant IFITM5 proteins were also produced using an E. coli recombinant expression system. E. coli BL21(DE3) cells transformed by the expression plasmid were grown at 37°C in LB medium containing 50 μg/mL ampicillin. After four-hour induction by 1 mM isopropyl β-Dthiogalactopyranoside (IPTG), cells were harvested by centrifugation (6,400 × g for 10 min at 4°C). The cells were suspended in 50 mM Tris-HCl buffer (pH 8) and disrupted by a French press (Ohtake, Tokyo, Japan) (100 MPa × 4 times). The crude membrane fraction was collected by ultracentrifugation (178,000 × g for 90 min at 4°C). The collected fraction was solubilized with 1.5% n-dodecyl-β-Dmaltopyranoside (DDM) (Dojindo Lab, Kumamoto, Japan) in 50 mM Tris-HCl, pH 8, containing 0.3 M NaCl and 5 mM imidazole. After the ultracentrifugation, the supernatant was incubated with Ni 2+ -NTA agarose resin (Qiagen, Hilden, Germany). The resin was applied to a chromatography column and washed with 50 mM imidazole containing 50 mM Tris-HCl (pH 8), 0.3 M NaCl and 0.1% DDM. The DDM-solubilized IFITM5 was collected by elution with the same buffer containing 0.3 M imidazole. The sample media were replaced by the appropriate buffer solution by two passages over a PD-10 column (GE Healthcare UK, Ltd., Amersham Place, England). The experimental details are described in previous reports [19, 28] . Briefly, total proteins were extracted from the osteoblast cells which co-expressed IFITM5 and FKBP11-FLAG using a total protein extraction kit (BioChain Institute Inc., Newark, CA). Then, the cell lysate was incubated with anti-FLAG M2 agarose gel (Sigma-Aldrich) at 4°C for 2 h. To recover FKBP11-FLAG, 500 ng/μL 3 × FLAG peptide (Sigma-Aldrich) dissolved in Tris-buffered saline was added to the collected gel at 4°C for 1 h. The recovered proteins and the cell lysate containing total proteins were analyzed by SDS-PAGE (15% ePAGEL; ATTO, Tokyo, Japan) and western blot. The anti-IFITM5 polyclonal antibody, which was prepared from the amino-terminal peptide sequence (TSYPREDPRAPSSRC), and anti-FLAG monoclonal antibody (Sigma-Aldrich) were used as primary antibodies. The HRP-conjugated goat anti-rabbit IgG (H+L) (Zymed Laboratories, San Francisco, CA) and goat anti-mouse IgG (H+L) (Sigma-Aldrich) antibodies were used as secondary antibodies for the anti-IFITM5 and anti-FLAG primary antibodies, respectively. The proteins were detected by chemiluminescent reaction (MercK-Millipore, Billerica, MA). The cell lysate extracted from the osteoblast cells metabolically labeled by 17-ODYA was incubated with anti-FLAG M2 agarose gel to obtain purified FLAG-fused IFITM5 proteins. The 17-ODYA-labeled proteins were chemically labeled with azide-PEG 3 -5(6)-carboxytetramethylrhodamine (TAMRA-azide; Click Chemistry Tools, Scottsdale, AZ) with reference to previous studies [10, 29, 30, 32] and the manufacturer's guide. The proteins separated by SDS-PAGE were visualized using a 532-nm laser for excitation and the fluorescence by TAMRA (565 nm) was detected using a 575nm long-path filter (Typhoon FLA 9000; GE Healthcare). The subcultured osteoblast MC3T3 cells were seeded at a density of 5,000 cells/cm 2 in 40 mm dishes and cultured in α-Modified Eagle's Medium (α-MEM; Sigma-Aldrich) containing 10% (v/v) fetal bovine serum (FBS; Nichirei Biosciences Inc., Tokyo, Japan). On the next day, this was replaced with differentiation medium, containing 2 mM glycerophosphate and 50 μg/mL sodium ascorbate at final concentrations, to induce osteoblast differentiation. When necessary, 100 μM 2BP in less than 0.1% DMSO, or 0.1% DMSO alone was added to the differentiation medium at final concentrations. All cultures were incubated at 37°C in a humidified atmosphere containing 5% CO 2 for 27 days. Mineralized nodules were stained with Alizarin Red S (Sigma-Aldrich). The standard staining procedure was used. The mineralized nodules were checked every three days. To identify the S-palmitoylation on IFITM5, the osteoblast cells harboring the plasmid DNA encoding IFITM5-WT were cultured in the absence and presence of 2BP, which inhibits the S-palmitoylation (Figure 2-A) [31] . Then, the cell lysate containing total protein was extracted for use in the SDS-PAGE and western blot analyses. For purposes of comparison, E. coli cells were also cultured in the absence of 2BP and the cell lysate was extracted. Figure 2 -B shows the results of the western blot assay for IFITM5-WT expressed in the osteoblast and the E. coli cells. In the osteoblast cells, IFITM5-WT exhibited a single band near the 17.4 kDa molecular-mass marker (see lane 1) in the absence of 2BP. However, in the presence of 2BP (see lane 4), the band appeared at a lower position than that in the absence of 2BP (lane 1). These results suggested that IFITM5-WT has high and low molecular-mass forms in the absence and presence of 2BP, respectively. The S-palmitoylation is a reversible reaction, and therefore is depalmitoylated by a strong reductant such as hydroxylamine [10] . Following hydroxylamine treatment (see lane 2), the band appeared at the same position as in the presence of 2BP (lane 4). In prokaryote E. coli cells, the post-translational modification S-Palmitoylation on IFITM5 PLOS ONE | www.plosone.org does not occur. Hence, the band was also observed at the same lower position (see lane 3). In the case of IFITM3, the palmitoylation was also reported to induce a change in mobility on electrophoresis, just as in our present results [10] . For direct observation of the S-palmitoylation, an established chemical reporter, 17-ODYA (Figure 2-C) , was used. The osteoblast cells harboring the plasmid encoding IFITM5-WT were cultured in the presence of 17-ODYA to label the protein metabolically. Following the extraction and the purification of the cell lysate, the labeled IFITM5-WT was ligated with TAMRA-azide according to the Cu(I)-catalyzed [3+2] azidealkyne cycloaddition method [10, 29, 30, 32 ]. An in-gel fluorescence image of the 17-ODYA-TAMRA-labeled IFITM5-WT (see lane 2 in Figure 2 -D) showed that IFITM5 was Spalmitoylated in the osteoblast cells. The FLAG-tag attached to IFITM5 has no influence on the modification and chemical labeling (lanes 1 and 5). In addition, after the hydroxylamine treatment (see lane 6), the fluorescence became weak because of the dissociation of 17-ODYA from IFITM5, which was the same mechanism as the dissociation of the palmitic acid from IFITM5 by reduction as described above (lane 2 of Figure 2-B) . Therefore, we concluded that the IFITM5 expressed in the native osteoblast cells is S-palmitoylated. In addition, the bands corresponding to the high and the low molecular-mass forms shown in western blot analysis were tentatively assigned to the S-palmitoylated and the depalmitoylated forms, respectively. As described above in the Introduction, cysteine residues are the substrate for S-palmitoylation. IFITM5 possesses three cysteines, Cys52 and Cys53 in the TM1 domain, and Cys86 in the CP loop (Figure 1-A) . All of these cysteines are highly conserved among the mammalian IFITM family proteins (Figure 3-A) . To identify the modification site in IFITM5, we prepared cysteine-substituted mutants, IFITM5-C52A/C53A, -C86A, and -C52A/C53A/C86A (Cys-less). The osteoblast cells harboring each plasmid were cultured in the absence of 2BP, and then the cell lysate was extracted. Figure 3 -B shows the results of the western blot detecting the expression of all the mutants in the osteoblast cells. In the C52A/C53A and Cys-less mutants (see lanes 2 and 4), the low molecular-mass form was detected. This result indicates that either Cys52 or Cys53 is involved in the S-palmitoylation. In addition, as shown in Figure 2 -D, strong and weak fluorescence were detected in the C52A/ C53A mutant in the absence and presence of hydroxylamine (lanes 3 and 7) , respectively, but not in the Cys-less mutant (lanes 4 and 8) . These results suggested that the rest of the cysteine in the C52A/C53A mutant, Cys86, is S-palmitoylated and the Cys-less mutant completely lost the S-palmitoylation because all the cysteines were substituted. Therefore, we concluded that Cys86, plus one or two other cysteine residues in IFITM5, i.e., Cys52 and/or Cys53, are S-palmitoylated. In addition, it was found that the S-palmitoylation on the TM1 domain has a major effect on the mobility in the gel (lower panel of Figure 2 -D and Figure 3-B) . Therefore, we hereafter refer to the high and low molecular-mass forms as the TM1palmitoylated and the TM1-depalmitoylated forms, respectively. Finally, we reassigned the bands shown in the western blot analysis as follows: IFITM5-WT is fully palmitoylated, the C86A mutant is partially palmitoylated at Cys52 and/or Cys53, the C52A/C53A mutant is partially palmitoylated at Cys86, and the Cys-less mutant is completely depalmitoylated. Previous studies have revealed that IFITM5 interacts with FKBP11 [19] . FKBP11 belongs to the FK506-binding protein family and has a transmembrane domain. The interaction between IFITM5 and FKBP11 is important for the immune activity because formation of the IFITM5-FKBP11-CD81-FPRP complex induces the expression of interferon-induced genesnamely, the Bst2, Irgm, Ifit3, B2m, and MHC class I antigen gene [28] . To investigate the effect of the S-palmitoylation on the interaction of IFITM5 with FKBP11, we carried out an immunoprecipitation assay. The osteoblast cells co-transfected by the plasmids encoding IFITM5-WT and FKBP11-FLAG were cultured in the absence and the presence of 2BP. Then, the extracted cell-lysate was incubated with anti-FLAG agarose gel. The gel was washed several times. Finally, the proteins were competitively eluted by the addition of FLAG peptide. If IFITM5 interacted with FKBP11, it was expected that IFITM5 The conserved cysteines are highlighted in orange and numbered. In the lower panel, the numbers given in parenthesis correspond to the residual number for IFITM2. For the calculation of probability, a total of 23 IFITM2, 23 IFITM3, and 17 IFITM5 sequences derived from mammalian species in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database were used. Sequence alignment was carried out using CLUSTALW. Sequence logos were generated using WEBLOGO 3. B) Western blot for the wild-type and cysteine-substituted mutants of IFITM5 expressed in the osteoblast cells. For detection, the anti-IFITM5 antibody was used as a primary antibody. The upper arrow indicates that C52 and/or C53 in the TM1 domain is Spalmitoylated (lanes 1 and 3) . The C52A/C53A (lane 2) and Cys-less (lane 4) mutants are partially and completely depalmitoylated. The experiment was carried out 2 times. doi: 10.1371/journal.pone.0075831.g003 would be obtained during this step and detected by immunoblotting. Figure 4 -A shows the results of the western blot for the co-immunoprecipitation of IFITM5-WT with FKBP-FLAG. The band corresponding to FKBP11 appeared in all the lanes (upper panel). Lanes 1 and 2 are controls to ensure that IFITM5 and FKBP11 are both contained in the cell lysate before the immunoprecipitation. The controls also ensured that IFITM5 was S-palmitoylated in the absence of 2BP (see lane 1), whereas IFITM5 was not S-palmitoylated in the presence of 2BP (see lane 2). After the immunoprecipitation, a single band corresponding to the S-palmitoylated IFITM5 appeared in the absence of 2BP (see lane 3), indicating the interaction of the Spalmitoylated IFITM5 with FKBP11. However, in the presence of 2BP, no band corresponding to IFITM5 appeared (see lane 4) , indicating that the two molecules do not interact with each other. These results suggest that the S-palmitoylation on IFITM5 contributes to the interaction with FKBP11. Next, we further investigated the relationship between the Spalmitoylation and the interaction with FKBP11 by using the IFITM5 mutants described above. The osteoblast cells cotransfected by the plasmids encoding IFITM5 mutants (C52A/ C53A, C86A, and Cys-less) and FKBP11-FLAG were cultured. The immunoprecipitation assay was carried out in the same way as described above. Figure 4 -B shows the results of the western blot for the co-immunoprecipitation of the wild-type and the IFITM5 mutants with FKBP11. Figure 4 -C shows the results of the control experiment using the cell lysate before the immunoprecipitation. As described in the previous section 3-3, the band corresponding to FKBP11 appeared in all the lanes (upper panels) because the immunoprecipitation was carried out using the anti-FLAG agarose gel. In the lower panel of Figure 4 -B, single bands were observed for the IFITM5-WT and -C86A mutant (lanes 1 and 3) but not for the -C52A/C53A and Cys-less mutants (lanes 2 and 4) . This result indicates that the wild-type and the C86A mutant interact with FKBP11, whereas the other two mutants do not. Interestingly, this tendency mirrored the trend for the S-palmitoylation profiles, which means that Cys52 and/or Cys53 in the TM1 domain of the IFITM5-WT and -C86A mutants is S-palmitoylated, whereas these residues are not S-palmitoylated in the C52A/C53A and Cys-less mutants (see Figures 2-D, 3 -B and the lower panel of Figure 4 -C). Because the S-palmitoylation contributes to the IFITM5-FKBP11 interaction, as described in the previous section 3-3 (also in Figure 4-A) , the results of Figure 4 -B suggest that the mutants which lost the S-palmitoylation site(s), Cys52 and/or Cys53, are not able to interact with FKBP11. In other words, the S-palmitoylation on these cysteines is necessary for the interaction of IFITM5 with FKBP11. As described above in the Introduction, previous studies have revealed that IFITM5 also contributes to bone formation [18] [19] [20] [21] . Therefore, we investigated the influence of Spalmitoylation on the bone nodule formation in osteoblast cells, in which native IFITM5 is expressed. Figure 5 shows the time-dependent nodule formation in the absence and the presence of 2BP ( Figure 5-A and -B) . Figure 5 -C shows the results of the control trial to verify the effect of DMSO, which was used as the solvent for 2BP, on the nodule formation. The mineralized nodule was stained with Alizarin Red, which reacts with deposited calcium. In Figure 5 -D, the area of the mineralized nodule was plotted against experimental time. In the absence of 2BP (Figure 5-A, -C, and -D) , the mineralization was started 15 days after the initiation of the cell differentiation (Day 0). On the other hand, in the presence of 2BP ( Figure 5-B and -D) , the nodule was formed on Day 12. The halftime for the maximum mineralization in the presence of 2BP was estimated to be 7 days earlier than that in the absence of 2BP (Figure 5-D) . In addition, differences in the form of the mineralized nodules were observed. Figure 5 -E shows an enlarged view of each nodule on Day 21. The stained nodules were diffused in the presence of 2BP (panel b), whereas in the absence of 2BP the nodules formed a large cluster (panels a and c). Therefore, our observations in this study suggested that the S-palmitoylation affects the bone nodule formation in the osteoblast cells. In this study, we confirmed the S-palmitoylation on IFITM5 in the osteoblast cells, which was the same as that previously reported for IFITM3 and IFITM2. As reported previously, in IFITM3 and IFITM2, which share 85% sequence similarity (Figure 1-C) , two cysteines in the TM1 domain (Cys71 and Cys72 for IFITM3, Cys70 and Cys71 for IFITM2) and one cysteine in the CP loop (Cys105 for IFITM3, Cys104 for IFITM2) are all S-palmitoylated in cells [10, 24] . On the other hand, although IFITM5 shares 68% and 66% sequence similarity to IFITM3 and IFITM2, respectively, more than one cysteine in the TM1 domain (Cys52 or Cys53) and one cysteine in the CP loop (Cys86) are S-palmitoylated. Taking into account the high conservation of three cysteines in the IFITM proteins (Figures 1-A and 3-A) , all the cysteines in IFITM5 may be involved in the S-palmitoylation just as in the case of IFITM3 and IFITM2 [10, 24] . The roles of the S-palmitoylation on IFITM3 have been studied intensively, and the S-palmitoylation has been shown to be crucial for the correct positioning in the membrane and the resistance to viral infection and internalization [10] (the roles are summarized in Figure 6 -A and discussed in detail below). A recent study has revealed that the S-palmitoylation on IFITM2 is also important for the protein clustering in the membrane [24] . However, we do not know the role of the Spalmitoylation of IFITM5 for the clustering in the membrane at present because we have not yet succeeded in obtaining a proper antibody for immunohistochemistry, despite our allocating much time to the search and considering a considerable number of antibodies. Dr. Hanagata and co-workers previously reported that IFITM5 lacking the TM1 domain and the CP loop, which and IFITM5 (lower panels), the anti-FLAG and the anti-IFITM5 antibodies were used as primary antibodies, respectively. Arrows indicate the existence of each protein and the S-palmitoylation on IFITM5. A) Western blot for the co-immunoprecipitation of the wild-type IFITM5 with the FLAG-fused FKBP11 (FKBP11-FLAG) in the osteoblast cells in the absence and the presence of 2BP (denoted as "-" and "+", respectively). Lanes 1 and 2 are the results for the control trials used to verify the existence of IFITM5 and FKBP11 before the immunoprecipitation, and Lanes 3 and 4 show the results after the immunoprecipitation. The experiment was repeated 3 times. B) Western blot for the co-immunoprecipitation of the wild-type and the cysteine-substituted mutants of IFITM5 with FKBP11-FLAG in the osteoblast cells. The band corresponding to FLAG peptide is not shown because of the smaller molecular-mass of FLAG peptide relative to FKBP11-FLAG. C) The control experiment of Figure 4 -B used to verify that IFITM5 and FKBP11 were both present in the cell lysate before the immunoprecipitation. The experiment was repeated 2 times. A) The functional mechanism of IFITM3 is summarized from previous studies. (i) IFITM3 is S-palmitoylated at Cys71, Cys72, and Cys105, (ii) which induces clustering and correct positioning in the membrane, (iii) resulting in the antiviral activity against influenza virus. B) The functional mechanism of IFITM5 is summarized by combining the results from the present and the previous studies. (i) Cys86, plus one or two other cysteine residues in IFITM5, i.e., Cys52 and/or Cys53, are S-palmitoylated (ii). The S-palmitoylation allows IFITM5 to interact with FKBP11 in the osteoblast cells (iii). The dissociation of CD9 from the FKBP11-CD81-FPRP/CD9 complex is induced by formation of the IFITM5-FKBP11-CD81-FPRP complex and leads to the immunologically relevant gene expression. IFITM5 also contributes to the bone formation, but it is unknown which states as described in (i)-(iii) are important for the bone formation at present.At present, no interactive protein has been identified in IFITM3 and IFITM2. On the other hand, IFITM5 interacts with the partner protein, FKBP11, and the S-palmitoylation clearly makes a significant contribution to the interaction. Therefore, IFITM5 forms a hetero-oligomer in the cell membrane for its physiological function. contain the relevant modification sites, lost the ability to interact with FKBP11 [19] . In the present study, we determined that the S-palmitoylation on Cys52 and/or Cys53 in the TM1 domain is necessary for the interaction. From these results, we speculate that Cys52 and Cys53 face toward the interaction surface with FKBP11, and therefore IFITM5 and FKBP11 interact with each other through the palmitic acid(s) attached to the cysteine(s) (summarized in Figure 6 -B, discussed in detail later). Our investigation revealed that Cys86 is involved in the Spalmitoylation but does not contribute to the interaction with FKBP11. We speculate that some other residues in the CP loop located near the TM1 domain make some contribution to the interaction. Previous investigations also revealed that IFITM5 expressed in the heterologous fibroblast NIH3T3 cells exhibited direct interactions with CD81, the B cell receptor-associated protein 31 (BCAP31), and the hydroxysteroid (17-beta) dehydrogenase 7 (HSD17b7). These three proteins bind to the IFITM5 without the S-palmitoylation (low molecular-mass form; see Figure 3 -b in ref [19] . and Figure 1 -B in ref [28] .). In the fibroblast cells, the S-palmitoylation on IFITM5 is insufficient [19] . These interactions are not observed in the native osteoblast cells, and therefore are nonspecific. Taking these facts into consideration, we speculate that the S-palmitoylation on IFITM5 promotes the specific interaction with FKBP11 in the osteoblast cells. The role played by the S-palmitoylation of IFITM5 in immune activity of the osteoblast cells will be discussed by combining the results from the present and the previous studies. A specific interaction between IFITM5 and FKBP11 should be necessary to form the IFITM5-FKBP11-CD81-FPRP complex. CD81, also known as TAPA-1, is a member of the tetraspanin membrane protein family and a component of the B-cell coreceptor complex which mediates the B-cell signaling for immune responses. When forming this complex, CD9, a partner protein with CD81, dissociates from the FKBP11-CD81-FPRP/CD9 complex and consequently induces the osteoblastspecific expression of the interferon-induced genes, Bst2, Irgm, Ifit3, B2m, and the MHC class I antigen gene [28] . If the Spalmitoylation-mediated specific interaction of IFITM5 with FKBP11 were lost, the IFITM5-FKBP11-CD81-FPRP complex would not be formed, and consequently the interferon-induced gene expression would be inhibited because CD9 would remain associated with the FKBP11-CD81-FPRP/CD9 complex. In this respect, we speculate that IFITM5 is involved in the immune system activity in the osteoblast cells and the interaction of the S-palmitoylated IFITM5 with FKBP11 regulates the immune activity. In addition, it was suggested that the S-palmitoylation on IFITM5 contributes to the bone nodule formation, including morphology and time for mineralization, in the osteoblast cells ( Figure 5 ). It is difficult to conclude at present that the lack of the S-palmitoylation on IFITM5 causes the diffusion of the bone nodules (panel b of Figure 5 -E); we can say, however, that IFITM5 will probably not be S-palmitoylated in the cells in the presence of 2BP. While 2BP is commonly used as an inhibitor of palmitoylation, it also targets many metabolic enzymes [33, 34] . Thus, it is also difficult to interpret the results of the long-term incubation of the osteoblast cells in the presence of 2BP. In any case, these are interesting and key observations in terms of clarifying the role played by the S-palmitoylation of IFITM5 in bone formation, and further studies are required. Figure 6 describes a possible mechanism of the interaction of IFITM5 with FKBP11 and the role of IFITM5 in the osteoblast cell function by means of a comparison with IFITM3. In the case of IFITM3, as shown in Figure 6 -A, the following are observed. (i) The three cysteines are all S-palmitoylated (ii). The S-palmitoylation leads to the clustering and the correct positioning of IFITM3 molecules in the membrane (iii). The Spalmitoylation and the following clustering are crucial for the resistance to the influenza virus. When IFITM3 lacks the Spalmitoylation, the IFITM3 molecules do not cluster, which leads to the significant decrease in the antiviral activity. On the other hand, Figure 6 -B shows that the following observations are made in the case of IFITM5. (i) Cys86, plus one or two other cysteine residues in IFITM5, i.e., Cys52 and/or Cys53, are S-palmitoylated (ii). The S-palmitoylated IFITM5 is able to interact specifically with FKBP11. The interaction is presumed to be mediated by the palmitic acid(s) attached to the cysteine(s) facing toward the interaction surface on FKBP11. Cys86 is involved in the S-palmitoylation but not in the interaction of IFITM5 with FKBP11. At present, however, little is known about the role of the S-palmitoylation of IFITM5 for the localization in the membrane. When the S-palmitoylation affects the localization of IFITM5 as in the case of IFITM3 [10] , the S-palmitoylated IFITM5 molecules should be localized in the membrane or the depalmitoylated molecules should be delocalized. The loss of the interaction between IFITM5 and FKBP11 could be due to a relocalization of the depalmitoylated IFITM5 that prevents its association with FKBP11 (iii). The Spalmitoylated IFITM5 interacts with the FKBP11-CD81-FPRP/CD9 complex through FKBP11, which induces the dissociation of CD9 from the complex and the expression of 5 immunologically relevant genes. Finally, IFITM5 forms the IFITM5-FKBP11-CD81-FPRP complex. It is unknown at present which of the three states (i)~(iii) illustrated in Figure 6 -B is important for the bone mineralization of the osteoblast cells. The lack of the S-palmitoylation influences the interaction with FKBP11, which could account for the following complex formation and gene expression. In addition, the bone nodule formation is also affected. Note that the role of the Spalmitoylation has been involved in the bone formation [35] . It is indicated that the S-palmitoylation on IFITM5 plays roles not only for the regulation of the immune activity but also for the bone formation. In conclusion, we have revealed the S-palmitoylation on IFITM5 and its role in the interaction with FKBP11. Not only the immune activity but also the bone mineralization in the osteoblast cells is affected by the S-palmitoylation. In general, the functional role of the S-palmitoylation is different for each protein [36] . For many proteins, the palmitoylation and depalmitoylation cycle is constitutive and regulated by enzymes. Based on the present results, it is difficult to address (i) whether the S-palmitoylation on IFITM5 is constitutive or regulated, or (ii) when and where IFITM5 is S-palmitoylated in the osteoblast cells. Further studies are required and are currently underway.

What regulates the antiviral activity of IFITM3?

Virus-Vectored Influenza Virus Vaccines https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4147686/ SHA: f6d2afb2ec44d8656972ea79f8a833143bbeb42b Authors: Tripp, Ralph A.; Tompkins, S. Mark Date: 2014-08-07 DOI: 10.3390/v6083055 License: cc-by Abstract: Despite the availability of an inactivated vaccine that has been licensed for >50 years, the influenza virus continues to cause morbidity and mortality worldwide. Constant evolution of circulating influenza virus strains and the emergence of new strains diminishes the effectiveness of annual vaccines that rely on a match with circulating influenza strains. Thus, there is a continued need for new, efficacious vaccines conferring cross-clade protection to avoid the need for biannual reformulation of seasonal influenza vaccines. Recombinant virus-vectored vaccines are an appealing alternative to classical inactivated vaccines because virus vectors enable native expression of influenza antigens, even from virulent influenza viruses, while expressed in the context of the vector that can improve immunogenicity. In addition, a vectored vaccine often enables delivery of the vaccine to sites of inductive immunity such as the respiratory tract enabling protection from influenza virus infection. Moreover, the ability to readily manipulate virus vectors to produce novel influenza vaccines may provide the quickest path toward a universal vaccine protecting against all influenza viruses. This review will discuss experimental virus-vectored vaccines for use in humans, comparing them to licensed vaccines and the hurdles faced for licensure of these next-generation influenza virus vaccines. Text: Seasonal influenza is a worldwide health problem causing high mobility and substantial mortality [1] [2] [3] [4] . Moreover, influenza infection often worsens preexisting medical conditions [5] [6] [7] . Vaccines against circulating influenza strains are available and updated annually, but many issues are still present, including low efficacy in the populations at greatest risk of complications from influenza virus infection, i.e., the young and elderly [8, 9] . Despite increasing vaccination rates, influenza-related hospitalizations are increasing [8, 10] , and substantial drug resistance has developed to two of the four currently approved anti-viral drugs [11, 12] . While adjuvants have the potential to improve efficacy and availability of current inactivated vaccines, live-attenuated and virus-vectored vaccines are still considered one of the best options for the induction of broad and efficacious immunity to the influenza virus [13] . The general types of influenza vaccines available in the United States are trivalent inactivated influenza vaccine (TIV), quadrivalent influenza vaccine (QIV), and live attenuated influenza vaccine (LAIV; in trivalent and quadrivalent forms). There are three types of inactivated vaccines that include whole virus inactivated, split virus inactivated, and subunit vaccines. In split virus vaccines, the virus is disrupted by a detergent. In subunit vaccines, HA and NA have been further purified by removal of other viral components. TIV is administered intramuscularly and contains three or four inactivated viruses, i.e., two type A strains (H1 and H3) and one or two type B strains. TIV efficacy is measured by induction of humoral responses to the hemagglutinin (HA) protein, the major surface and attachment glycoprotein on influenza. Serum antibody responses to HA are measured by the hemagglutination-inhibition (HI) assay, and the strain-specific HI titer is considered the gold-standard correlate of immunity to influenza where a four-fold increase in titer post-vaccination, or a HI titer of ≥1:40 is considered protective [4, 14] . Protection against clinical disease is mainly conferred by serum antibodies; however, mucosal IgA antibodies also may contribute to resistance against infection. Split virus inactivated vaccines can induce neuraminidase (NA)-specific antibody responses [15] [16] [17] , and anti-NA antibodies have been associated with protection from infection in humans [18] [19] [20] [21] [22] . Currently, NA-specific antibody responses are not considered a correlate of protection [14] . LAIV is administered as a nasal spray and contains the same three or four influenza virus strains as inactivated vaccines but on an attenuated vaccine backbone [4] . LAIV are temperature-sensitive and cold-adapted so they do not replicate effectively at core body temperature, but replicate in the mucosa of the nasopharynx [23] . LAIV immunization induces serum antibody responses, mucosal antibody responses (IgA), and T cell responses. While robust serum antibody and nasal wash (mucosal) antibody responses are associated with protection from infection, other immune responses, such as CD8 + cytotoxic lymphocyte (CTL) responses may contribute to protection and there is not a clear correlate of immunity for LAIV [4, 14, 24] . Currently licensed influenza virus vaccines suffer from a number of issues. The inactivated vaccines rely on specific antibody responses to the HA, and to a lesser extent NA proteins for protection. The immunodominant portions of the HA and NA molecules undergo a constant process of antigenic drift, a natural accumulation of mutations, enabling virus evasion from immunity [9, 25] . Thus, the circulating influenza A and B strains are reviewed annually for antigenic match with current vaccines, Replacement of vaccine strains may occur regularly, and annual vaccination is recommended to assure protection [4, 26, 27] . For the northern hemisphere, vaccine strain selection occurs in February and then manufacturers begin production, taking at least six months to produce the millions of vaccine doses required for the fall [27] . If the prediction is imperfect, or if manufacturers have issues with vaccine production, vaccine efficacy or availability can be compromised [28] . LAIV is not recommended for all populations; however, it is generally considered to be as effective as inactivated vaccines and may be more efficacious in children [4, 9, 24] . While LAIV relies on antigenic match and the HA and NA antigens are replaced on the same schedule as the TIV [4, 9] , there is some suggestion that LAIV may induce broader protection than TIV due to the diversity of the immune response consistent with inducing virus-neutralizing serum and mucosal antibodies, as well as broadly reactive T cell responses [9, 23, 29] . While overall both TIV and LAIV are considered safe and effective, there is a recognized need for improved seasonal influenza vaccines [26] . Moreover, improved understanding of immunity to conserved influenza virus antigens has raised the possibility of a universal vaccine, and these universal antigens will likely require novel vaccines for effective delivery [30] [31] [32] . Virus-vectored vaccines share many of the advantages of LAIV, as well as those unique to the vectors. Recombinant DNA systems exist that allow ready manipulation and modification of the vector genome. This in turn enables modification of the vectors to attenuate the virus or enhance immunogenicity, in addition to adding and manipulating the influenza virus antigens. Many of these vectors have been extensively studied or used as vaccines against wild type forms of the virus. Finally, each of these vaccine vectors is either replication-defective or causes a self-limiting infection, although like LAIV, safety in immunocompromised individuals still remains a concern [4, 13, [33] [34] [35] . Table 1 summarizes the benefits and concerns of each of the virus-vectored vaccines discussed here. There are 53 serotypes of adenovirus, many of which have been explored as vaccine vectors. A live adenovirus vaccine containing serotypes 4 and 7 has been in use by the military for decades, suggesting adenoviruses may be safe for widespread vaccine use [36] . However, safety concerns have led to the majority of adenovirus-based vaccine development to focus on replication-defective vectors. Adenovirus 5 (Ad5) is the most-studied serotype, having been tested for gene delivery and anti-cancer agents, as well as for infectious disease vaccines. Adenovirus vectors are attractive as vaccine vectors because their genome is very stable and there are a variety of recombinant systems available which can accommodate up to 10 kb of recombinant genetic material [37] . Adenovirus is a non-enveloped virus which is relatively stable and can be formulated for long-term storage at 4 °C, or even storage up to six months at room temperature [33] . Adenovirus vaccines can be grown to high titers, exceeding 10 1° plaque forming units (PFU) per mL when cultured on 293 or PER.C6 cells [38] , and the virus can be purified by simple methods [39] . Adenovirus vaccines can also be delivered via multiple routes, including intramuscular injection, subcutaneous injection, intradermal injection, oral delivery using a protective capsule, and by intranasal delivery. Importantly, the latter two delivery methods induce robust mucosal immune responses and may bypass preexisting vector immunity [33] . Even replication-defective adenovirus vectors are naturally immunostimulatory and effective adjuvants to the recombinant antigen being delivered. Adenovirus has been extensively studied as a vaccine vector for human disease. The first report using adenovirus as a vaccine vector for influenza demonstrated immunogenicity of recombinant adenovirus 5 (rAd5) expressing the HA of a swine influenza virus, A/Swine/Iowa/1999 (H3N2). Intramuscular immunization of mice with this construct induced robust neutralizing antibody responses and protected mice from challenge with a heterologous virus, A/Hong Kong/1/1968 (H3N2) [40] . Replication defective rAd5 vaccines expressing influenza HA have also been tested in humans. A rAd5-HA expressing the HA from A/Puerto Rico/8/1934 (H1N1; PR8) was delivered to humans epicutaneously or intranasally and assayed for safety and immunogenicity. The vaccine was well tolerated and induced seroconversion with the intranasal administration had a higher conversion rate and higher geometric meant HI titers [41] . While clinical trials with rAd vectors have overall been successful, demonstrating safety and some level of efficacy, rAd5 as a vector has been negatively overshadowed by two clinical trial failures. The first trial was a gene therapy examination where high-dose intravenous delivery of an Ad vector resulted in the death of an 18-year-old male [42, 43] . The second clinical failure was using an Ad5-vectored HIV vaccine being tested as a part of a Step Study, a phase 2B clinical trial. In this study, individuals were vaccinated with the Ad5 vaccine vector expressing HIV-1 gag, pol, and nef genes. The vaccine induced HIV-specific T cell responses; however, the study was stopped after interim analysis suggested the vaccine did not achieve efficacy and individuals with high preexisting Ad5 antibody titers might have an increased risk of acquiring HIV-1 [44] [45] [46] . Subsequently, the rAd5 vaccine-associated risk was confirmed [47] . While these two instances do not suggest Ad-vector vaccines are unsafe or inefficacious, the umbra cast by the clinical trials notes has affected interest for all adenovirus vaccines, but interest still remains. Immunization with adenovirus vectors induces potent cellular and humoral immune responses that are initiated through toll-like receptor-dependent and independent pathways which induce robust pro-inflammatory cytokine responses. Recombinant Ad vaccines expressing HA antigens from pandemic H1N1 (pH1N1), H5 and H7 highly pathogenic avian influenza (HPAI) virus (HPAIV), and H9 avian influenza viruses have been tested for efficacy in a number of animal models, including chickens, mice, and ferrets, and been shown to be efficacious and provide protection from challenge [48, 49] . Several rAd5 vectors have been explored for delivery of non-HA antigens, influenza nucleoprotein (NP) and matrix 2 (M2) protein [29, [50] [51] [52] . The efficacy of non-HA antigens has led to their inclusion with HA-based vaccines to improve immunogenicity and broaden breadth of both humoral and cellular immunity [53, 54] . However, as both CD8 + T cell and neutralizing antibody responses are generated by the vector and vaccine antigens, immunological memory to these components can reduce efficacy and limit repeated use [48] . One drawback of an Ad5 vector is the potential for preexisting immunity, so alternative adenovirus serotypes have been explored as vectors, particularly non-human and uncommon human serotypes. Non-human adenovirus vectors include those from non-human primates (NHP), dogs, sheep, pigs, cows, birds and others [48, 55] . These vectors can infect a variety of cell types, but are generally attenuated in humans avoiding concerns of preexisting immunity. Swine, NHP and bovine adenoviruses expressing H5 HA antigens have been shown to induce immunity comparable to human rAd5-H5 vaccines [33, 56] . Recombinant, replication-defective adenoviruses from low-prevalence serotypes have also been shown to be efficacious. Low prevalence serotypes such as adenovirus types 3, 7, 11, and 35 can evade anti-Ad5 immune responses while maintaining effective antigen delivery and immunogenicity [48, 57] . Prime-boost strategies, using DNA or protein immunization in conjunction with an adenovirus vaccine booster immunization have also been explored as a means to avoided preexisting immunity [52] . Adeno-associated viruses (AAV) were first explored as gene therapy vectors. Like rAd vectors, rAAV have broad tropism infecting a variety of hosts, tissues, and proliferating and non-proliferating cell types [58] . AAVs had been generally not considered as vaccine vectors because they were widely considered to be poorly immunogenic. A seminal study using AAV-2 to express a HSV-2 glycoprotein showed this virus vaccine vector effectively induced potent CD8 + T cell and serum antibody responses, thereby opening the door to other rAAV vaccine-associated studies [59, 60] . AAV vector systems have a number of engaging properties. The wild type viruses are non-pathogenic and replication incompetent in humans and the recombinant AAV vector systems are even further attenuated [61] . As members of the parvovirus family, AAVs are small non-enveloped viruses that are stable and amenable to long-term storage without a cold chain. While there is limited preexisting immunity, availability of non-human strains as vaccine candidates eliminates these concerns. Modifications to the vector have increased immunogenicity, as well [60] . There are limited studies using AAVs as vaccine vectors for influenza. An AAV expressing an HA antigen was first shown to induce protective in 2001 [62] . Later, a hybrid AAV derived from two non-human primate isolates (AAVrh32.33) was used to express influenza NP and protect against PR8 challenge in mice [63] . Most recently, following the 2009 H1N1 influenza virus pandemic, rAAV vectors were generated expressing the HA, NP and matrix 1 (M1) proteins of A/Mexico/4603/2009 (pH1N1), and in murine immunization and challenge studies, the rAAV-HA and rAAV-NP were shown to be protective; however, mice vaccinated with rAAV-HA + NP + M1 had the most robust protection. Also, mice vaccinated with rAAV-HA + rAAV-NP + rAAV-M1 were also partially protected against heterologous (PR8, H1N1) challenge [63] . Most recently, an AAV vector was used to deliver passive immunity to influenza [64, 65] . In these studies, AAV (AAV8 and AAV9) was used to deliver an antibody transgene encoding a broadly cross-protective anti-influenza monoclonal antibody for in vivo expression. Both intramuscular and intranasal delivery of the AAVs was shown to protect against a number of influenza virus challenges in mice and ferrets, including H1N1 and H5N1 viruses [64, 65] . These studies suggest that rAAV vectors are promising vaccine and immunoprophylaxis vectors. To this point, while approximately 80 phase I, I/II, II, or III rAAV clinical trials are open, completed, or being reviewed, these have focused upon gene transfer studies and so there is as yet limited safety data for use of rAAV as vaccines [66] . Alphaviruses are positive-sense, single-stranded RNA viruses of the Togaviridae family. A variety of alphaviruses have been developed as vaccine vectors, including Semliki Forest virus (SFV), Sindbis (SIN) virus, Venezuelan equine encephalitis (VEE) virus, as well as chimeric viruses incorporating portions of SIN and VEE viruses. The replication defective vaccines or replicons do not encode viral structural proteins, having these portions of the genome replaces with transgenic material. The structural proteins are provided in cell culture production systems. One important feature of the replicon systems is the self-replicating nature of the RNA. Despite the partial viral genome, the RNAs are self-replicating and can express transgenes at very high levels [67] . SIN, SFV, and VEE have all been tested for efficacy as vaccine vectors for influenza virus [68] [69] [70] [71] . A VEE-based replicon system encoding the HA from PR8 was demonstrated to induce potent HA-specific immune response and protected from challenge in a murine model, despite repeated immunization with the vector expressing a control antigen, suggesting preexisting immunity may not be an issue for the replicon vaccine [68] . A separate study developed a VEE replicon system expressing the HA from A/Hong Kong/156/1997 (H5N1) and demonstrated varying efficacy after in ovo vaccination or vaccination of 1-day-old chicks [70] . A recombinant SIN virus was use as a vaccine vector to deliver a CD8 + T cell epitope only. The well-characterized NP epitope was transgenically expressed in the SIN system and shown to be immunogenic in mice, priming a robust CD8 + T cell response and reducing influenza virus titer after challenge [69] . More recently, a VEE replicon system expressing the HA protein of PR8 was shown to protect young adult (8-week-old) and aged (12-month-old) mice from lethal homologous challenge [72] . The VEE replicon systems are particularly appealing as the VEE targets antigen-presenting cells in the lymphatic tissues, priming rapid and robust immune responses [73] . VEE replicon systems can induce robust mucosal immune responses through intranasal or subcutaneous immunization [72] [73] [74] , and subcutaneous immunization with virus-like replicon particles (VRP) expressing HA-induced antigen-specific systemic IgG and fecal IgA antibodies [74] . VRPs derived from VEE virus have been developed as candidate vaccines for cytomegalovirus (CMV). A phase I clinical trial with the CMV VRP showed the vaccine was immunogenic, inducing CMV-neutralizing antibody responses and potent T cell responses. Moreover, the vaccine was well tolerated and considered safe [75] . A separate clinical trial assessed efficacy of repeated immunization with a VRP expressing a tumor antigen. The vaccine was safe and despite high vector-specific immunity after initial immunization, continued to boost transgene-specific immune responses upon boost [76] . While additional clinical data is needed, these reports suggest alphavirus replicon systems or VRPs may be safe and efficacious, even in the face of preexisting immunity. Baculovirus has been extensively used to produce recombinant proteins. Recently, a baculovirus-derived recombinant HA vaccine was approved for human use and was first available for use in the United States for the 2013-2014 influenza season [4] . Baculoviruses have also been explored as vaccine vectors. Baculoviruses have a number of advantages as vaccine vectors. The viruses have been extensively studied for protein expression and for pesticide use and so are readily manipulated. The vectors can accommodate large gene insertions, show limited cytopathic effect in mammalian cells, and have been shown to infect and express genes of interest in a spectrum of mammalian cells [77] . While the insect promoters are not effective for mammalian gene expression, appropriate promoters can be cloned into the baculovirus vaccine vectors. Baculovirus vectors have been tested as influenza vaccines, with the first reported vaccine using Autographa californica nuclear polyhedrosis virus (AcNPV) expressing the HA of PR8 under control of the CAG promoter (AcCAG-HA) [77] . Intramuscular, intranasal, intradermal, and intraperitoneal immunization or mice with AcCAG-HA elicited HA-specific antibody responses, however only intranasal immunization provided protection from lethal challenge. Interestingly, intranasal immunization with the wild type AcNPV also resulted in protection from PR8 challenge. The robust innate immune response to the baculovirus provided non-specific protection from subsequent influenza virus infection [78] . While these studies did not demonstrate specific protection, there were antigen-specific immune responses and potential adjuvant effects by the innate response. Baculovirus pseudotype viruses have also been explored. The G protein of vesicular stomatitis virus controlled by the insect polyhedron promoter and the HA of A/Chicken/Hubei/327/2004 (H5N1) HPAIV controlled by a CMV promoter were used to generate the BV-G-HA. Intramuscular immunization of mice or chickens with BV-G-HA elicited strong HI and VN serum antibody responses, IFN-γ responses, and protected from H5N1 challenge [79] . A separate study demonstrated efficacy using a bivalent pseudotyped baculovirus vector [80] . Baculovirus has also been used to generate an inactivated particle vaccine. The HA of A/Indonesia/CDC669/2006(H5N1) was incorporated into a commercial baculovirus vector controlled by the e1 promoter from White Spot Syndrome Virus. The resulting recombinant virus was propagated in insect (Sf9) cells and inactivated as a particle vaccine [81, 82] . Intranasal delivery with cholera toxin B as an adjuvant elicited robust HI titers and protected from lethal challenge [81] . Oral delivery of this encapsulated vaccine induced robust serum HI titers and mucosal IgA titers in mice, and protected from H5N1 HPAIV challenge. More recently, co-formulations of inactivated baculovirus vectors have also been shown to be effective in mice [83] . While there is growing data on the potential use of baculovirus or pseudotyped baculovirus as a vaccine vector, efficacy data in mammalian animal models other than mice is lacking. There is also no data on the safety in humans, reducing enthusiasm for baculovirus as a vaccine vector for influenza at this time. Newcastle disease virus (NDV) is a single-stranded, negative-sense RNA virus that causes disease in poultry. NDV has a number of appealing qualities as a vaccine vector. As an avian virus, there is little or no preexisting immunity to NDV in humans and NDV propagates to high titers in both chicken eggs and cell culture. As a paramyxovirus, there is no DNA phase in the virus lifecycle reducing concerns of integration events, and the levels of gene expression are driven by the proximity to the leader sequence at the 3' end of the viral genome. This gradient of gene expression enables attenuation through rearrangement of the genome, or by insertion of transgenes within the genome. Finally, pathogenicity of NDV is largely determined by features of the fusion protein enabling ready attenuation of the vaccine vector [84] . Reverse genetics, a method that allows NDV to be rescued from plasmids expressing the viral RNA polymerase and nucleocapsid proteins, was first reported in 1999 [85, 86] . This process has enabled manipulation of the NDV genome as well as incorporation of transgenes and the development of NDV vectors. Influenza was the first infectious disease targeted with a recombinant NDV (rNDV) vector. The HA protein of A/WSN/1933 (H1N1) was inserted into the Hitchner B1 vaccine strain. The HA protein was expressed on infected cells and was incorporated into infectious virions. While the virus was attenuated compared to the parental vaccine strain, it induced a robust serum antibody response and protected against homologous influenza virus challenge in a murine model of infection [87] . Subsequently, rNDV was tested as a vaccine vector for HPAIV having varying efficacy against H5 and H7 influenza virus infections in poultry [88] [89] [90] [91] [92] [93] [94] . These vaccines have the added benefit of potentially providing protection against both the influenza virus and NDV infection. NDV has also been explored as a vaccine vector for humans. Two NHP studies assessed the immunogenicity and efficacy of an rNDV expressing the HA or NA of A/Vietnam/1203/2004 (H5N1; VN1203) [95, 96] . Intranasal and intratracheal delivery of the rNDV-HA or rNDV-NA vaccines induced both serum and mucosal antibody responses and protected from HPAIV challenge [95, 96] . NDV has limited clinical data; however, phase I and phase I/II clinical trials have shown that the NDV vector is well-tolerated, even at high doses delivered intravenously [44, 97] . While these results are promising, additional studies are needed to advance NDV as a human vaccine vector for influenza. Parainfluenza virus type 5 (PIV5) is a paramyxovirus vaccine vector being explored for delivery of influenza and other infectious disease vaccine antigens. PIV5 has only recently been described as a vaccine vector [98] . Similar to other RNA viruses, PIV5 has a number of features that make it an attractive vaccine vector. For example, PIV5 has a stable RNA genome and no DNA phase in virus replication cycle reducing concerns of host genome integration or modification. PIV5 can be grown to very high titers in mammalian vaccine cell culture substrates and is not cytopathic allowing for extended culture and harvest of vaccine virus [98, 99] . Like NDV, PIV5 has a 3'-to 5' gradient of gene expression and insertion of transgenes at different locations in the genome can variably attenuate the virus and alter transgene expression [100] . PIV5 has broad tropism, infecting many cell types, tissues, and species without causing clinical disease, although PIV5 has been associated with -kennel cough‖ in dogs [99] . A reverse genetics system for PIV5 was first used to insert the HA gene from A/Udorn/307/72 (H3N2) into the PIV5 genome between the hemagglutinin-neuraminidase (HN) gene and the large (L) polymerase gene. Similar to NDV, the HA was expressed at high levels in infected cells and replicated similarly to the wild type virus, and importantly, was not pathogenic in immunodeficient mice [98] . Additionally, a single intranasal immunization in a murine model of influenza infection was shown to induce neutralizing antibody responses and protect against a virus expressing homologous HA protein [98] . PIV5 has also been explored as a vaccine against HPAIV. Recombinant PIV5 vaccines expressing the HA or NP from VN1203 were tested for efficacy in a murine challenge model. Mice intranasally vaccinated with a single dose of PIV5-H5 vaccine had robust serum and mucosal antibody responses, and were protected from lethal challenge. Notably, although cellular immune responses appeared to contribute to protection, serum antibody was sufficient for protection from challenge [100, 101] . Intramuscular immunization with PIV5-H5 was also shown to be effective at inducing neutralizing antibody responses and protecting against lethal influenza virus challenge [101] . PIV5 expressing the NP protein of HPAIV was also efficacious in the murine immunization and challenge model, where a single intranasal immunization induced robust CD8 + T cell responses and protected against homologous (H5N1) and heterosubtypic (H1N1) virus challenge [102] . Currently there is no clinical safety data for use of PIV5 in humans. However, live PIV5 has been a component of veterinary vaccines for -kennel cough‖ for >30 years, and veterinarians and dog owners are exposed to live PIV5 without reported disease [99] . This combined with preclinical data from a variety of animal models suggests that PIV5 as a vector is likely to be safe in humans. As preexisting immunity is a concern for all virus-vectored vaccines, it should be noted that there is no data on the levels of preexisting immunity to PIV5 in humans. However, a study evaluating the efficacy of a PIV5-H3 vaccine in canines previously vaccinated against PIV5 (kennel cough) showed induction of robust anti-H3 serum antibody responses as well as high serum antibody levels to the PIV5 vaccine, suggesting preexisting immunity to the PIV5 vector may not affect immunogenicity of vaccines even with repeated use [99] . Poxvirus vaccines have a long history and the notable hallmark of being responsible for eradication of smallpox. The termination of the smallpox virus vaccination program has resulted in a large population of poxvirus-naï ve individuals that provides the opportunity for the use of poxviruses as vectors without preexisting immunity concerns [103] . Poxvirus-vectored vaccines were first proposed for use in 1982 with two reports of recombinant vaccinia viruses encoding and expressing functional thymidine kinase gene from herpes virus [104, 105] . Within a year, a vaccinia virus encoding the HA of an H2N2 virus was shown to express a functional HA protein (cleaved in the HA1 and HA2 subunits) and be immunogenic in rabbits and hamsters [106] . Subsequently, all ten of the primary influenza proteins have been expressed in vaccine virus [107] . Early work with intact vaccinia virus vectors raised safety concerns, as there was substantial reactogenicity that hindered recombinant vaccine development [108] . Two vaccinia vectors were developed to address these safety concerns. The modified vaccinia virus Ankara (MVA) strain was attenuated by passage 530 times in chick embryo fibroblasts cultures. The second, New York vaccinia virus (NYVAC) was a plaque-purified clone of the Copenhagen vaccine strain rationally attenuated by deletion of 18 open reading frames [109] [110] [111] . Modified vaccinia virus Ankara (MVA) was developed prior to smallpox eradication to reduce or prevent adverse effects of other smallpox vaccines [109] . Serial tissue culture passage of MVA resulted in loss of 15% of the genome, and established a growth restriction for avian cells. The defects affected late stages in virus assembly in non-avian cells, a feature enabling use of the vector as single-round expression vector in non-permissive hosts. Interestingly, over two decades ago, recombinant MVA expressing the HA and NP of influenza virus was shown to be effective against lethal influenza virus challenge in a murine model [112] . Subsequently, MVA expressing various antigens from seasonal, pandemic (A/California/04/2009, pH1N1), equine (A/Equine/Kentucky/1/81 H3N8), and HPAI (VN1203) viruses have been shown to be efficacious in murine, ferret, NHP, and equine challenge models [113] . MVA vaccines are very effective stimulators of both cellular and humoral immunity. For example, abortive infection provides native expression of the influenza antigens enabling robust antibody responses to native surface viral antigens. Concurrently, the intracellular influenza peptides expressed by the pox vector enter the class I MHC antigen processing and presentation pathway enabling induction of CD8 + T cell antiviral responses. MVA also induces CD4 + T cell responses further contributing to the magnitude of the antigen-specific effector functions [107, [112] [113] [114] [115] . MVA is also a potent activator of early innate immune responses further enhancing adaptive immune responses [116] . Between early smallpox vaccine development and more recent vaccine vector development, MVA has undergone extensive safety testing and shown to be attenuated in severely immunocompromised animals and safe for use in children, adults, elderly, and immunocompromised persons. With extensive pre-clinical data, recombinant MVA vaccines expressing influenza antigens have been tested in clinical trials and been shown to be safe and immunogenic in humans [117] [118] [119] . These results combined with data from other (non-influenza) clinical and pre-clinical studies support MVA as a leading viral-vectored candidate vaccine. The NYVAC vector is a highly attenuated vaccinia virus strain. NYVAC is replication-restricted; however, it grows in chick embryo fibroblasts and Vero cells enabling vaccine-scale production. In non-permissive cells, critical late structural proteins are not produced stopping replication at the immature virion stage [120] . NYVAC is very attenuated and considered safe for use in humans of all ages; however, it predominantly induces a CD4 + T cell response which is different compared to MVA [114] . Both MVA and NYVAC provoke robust humoral responses, and can be delivered mucosally to induce mucosal antibody responses [121] . There has been only limited exploration of NYVAC as a vaccine vector for influenza virus; however, a vaccine expressing the HA from A/chicken/Indonesia/7/2003 (H5N1) was shown to induce potent neutralizing antibody responses and protect against challenge in swine [122] . While there is strong safety and efficacy data for use of NYVAC or MVA-vectored influenza vaccines, preexisting immunity remains a concern. Although the smallpox vaccination campaign has resulted in a population of poxvirus-naï ve people, the initiation of an MVA or NYVAC vaccination program for HIV, influenza or other pathogens will rapidly reduce this susceptible population. While there is significant interest in development of pox-vectored influenza virus vaccines, current influenza vaccination strategies rely upon regular immunization with vaccines matched to circulating strains. This would likely limit the use and/or efficacy of poxvirus-vectored influenza virus vaccines for regular and seasonal use [13] . Intriguingly, NYVAC may have an advantage for use as an influenza vaccine vector, because immunization with this vector induces weaker vaccine-specific immune responses compared to other poxvirus vaccines, a feature that may address the concerns surrounding preexisting immunity [123] . While poxvirus-vectored vaccines have not yet been approved for use in humans, there is a growing list of licensed poxvirus for veterinary use that include fowlpox-and canarypox-vectored vaccines for avian and equine influenza viruses, respectively [124, 125] . The fowlpox-vectored vaccine expressing the avian influenza virus HA antigen has the added benefit of providing protection against fowlpox infection. Currently, at least ten poxvirus-vectored vaccines have been licensed for veterinary use [126] . These poxvirus vectors have the potential for use as vaccine vectors in humans, similar to the first use of cowpox for vaccination against smallpox [127] . The availability of these non-human poxvirus vectors with extensive animal safety and efficacy data may address the issues with preexisting immunity to the human vaccine strains, although the cross-reactivity originally described with cowpox could also limit use. Influenza vaccines utilizing vesicular stomatitis virus (VSV), a rhabdovirus, as a vaccine vector have a number of advantages shared with other RNA virus vaccine vectors. Both live and replication-defective VSV vaccine vectors have been shown to be immunogenic [128, 129] , and like Paramyxoviridae, the Rhabdoviridae genome has a 3'-to-5' gradient of gene expression enabling attention by selective vaccine gene insertion or genome rearrangement [130] . VSV has a number of other advantages including broad tissue tropism, and the potential for intramuscular or intranasal immunization. The latter delivery method enables induction of mucosal immunity and elimination of needles required for vaccination. Also, there is little evidence of VSV seropositivity in humans eliminating concerns of preexisting immunity, although repeated use may be a concern. Also, VSV vaccine can be produced using existing mammalian vaccine manufacturing cell lines. Influenza antigens were first expressed in a VSV vector in 1997. Both the HA and NA were shown to be expressed as functional proteins and incorporated into the recombinant VSV particles [131] . Subsequently, VSV-HA, expressing the HA protein from A/WSN/1933 (H1N1) was shown to be immunogenic and protect mice from lethal influenza virus challenge [129] . To reduce safety concerns, attenuated VSV vectors were developed. One candidate vaccine had a truncated VSV G protein, while a second candidate was deficient in G protein expression and relied on G protein expressed by a helper vaccine cell line to the provide the virus receptor. Both vectors were found to be attenuated in mice, but maintained immunogenicity [128] . More recently, single-cycle replicating VSV vaccines have been tested for efficacy against H5N1 HPAIV. VSV vectors expressing the HA from A/Hong Kong/156/97 (H5N1) were shown to be immunogenic and induce cross-reactive antibody responses and protect against challenge with heterologous H5N1 challenge in murine and NHP models [132] [133] [134] . VSV vectors are not without potential concerns. VSV can cause disease in a number of species, including humans [135] . The virus is also potentially neuroinvasive in some species [136] , although NHP studies suggest this is not a concern in humans [137] . Also, while the incorporation of the influenza antigen in to the virion may provide some benefit in immunogenicity, changes in tropism or attenuation could arise from incorporation of different influenza glycoproteins. There is no evidence for this, however [134] . Currently, there is no human safety data for VSV-vectored vaccines. While experimental data is promising, additional work is needed before consideration for human influenza vaccination. Current influenza vaccines rely on matching the HA antigen of the vaccine with circulating strains to provide strain-specific neutralizing antibody responses [4, 14, 24] . There is significant interest in developing universal influenza vaccines that would not require annual reformulation to provide protective robust and durable immunity. These vaccines rely on generating focused immune responses to highly conserved portions of the virus that are refractory to mutation [30] [31] [32] . Traditional vaccines may not be suitable for these vaccination strategies; however, vectored vaccines that have the ability to be readily modified and to express transgenes are compatible for these applications. The NP and M2 proteins have been explored as universal vaccine antigens for decades. Early work with recombinant viral vectors demonstrated that immunization with vaccines expressing influenza antigens induced potent CD8 + T cell responses [107, [138] [139] [140] [141] . These responses, even to the HA antigen, could be cross-protective [138] . A number of studies have shown that immunization with NP expressed by AAV, rAd5, alphavirus vectors, MVA, or other vector systems induces potent CD8 + T cell responses and protects against influenza virus challenge [52, 63, 69, 102, 139, 142] . As the NP protein is highly conserved across influenza A viruses, NP-specific T cells can protect against heterologous and even heterosubtypic virus challenges [30] . The M2 protein is also highly conserved and expressed on the surface of infected cells, although to a lesser extent on the surface of virus particles [30] . Much of the vaccine work in this area has focused on virus-like or subunit particles expressing the M2 ectodomain; however, studies utilizing a DNA-prime, rAd-boost strategies to vaccinate against the entire M2 protein have shown the antigen to be immunogenic and protective [50] . In these studies, antibodies to the M2 protein protected against homologous and heterosubtypic challenge, including a H5N1 HPAIV challenge. More recently, NP and M2 have been combined to induce broadly cross-reactive CD8 + T cell and antibody responses, and rAd5 vaccines expressing these antigens have been shown to protect against pH1N1 and H5N1 challenges [29, 51] . Historically, the HA has not been widely considered as a universal vaccine antigen. However, the recent identification of virus neutralizing monoclonal antibodies that cross-react with many subtypes of influenza virus [143] has presented the opportunity to design vaccine antigens to prime focused antibody responses to the highly conserved regions recognized by these monoclonal antibodies. The majority of these broadly cross-reactive antibodies recognize regions on the stalk of the HA protein [143] . The HA stalk is generally less immunogenic compared to the globular head of the HA protein so most approaches have utilized -headless‖ HA proteins as immunogens. HA stalk vaccines have been designed using DNA and virus-like particles [144] and MVA [142] ; however, these approaches are amenable to expression in any of the viruses vectors described here. The goal of any vaccine is to protect against infection and disease, while inducing population-based immunity to reduce or eliminate virus transmission within the population. It is clear that currently licensed influenza vaccines have not fully met these goals, nor those specific to inducing long-term, robust immunity. There are a number of vaccine-related issues that must be addressed before population-based influenza vaccination strategies are optimized. The concept of a -one size fits all‖ vaccine needs to be updated, given the recent ability to probe the virus-host interface through RNA interference approaches that facilitate the identification of host genes affecting virus replication, immunity, and disease. There is also a need for revision of the current influenza virus vaccine strategies for at-risk populations, particularly those at either end of the age spectrum. An example of an improved vaccine regime might include the use of a vectored influenza virus vaccine that expresses the HA, NA and M and/or NP proteins for the two currently circulating influenza A subtypes and both influenza B strains so that vaccine take and vaccine antigen levels are not an issue in inducing protective immunity. Recombinant live-attenuated or replication-deficient influenza viruses may offer an advantage for this and other approaches. Vectored vaccines can be constructed to express full-length influenza virus proteins, as well as generate conformationally restricted epitopes, features critical in generating appropriate humoral protection. Inclusion of internal influenza antigens in a vectored vaccine can also induce high levels of protective cellular immunity. To generate sustained immunity, it is an advantage to induce immunity at sites of inductive immunity to natural infection, in this case the respiratory tract. Several vectored vaccines target the respiratory tract. Typically, vectored vaccines generate antigen for weeks after immunization, in contrast to subunit vaccination. This increased presence and level of vaccine antigen contributes to and helps sustain a durable memory immune response, even augmenting the selection of higher affinity antibody secreting cells. The enhanced memory response is in part linked to the intrinsic augmentation of immunity induced by the vector. Thus, for weaker antigens typical of HA, vectored vaccines have the capacity to overcome real limitations in achieving robust and durable protection. Meeting the mandates of seasonal influenza vaccine development is difficult, and to respond to a pandemic strain is even more challenging. Issues with influenza vaccine strain selection based on recently circulating viruses often reflect recommendations by the World Health Organization (WHO)-a process that is cumbersome. The strains of influenza A viruses to be used in vaccine manufacture are not wild-type viruses but rather reassortants that are hybrid viruses containing at least the HA and NA gene segments from the target strains and other gene segments from the master strain, PR8, which has properties of high growth in fertilized hen's eggs. This additional process requires more time and quality control, and specifically for HPAI viruses, it is a process that may fail because of the nature of those viruses. In contrast, viral-vectored vaccines are relatively easy to manipulate and produce, and have well-established safety profiles. There are several viral-based vectors currently employed as antigen delivery systems, including poxviruses, adenoviruses baculovirus, paramyxovirus, rhabdovirus, and others; however, the majority of human clinical trials assessing viral-vectored influenza vaccines use poxvirus and adenovirus vectors. While each of these vector approaches has unique features and is in different stages of development, the combined successes of these approaches supports the virus-vectored vaccine approach as a whole. Issues such as preexisting immunity and cold chain requirements, and lingering safety concerns will have to be overcome; however, each approach is making progress in addressing these issues, and all of the approaches are still viable. Virus-vectored vaccines hold particular promise for vaccination with universal or focused antigens where traditional vaccination methods are not suited to efficacious delivery of these antigens. The most promising approaches currently in development are arguably those targeting conserved HA stalk region epitopes. Given the findings to date, virus-vectored vaccines hold great promise and may overcome the current limitations of influenza vaccines.

What did the clinical trial with VRP show?

Genomic characterization of the 2019 novel human-pathogenic coronavirus isolated from a patient with atypical pneumonia after visiting Wuhan https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7067204/ SHA: c097a8a9a543d69c34f10e5c3fd78019e560026a Authors: Chan, Jasper Fuk-Woo; Kok, Kin-Hang; Zhu, Zheng; Chu, Hin; To, Kelvin Kai-Wang; Yuan, Shuofeng; Yuen, Kwok-Yung Date: 2020-01-28 DOI: 10.1080/22221751.2020.1719902 License: cc-by Abstract: A mysterious outbreak of atypical pneumonia in late 2019 was traced to a seafood wholesale market in Wuhan of China. Within a few weeks, a novel coronavirus tentatively named as 2019 novel coronavirus (2019-nCoV) was announced by the World Health Organization. We performed bioinformatics analysis on a virus genome from a patient with 2019-nCoV infection and compared it with other related coronavirus genomes. Overall, the genome of 2019-nCoV has 89% nucleotide identity with bat SARS-like-CoVZXC21 and 82% with that of human SARS-CoV. The phylogenetic trees of their orf1a/b, Spike, Envelope, Membrane and Nucleoprotein also clustered closely with those of the bat, civet and human SARS coronaviruses. However, the external subdomain of Spike’s receptor binding domain of 2019-nCoV shares only 40% amino acid identity with other SARS-related coronaviruses. Remarkably, its orf3b encodes a completely novel short protein. Furthermore, its new orf8 likely encodes a secreted protein with an alpha-helix, following with a beta-sheet(s) containing six strands. Learning from the roles of civet in SARS and camel in MERS, hunting for the animal source of 2019-nCoV and its more ancestral virus would be important for understanding the origin and evolution of this novel lineage B betacoronavirus. These findings provide the basis for starting further studies on the pathogenesis, and optimizing the design of diagnostic, antiviral and vaccination strategies for this emerging infection. Text: Coronaviruses (CoVs) are enveloped, positive-sense, single-stranded RNA viruses that belong to the subfamily Coronavirinae, family Coronavirdiae, order Nidovirales. There are four genera of CoVs, namely, Alphacoronavirus (αCoV), Betacoronavirus (βCoV), Deltacoronavirus (δCoV), and Gammacoronavirus (γCoV) [1] . Evolutionary analyses have shown that bats and rodents are the gene sources of most αCoVs and βCoVs, while avian species are the gene sources of most δCoVs and γCoVs. CoVs have repeatedly crossed species barriers and some have emerged as important human pathogens. The best-known examples include severe acute respiratory syndrome CoV (SARS-CoV) which emerged in China in 2002-2003 to cause a large-scale epidemic with about 8000 infections and 800 deaths, and Middle East respiratory syndrome CoV (MERS-CoV) which has caused a persistent epidemic in the Arabian Peninsula since 2012 [2, 3] . In both of these epidemics, these viruses have likely originated from bats and then jumped into another amplification mammalian host [the Himalayan palm civet (Paguma larvata) for SARS-CoV and the dromedary camel (Camelus dromedarius) for MERS-CoV] before crossing species barriers to infect humans. Prior to December 2019, 6 CoVs were known to infect human, including 2 αCoV (HCoV-229E and HKU-NL63) and 4 βCoV (HCoV-OC43 [ HCoV-OC43 and HCoV-HKU1 usually cause self-limiting upper respiratory infections in immunocompetent hosts and occasionally lower respiratory tract infections in immunocompromised hosts and elderly [4] . In contrast, SARS-CoV (lineage B βCoV) and MERS-CoV (lineage C βCoV) may cause severe lower respiratory tract infection with acute respiratory distress syndrome and extrapulmonary manifestations, such as diarrhea, lymphopenia, deranged liver and renal function tests, and multiorgan dysfunction syndrome, among both immunocompetent and immunocompromised hosts with mortality rates of ∼10% and ∼35%, respectively [5, 6] . On 31 December 2019, the World Health Organization (WHO) was informed of cases of pneumonia of unknown cause in Wuhan City, Hubei Province, China [7] . Subsequent virological testing showed that a novel CoV was detected in these patients. As of 16 January 2020, 43 patients have been diagnosed to have infection with this novel CoV, including two exported cases of mild pneumonia in Thailand and Japan [8, 9] . The earliest date of symptom onset was 1 December 2019 [10] . The symptomatology of these patients included fever, malaise, dry cough, and dyspnea. Among 41 patients admitted to a designated hospital in Wuhan, 13 (32%) required intensive care and 6 (15%) died. All 41 patients had pneumonia with abnormal findings on chest computerized tomography scans [10] . We recently reported a familial cluster of 2019-nCoV infection in a Shenzhen family with travel history to Wuhan [11] . In the present study, we analyzed a 2019-nCoV complete genome from a patient in this familial cluster and compared it with the genomes of related βCoVs to provide insights into the potential source and control strategies. The complete genome sequence of 2019-nCoV HKU-SZ-005b was available at GenBank (accession no. MN975262) ( Table 1 ). The representative complete genomes of other related βCoVs strains collected from human or mammals were included for comparative analysis. These included strains collected from human, bats, and Himalayan palm civet between 2003 and 2018, with one 229E coronavirus strain as the outgroup. Phylogenetic tree construction by the neighbour joining method was performed using MEGA X software, with bootstrap values being calculated from 1000 trees [12] . The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (1000 replicates) was shown next to the branches [13] . The tree was drawn to scale, with branch lengths in the same units as those of the evolutionary distances used to infer the phylogenetic tree. The evolutionary distances were computed using the Poisson correction method and were in the units of the number of amino acid substitutions per site [14] . All ambiguous positions were removed for each sequence pair (pairwise deletion option). Evolutionary analyses were conducted in MEGA X [15] . Multiple alignment was performed using CLUSTAL 2.1 and further visualized using BOX-SHADE 3.21. Structural analysis of orf8 was performed using PSI-blast-based secondary structure PREDiction (PSIPRED) [16] . For the prediction of protein secondary structure including beta sheet, alpha helix, and coil, initial amino acid sequences were input and analysed using neural networking and its own algorithm. Predicted structures were visualized and highlighted on the BOX-SHADE alignment. Prediction of transmembrane domains was performed using the TMHMM 2.0 server (http://www.cbs.dtu.dk/services/TMHMM/). Secondary structure prediction in the 5 ′ -untranslated region (UTR) and 3 ′ -UTR was performed using the RNAfold WebServer (http://rna.tbi.univie.ac.at/cgi-bin/ RNAWebSuite/RNAfold.cgi) with minimum free energy (MFE) and partition function in Fold algorithms and Table 2 . Putative functions and proteolytic cleavage sites of 16 nonstructural proteins in orf1a/b as predicted by bioinformatics. Putative function/domain Amino acid position Putative cleave site complex with nsp3 and 6: DMV formation complex with nsp3 and 4: DMV formation short peptide at the end of orf1a basic options. The human SARS-CoV 5 ′ -and 3 ′ -UTR were used as references to adjust the prediction results. The single-stranded RNA genome of the 2019-nCoV was 29891 nucleotides in size, encoding 9860 amino acids. The G + C content was 38%. Similar to other (Table 2 ). There are no remarkable differences between the orfs and nsps of 2019-nCoV with those of SARS-CoV (Table 3) . The major distinction between SARSr-CoV and SARS-CoV is in orf3b, Spike and orf8 but especially variable in Spike S1 and orf8 which were previously shown to be recombination hot spots. Spike glycoprotein comprised of S1 and S2 subunits. The S1 subunit contains a signal peptide, followed by an N-terminal domain (NTD) and receptor-binding domain (RBD), while the S2 subunit contains conserved fusion peptide (FP), heptad repeat (HR) 1 and 2, transmembrane domain (TM), and cytoplasmic domain (CP). We found that the S2 subunit of 2019-nCoV is highly conserved and shares 99% identity with those of the two bat SARS-like CoVs (SL-CoV ZXC21 and ZC45) and human SARS-CoV (Figure 2 ). Thus the broad spectrum antiviral peptides against S2 would be an important preventive and treatment modality for testing in animal models before clinical trials [18] . Though the S1 subunit of 2019-nCoV shares around 70% identity to that of the two bat SARS-like CoVs and human SARS-CoV (Figure 3(A) ), the core domain of RBD (excluding the external subdomain) are highly conserved (Figure 3(B) ). Most of the amino acid differences of RBD are located in the external subdomain, which is responsible for the direct interaction with the host receptor. Further investigation of this soluble variable external subdomain region will reveal its receptor usage, interspecies transmission and pathogenesis. Unlike 2019-nCoV and human SARS-CoV, most known bat SARSr-CoVs have two stretches of deletions in the spike receptor binding domain (RBD) when compared with that of human SARS-CoV. But some Yunnan strains such as the WIV1 had no such deletions and can use human ACE2 as a cellular entry receptor. It is interesting to note that the two bat SARS-related coronavirus ZXC21 and ZC45, being closest to 2019-nCoV, can infect suckling rats and cause inflammation in the brain tissue, and pathological changes in lung & intestine. However, these two viruses could not be isolated in Vero E6 cells and were not investigated further. The two retained deletion sites in the Spike genes of ZXC21 and ZC45 may lessen their likelihood of jumping species barriers imposed by receptor specificity. A novel short putative protein with 4 helices and no homology to existing SARS-CoV or SARS-r-CoV protein was found within Orf3b ( Figure 4 ). It is notable that SARS-CoV deletion mutants lacking orf3b replicate to levels similar to those of wildtype virus in several cell types [19] , suggesting that orf3b is dispensable for viral replication in vitro. But orf3b may have a role in viral pathogenicity as Vero E6 but not 293T cells transfected with a construct expressing Orf3b underwent necrosis as early as 6 h after transfection and underwent simultaneous necrosis and apoptosis at later time points [20] . Orf3b was also shown to inhibit expression of IFN-β at synthesis and signalling [21] . Subsequently, orf3b homologues identified from three bat SARSrelated-CoV strains were C-terminally truncated and lacked the C-terminal nucleus localization signal of SARS-CoV [22] . IFN antagonist activity analysis demonstrated that one SARS-related-CoV orf3b still possessed IFN antagonist and IRF3-modulating activities. These results indicated that different orf3b proteins display different IFN antagonist activities and this function is independent of the protein's nuclear localization, suggesting a potential link between bat SARS-related-CoV orf3b function and pathogenesis. The importance of this new protein in 2019-nCoV will require further validation and study. Orf8 orf8 is an accessory protein found in the Betacoronavirus lineage B coronaviruses. Human SARS-CoVs isolated from early-phase patients, all civet SARS-CoVs, and other bat SARS-related CoVs contain fulllength orf8 [23] . However, a 29-nucleotide deletion, Bat SL-CoV ZXC21 2018 Bat which causes the split of full length of orf8 into putative orf8a and orf8b, has been found in all SARS-CoV isolated from mid-and late-phase human patients [24] . In addition, we have previously identified two bat SARS-related-CoV (Bat-CoV YNLF_31C and YNLF_34C) and proposed that the original SARS-CoV full-length orf8 is acquired from these two bat SARS-related-CoV [25] . Since the SARS-CoV is the closest human pathogenic virus to the 2019-nCoV, we performed phylogenetic analysis and multiple alignments to investigate the orf8 amino acid sequences. The orf8 protein sequences used in the analysis derived from early phase SARS-CoV that includes full-length orf8 (human SARS-CoV GZ02), the mid-and late-phase SARS-CoV that includes the split orf8b (human SARS-CoV Tor2), civet SARS-CoV (paguma SARS-CoV), two bat SARS-related-CoV containing full-length orf8 (bat-CoV YNLF_31C and YNLF_34C), 2019-nCoV, the other two closest bat SARS-related-CoV to 2019-nCoV SL-CoV ZXC21 and ZC45), and bat SARS-related-CoV HKU3-1 ( Figure 5(A) ). As expected, orf8 derived from 2019-nCoV belongs to the group that includes the closest genome sequences of bat SARS-related-CoV ZXC21 and ZC45. Interestingly, the new 2019-nCoV orf8 is distant from the conserved orf8 or Figure 5(B) ) which was shown to trigger intracellular stress pathways and activates NLRP3 inflammasomes [26] , but this is absent in this novel orf8 of 2019-nCoV. Based on a secondary structure prediction, this novel orf8 has a high possibility to form a protein with an alpha-helix, following with a betasheet(s) containing six strands ( Figure 5(C) ). The genome of 2019-nCoV has overall 89% nucleotide identity with bat SARS-related-CoV SL-CoVZXC21 (MG772934.1), and 82% with human SARS-CoV BJ01 2003 (AY278488) and human SARS-CoV Tor2 (AY274119). The phylogenetic trees constructed using the amino acid sequences of orf1a/b and the 4 structural genes (S, E, M, and N) were shown (Figure 6(A-E) ). For all these 5 genes, the 2019-nCoV was clustered with lineage B βCoVs. It was most closely related to the bat SARS-related CoVs ZXC21 and ZC45 found in Chinese horseshoe As shown in Figure 7 (A-C), the SARS-CoV 5 ′ -UTR contains SL1, SL2, SL3, SL4, S5, SL5A, SL5B, SL5C, SL6, SL7, and SL8. The SL3 contains trans-cis motif [27] . The SL1, SL2, SL3, SL4, S5, SL5A, SL5B, and SL5C structures were similar among the 2019-nCoV, human SARS-CoV and the bat SARS-related ZC45. In the 2019-nCoV, part of the S5 found was inside Figure 7 Continued the orf1a/b (marked in red), which was similar to SARS-CoV. In bat SARS-related CoV ZC45, the S5 was not found inside orf1a/b. The 2019-nCoV had the same SL6, SL7, and SL8 as SARS-CoV, and an additional stem loop. Bat SARS-related CoV ZC45 did not have the SARS-COV SL6-like stem loop. Instead, it possessed two other stem loops in this region. All three strains had similar SL7 and SL8. The bat SARS-like CoV ZC45 also had an additional stem loop between SL7 and SL8. Overall, the 5 ′ -UTR of 2019-nCoV was more similar to that of SARS-CoV than the bat SARS-related CoV ZC 45. The biological relevance and effects of virulence of the 5 ′ -UTR structures should be investigated further. The 2019-nCoV had various 3 ′ -UTR structures, including BSL, S1, S2, S3, S4, L1, L2, L3, and HVR (Figure 7(D-F) ). The 3 ′ -UTR was conserved among 2019-nCoV, human SARS-CoV and SARS-related CoVs [27] . In summary, 2019-nCoV is a novel lineage B Betacoronavirus closely related to bat SARS-related coronaviruses. It also has unique genomic features which deserves further investigation to ascertain their roles in viral replication cycle and pathogenesis. More animal sampling to determine its natural animal reservoir and intermediate animal host in the market is important. This will shed light on the evolutionary history of this emerging coronavirus which has jumped into human after the other two zoonotic Betacoroanviruses, SARS-CoV and MERS-CoV.

What is the relation between the new2019-nCOV and the conserved orf8?

Pre-existing immunity against vaccine vectors – friend or foe? https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3542731/ SHA: f5bdf18567bb3760e1ce05008135f0270badbd5c Authors: Saxena, Manvendra; Van, Thi Thu Hao; Baird, Fiona J.; Coloe, Peter J.; Smooker, Peter M. Date: 2013-01-27 DOI: 10.1099/mic.0.049601-0 License: cc-by Abstract: Over the last century, the successful attenuation of multiple bacterial and viral pathogens has led to an effective, robust and safe form of vaccination. Recently, these vaccines have been evaluated as delivery vectors for heterologous antigens, as a means of simultaneous vaccination against two pathogens. The general consensus from published studies is that these vaccine vectors have the potential to be both safe and efficacious. However, some of the commonly employed vectors, for example Salmonella and adenovirus, often have pre-existing immune responses in the host and this has the potential to modify the subsequent immune response to a vectored antigen. This review examines the literature on this topic, and concludes that for bacterial vectors there can in fact, in some cases, be an enhancement in immunogenicity, typically humoral, while for viral vectors pre-existing immunity is a hindrance for subsequent induction of cell-mediated responses. Text: In the fields of medicine and veterinary medicine, there are numerous live, attenuated bacterial and viral vaccines in use today worldwide. The safety and efficacy of such vaccines is well established and allows further development as vector systems to deliver antigen originating from other pathogens. Various attenuated bacteria, including Escherichia coli, Vibrio cholerae, lactic acid bacteria (LAB), specifically Lactococcus lactis, Mycobacterium, Listeria, Shigella and Salmonella, have been tested for the targeted delivery of heterologous antigens of bacterial, viral and parasitic origin into a variety of animal hosts (Bahey-El-Din et al., 2010; Innocentin et al., 2009; Johnson et al., 2011; Tobias et al., 2008 Tobias et al., , 2010 Tobias & Svennerholm, 2012) . Bacteria such as E. coli and lactic acid bacteria have recently gained favour, as E. coli is a commensal and lactic acid bacteria are present in most fermented food items and are therefore naturally present in the host. They are also a much safer option than traditional attenuated vaccines in children and immunecompromised people. As this review discusses the effects of pre-existing immune responses to attenuated vaccines, further discussion of LAB and E. coli as potential vectors will not be undertaken; however, the reader is directed to several interesting reviews (Bermú dez-Humarán et al., 2011; Wells & Mercenier, 2008) . Intracellular bacteria from the genera Mycobacterium (Guleria et al., 1996) , Listeria (Gentschev et al., 2001) , Shigella (Levine et al., 1997) and Salmonella (Dougan et al., 1987) are considered to be suitable candidates for the delivery of vaccine antigens due to their capability to induce robust T cell immune responses (Alderton et al., 1991; Lo et al., 1999; Mastroeni et al., 2001; Mittrücker & Kaufmann, 2000; Nauciel, 1990) . Salmonella is one genus that has been well examined as a vector, building on the extensive research available on the micro-organism's physiology and pathogenesis (Basso et al., 2000; Killeen & DiRita, 2000; Sirard et al., 1999; Ward et al., 1999) . There exist several commercial vaccines that are used as anti-Salmonella vaccines in humans and animals (e.g. Ty21a for typhoid fever in humans, several Salmonella serovars against salmonellosis in chickens and other animals). The general strategy for vectoring heterologous antigen is depicted in Fig. 1 . The first clinical trial of a recombinant, which was conducted over 20 years ago using an attenuated Salmonella as a delivery vector, led to the widespread testing of this bacterium as a mucosal delivery system for antigens from non-Salmonella pathogens (Dougan et al., 1987) . These studies have demonstrated the utility of live bacteria to deliver expressed antigens and DNA vaccines to the host immune system (Atkins et al., 2006; Husseiny & Hensel, 2008; Jiang et al., 2004; Kirby et al., 2004) . Since then several other intracellular bacterial vectors have been successfully tested for their capability to deliver a variety of antigens from various pathogens, as well as vaccination against cancer. One genus which has been widely tested as vector is Listeria. Listeria species are Gram-positive intracellular food-borne pathogens. The advantages of Listeria are that it can invade a variety of cells, including antigen presenting cells (APCs). After invading the host cell, Listeria resides inside the phagosome; however, it can escape the phagosome with the help of listeriolysin O (LLO; Hly) and reside in the cytoplasm of the cells, thereby efficiently presenting antigen to both CD8 and CD4 T cells (Cossart & Mengaud, 1989; Kaufmann, 1993; Pamer et al., 1997) . Several studies have demonstrated the effectiveness and ease of using Listeria monocytogenes to deliver heterologous vaccine antigens and DNA vaccines Jensen et al., 1997; Johnson et al., 2011; Peters et al., 2003; Shen et al., 1995; Yin et al., 2011) . Similarly, various viral vectors have been successfully tested for their capability to deliver heterologous vaccine antigens, and this generally results in the induction of strong CTL immune responses. In the veterinary field, there are numerous viral vector vaccines that are currently licensed for use in livestock and domesticated animals. These recombinant vaccines are based on both DNA viruses (such as fowlpox virus-based vaccines which target avian influenza virus and fowlpox virus, or vaccinia virusbased vectors against the rabies virus in wildlife) and RNA viruses [such as Newcastle disease virus-based vaccines to be used in poultry or yellow fever virus (YFV)-based vaccines to be used in horses against West Nile virus] (Draper & Heeney, 2010) . Based on the safety record in the veterinary field, many viruses have been studied for human use as a vector in vaccine development (Beukema et al., 2006; Esteban, 2009; Schirrmacher & Fournier, 2009; Stoyanov et al., 2010; Weli & Tryland, 2011) . Amongst them, YFV (YF-17D strain) was the first to be licensed for use in humans, where the cDNAs encoding the envelope proteins of YFV were replaced with the corresponding genes of an attenuated Japanese encephalitis virus strain, SA14-14-2 (Appaiahgari & Vrati, 2010; Rollier et al., 2011) . Poxviruses are also studied extensively as candidate vectors for human use, among which attenuated derivatives of vaccinia virus [such as modified vaccinia virus Ankara (MVA) and New York attenuated vaccinia virus NYVAC strains] are the most promising vectors (Esteban, 2009; Gó mez et al., 2008; Rimmelzwaan & Sutter, 2009 ). They are ideal candidate vectors due to their large DNA-packing capacity and their thermal and genetic stability (Minke et al., 2004) . The NYVAC vector has been shown to induce CD4 + T cell-dominant responses, and MVA induces both CD4 + and CD8 + T cell responses (Mooij et al., 2008) . The adenovirus (Ad) vector is another of the most widely evaluated vectors to date to express heterologous antigens, due to ease of production, safety profile, genetic stability, the ease of DNA genome manipulation, and the ability to stimulate both innate and adaptive immune responses and induce both T and B cell responses (Alexander et al., 2012; Fitzgerald et al., 2003; Gabitzsch & Jones, 2011; Lasaro & Ertl, 2009; Vemula & Mittal, 2010; Weyer et al., 2009) . They have been extensively examined as a delivery vector in several preclinical and clinical studies for infectious diseases such as anthrax, hepatitis B, human immunodeficiency virus (HIV)-1, influenza, measles, severe acute respiratory syndrome (SARS), malaria and tuberculosis M. Saxena and others (Chengalvala et al., 1994; Gao et al., 2006; Hashimoto et al., 2005; Hsu et al., 1992; Limbach & Richie, 2009; Radosevic et al., 2007; Shiver et al., 2002) . However, before vectored vaccines can be used in the human population they need to satisfy several important criteria. Safety is a major concern, as even a low level of toxicity is unacceptable (of course the minor discomfort that accompanies many vaccinations is normal). Secondly, a vaccine should be inexpensive, so that it can be administered to a large population at minimal cost, and this is particularly important in resource-poor countries (Killeen & DiRita, 2000) . Similar constraints apply to veterinary vaccines, with cost often an even more important consideration. Finally, long-lasting cellular and (where appropriate) humoral immune responses to the vectored antigen must be induced following administration of these vaccines, preferably with a single dose (Atkins et al., 2006) . As some of the vectors in use will have been seen by the host immune system prior to vaccination, whether the presence of pre-existing immune responses is detrimental for the further development of a vector-based vaccine scheme, or can augment responses to the vectored antigen, needs to be considered in detail. This is the subject of this review. In discussing the possible effects on pre-existing immunity, the natural immunity to the vector needs to be considered. Therefore, considering a vector such as Salmonella, if a host has previously been infected there will exist robust B and T memory responses, and as such, when a vaccination is delivered, an anamnestic response to the Salmonella antigens will be induced (while the response to the vectored antigen will be a primary response). This will theoretically reduce the exposure of the heterologous antigen to the immune system, as the vector is rapidly cleared. Surprisingly, as will be seen in some of the examples given below, this can have results that differ depending on the magnitude of the response to the vectored antigen. Similarly, for virally vectored antigens, the existence of pre-existing immunity to the vector (particularly neutralizing antibody) will restrict delivery of the virus into cells, thereby effectively reducing the dose of the vectored antigen. Again, this might be expected to result in a reduction in the antigenicity of the vectored antigen. In the case of bacterial vectors, the effect of pre-existing immune responses has only been tested using Salmonella serovars and Listeria spp. Concern that prior immunological experience of the host with either the homologous Salmonella vector strain or a related strain might compromise its ability to deliver heterologous vaccine antigen was first raised in 1987 (Dougan et al., 1987) . Bao and Clements subsequently reported experimental evidence of the consequences of prior exposure of animals to the vector strain (Bao & Clements, 1991) . This work showed that both serum and mucosal antibody responses against the foreign antigen were in fact upregulated in animals with prior exposure to the vector strain. Whittle & Verma (1997) reported similar findings. Mice immunized via the intra-peritoneal route with a Salmonella dublin aroA mutant expressing heterologous antigen after being exposed to the same vector showed a higher immune response to the vectored antigen in comparison to mice without any immunological memory against the vector. Subsequently, several studies have been conducted to examine the effect of pre-existing immunity in the host against Salmonella. These results are summarized in Table 1 . The various reports are contradictory in their findings and seem to paint a rather confusing picture. Some studies concluded that pre-existing immunity against the Salmonella vector leads to stronger immune responses against the delivered antigen (Bao & Clements, 1991; Jespersgaard et al., 2001; Kohler et al., 2000a, b; Metzger et al., 2004; Saxena et al., 2009; Sevil Domènech et al., 2008; Whittle & Verma, 1997) , with others considering pre-existing immunity to be a limiting factor in the long-term use of Salmonella as an efficient vector for antigen delivery (Attridge et al., 1997; Gahan et al., 2008; Roberts et al., 1999; Sevil Domènech et al., 2007; Vindurampulle & Attridge, 2003a, b) . A slight majority of the studies listed in Table 1 (10 versus eight) indicate the upregulation of immune responses after animals have been exposed to either homologous or related strains before the delivery of heterologous antigen using a Salmonella vector. A study by Metzger and co-workers on human volunteers using Salmonella Typhi as a vector suggested that there was no change in the T cell immune response against the heterologous antigen in human volunteers who were exposed to empty vector in comparison with volunteers who were immunologically naive of the vector strain (Metzger et al., 2004) . In these subjects, humoral responses were moderately elevated in preexposed individuals. Similarly, Saxena et al. (2009) indicated higher humoral and T cell responses in mice pre-exposed to homologous or heterologous Salmonella strains. The interleukin 4 (IL4) response was significantly higher when the animal host was exposed to the homologous strain, whereas pre-exposure to a related species did not have such an impact on IL4 responses. Conversely interferon (IFN)-c responses were higher, irrespective of the strain to which mice were pre-exposed. This study also indicated that the presence of homologous or heterologous opsonizing antibodies leads to a higher uptake of Salmonella by macrophages in vitro, which may explain the higher immune responses in exposed mice. As may be expected, uptake was higher when homologous sera were used as the opsonin rather than heterologous sera. This is depicted in Fig. 2 . Conversely, there are reports that indicate that pre-existing immunity against the bacterial vector downregulates immune responses against the delivered heterologous antigen using similar or related vectors. Attridge and coworkers reported that the presence of immunity against the bacterial vector prior to the delivery of vectored antigenic Microbiology 159 protein can downregulate immune responses in mice against the delivered antigen (Attridge et al., 1997) . Similar results were reported by Roberts et al. (1999) and Vindurampulle & Attridge (2003a, b) . However, the latter authors found that the hypo-responsiveness could be largely eliminated by exposing animals to the foreign antigen prior to vectorpriming (Vindurampulle & Attridge, 2003b) . Unfortunately, this would appear to be impractical for an immunization regimen! A study presented by Gahan et al. (2008) immunized mice with S. Typhimurium expressing C fragment of tetanus toxin antigen from an expression plasmid or as a DNA vaccine. Vaccinated mice developed humoral responses to LPS and tetC (for the plasmid-bearing vaccines). Animals from all groups (including a previously unvaccinated group) were immunized on day 182 with Salmonella expressing tetC. At this time, the anti-LPS and tetC titres were beginning to wane. Fourteen days after the second immunization, the colonization of various mouse organs was assessed. The ability to colonize was found to be significantly reduced in groups that had been previously vaccinated with Salmonella. In view of this finding, it was perhaps not surprising that at day 210 the LPS titres were not significantly different between groups receiving one or two vaccinations. More interestingly, mice that had been primed with Salmonella alone, and then boosted with Salmonella expressing tetC, induced much lower anti-tetC responses than mice that had not been primed. This argues strongly that prior immunological immunity to the vector can seriously dampen subsequent antigen-specific humoral responses. Whether the same is true for cellular responses was not evaluated. Other studies have evaluated cellular responses. A study by Sevil Domènech and colleagues reported that pre-existing anti-vector immunity seriously compromises CD8 + responses in mice when exposed to a similar strain used as vector (Sevil Domènech et al., 2007) . In contrast, another study by the same authors reported that animals exposed to related vectors induce much higher CD8 + responses when compared with animals which do not have any pre-existing Salmonella immunity (Sevil Domènech et al., 2008) . The difference between these two studies was that in the first, the prime and boost were with identical serovars, while in the second study, different serovars were used. This may point to a way of avoiding downregulation of CD8 responses by pre-existing immunity. This is important, as one of the advantages of using Salmonella (an intracellular pathogen) is that strong cellular immune responses can be induced. It must be noted that in the case of Salmonella vaccines, effects other than strictly immunological responses (particularly adaptive responses) should be considered. In the context of innate immunity, it was shown that administration of non-virulent Salmonella to gnobiotic pigs eliminated disease following challenge with a virulent strain (Foster et al., 2003) . Interestingly, protection was not by competitive exclusion, as the virulent strain was in high numbers in the gut but did not distribute systemically. The protection was proposed to be mediated by the infiltration of a large number of polymorphonuclear leukocytes into the gut, and although perhaps impractical as a general prophylactic (as the time between vaccination and infection is short), this may be an option for short-term or perhaps therapeutic vaccination (as reviewed by Foster et al., 2012) . Chickens (Gallus gallus) are a natural animal reservoir for Salmonella, which makes them an important source of Salmonella-associated gastroenteritis in humans. The ability to use oral Salmonella vaccines to immunize against heterologous pathogens would be of enormous benefit to Uptake of STM-1 by J774 macrophages, relative to the highest uptake percentage. X, Opsonized with naive sera; m, opsonized with serum from mice exposed to Salmonella enteriditis; &, opsonized with serum from mice exposed to STM-1. Pre-existing immunity against vaccine vectors the poultry industry in both broiler and layer flocks. Both vertical and horizontal transmission is associated with Salmonella in chickens (Liljebjelke et al., 2005) . Vertical transmission via in ovo transmission is particularly important, because if there is prior exposure to the vaccine strain, subsequent vaccination using an oral Salmonella vector could be severely compromised. A considerable number of studies on cross-protective immunity and competitive exclusion have been undertaken in chickens. Protective cross-reactive immunity against Salmonella strains has been demonstrated against both homologous and heterologous challenges (Beal et al., 2006) , although cross-serogroup protection was not strong. Furthermore, a recent study reported that pretreatment of newly hatched chickens with different Salmonella strains could produce a complete invasioninhibition effect on any subsequent exposure to both homologous and heterologous strains (Methner et al., 2010) . Pre-exposure with a highly invasive form of Salmonella Enteritidis caused a large influx of heterophils to the caecal mucosa in 1-day-old chicks, and subsequent heterologous caecal colonization was inhibited for a period of 48 h (Methner et al., 2010) . The implications of this kind of colonization-inhibition study on the immunological status of the affected chickens are yet to be fully elucidated. It should be noted that the studies listed in Tables 1 and 2 are controlled laboratory studies, with the possibility of a competitive exclusion component to immunity not discussed. Similarly studies of L. monocytogenes and the effects of preexisting immune responses indicate conflicting results. A study by Bouwer et al. (1999) indicates that pre-existing immune responses against the Listeria vector do not diminish immune responses against the delivered heterologous antigen, and a similar study by Starks et al. (2004) also concluded that prior exposure of mice to the empty Listeria vector did not influence anti-cancer immune responses when a similar mutant was used as a carrier of a melanoma cancer antigen. Similar findings were reported by Whitney et al. (2011) in rhesus macaques in which L. monocytyogens was used as a carrier of gag-HIV antigen. Conversely, studies by Stevens et al. (2005) in which L. monocytogens was used to deliver feline immunodeficiency virus (FIV) gag protein and as a carrier of DNA vaccines to vaccinate cats against FIV envelope protein indicated lower immune responses against the delivered antigen in cats exposed to empty Listeria vector in comparison with naive animals (Stevens et al., 2005) . Similar findings have been reported by Tvinnereim et al. (2002) and Leong et al. (2009) . However, taken together, these studies conclude that prior exposure of host animals to empty vector does not abrogate immune responses to the vectored antigen, but only reduces them somewhat. Only the study by Vijh et al. (1999) indicated that exposure to the empty vector may completely abrogate immune responses against the delivered antigens (Vijh et al., 1999) . However, these studies also indicate that downregulation of antigenspecific immune responses is highly dependent on dose and time. Leong et al. (2009) also demonstrated that the negative impact of vector-specific immune responses can also be countered by repeated immunization with the same vaccine and dose; this in effect leads to higher priming of naive T cells against the delivered antigen. Of course, such repeated vaccination may not be practicable in real-world situations. Despite the many advantages which viral vectoring can offer, pre-existing immunity is a major obstacle of many viralvectored vaccines, such as Ad serotype 5 or herpes simplex virus type 1 (HSV-1), where the rate of seroprevalence to these viruses is very high [40-45 % and 70 % (or more) of the US population, respectively] (Hocknell et al., 2002; Pichla-Gollon et al., 2009) . Vector-specific antibodies may impede the induction of immune responses to the vaccine-encoded antigens, as they may reduce the dose and time of exposure of the target cells to the vaccinated antigens (Pichla-Gollon et al., 2009; Pine et al., 2011) . In a large-scale clinical trial (STEP) of an Ad serotype 5 (AdHu5)-based HIV-1 vaccine, the vaccines showed a lack of efficacy and tended to increase the risk of HIV-1 infection in vaccine recipients who had pre-existing neutralizing antibodies to AdHu5 (Buchbinder et al., 2008) . For an HSV-1-based vector vaccine, it has been demonstrated that pre-existing anti-HSV-1 immunity reduced, but did not abolish, humoral and cellular immune responses against the vaccine-encoded antigen (Hocknell et al., 2002; Lauterbach et al., 2005) . However, Brockman and Knipe found that the induction of durable antibody responses and cellular proliferative responses to HSVencoded antigen were not affected by prior HSV immunity (Brockman & Knipe, 2002) . Similarly, pre-existing immunity to poliovirus has little effect on vaccine efficacy in a poliovirus-vectored vaccine (Mandl et al., 2001) . Different effects of pre-existing immunity on the efficacy of recombinant viral vaccine vectors are summarized in Table 2 . There are several approaches to avoiding pre-existing vector immunity, such as the use of vectors derived from nonhuman sources, using human viruses of rare serotypes (Kahl et al., 2010; Lasaro & Ertl, 2009) , heterologous prime-boost approaches (Liu et al., 2008) , homologous reimmunization (Steffensen et al., 2012) and removing key neutralizing epitopes on the surface of viral capsid proteins (Gabitzsch & Jones, 2011; Roberts et al., 2006) . The inhibitory effect of pre-existing immunity can also be avoided by masking the Ad vector inside dendritic cells (DCs) (Steffensen et al., 2012) . In addition, mucosal vaccination or administration of higher vaccine doses can overcome pre-existing immunity problems (Alexander et al., 2012; Belyakov et al., 1999; Priddy et al., 2008; Xiang et al., 2003) . As we search for new vaccine approaches for the array of pathogens for which none is yet available, revisiting proven vaccines and developing these further has gained M. Saxena and others momentum. Hence, attenuated bacteria and viruses which have a long history of efficacy and safety are being brought into use. While very attractive, a common theme in these experimental approaches has been the limitations that preexisting immunity to the vector may pose. However, as this examination of the relevant literature shows, there is a rather confusing picture, with some studies in fact indicating that pre-existing immunity may be a friend, rather than foe. Few studies using viral vectors have reported on the influence of pre-existing immunity on humoral responses. Generally speaking, for bacterial-delivered antigens, the humoral responses were influenced by pre-existing immunity, with slightly more studies finding augmentation rather than diminution. Why is there variation? This may be due to several factors, including the type of Salmonella used and its invasiveness. Dunstan and colleagues tested the ability of six isogenic Salmonella serovar Typhimurium strains harbouring different mutations for their ability to induce immune responses against the C fragment of tetanus toxin and concluded that the strain which had the least ability to colonize Peyer's patches induced the lowest immune responses (Dunstan et al., 1998) . Similarly, the boosting time and nature of the antigen used might be important. Attridge and colleagues indicated the importance of boosting time. In one experiment, boosting mice at 10 weeks led to complete inhibition of antibody responses against the delivered heterologous antigen; however, when the mice were boosted at 4 weeks, the downregulation of antibody responses was not so prominent (Attridge et al., 1997) . A similar study conducted by Kohlers and colleagues shows that boosting at 7 weeks after pre-exposing animals to empty vector leads to lower antigen-specific IgG and secretory IgA responses; however, boosting at 14 weeks leads to higher IgG and secretory IgA responses (Kohler et al., 2000b) . This is in conflict with the above result, although it should be mentioned that they used different Salmonella species. Vindurampulle and Attridge also examined the impact of the Salmonella strain and the nature of the antigens used. In their study, they used S. Dublin and Salmonella Stanley aroA mutants to deliver E. coli K88 and LT-B antigens, and concluded that the effect of pre-existing immunity depends on both the strain used and the type of antigen delivered (Vindurampulle & Attridge, 2003b) . All these studies on the effect of pre-existing immunity discuss the impact on humoral responses. Sevil Domenech and colleagues reported that pre-exposing animals to the homologous Salmonella vector leads to a significant reduction in CD8 + responses; however, exposure of animals to a heterologous strain leads to significantly higher CD8 + responses (Sevil Domènech et al., 2007 , 2008 . Saxena and colleagues also reported that antigenspecific T cell responses were either similar or significantly higher, with no downregulation in T cell responses observed after pre-exposing mice to either homologous or heterologous strains (Saxena et al., 2009) . For viral vectors, the impact of cell-mediated immunity was more pronounced, and as depicted in Table 2 , almost always resulted in a reduction in the subsequent immune response. Presumably this is because viruses will induce neutralizing antibody on the first dose, and in subsequent doses this antibody will limit the number of transduced cells, therefore limiting the responses. This is particularly a problem with a common viral vector such as Ad, where a large proportion of the population will have immunological memory against common serotypes (Lasaro & Ertl, 2009) . As these authors conclude, it will be possible to utilize such vectors only by developing vaccines from alternative serotypes. It may be that a vector such as Pre-existing immunity against vaccine vectors attenuated influenza virus, with the ability to easily develop reassortants, will be useful in this context. In addition, immunological memory in the form of opsonizing antibody certainly plays an important role in the early uptake of Salmonella by macrophages and DC. This may be beneficial, as the live bacterial vector used for delivery purposes harbours mutations in genes encoding proteins responsible for their survival in the animal host. This not only encumbers their ability to cause disease, making them safe live vectors, but also limits the number of replications. The presence of opsonizing antibodies should mean a higher level of bacterial uptake, leading to higher presentation to the immune system and therefore a better immune response. We have previously shown that this is indeed the case (Saxena et al., 2009 ) (depicted in Fig. 2 ). It would be of great benefit to address these issues not only in mice but also in other organisms such as chickens, which are the most likely host to be targeted for the use of live Salmonella vectors, specifically where the vaccines are developed for use in livestock and poultry. To summarize, bacterial vectors such as Salmonella and viral vectors such as Ad show great promise as delivery vehicles for heterologous antigens; however, prior exposure to the vector must be considered. By judicious selection of the strain/serotype it will be possible to avoid the negative effects and it may indeed be possible to positively influence the response, particularly for humoral immunity.

What are important criteria for selecting vaccine delivery vectors?

Venezuelan Equine Encephalitis Virus Induces Apoptosis through the Unfolded Protein Response Activation of EGR1 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4794670/ SHA: f4aa788ab898b28b00ee103e4d4ab24a2c684caf Authors: Baer, Alan; Lundberg, Lindsay; Swales, Danielle; Waybright, Nicole; Pinkham, Chelsea; Dinman, Jonathan D.; Jacobs, Jonathan L.; Kehn-Hall, Kylene Date: 2016-03-11 DOI: 10.1128/jvi.02827-15 License: cc-by Abstract: Venezuelan equine encephalitis virus (VEEV) is a previously weaponized arthropod-borne virus responsible for causing acute and fatal encephalitis in animal and human hosts. The increased circulation and spread in the Americas of VEEV and other encephalitic arboviruses, such as eastern equine encephalitis virus and West Nile virus, underscore the need for research aimed at characterizing the pathogenesis of viral encephalomyelitis for the development of novel medical countermeasures. The host-pathogen dynamics of VEEV Trinidad donkey-infected human astrocytoma U87MG cells were determined by carrying out RNA sequencing (RNA-Seq) of poly(A) and mRNAs. To identify the critical alterations that take place in the host transcriptome following VEEV infection, samples were collected at 4, 8, and 16 h postinfection and RNA-Seq data were acquired using an Ion Torrent PGM platform. Differential expression of interferon response, stress response factors, and components of the unfolded protein response (UPR) was observed. The protein kinase RNA-like endoplasmic reticulum kinase (PERK) arm of the UPR was activated, as the expression of both activating transcription factor 4 (ATF4) and CHOP (DDIT3), critical regulators of the pathway, was altered after infection. Expression of the transcription factor early growth response 1 (EGR1) was induced in a PERK-dependent manner. EGR1(−/−) mouse embryonic fibroblasts (MEFs) demonstrated lower susceptibility to VEEV-induced cell death than isogenic wild-type MEFs, indicating that EGR1 modulates proapoptotic pathways following VEEV infection. The influence of EGR1 is of great importance, as neuronal damage can lead to long-term sequelae in individuals who have survived VEEV infection. IMPORTANCE Alphaviruses represent a group of clinically relevant viruses transmitted by mosquitoes to humans. In severe cases, viral spread targets neuronal tissue, resulting in significant and life-threatening inflammation dependent on a combination of virus-host interactions. Currently there are no therapeutics for infections cause by encephalitic alphaviruses due to an incomplete understanding of their molecular pathogenesis. Venezuelan equine encephalitis virus (VEEV) is an alphavirus that is prevalent in the Americas and that is capable of infecting horses and humans. Here we utilized next-generation RNA sequencing to identify differential alterations in VEEV-infected astrocytes. Our results indicated that the abundance of transcripts associated with the interferon and the unfolded protein response pathways was altered following infection and demonstrated that early growth response 1 (EGR1) contributed to VEEV-induced cell death. Text: V enezuelan equine encephalitis virus (VEEV) is a New World alphavirus in the family Togaviridae that is endemic to the Americas. VEEV is a positive-strand RNA virus that is transmitted by mosquitoes and that is naturally present in rodent reservoirs (1) . There are six subtypes that are categorized by their geographic range and pathology in equines and humans. The two epizootic strains, IA/B and IC, arose from mutations among the enzootic strains (2) . The IA/B and IC strains are of particular concern due to increased rates of morbidity and mortality and the risks associated with viral amplification and potential species spillover (2) . In humans, VEEV causes a febrile illness typified by fever, malaise, and vomiting. In some cases, infection progresses to the central nervous system (CNS) and neurological symptoms, such as confusion, ataxia, and seizures, manifest. The mortality rate among cases with neurological symptoms can be as high as 35% in children and 10% in adults, with long-term neurological deficits often being seen in survivors (2) . In 1995, an outbreak of VEEV in Colombia and Venezuela resulted in over 100,000 human cases (3) . In addition to natural outbreaks, VEEV is also a concern from a bioterrorism perspective, as it can be grown to high titers, requires a low infectious dose, and contains multiple serotypes. Both the former Soviet Union and the United States previously weaponized the virus, producing large quantities for their now defunct offensive bioweapons programs (4) . Currently, vaccine strain TC83 is used in horses and for high-risk personnel; however, due to the low rate of seroconversion achieved with this vaccine (5) and its reliance on two single attenuating mutations (6) , it is considered unfit for mass distribution (7) . To date there are no FDA-approved therapeutics for VEEV infection, and further studies are required for clarification of the mechanisms associated with the underlying pathogenesis of VEEV. Viral and host transcriptomic studies can provide a wealth of information on the underlying pathogenic mechanisms and interactions following the course of an infection. The use of highthroughput next-generation sequencing has led to the discovery of previously uncharacterized viruses and the establishment of numerous novel experimental systems redefining virus-host interactions. To date a number of studies have examined the alterations in the host transcriptome following VEEV infection. A comparative microarray analysis between cells persistently infected with VEEV and cells able to clear VEEV resulted in the identification of PARP12L as an antiviral factor (8) . A molecular comparison utilizing microarrays of host-based responses to the TC83 strain was able to identify biomarkers differentiating between vaccine responder and vaccine nonresponder groups, as well as the involvement of interferon (IFN), interferon-induced pathways, Toll-like receptor (TLR), and interleukin 12 (IL-12)related pathways (9) . A study examining the role of adhesion and inflammatory factors in VEEV-infected CD-1 mice found viral modulation of the expression of extracellular matrix and adhesion genes, such as integrins (Itg␣X, Itg2, 3, and 7), cadherins 1 and 2, vascular cell adhesion molecule 1, and intracellular adhesion molecule 1 (ICAM-1), in the brains of VEEV-infected mice (10) . Follow-up experiments utilizing ICAM-1-knockout mice demonstrated reduced inflammation in the brain and a subsequent delay in the onset of neurological sequelae (10) . A study by Sharma et al. utilized microarrays to analyze gene expression changes in the brain tissue of VEEV-infected mice over the course of an infection, discovering numerous immune pathways involved in antigen presentation, inflammation, apoptosis, and the traditional antiviral response (Cxcl10, CxCl11, Ccl5, Ifr7, Ifi27, Oas1b, Fcerg1, Mif, clusterin, and major histocompatibility complex [MHC] class II) (11) . A second study by the same group identified the regulation of microRNAs (miRNAs) in the brains of VEEV-infected mice, which enabled the correlation of the miRNA changes with earlier mRNA expression data (11, 12) . These analyses suggest that VEEV may be utilizing cellular miRNAs in order to regulate downstream mRNA, which may correspond with the VEEV-induced histological changes to the nervous system (11, 12) . In the current study, next-generation RNA sequencing (RNA-Seq) was used to identify clinically relevant alterations in the mRNA transcriptome of human astrocytes infected with wildtype (WT) VEEV strain Trinidad donkey (TrD). The analysis of host mRNAs by RNA-Seq provides novel insight into how a host responds to a viral infection through the identification of a wide and dynamic range of transcripts in an unbiased manner. Selective sequencing of mRNAs, specifically, polyadenylated [poly(A)] transcripts, which account for ϳ1% of the entire transcriptome, enhances the detection of the most relevant and low-abundance transcripts (13) . As VEEV has been shown to productively infect astrocytes both in vitro and in vivo (14, 15) , we chose astrocytes as our model of interest. Astrocytes are the most abundant cell in the brain, outnumbering neurons by at least 5-fold (16) , providing an abundant resource for viral replication within the brain. In addition to their well-described structural role in neuronal tissue, as-trocytes play critical roles in other processes, including the regulation of blood flow and of the blood-brain barrier, synapse transmission, and the response to infection (16) . VEEV-infected astrocytes have been shown to produce multiple cytokines, including IL-8, IL-17, interferon gamma (IFN-␥), and gamma interferon-induced protein 10, all of which were found to be associated with viral attenuation (14) . In order to obtain a dynamic view of the virus-host interactome, RNA-Seq was used to monitor changes in gene expression in VEEV TrD-infected astrocytes at 4, 8, and 16 h postinfection (hpi). By viewing the alterations at multiple early time points using triplicate biological replicates, a robust and dynamic range of information is generated, and this information provides an increase in both the power and the accuracy of detection of differentially expressed transcripts in a highly relevant clinical model (17) . Among VEEV-infected cells, an increase in interferon-regulated genes, including IFIT1, IFIT2, IFIT3, and OASL, was observed. The increased expression of genes involved in the stressinduced unfolded protein response (UPR) pathway was also noted. Interestingly, VEEV infection resulted in an increase in early growth response protein 1 (EGR1), which may serve as a link between the two pathways. The identification of host mRNAs whose expression is altered following VEEV replication, specifically, EGR1 and its interactors up-and downstream, may provide novel host-based therapeutic targets critical for VEEV replication and a greater understanding of the underlying mechanisms underpinning alphavirus replication. Viral infections and plaque assays. VEEV TrD was obtained from BEI Resources. All experiments with VEEV TrD were performed under biosafety level 3 (BSL-3) conditions. All work involving select agents is registered with the Centers for Disease Control and Prevention and was conducted at George Mason University's Biomedical Research Laboratory, which is registered in accordance with federal select agent regulations. For infections, VEEV was added to supplemented Dulbecco modified Eagle medium (DMEM) to achieve a multiplicity of infection (MOI) of 0.05, 0.5, or 5. Cells were infected for 1 h at 37°C and rotated every 15 min to ensure adequate coverage. The cells were then washed with phosphatebuffered saline (PBS), and complete growth medium was added back to the cells. Viral supernatants and cells were collected at various times postinfection for further analysis. Plaque assays were performed as previously described (18) . mRNA isolation and poly(A) library preparation. RNA from U87MG cells was purified from both VEEV TrD-infected (biosafety level 3) and mock-infected U87MG cells at 4, 8, and 16 hpi utilizing a mirVana isolation kit (Life Technologies). Quality control of purified RNA was then performed using an Agilent 2100 bioanalyzer, and an RNA integrity number (RIN) cutoff of 8 was utilized for all samples. An External RNA Controls Consortium (ERCC) RNA spike-in control mix was then added to the total RNA inputs (10 g RNA) before poly(A) selection using a Life Technologies Dynabeads mRNA Direct kit. Preparation of a whole-transcriptome RNA library from purified mRNA was then performed using an Ion Total RNA-Seq kit (v2; Life Technologies). Quality control of the cDNA libraries was then performed using the Agilent 2100 bioanalyzer along with sterility testing for removal of libraries for sequencing from a BSL-3 to BSL-2 laboratory. RNA sequencing. Library template preparation was performed on a One Touch 2 platform (Life Technologies). Next-generation RNA sequencing was performed on an Ion Torrent PGM platform and was carried out for each sample to assess the differential gene expression of infected versus uninfected cells over time. Data filtering and RNA-Seq analysis pipeline. A total of ϳ119 million sequencing reads and an average of 6.6 million reads per sample were used as the input into our analysis pipeline. Unless otherwise noted, downstream RNA-Seq analysis was carried out using the CLC bio Genomics Workbench (v7). Raw RNA-Seq reads were trimmed to remove any residual sequencing adapter fragments that remained on the 5= or 3= ends after sequencing. In addition, end trimming of reads was done using the modified Mott algorithm with a Q20 quality score, and any reads of less than 15 bp were discarded. Following read trimming, the reads were mapped to human genome hg19 with the following RNA-Seq parameters: a 10-hit limit for multiple mapped positions, a similarity fraction of 0.8, a length fraction of 0.8, a mismatch cost of 2, and an indel cost of 3. The expression level of individual genes and transcripts was calculated using the number of reads per kilobase of the exon model per million mapped reads (RPKM) method of Mortazavi et al. (19) . In addition, unmapped reads were also mapped to the ERCC92 synthetic RNA sequence set (20) , as well as to the VEEV reference genome (GenBank accession number L01442). In all samples, the correlation coefficient (R 2 ) between the expected and the mapped number of reads for the ERCC92 spike-in controls was above 0.90. A summary of the overall sequencing results is shown in Table 1 . Postmapping filtering of all RNA-Seq data was carried out next to include only genes with at least one uniquely mapped read (26,230 genes remained across all data sets) and only those with a nonzero interquartile range across the entire experiment. Principal component analysis of the resulting filtered data set (13,906 genes in total) was carried out using raw counts of uniquely mapped reads (see Fig. 2A ). The remaining RPKM expression values for each gene included in the filtered data set were subjected to quantile normalization with a 5% cutoff. A box plot of log 2transformed RPKM values for each sample before normalization is shown in Fig. 2B . The R 2 value for pairwise sample-to-sample variation within each biological replicate set was observed to range from 0.89 to 0.99, indicating that our biological replicates were consistent and showed no strong bias (data not shown). Differential gene expression analysis. Differentially expressed genes (DEGs) were identified using two approaches. First, the empirical analysis of differential gene expression algorithm, part of the edgeR Bioconductor package (21) , was applied to the integrated data set of all 18 experiments using the default parameters and a false discovery rate-corrected P value. At each time point, infected and mock-infected samples were compared, and genes whose expression differed by more than 2-fold with a significance with a P value of Յ0.05 were provisionally considered to be differentially expressed. In addition to the method described above, an orthogonal statistical test of differential expression was applied to the data using a statistical test developed by Baggerly et al. (22) to count the number of expressed sequence tags associated with individual genes, a common feature of both serial analysis of gene expression (SAGE) data and RNA-Seq data. When infected and mock-infected samples were compared, individual genes were provisionally considered differentially expressed when their expression differed by more than 2-fold with a significance with a P value of Յ0.05. Differentially expressed genes found to be in the intersection of the sets of genes identified by both of the methods outlined above were considered high-quality candidates and used as the starting point for further investigation. Clustering and GSEA. Filtered, normalized expression data were subjected to k-means clustering using a Euclidian distance metric where genes were grouped by means of normalized gene expression (RPKM) values for each experimental condition. Clustering was fitted to 20 distinct clustering groups, and the individual gene expression profiles clustered were further tested for enrichment of gene ontology (GO) terms associated with individual genes. Gene annotations were obtained from Reactome, a database of biological pathway and gene functional annotations (23) . Enrichment analysis was performed using two approaches. First, a hypergeometric test on GO annotations was carried out using an implementation of the GOStats package on each of the individual clusters obtained from k-means clustering (24) . In addition, gene set enrichment analysis (GSEA) was carried out on the entire filtered data set using 100,000 permutations, while duplicates were removed and an analysis of variance was applied. A total of 1,419 categories passed a minimum feature size of 10 and were used for further investigation. Cohorts of genes with shared patterns of expression over time were identified by k-means clustering. Those found to be enriched for DEGs were subsequently subjected to pathway analysis using the GeneMania system (25) . Using an ad hoc manual approach, relevant pathways and the connections between them were identified on the basis of existing data in the literature coupled with the temporal gene expression data obtained from this study. qRT-PCR analysis. Purified mRNA was converted to cDNA using a high-capacity RNA-to-cDNA kit (Life Technologies) according to the manufacturer's instructions. Analysis of the viral copy numbers was performed by quantitative reverse transcription-PCR (qRT-PCR) as previously described (26) . Host expression of the following genes was assayed with TaqMan assays (indicated in parentheses): activating transcription factor 3 (ATF3; Hs00231069_m1), ATF4 (Hs00909569_g1), CEBPB (Hs00270923_s1), CEBPD (Hs00270931_s1), DDIT3 (Hs00358796_g1), FOS (Hs04194186_s1), JUN (Hs01103582_s1), EGR1 (Hs00152928_m1), IFI6 (Hs00242571_m1), IFIT1 (Hs01911452_s1), IFIT2 (Hs01922738_s1), IFIT3 (Hs01922738_s1), ISG15 (Hs01921425_s1), ISG20 (Hs00158122_m1), OASL (Hs00984387_m1), BIRC5 (Mm00599749_m1), and XIAP (Mm01311594_mH). Assays for 18S rRNA (Hs99999901_s1 or Mm04277571_s1) were used for normalization. Assays were performed according to the manufacturer's instructions using an ABI StepOne Plus instrument. Treatment with PERKi and collection for Western blot analysis. U87MG cells were pretreated for 2 h with 10 M the protein kinase RNAlike endoplasmic reticulum (ER) kinase (PERK) inhibitor (PERKi) GSK2606414 (catalog number 516535; EMD Millipore) or dimethyl sulfoxide (DMSO) in DMEM prior to infection with VEEV TrD (MOI, 5). After 1 h, the viral inoculum was removed and cells were washed with sterile PBS (1ϫ). The medium was replaced with medium containing the inhibitor or DMSO. At 16 hpi, the medium was removed, and the cells were washed with PBS and then collected for Western blot analysis. Knockdown of EGR1 with siRNA. U87MG cells seeded at 6.7 ϫ 10 4 cells per well in a 12-well plate were transfected with 50 nM siGenome Protein lysate preparation and Western blot analysis. Protein lysate preparation and Western blot analysis were performed as previously described (27) . Primary antibodies to the following were used: EGR1 (antibody 44D5; catalog number 4154; Cell Signaling), polyclonal anti-Venezuelan equine encephalitis virus TC83 (subtype IA/B) capsid protein (BEI Resources), CHOP (antibody L63F7; catalog number 2895; Cell Signaling), phosphorylated ␣ subunit of eukaryotic initiation factor 2 (p-eIF2␣; Ser51; antibody D9G8; catalog number 3398; Cell Signaling), ATF4 (antibody D4B8; catalog number 11815; Cell Signaling), activated caspase 3 (antibody Asp175; catalog number 9661; Cell Signaling), and horseradish peroxidase-conjugated ␤-actin (catalog number ab49900-100; Abcam). Immunofluorescence analysis. U87MG cells were grown on coverslips in a 6-well plate, infected with VEEV TrD as described above, washed with PBS (without Ca and Mg), and then fixed with 4% formaldehyde. Cells were permeabilized with 0.5% Triton X-100 in PBS for 20 min and then washed twice with PBS. The cells were blocked for 10 min at room temperature in 3% bovine serum albumin in PBS. Primary antibodies consisting of a VEEV capsid protein (catalog number NR-9403; BEI Resources) diluted 1:600 and an EGR1 antibody (antibody 44D5; catalog number 4154; Cell Signaling) diluted 1:400 were incubated in fresh blocking buffer at 37°C for 1 h and washed 3 times for 3 min each time in 300 mM NaCl with 0.1% Triton X-100. Alexa Fluor 568 donkey anti-goat secondary antibody (catalog number A11057; Invitrogen) and Alexa Fluor 488 donkey anti-mouse secondary antibody (catalog number A21202; Invitrogen) diluted 1:400 were used as secondary antibodies and treated in the same manner as the primary antibodies. DAPI (4=,6-di- amidino-2-phenylindole) diluted 1:1,000 was used to visualize the nuclei. Coverslips were mounted onto glass slides using 10 l of Fluoromount G mounting medium (catalog number 0100-01; Southern Biotech). A Nikon Eclipse TE2000-U fluorescence microscope was used for fluorescence microscopy. Images were viewed using a 60ϫ objective oil immersion lens. Five images of each sample were obtained, and a representative image of each sample is shown below. All images were subjected to fourline averaging. The images were processed through Nikon NIS-Elements AR Analysis (v3.2) software. CellTiter Glo and Caspase 3/7 Glo assays. Wild-type and EGR1 Ϫ/Ϫ mouse embryonic fibroblasts (MEFs) were infected with TrD at various MOIs for an hour and then washed with PBS, and the medium was replaced. Cell viability was measured at 24 h postinfection using a Promega CellTiter luminescent cell viability assay (catalog number G7571) according to the manufacturer's protocol. Luminescence was read using a Beckman Coulter DTX 880 multimode detector with an integration time of 100 ms per well. Similarly, caspase activation in infected wildtype and EGR1 Ϫ/Ϫ MEFs was measured at 24 h postinfection using a Promega Caspase 3/7 Glo assay (catalog number G8090) according to the manufacturer's protocol. Luminescence was read using the DTX 880 multimode detector with an integration time of 100 ms per well. Nucleotide sequence accession numbers. The raw sequencing data for all RNA-Seq runs included in this work are publically available in the NCBI BioProject database under accession number PRJNA300864 (http: //www.ncbi.nlm.nih.gov/bioproject/PRJNA300864). VEEV replication kinetics in U87MG astrocytes. VEEV replicates in vivo in monocytes, macrophages, neurons, and astrocytes (14) . Common cell lines used to study VEEV infection include Vero and BHK cells; in this study, U87MG astrocytes were chosen as an in vitro model due to their physiological relevance and greater clinical significance. Initial experiments were performed to characterize viral replication in U87MG cells. VEEV replication kinetics in U87MG cells were measured using plaque assays and by monitoring viral protein and RNA expression levels and the cytopathic effect (CPE) on the infected cells (Fig. 1) . Viral release was observed as early as 4 hpi, with ϳ4 log units of virus being observed, followed by a consistent increase in replication at 8 and 16 hpi (Fig. 1A) . Viral replication peaked at 16 hpi, and no additional increase in viral titers was observed at 24 hpi. Viral capsid expression followed a similar pattern, with protein being detected at 8 hpi and expression plateauing at 16 hpi (Fig. 1B) . Among infected U87MG cells, a significant CPE was observed by microscopy at 24 hpi, with little to no CPE being detected at 16 hpi (data not shown). Consistent with these observations, increased caspase 3/7 activity was observed only at 24 hpi (Fig. 1C) . On the basis of these data, times of 4, 8, and 16 hpi, reflecting the early, middle, and late stages of the viral life cycle, respectively, were selected for RNA-Seq analysis in order to provide a dynamic view of the host-pathogen transcriptome profile. RNA sequencing analysis of VEEV-infected astrocytes. mRNA from triplicate sets of mock-and VEEV-infected U87MG cell cultures was isolated, purified at 4, 8, and 16 hpi, and used to prepare cDNA libraries for downstream RNA-Seq (see Materials and Methods). A high-level summary of the RNA-Seq results is shown in Table 1 . VEEV RNA samples were assayed by quantitative RT-PCR at each time point as a control to demonstrate the increasing viral RNA load over time (Fig. 1D) , consistent with the increasing number of RNA-Seq reads mapped to the VEEV genome at later time points (Table 1) . For RNA-Seq analysis, individual genes were expressed as the number of reads per kilobase of the exon model per million mapped reads (RPKM) (19) . Log 2 -normalized RPKM expression values for each experimental sample are shown in Fig. 2A and can be found in Data Set S1 in the supplemental material. Minimal sample-to-sample variation in expression values within biological replicates was consistently detected (R 2 Ͼ 0.89 for all replicates; data not shown). In addition, intersample variation was also found to be minimal when it was tested pairwise across the entire experiment by using RPKM values for ERCC97 synthetic spike-in control RNAs (R 2 Ͼ 0.90 for all comparisons; data not shown). As anticipated, two-component principal component analysis of the RNA-Seq data for mock-infected cells versus VEEV-infected cells showed a clear separation of the samples at 16 hpi from the samples at earlier time points (Fig. 2B) . However, the clustering of VEEV-infected samples with mock-infected samples at earlier time points suggested that the response to viral infection was limited to a narrow subset of early response genes, thus placing a higher burden of proof on identifying differentially expressed genes (DEGs) during the first few hours of infection. Along these lines, two orthogonal methods were used to identify DEGs suitable for further characterization: the edgeR method (21) and the method developed by Baggerly et al. (22) . Genes identified by one method were provisionally considered DEGs, and those identified by both methods were candidate DEGs to be confirmed by qRT-PCR. In addition to comparing individual gene expression values for mock-infected cells and VEEV-infected cells at each time point, gene expression values were also compared serially within each time series of VEEV-infected cells for genes that did not show any statistically significant changes in expression in mock-infected cells. A schematic of the comparative analysis is shown in Fig. 2C . The number of statistically significant DEGs identified by each of these comparisons is shown in Fig. 2D . Furthermore, k-means clustering (against normalized RPKM values) was employed to identify gross changes in gene expression over time for cohorts of genes potentially sharing the same pathway or regulatory triggers ( Fig. 3 ; see also Data Set S2 in the supplemental material). Gene set enrichment analysis (GSEA; see Material and Methods and Data Set S3 in the supplemental material) was carried out on each kmeans cluster. In particular, cluster 20 (Table 2) was significantly enriched for genes involved in translational control, the type I interferon-mediated signaling pathway, and the unfolded protein response (UPR) pathway (GSEA P value Ͻ 0.01). Although there is a well-established connection between translational control and UPR, a novel connection between UPR and the type I interferonmediated response in response to viral replication was suggested by pathway analysis (see Materials and Methods), implicating early growth response 1 (EGR1) as a potential bridge between these two pathways (Fig. 4) . EGR1 belongs to cluster 20 and is strongly induced during VEEV infection, and several other genes associated with the interferon response belong to the same cluster: IRF1, IFIT1, IFIT2, ISG15, and ILF3. EGR1 has been associated with increases in the expression of activating transcription factor 3 (ATF3) (28) , which is a key component of the UPR and which also belongs to cluster 20. This connection represented a potential a Biological process annotations obtained from Reactome for cluster 20. Reactome annotation identifiers are indicated for each annotation. Only traceable author submission (TAS)-classified annotations are considered. TAP, transporter associated with antigen processing; SRP, signal recognition particle. b Full set, the total number of genes in the genome with an annotated biological process; subset, total number of differentially expressed genes with an annotated biological process. Network of type I interferon response-and UPR-related genes. Large circles, differentially expressed genes; small circles, genes with no significant change in expression; red circles, type I interferon response factors; yellow circles, genes regulating DNA transcription; blue circles, unfolded protein response genes; red lines, genes involved in physical protein-protein interactions; blue lines, genes involved in a common pathway. This network was seeded with k-means clusters 18 and 20, and many ribosomal protein genes were removed. bridge between the UPR pathway and the interferon response pathway, with EGR1 being one of the potential key transcription factors driving this connection. Consequently, 15 genes from this analysis were selected for further characterization by qRT-PCR (see below): ATF3, activating transcription factor 4 (ATF4), CEBPB, CEBPD, DDIT3/CHOP, EGR1, FOS, IFI6, IFIT1, IFIT2, IFIT3, ISG15, ISG20, JUN, and OASL. The expression values of these genes, as measured by RNA-Seq, are shown in Fig. 5A and B. Confirmatory qRT-PCR analysis indicated concordant gene expression ( Fig. 5C and D) . The interferon response genes induced are in agreement with those detected in previously published studies (11, 29, 30) , and these genes served as an internal positive control. Moreover, the link between EGR1 and the interferon pathway has been demonstrated; EGR1 is induced by IFN-␥ in mouse fibroblasts and by IFN-␣, -␤, and -␥ in human fibroblasts (31, 32) . EGR1 and the UPR pathway were selected for further analysis, as their role in VEEV infection has not been elucidated. The RNA-Seq and pathway analysis data indicated that UPR and stress response genes were induced after VEEV infection. During an infection, host cells respond to cellular stresses resulting from increased viral protein translation and secretion by triggering the onset of the UPR pathway. The UPR pathway is an adaptive cellular response activated by endoplasmic reticulum (ER) stress due to protein misfolding. In order to regulate cellular homeostasis during protein folding and secretion, the UPR pathway has developed three classes of sensors to ensure proper cellular regulation: inositolrequiring enzyme 1 (IRE1), protein kinase RNA-like ER kinase (PERK), and activating transcription factor 6 (ATF6) (33, 34) . During VEEV infection, the PERK arm of the UPR appeared to be altered, as two critical regulators of this pathway were differentially expressed: ATF4 and CHOP (DDIT3) (35) . To determine if DEGs altered subsequent protein expression, Western blot analysis was performed for CHOP, ATF4, and phosphorylated eIF2␣ (p-eIF2␣). Tunicamycin, a glycosylation inhibitor and inducer of UPR (36) , was included as a positive control. A time course analysis of U87MG cells treated with 1 M tunicamycin indicated that 8 h of treatment provided the most robust induction of UPR proteins (data not shown). VEEV-infected but not mock-infected or UV-inactivated VEEV (UV-VEEV)-infected cells displayed a dramatic increase in p-eIF2␣ expression and a modest but consistent increase in CHOP and ATF4 expression at 16 hpi (Fig. 6A) . No change in protein expression was observed at 4 hpi (data not shown). Confocal microscopy confirmed CHOP and ATF4 up- regulation, demonstrating a more robust and nuclear staining pattern in VEEV-infected cells than in mock-infected cells (Fig. 6C to E). While ATF4 protein expression levels increased, ATF4 mRNA abundances decreased following VEEV infection ( Fig. 5B and D). These results are consistent with the observation that ATF4 expression is regulated at the translational level upon UPR induction (37) . As eIF2␣ can be phosphorylated by multiple kinases (PERK, protein kinase double-stranded RNA dependent [PKR], general control nonderepressible-2 [GCN2], and hemeregulated inhibitor [HRI]) (38) , the PERK inhibitor (PERKi) GSK2606414 was used to determine if the observed phosphorylation was PERK dependent. Treatment of VEEV-infected cells with PERKi resulted in a marked decrease in eIF2␣ phosphorylation (Fig. 6B) . These results indicate that PERK contributes to eIF2␣ phosphorylation but that there is likely an additional kinase contributing to the phosphorylation event. Collectively, these findings indicate that the PERK arm of the UPR pathway is induced at later time points following VEEV infection. EGR1 is upregulated in infected cells and localizes to the nucleus. EGR1 is a transcription factor that can be induced by numerous signals, including oxidative stress, hypoxemia, and growth factors (39, 40) . It can also be activated upon infection by both DNA and RNA viruses, including Epstein-Barr virus, mouse hepatitis virus, murine coronavirus, and Japanese encephalitis virus (41) (42) (43) . Treatment of MEFs with the UPR activator thapsigargin has been shown to induce EGR1 expression in a PERK-dependent manner (44) . Given the link between EGR1 and UPR and the robust induction of EGR1 mRNA expression following VEEV infection ( Fig. 4 and 5) , EGR1 was chosen for further study. EGR1 protein expression after VEEV infection was analyzed by Western blot analysis. As previous studies have indicated that EGR1 can be activated by mouse hepatitis virus independently of virus replication (likely due to cellular membrane disruption following entry) (41), a UV-inactivated virus control (UV-VEEV) was included. EGR1 protein levels were increased following VEEV infection compared to those in mock-infected cells and UV-VEEV-infected cells (Fig. 7A; compare lanes 3, 6, and 9 ). The most dramatic upregulation of EGR1 occurred at 16 hpi; this correlates with the highest levels of VEEV capsid production (Fig. 1B) . Following induction, EGR1 has been shown to translocate to the nucleus to induce gene expression through binding to the Egr binding sequence (EBS) [GCG(G/T)GGCG] (40, 45) . Confocal microcopy revealed high levels of EGR1 in the nuclei of infected cells, whereas only low levels of both nuclear and cytoplasmic EGR1 were detected in mock-infected cells (Fig. 7B) . PERKi treatment of VEEV-infected cells resulted in a complete loss of EGR1 induction (Fig. 7C) , indicating that EGR1 was induced in a PERK-dependent fashion. These results demonstrate that EGR1 protein levels and nuclear localization are increased following VEEV infection and that the induction of EGR1 is dependent on PERK. The loss of EGR1 inhibits VEEV-induced apoptosis but does not alter VEEV replication kinetics. As EGR1 influences cell survival and apoptosis (46) , the impact of EGR1 on VEEV-induced cell death was assessed. Caspase 3 cleavage was observed in WT MEFs at 24 hpi when they were infected at an MOI of 0.5 and started as early as 16 hpi when they were infected at an MOI of 5 (Fig. 8A ). In contrast, EGR1 Ϫ/Ϫ cells showed little to no detectable caspase cleavage following infection with VEEV. Two sets of experiments were performed to quantitatively confirm these results: CellTiter Glo assays to measure total cell viability (ATP production) and Caspase 3/7 Glo assays to measure caspase 3/7 activity. Both WT and EGR1 Ϫ/Ϫ MEFs displayed dose-dependent decreases in cell viability following VEEV infection, with EGR1 Ϫ/Ϫ cells having significantly more viable cells at each MOI examined (Fig. 8B) . Concordantly, a dose-dependent increase in caspase 3/7 activity was observed following VEEV infection, with EGR1 Ϫ/Ϫ cells demonstrating reduced caspase 3 activity at MOIs of 0.5 and 5 (Fig. 8C) . These results were replicated in U87MG cells transfected with siRNA targeting EGR1 (Fig. 8D) . EGR1 has been shown to negatively regulate the transcription of BIRC5 (survivin), an inhibitor of apoptosis (IAP) family member (47) . RNA-Seq data indicated that BIRC5 gene expression was decreased following VEEV infection: log 2 -transformed fold change values of normalized gene expression were Ϫ1.16, Ϫ1.18, and Ϫ1.50 at 4, 8, and 16 hpi, respectively (see Table S1 in the supplemental material and NCBI BioProject accession number PRJNA300864). WT and EGR1 Ϫ/Ϫ MEFs were used to determine if EGR1 influenced BIRC5 gene expression following VEEV infection. BIRC5 expression was significantly decreased at 16 hpi in VEEV-infected WT MEFs, but this reduction was not observed in VEEV-infected EGR1 Ϫ/Ϫ MEFs (Fig. 8E) . Ex-pression of the gene for the X-linked inhibitor of apoptosis (XIAP), another IAP family member, was not significantly differentially altered after infection (data not shown). Collectively, these results demonstrate that EGR1 contributes to VEEV-induced apoptosis. VEEV replication kinetics were determined for both EGR1 Ϫ/Ϫ and WT MEFs to determine the relevance of EGR1 in viral replication. Cells were infected at two different MOIs (0.5 and 5), and viral supernatants were collected at 4, 8, 16, and 24 hpi and analyzed by plaque assay. The replication kinetics were similar between EGR1 Ϫ/Ϫ and WT MEFs at both MOIs, with titers peaking at 16 hpi (Fig. 9A) . A lack of EGR1 expression was confirmed by Western blotting (Fig. 9B) . These results were replicated in U87MG cells transfected with siRNA targeting EGR1. Transfection of siRNA targeting EGR1 resulted in a Ͼ90% decrease in EGR1 protein expression (Fig. 9D ) without any significant effect on viral replication (Fig. 9C) . These results suggest that the decrease in apoptosis observed in EGR1 Ϫ/Ϫ MEFs was not due to altered VEEV replication kinetics. Despite being recognized as an emerging threat, relatively little is known about the virulence mechanisms of alphaviruses, largely due to a knowledge gap in the host-pathogen interactome. VEEV infection often results in fatal encephalitis and is known to inhibit both cellular transcription and translation in order to downregulate the innate immune response (1, 48) . In contrast, in the CNS VEEV has been shown to upregulate numerous genes in both the inflammatory response and apoptotic pathways (1, 48) . Specifically, numerous proinflammatory cytokines, including interleu-kin-1␤ (IL-1␤), IL-6, IL-12, glycogen synthase kinase 3␤, inducible nitric oxide synthase, and tumor necrosis factor alpha (TNF-␣), have all been shown to play a role in VEEV pathogenesis (49) (50) (51) (52) (53) . The use of high-throughput next-generation sequencing technologies, such as RNA-Seq, allows an in-depth and unbiased look into the virus-host transcriptome, thus enabling changes in the expression of specific mRNAs to be connected with phenotypic outcomes. To this end, identification of critical differentially expressed transcripts among clinically relevant infected cells will help lead to a greater understanding of viral pathogenesis and may prove beneficial for the identification of therapeutic targets. In this study, network analysis/RNA-Seq data and the results of protein expression studies revealed that VEEV infection resulted in activation of the PERK arm of the UPR pathway, including the activation of ATF4, CHOP, and eIF2␣ phosphorylation. Several alphaviruses have previously been reported to hijack key components of the UPR pathway in order to promote viral replication, as the reliance of enveloped viruses on the ER for the synthesis of viral envelope-associated glycoproteins and their transport to the plasma membrane often stresses the ER due to rapid viral protein production (54, 55) . Modulation of the UPR is not unique to alphaviruses; rather, it is a shared trait of many positive-sense RNA viruses. Dengue virus has been shown to suppress PERK by inhibiting continued eIF2␣ phosphorylation in order to inhibit immediate apoptosis, increasing viral protein translation and extending the length of productive viral replication (34) . Studies with hepatitis E virus (HEV) have demonstrated that expression of HEV capsid protein open reading frame 2 (ORF2) activates the expression of CHOP and ATF4 (56) . In HEV, ORF2 was shown to stimulate CHOP through both ER stressors and amino acid response elements (AARE) through interaction with ATF4 (56) . The results shown here indicate that during VEEV infection, initiation of the UPR pathway and subsequent activation of EGR1 play a role in the outcome of virus-induced apoptosis. During the initial detection of ER stress, PERK is able to identify misfolded proteins in the lumen of the ER and phosphorylates eIF2␣ in order to initiate prosurvival pathways in the UPR through the general At 24 hpi caspase 3/7 activity was analyzed using the Caspase 3/7 Glo assay. The fold change values for mock-infected cells were set to a value of 1. **, P Ͻ 0.001. (E) EGR1 Ϫ/Ϫ and WT MEFs were mock or VEEV infected (MOI, 5). RNA was prepared, and gene expression was determined by qRT-PCR using a TaqMan assays for BIRC5 (survivin). The data shown are the values of the fold change of normalized gene expression determined by the ⌬⌬C T threshold cycle (C T ) method. *, P Ͻ 0.005 (comparison of VEEV-infected WT and EGR1 Ϫ/Ϫ cells). inhibition of protein synthesis (33, 34) . VEEV appears to induce the UPR and promote increased eIF2␣ phosphorylation, which results in the translational inhibition of most mRNAs, while UPR selectively increases the translation of ATF4. ATF4 is responsible for the expression of genes that encode proteins involved in apoptosis, redox processes, amino acid metabolism, and ER chaperone recruitment and is a well-known mediator of the PERK pathway and CHOP (33, 34) . CHOP activation facilitates the increased expression of cellular chaperones in order to counteract the buildup of misfolded proteins (57) . Failure to suppress protein misfolding in persistently stressed cells, such as during a viral infection, can then result in activation of the proapoptotic transcription factor CHOP, leading to suppression of the antiapoptotic protein B cell lymphoma-2 (Bcl-2). CHOP can also function as a prosurvival transcription factor by dephosphorylating eIF2␣ through activation of the DNA damage-inducible protein (GADD34) in a self-regulating feedback look (33, 34) . However, the data presented here support a model whereby VEEV infection leads CHOP to function in its proapoptotic role, as no change in GADD34 gene expression was detected by RNA-Seq analysis. While the UPR was induced following VEEV infection, robust activation was not observed until later time points after infection. This is somewhat surprising, as VEEV infection is expected to induce significant ER stress due to the massive production of viral proteins during the course of an acute robust infection. The structural proteins of VEEV are translated from the viral subgenomic RNA into polyproteins on the rough ER. The E1 and pE2 precur-sor glycoproteins are then assembled as heterodimers in the ER, undergoing conformational changes requiring numerous chaperones (1, 58) . It is possible that VEEV has developed mechanisms to subvert the induction of the UPR. In order to counteract the UPR, the nonstructural proteins (nsPs) of Chikungunya virus (CHIKV) have been shown to inhibit expression of ATF4 and other known UPR target genes, including GRP78/BiP, GRP94, and CHOP (59) . Through nsP activity, CHIKV has developed a means of suppressing the UPR activity resulting from viral glycoprotein-induced ER stress, thus preventing immediate autophagy and apoptotic activation. The VEEV capsid is responsible for interfering with nucleocytoplasmic trafficking and inhibiting rRNA and mRNA transcription and has been implicated in the regulation of type I IFN signaling and the antiviral response through the regulation of both viral RNA and protein production (1, 48, 60) . Therefore, we hypothesize that the ability of the VEEV capsid to inhibit cellular transcription and block nucleocytoplasmic trafficking results in delayed induction of the UPR. The results of a detailed network analysis based on existing data in the literature, coupled with the temporal gene expression profiles obtained from this study, point toward EGR1 being an important node in the novel link between VEEV activation of the type I interferon response and UPR. EGR1 is known to form a DNA binding complex with C/EBPB, a critical dimerization partner of CHOP (61) . Previous studies have demonstrated that the nuclear localization of CHOP may act as an inducer of EGR1 and that CHOP may act as a transcriptional cofactor for regulation of C/EBPB-EGR1 target genes (61) . The results of the Western blot and microscopy analysis presented in this study support this model, as VEEV infection was found to increase both the overall levels and the nuclear distribution of CHOP along with those of EGR1. Previous studies demonstrated EGR1 mRNA induction by IFN-␥ in mouse fibroblasts and by TNF-␣, TNF-␤, IL-1, IFN-␣, IFN-␤, and IFN-␥ in human fibroblasts (31, 32) . EGR1, also known as Zif268 and NGF1-A, is a zinc finger protein and mammalian transcription factor. It has been implicated in cellular proliferation and differentiation, but it may also have proapoptotic functions, depending on the cell type and stimulus (62) . Of particular interest, EGR1 directly controls proliferation when activated by the mitogen-activated protein kinase/extracellular signal-regulated kinase pathway in mitogen-stimulated astrocytes (63) . Virus-induced changes in EGR1 expression have been observed in several in vitro systems. In HIV-1-infected astrocytes, EGR1 upregulation was found to be induced by Tat through transactivation of the EGR1 promoter, leading to cellular dysfunction and Tat-induced neurotoxicity (64) . Increased amounts of EGR1 mRNA have also been demonstrated to act in a region-specific manner, corresponding temporally with viral RNA production in the brain tissues of rats infected with either rabies virus or Borna disease virus (65) . In summary, the current study demonstrates a potential link between UPR activation and EGR1. EGR1 Ϫ/Ϫ MEFs demonstrated lower levels of susceptibility to VEEV-induced cell death than wild-type MEFs, indicating that EGR1 modulates proapoptotic pathways following infection. Studies are under way to determine if alteration of the UPR through small molecule inhibitors or siRNA interference influences VEEV replication and/or cell death. To date the mechanisms underlying VEEV pathogenesis and subsequent neuronal degeneration have been only partially elucidated. Therefore, determining the role of EGR1 and UPR may play a significant role in the development of a novel therapeutic target resulting in decreased neuronal death and the subsequent neuronal sequelae that result from infection.

What is EGR1?

Responding to the COVID-19 pandemic in complex humanitarian crises https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7085188/ SHA: d013e42811c6442b184da3b9bbfd9e334031a975 Authors: Poole, Danielle N.; Escudero, Daniel J.; Gostin, Lawrence O.; Leblang, David; Talbot, Elizabeth A. Date: 2020-03-21 DOI: 10.1186/s12939-020-01162-y License: cc-by Abstract: nan Text: Over 168 million people across 50 countries are estimated to need humanitarian assistance in 2020 [1] . Response to epidemics in complex humanitarian crisessuch as the recent cholera epidemic in Yemen and the Ebola epidemic in the Democratic Republic of Congois a global health challenge of increasing scale [2] . The thousands of Yemeni and Congolese who have died in these years-long epidemics demonstrate the difficulty of combatting even well-known pathogens in humanitarian settings. The novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) may represent a still greater threat to those in complex humanitarian crises, which lack the infrastructure, support, and health systems to mount a comprehensive response. Poor governance, public distrust, and political violence may further undermine interventions in these settings. Populations affected by humanitarian crises are expected to be particularly susceptible to COVID-19, the disease caused by SARS-CoV-2, due to displacement, crowded housing, malnutrition, inadequate water, sanitation, and hygiene (WASH) tools, and stigmatization. Disease outbreaks further reduce access to limited healthcare, which is increasingly disrupted by attacks on health facilities and the persistent overburdening of health systems. These situations escalate both the necessity and the difficulty of delivering accurate and actionable information to potentially affected populations [3] . As the international community responds to SARS-CoV-2, public health authorities in humanitarian crises begin at a disadvantage to enact appropriate infection control to prevent transmission in healthcare settings, identify infectious cases, administer supportive care and novel treatments for the seriously ill, and trace contacts. These standard public health measures are particularly difficult to perform in humanitarian settings. For example, limited public health, laboratory, and primary care services represent a barrier to testing. Providing the limited healthcare worker cadre with appropriate training and personal protective equipment, and ensuring a continuous supply chain for such, is a challenge in all settings, exacerbated in complex humanitarian crises. Frequent displacement and limited contact information may prevent effective contact tracing. Finally, intractable structural challenges such as overcrowding limit the implementation of both quarantine of those exposed and isolation of those who are ill. Given these increased vulnerabilities, humanitarian crises should be viewed as a priority for national and international bodies that seek to combat this unfolding pandemic. Resources must be identified to protect healthcare workers, develop and deploy rapid testing, improve surveillance, and enact quarantine and isolation of contacts and cases. To mitigate the impact of COVID-19 on crisesaffected populations, governments and agencies will implement the familiar, global evidence-based approaches for combatting respiratory viruses. Respiratory hygiene is a highly effective public health intervention, supported by evidence demonstrating that the spread of respiratory viruses, such as SARS-CoV-2, can be prevented by hand hygiene, safe cough practice, and social distancing [4] . Hand hygiene is a readily implemented behavior: the distribution of soap to households in humanitarian settings has been shown to increase handwashing by over 30% [5] . Furthermore, hand hygiene is an avenue of agency for protecting one's own health, consistent with the rights to dignity and to fully participate in decisions related to assistance in humanitarian crises. Widespread introduction of alcohol-based hand rubs is also possible in many resource-limited settings, with published protocols for local production [6] . The Sphere Handbook, a collection of rights-based guidelines for humanitarian response, is the foremost authority on minimum standards for humanitarian assistance [7] . However, despite the indisputable evidence for the efficacy of hand hygiene for reducing both bacterial and viral pathogen transmission, humanitarian WASH standards are based on evidence pertaining to the prevention of illnesses transmitted by the faecal-oral route, with the focus on hand hygiene proximate to latrines [5, 8] . And yet, latrines in crisis settings are often shared and distant from residential shelters, conferring a high risk of gender-based violence [9] . Gender-based violence around latrines is an important deterrent for accessing latrine-adjacent handwashing stations, particularly for hand hygiene to prevent respiratory pathogen transmission. Evidence-based guidelines alone in complex humanitarian crises may not suffice during the emergence of the current SARS-CoV-2 pandemic. Without the adaptation of existing standards, mitigation plans will fall short of health and human rights obligations in outbreak response. Crisis-affected community engagement is integral in pandemic planning, in order to maximize the real-world effectiveness of efficacious interventions. Transparent and credible information-sharing mechanisms are increasingly essential when pandemics threaten vulnerable populations [10] . Diplomacy bridging long-standing mistrust of public health and biomedical interventions and facilitating engagement with contentious actors is a necessary component of effective health governance in complex crisis settings [2] . Interventions tailored to the needs of crisis-affected populations, delivered with transparent information, in the context of inclusive governance practices, are urgently needed in the global response to the COVID-19 pandemic.

What resources need to be identified?

Nucleolar Protein Trafficking in Response to HIV-1 Tat: Rewiring the Nucleolus https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3499507/ SHA: efa871aeaf22cbd0ce30e8bd1cb3d1afff2a98f9 Authors: Jarboui, Mohamed Ali; Bidoia, Carlo; Woods, Elena; Roe, Barbara; Wynne, Kieran; Elia, Giuliano; Hall, William W.; Gautier, Virginie W. Date: 2012-11-15 DOI: 10.1371/journal.pone.0048702 License: cc-by Abstract: The trans-activator Tat protein is a viral regulatory protein essential for HIV-1 replication. Tat trafficks to the nucleoplasm and the nucleolus. The nucleolus, a highly dynamic and structured membrane-less sub-nuclear compartment, is the site of rRNA and ribosome biogenesis and is involved in numerous cellular functions including transcriptional regulation, cell cycle control and viral infection. Importantly, transient nucleolar trafficking of both Tat and HIV-1 viral transcripts are critical in HIV-1 replication, however, the role(s) of the nucleolus in HIV-1 replication remains unclear. To better understand how the interaction of Tat with the nucleolar machinery contributes to HIV-1 pathogenesis, we investigated the quantitative changes in the composition of the nucleolar proteome of Jurkat T-cells stably expressing HIV-1 Tat fused to a TAP tag. Using an organellar proteomic approach based on mass spectrometry, coupled with Stable Isotope Labelling in Cell culture (SILAC), we quantified 520 proteins, including 49 proteins showing significant changes in abundance in Jurkat T-cell nucleolus upon Tat expression. Numerous proteins exhibiting a fold change were well characterised Tat interactors and/or known to be critical for HIV-1 replication. This suggests that the spatial control and subcellular compartimentaliation of these cellular cofactors by Tat provide an additional layer of control for regulating cellular machinery involved in HIV-1 pathogenesis. Pathway analysis and network reconstruction revealed that Tat expression specifically resulted in the nucleolar enrichment of proteins collectively participating in ribosomal biogenesis, protein homeostasis, metabolic pathways including glycolytic, pentose phosphate, nucleotides and amino acids biosynthetic pathways, stress response, T-cell signaling pathways and genome integrity. We present here the first differential profiling of the nucleolar proteome of T-cells expressing HIV-1 Tat. We discuss how these proteins collectively participate in interconnected networks converging to adapt the nucleolus dynamic activities, which favor host biosynthetic activities and may contribute to create a cellular environment supporting robust HIV-1 production. Text: The nucleolus is a highly ordered subnuclear compartment organised around genetic loci called nucleolar-organising regions (NORs) formed by clusters of hundreds of rDNA gene repeats organised in tandem head-to-tail repeat [1, 2] . A membrane-less organelle originally described as the ''Ribosome Factory'', the nucleolus is dedicated to RNA-polymerase-I-directed rDNA transcription, rRNA processing mediated by small nucleolar ribonucleoproteins (soRNPs) and ribosome assembly. Ribosome biogenesis is essential for protein synthesis and cell viability [2] and ultimately results in the separate large (60S) and small (40S) ribosomal subunits, which are subsequently exported to the cytoplasm. This fundamental cellular process, to which the cell dedicates most of its energy resources, is tightly regulated to match dynamic changes in cell proliferation, growth rate and metabolic activities [3] . The nucleolus is the site of additional RNA processing, including mRNA export and degradation, the maturation of uridine-rich small nuclear RNPs (U snRNPs), which form the core of the spliceosome, biogenesis of t-RNA and microRNAs (miRNAs) [4] . The nucleolus is also involved in other cellular processes including cell cycle control, oncogenic processes, cellular stress responses and translation [4] . The concept of a multifunctional and highly dynamic nucleolus has been substantiated by several studies combining organellar proteomic approaches and quantitative mass spectrometry, and describing thousands of proteins transiting through the nucleolus in response to various metabolic conditions, stress and cellular environments [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16] . Collectively, the aforementioned studies represent landmarks in understanding the functional complexity of the nucleolus, and demonstrated that nucleolar proteins are in continuous exchange with other nuclear and cellular compartments in response to specific cellular conditions. Of importance, the nucleolus is also the target of viruses including HIV-1, hCMV, HSV and KSHV, as part of their replication strategy [2, 17] . Proteomics studies analysing the nucleoli of cells infected with Human respiratory syncytial virus (HRSV), influenza A virus, avian coronavirus infectious bronchitis virus (IBV) or adenovirus highlighted how viruses can distinctively disrupt the distribution of nucleolar proteins [2, 17, 18, 19, 20, 21, 22, 23, 24] . Interestingly, both HIV-1 regulatory proteins Tat and Rev localise to the nucleoplasm and nucleolus. Both their sequences encompass a nucleolar localisation signal (NoLS) overlapping with their nuclear localisation signal (NLS), which governs their nucleolar localisation [25, 26, 27, 28, 29, 30, 31] . Furthermore, Tat and Rev interact with the nucleolar antigen B23, which is essential for their nucleolar localisation [25, 26, 27, 28, 29, 30] . Nevertheless, a recent study described that in contrast to Jurkat T-cells and other transformed cell lines where Tat is associated with the nucleus and nucleolus, in primary T-cells Tat primarily accumulates at the plasma membrane, while trafficking via the nucleus where it functions [32] . While the regulation of their active nuclear import and/or export, as mediated by the karyopherin/importin family have been well described, the mechanisms distributing Tat and Rev between the cytoplasm, nucleoplasm and the nucleolus remains elusive [33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48] . Importantly, two major studies by Machienzi et al. have revealed important functional links between HIV-1 replication and the nucleolus [49, 50] . First, they could inhibit HIV-1 replication and Tat transactivation function employing a TAR decoy specifically directed to the nucleolus. Furthermore, using a similar approach, with an anti-HIV-1 hammerhead ribozyme fused to the U16 small nucleolar RNA and therefore targeted to the nucleolus, they could dramatically suppress HIV-1 replication. Collectively, these findings strongly suggest that HIV-1 transcripts and Tat nucleolar trafficking are critical for HIV-1 replication. However the nature of these contributions remains to be elucidated. In this report, we systematically analysed the nucleolar proteome perturbations occurring in Jurkat T-cells constitutively expressing HIV-1 Tat, using a quantitative mass spectrometry approach. Following the detailed annotation of the quantitative abundance changes in the nucleolar protein composition upon Tat expression, we focussed on the Tat-affected cellular complexes and signalling pathways associated with ribosome biogenesis, spliceosome, molecular chaperones, DNA replication and repair and metabolism and discuss their potential involvement in HIV-1 pathogenesis. In this study, we investigated the quantitative changes in the nucleolar proteome of Jurkat T cells constitutively expressing HIV-1 Tat (86aa) versus their Tat-negative counterpart, using stable isotope labelling with amino acids in cell culture (SILAC) technology, followed by ESI tandem mass spectrometry and implemented the experimental approach described in Figure 1A . First, using retroviral gene delivery, we transduced HIV-1 Tat fused to a tandem affinity purification (TAP) tag (consisting of two protein G and a streptavidin binding peptide) or TAP tag alone (control vector) in Jurkat leukemia T cell clone E6-1 and sorted the transduced cells (GFP positive) by FACS. This resulted in a highly enriched population of polyclonal transduced cells presenting different expression levels of the transgene ( Figure 1B) . The functionality of TAP-Tat was confirmed by transfecting Jurkat TAP-Tat and TAP cells with a luciferase reporter gene vector under the control of the HIV-1 LTR (pGL3-LTR) [36] . TAP-Tat up regulated gene expression from the HIV-1 LTR by up to 28 fold compared to control ( Figure 1C ). To further address the functionality of Tat fused to TAP, we compared Jurkat TAP-Tat with Jurkat-tat, a cell line stably expressing untagged Tat [51] . Both cell line exhibited comparable HIV-1 LTR activity following transfection with pGL3-LTR ( Figure S1 ). Next, Tat expression and subcellular localization was verified by subcellular fractionation followed by WB analysis ( Figure 1E ). TAP-Tat displayed a prominent nuclear/nucleolar localization but could also be detected in the cytoplasm. These observations were further validated by immunofluorescence microscopy ( Figure 1E ). Of note, Jurkat-tat presented similar patterns for Tat subcellular distribution as shown by immunofluorescence microscopy and subcellular fractionation followed by WB analysis (Figure S2 and S3). We next compared the growth rate and proliferation of the Jurkat TAP and TAP-Tat cell lines (Materials and Methods S1), which were equivalent ( Figure S4A ). Similarly, FACS analysis confirmed that the relative populations in G1, S, and G2/M were similar for Jurkat TAP-Tat and TAP cells ( Figure S4B ). We labeled Jurkat TAP-Tat and Jurkat TAP cells with light (R0K0) and heavy (R6K6) isotope containing arginine and lysine, respectively. Following five passages in their respective SILAC medium, 85 million cells from each culture were harvested, pooled and their nucleoli were isolated as previously described ( Figure 1A ) [52] . Each step of the procedure was closely monitored by microscopic examination. To assess the quality of our fractionation procedure, specific enrichment of known nucleolar antigens was investigated by Western Blot analysis ( Figure 1D ). Nucleolin (110 kDa) and Fibrillarin (FBL) (34 kDa), two major nucleolar proteins known to localise to the granular component of the nucleolus, were found to be highly enriched in the mixed nucleolar fraction. Of note, nucleolin was equally distributed between the nuclear and cytoplasmic fractions. This distribution pattern for nucleolin appears to be specific for Jurkat T-cells as show previously [52, 53] . The nuclear protein PARP-1 (Poly ADPribose polymerase 1) (113 kDa) was present in the nuclear and nucleoplasmic fraction but was depleted in the nucleolar fraction. Alpha-tubulin (50 kDa) was highly abundant in the cytoplasmic fraction and weakly detected in the nuclear fractions. Collectively, these results confirmed that our methods produced a highly enriched nucleolar fraction without significant cross contamination. Subsequently, the nucleolar protein mixture was trypsindigested and the resulting peptides were analysed by mass spectrometry. Comparative quantitative proteomic analysis was performed using MaxQuant to analyse the ratios in isotopes for each peptide identified. A total of 2427 peptides were quantified, representing 520 quantified nucleolar proteins. The fully annotated list of the quantified nucleolar proteins is available in Table S1 and the raw data from the mass spectrometry analysis was deposited in the Tranche repository database (https:// proteomecommons.org/tranche/), which can be accessed using the hash keys described in materials and methods. We annotated the quantified proteins using the ToppGene Suite tools [54] and extracted Gene Ontology (GO) and InterPro annotations [55] . The analysis of GO biological processes ( Figure 1F ) revealed that the best-represented biological processes included transcription (24%), RNA processing (23%), cell cycle process (13%) and chromosome organisation (15%), which reflects nucleolar associated functions and is comparable to our previous characterisation of Jurkat T-cell nucleolar proteome [52] . Subcellular distribution analysis ( Figure 1F ) revealed that our dataset contained proteins known to localise in the nucleolus (49%), in the nucleus (24%) while 15% of proteins were previously described to reside exclusively in the cytoplasm. The subcellular distribution was similar to our previous analysis of the Jurkat T-cell nucleolar proteome [52] . Table S1 . The distribution of protein ratios are represented in Figure 1G as log 2 (abundance change). The SILAC ratios indicate changes in protein abundance in the nucleolar fraction of Jurkat TAP-Tat cells in comparison with Jurkat TAP cells. The distribution of the quantified proteins followed a Gaussian distribution ( Figure 1G ). A total of 49 nucleolar proteins exhibited a 1.5 fold or greater significant change (p,0.05) upon Tat expression (Table 1) . Of these, 30 proteins were enriched, whereas 19 proteins were depleted. Cells displayed no changes in the steady state content of some of the major and abundant constituents of the nucleolus, including nucleophosmin (NPM1/ B23), C23, FBL, nucleolar protein P120 (NOL1), and nucleolar protein 5A (NOL5A). The distinct ratios of protein changes upon Tat expression could reflect specific nucleolar reorganization and altered activities of the nucleolus. We performed WB analysis to validate the SILAC-based results obtained by our quantitative proteomic approach ( Figure 2 ). 15 selected proteins displayed differential intensity in the nucleolar fractions upon Tat expression, including 9 enriched (HSP90b, STAT3, pRb, CK2a, CK2a', HSP90a, Transportin, ZAP70, DDX3), and 3 depleted (ILF3, BOP1, and SSRP1) proteins. In addition, we also tested by WB analysis, protein abundance not affected by Tat expression (Importin beta, FBL, B23, C23). These results highlight the concordance in the trend of the corresponding SILAC ratios, despite some differences in the quantitative ranges. Of note, using WB, we could observe a change of intensity for protein with a SILAC fold change as low as 1.25-fold. Of note, the question remains as to which fold change magnitude might constitute a biologically relevant consequence. On the one hand, the threshold of protein abundance changes can be determined statistically and would then highlight the larger abundance changes as illustrated in Table 1 . Alternatively, the coordinated enrichment or depletion of a majority of proteins belonging to a distinct cellular complex or pathway would allow the definition of a group of proteins of interest and potential significance. Therefore, we next focused on both enriched or depleted individual proteins with activities associated with HIV-1 or Tat molecular pathogenesis, and on clustered modifications affecting entire cellular signaling pathways and macromolecular complexes. We initially focused on signaling proteins interacting with Tat and/or associated HIV-1 molecular pathogenesis and whose abundance in the nucleolus was modulated by Tat expression. Phospho-protein phosphatases. Phospho-protein phosphatase PP1 and PP2A are essential serine/threonine phosphatases [56, 57] . Importantly, PP1 accounts for 80% of the Ser/Thr phosphatase activity within the nucleolus. In our study, PP1 was found to be potentially enriched by 1.52-fold in the nucleolus of Jurkat cells expressing Tat, which supports previous studies describing the nuclear and nucleolar targeting of PP1a by HIV-1 Tat and how PP1 upregulates HIV-1 transcription [58, 59, 60, 61, 62] . PP1 c was also identified as part of the in vitro nuclear interactome [63] . Similarly, PPP2CA, the PP2A catalytic subunit (1.29-fold) and its regulatory subunit PP2R1A (1.27-fold) were similarly enriched upon Tat expression. Interestingly, Tat association with the PP2A subunit promoters results in the overexpression and up regulation of PP2A activity in lymphocytes [64, 65] . Furthermore, PP2A contributes to the regulation of HIV-1 transcription and replication [61, 66] . Retinoblastoma Protein. The tumour suppressor gene pRb protein displayed a 1.4-fold change in the nucleolus upon Tat expression [67] . Furthermore, WB analysis confirmed the distinct translocation of pRb from the nucleoplasm to the nucleolus by Tat ( Figure 2 ). Depending on the cell type, pRb can be hyperphosphorylated or hypophosphorylated upon Tat expression and can negatively or positively regulate Tat-mediated transcription respectively [68, 69, 70] . Interestingly, the hyperphosphorylation of pRB triggers in its translocation into the nucleolus [71] . Phosphorylation of pRB is also associated with an increase in ribosomal biogenesis and cell growth [72] . STAT3. The transcription factor signal transducer and activator of transcription 3 (STAT3) was significantly enriched (1.86-fold) in the nucleolar fraction by Tat constitutive expression. Furthermore, WB analysis indicated that Tat expression could promote the relocalisation of STAT3 from the cytoplasm to the nucleus, with a distinct enrichment in the nucleolus ( Figure 2) . Interestingly, previous studies have demonstrated Tat-mediated activation of STAT3 signaling, as shown by its phosphorylation status [73] . Interestingly, STAT3 phosphorylation induced dimerisation of the protein followed its translocation to the nucleus [74] . YBX1. YBX1, the DNA/RNA binding multifunctional protein was enriched by 1.38-fold in the nucleolus of Jurkat cells upon Tat expression. Interestingly, YBX1 interacts with Tat and TAR and modulates HIV-1 gene expression [63, 75] . ZAP70. The protein tyrosine kinase ZAP70 (Zeta-chainassociated protein kinase 70) was enriched by 1.24-fold in the nucleolus of Jurkat cells expressing Tat [76] . Furthermore, WB analysis revealed that Tat expression could promote the relocalisation of ZAP70 from the cytoplasm to the nucleus, with a distinct enrichment in the nucleolus ( Figure 2 ). Of note, ZAP70 is part of the in vitro nuclear Tat interactome [63] . Matrin 3. The inner nuclear matrix protein, Matrin 3 (MATR3), presented a 1.39-fold change in the nucleolus of Jurkat cells expressing Tat. It localizes in the nucleolasm with a diffuse pattern excluded from the nucleoli [77] . Matrin 3 has been identified as part of the in vitro HIV-1 Tat nuclear interactome [63] . Two recent studies have described Matrin 3 as part of ribonucleoprotein complexes also including HIV-1 Rev and (Rev Response Element) RRE-containing HIV-1 RNA, and promoting HIV-1 post-transcriptional regulation [78, 79, 80] . CASP10. The pro-apototic signaling molecule, Caspase 10 (CASP10), was significantly depleted from the nucleolus of Jurkat-Tat cells (0.82-fold) [81] . Importantly, Tat expression downregulates CASP10 expression and activity in Jurkat cells [82] . ADAR1. Adenosine deaminase acting on RNA (ADAR1), which converts adenosines to inosines in double-stranded RNA, was significantly depleted from the nucleolus of Jurkat-Tat cells (0.78-fold). Interestingly, ADAR1 over-expression up-regulates HIV-1 replication via an RNA editing mechanism [83, 84, 85, 86, 87, 88] . Furthermore, ADAR1 belongs to the in vitro HIV-1 Tat nuclear interactome [63] . To underline the structural and functional relationships of the nucleolar proteins affected by HIV-1 Tat, we constructed a network representation of our dataset. We employed Cytoscape version 2.6.3 [89] and using the MiMI plugin [90] to map previously characterised interactions, extracted from protein interaction databases (BIND, DIP, HPRD, CCSB, Reactome, IntAct and MINT). This resulted in a highly dense and connected network comprising 416 proteins (nodes) out of the 536 proteins, linked by 5060 undirected interactions (edges) ( Figure 3A ). Centrality analysis revealed a threshold of 23.7 interactions per protein. Topology analysis using the CentiScaPe plugin [91] showed that the node degree distribution follows a power law ( Figure S5 ), characteristic of a scale-free network. Importantly, when we analysed the clustering coefficient distribution ( Figure S6 ) we found that the network is organised in a hierarchical architecture [92] , where connected nodes are part of highly clustered areas maintained by few hubs organised around HIV-1 Tat. Furthermore, node degree connection analysis of our network identified HIV-1 Tat as the most connected protein ( Figure S6 ). Specifically, the topology analysis indicated that the values for Tat centralities were the highest (Node degree, stress, radiality, closeness, betweeness and centroid), characterising Tat as the main hub protein of the nucleolar network. Indeed, a total of 146 proteins have been previously described to interact with Tat ( Figure 3B , Table S2 ). These proteins are involved in a wide range of cellular processes including chromosomal organization, DNA and RNA processing and cell cycle control. Importantly, aver the third of these proteins exhibit an increase in fold ratio change (59 proteins with a ratio .1.2 fold). In parallel, we characterised the magnitude of the related protein abundance changes observed in distinct cellular pathways ( Figure 4) . Ribosomal biogenesis. We initially focused on ribosome biogenesis, the primary function of the nucleolus. We could observe a general and coordinated increase in the abundance of ribosomal proteins in the nucleolus by Tat expression (Figure 4 ). While some ribosomal proteins remained unaffected, Tat caused the nucleolar accumulation of several distinct large and small ribosomal proteins, except RPL35A, for which Tat expression caused a marked decrease at the nucleolar level (0.29-fold). Similarly, several proteins involved in rRNA processing exhibited an overall increase in nucleolar accumulation upon Tat expression. These include human canonical members of the L7ae family together with members participating in Box C/D, H/ACA and U3 snoRNPs ( Figure 4) . Conversely, BOP1, a component of the PeBoW (Pescadillo Bop1 WDR12) complex essential for maturation of the large ribosomal subunit, was significantly depleted from the nucleolus of Jurkat TAP-Tat cells (0.81-fold) and this was confirmed by WB analysis (Figure 2 ) [93] . Nevertheless, the other PeBoW complex components, Pes1 (0.94-fold) and WDR12 (1.1fold), were not affected by Tat expression. Of note, we did not detect change in the abundance of protein participating in rDNA transcription such as RNAPOLI, UBF. Spliceosome. We identified and quantified in our dataset 55 proteins out of the 108 known spliceosomal proteins [94] . These proteins include the small nuclear ribonucleoproteins U1, U2 and U5, Sm D1, D2, D3, F and B, and the heterogeneous nuclear ribonucleoproteins. Our data suggested a distinct increase in the abundance of specific spliceosome complex proteins upon expression of HIV-1 Tat in Jurkat T-cells (Figure 3 and 4) . The only three proteins that were significantly depleted from the nucleolus upon expression of HIV-1 Tat were RBMX (0.89-fold), HNRNPA2B1 (0.84-fold) and SNRPA (0.81-fold). Several investigations showed expression alteration in cellular splicing factors in HIV-1 infected cells [95, 96] . Molecular chaperones. We have identified several molecular chaperones, co-chaperones and other factors involved into proteostasis to be highly enriched in the nucleolus of T-cells upon Tat expression (Figure 3 and 4) , many of which were previously characterised as part of the Tat nuclear interactome [63] . Several heat-shock proteins including DNAJs, specific HSP90, HSP70 and HSP40 isoforms and their co-factors were distinctively enriched in the nucleolar fraction of Jurkat cells expressing Tat ( Figure 4 ). As shown by WB, while HSP90a and b are mostly cytoplasmic, Tat expression triggers their relocalisation to the nucleus and nucleolus, corroborating our proteomic quantitative approach (Figure 2) . Similarly, heat-shock can cause the HSP90 and HSP70 to relocalise to the nucleolus [97, 98, 99, 100, 101] . In a recent study, Fassati's group has shown that HSP90 is present at the HIV-1 promoter and may directly regulate viral gene expression [102] . We also observed the coordinated increased abundance of class I (GroEL and GroES) and class II (chaperonin containing TCP-1 (CTT)) chaperonin molecules (Figure 3 and 4) upon Tat expression. Ubiquitin-proteasome pathway. The ubiquitin-proteasome pathway is the major proteolytic system of eukaryotic cells [103] . Importantly, the nuclear ubiquitin-proteasome pathway controls the supply of ribosomal proteins and is important to ribosome biogenesis [104, 105] . The 26S proteasome is composed of the 20S core particle (CP) and the 19S regulatory particle (RP). Alternatively, CP can associate with the 11S RP to form the immunoproteasome. All the quantified proteins in our study are part of the 19S regulatory complex and include PSMD2 (1.5-fold), PSMD3 (1.32-fold), PSMD11 (1.25-fold) and PSMD13 (0.72-fold), the only proteasome component significantly depleted from the nucleolus in the presence of Tat (Figure 4) . Interestingly, Tat interacts with distinct subunits of the proteasome system, including the 19S, 20S and 11S subunits. The consequences of these interactions include the competition of Tat with 11S RP or 19S RP for binding to the 20S CP, which resulted in the inhibition of the 20S peptidase activity [106, 107, 108, 109, 110, 111] . Furthermore, Tat was shown to modify the proteasome composition and activity, which affects the generation of peptide antigens recognized by cytotoxic T-lymphocytes [112] . Importantly, a recent study demonstrated that in the absence of Tat, proteasome components are associated to the HIV-1 promoter and proteasome activity limits transcription [113] . Addition of Tat promoted the dissociation of the 19S subunit from the 20S proteasome, followed by the distinct enrichment of the 19S-like complex in nuclear extracts together with the Tat-mediated recruitment of the 19S subunits to the HIV-1 promoter, which facilitated its transcriptional elongation [113] . We also quantified UBA1 (1.36-fold), the E3 ubiquitin-protein ligase UHRF1 (1.13-fold), UBC (1-fold) and two Ubiquitinspecific-peptidases, USP30 (1.28-fold) and USP20 (0.06-fold) (Figure 4) . DNA replication and repair. Upon HIV-1 Tat expression, we observed the coordinated nucleolar enrichment of several cellular factors associated with DNA replication and repairs pathways (Figure 4) . Tat induced the coordinated enrichment of the miniature chromosome maintenance MCM2-7 complex (from 1.23-to 3.30fold, respectively) [114] . MCM7, 6 and 3 were identified as part of the in vitro nuclear interactome of HIV-1 Tat [63] . The structural maintenance of chromosomes 2, SMC2, was enriched (1.35-fold) in the nucleolar fraction by Tat expression. SMC2 was identified as part of the in vitro nuclear interactome of HIV-1 Tat [63] . While replication factor C1 (RFC1) and RFC2 (1.31-and 1.28-fold respectively) displayed an increased fold change and RFC5/3 were not affected, RFC4 was severely depleted (0.69-fold) from the nucleolar fraction upon Tat expression [115] . RFC1 and RFC2 were identified as part of the in vitro nuclear interactome of HIV-1 Tat [63] . Tat induced the enrichment of XRCC6 (1.27-fold) and XRCC5 (1.36-fold) in the nucleolus, which are involved in the repair of non-homologous DNA end joining (NHEJ) [116] . XRCC6 associates with viral preintegration complexes containing HIV-1 Integrase and also interact with Tat and TAR [117, 118, 119] . Furthermore, in a ribozyme-based screen, XRCC5 (Ku80) knockdown decreased both retroviral integration and Tatmediated transcription [120] . As part of the base excision repair (BER), we have identified a major apurinic/apyrimidinic endonuclease 1 (APEX1) (1.29-fold) . Importantly, in a siRNA screen targeting DNA repair factors, APEX1 knockdown was found to inhibit HIV-1 infection by more 60% [121] . The high mobility group (HMG) protein, HMGA1 (1.30-fold), was enriched in the nucleolus following Tat expression [122] . HMGA1 interact with HIV-1 Integrase and is part of the HIV-1 pre-integration complex [123, 124] . Importantly, HMGA1 has been identified in a proteomic screen, as a cellular cofactor interacting with the HIV-1 59leader [125] . Metabolism. Our proteomic data suggest that Tat induces perturbations in glycolysis, the pentose phosphate pathway, and nucleotide and amino acid biosynthesis (Figure 4 and Figure S7 ). Notably, in T cells expressing Tat, we detected co-ordinated changes in the abundance of proteins not previously known to be associated with Tat pathogenesis, which revealed unexpected connections with with glycolysis and the pentose phosphate pathway, including the following glycolitic enzymes, lactate dehydrogenase B (LDHB) (1.37-fold), glyceraldehyde 3-phosphate dehydrogenase (GAPDH) (1.17-fold) and phosphoglyceric acid mutase (PGAM1) (0.89-fold) ( Figure 4 and Figure S7 ). Briefly, GPI catalyzes the reversible isomerization of glucose-6-phosphate in fructose-6-phosphate. Subsequently, PFKP catalyzes the irreversible conversion of fructose-6-phosphate to fructose-1,6-bisphosphate and is a key regulatory enzyme in glycolysis. At the end of the glycolytic pathway, PKM2, in its tetrameric form, is known to generate ATP and pyruvate, while LDHB diverts the majority of the pyruvate to lactate production and regeneration of NAD+ in support to continued glycolysis, a phenomenon described for proliferative Tcells [126] . Of note, in highly proliferating cells, PKM2 can be found in its dimeric form and its activity is altered. This upregulates the availibility of glucose intermediates, which are rerouted to the pentose phosphate and serine biosynthesis pathways for the production of biosynthetic precursors of nucleotides, phospholipids and amino acids. As part of the pentose phosphate pathway, we have characterised the significant enrichment of glucose-6-phosphate dehydrogenase (G6PD) (2.11-fold), which branches of the glycolysis pathway to generate NADPH, ribose-5phosphate an important precursor for the synthesis of nucleotides. Consistent with this, we detected the coordinated increase in the abundance of enzymes which plays a central role in the synthesis of purines and pyrimidines. More specifically, IMPDH2 (1.66fold), a rate-limiting enzyme at the branch point of purine nucleotide biosynthesis, leading to the generation of guanine nucleotides, phosphoribosyl pyrophosphate synthetase 2 (PRPS2) (1.41-fold), cytidine-5-prime-triphosphate synthetase (CTPS) (1.74-fold) which catalyses the conversion of UTP to CTP and the ribonucleotide reductase large subunit (RRM1) (1.56-fold). In parralel, we noted the increased abundance of the phosphoserine aminotransferase PSAT1 (1.90-fold), an enzyme implicated in serine biosynthesis, which has been linked with cell proliferation in vitro. The host-virus interface is a fundamental aspect in defining the molecular pathogenesis of HIV-1 [127, 128, 129, 130, 131, 132, 133] . Indeed, with its limited repertoire of viral proteins, HIV-1 relies extensively on the host cell machinery for its replication. Several recent studies have capitalized on the recent advances in the ''OMICS'' technologies, and have revealed important insights into this finely tuned molecular dialogue [132, 134] . HIV-1 Tat is essential for viral replication and orchestrates HIV-1 gene expression. The viral regulatory protein is known to interact with an extensive array of cellular proteins and to modulate cellular gene expression and signaling pathway [135, 136] . We and others have employed system-level approaches to investigate Tat interplay with the host cell machinery, which have characterised HIV-1 Tat as a critical mediator of the host-viral interface [137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149] . Here, we have investigated the nucleolar proteins trafficking in response to HIV-1 Tat expression in T-cells, with the view to provide unique and novel insights on the role of proteins compartimentalisation by Tat in the fine-tuning of protein availability and function. We have developed for this study, a cellular model using Jurkat T-cells stably expressing Tat fused in its N-ternminal to TAP-tag. Jurkat T-cells are robust and present the advantage to grow without stimulations and are easely transduced using retroviral gene delivery. Importantly, they have been widely employed to evaluate Tat-mediated pathogenesis using system-wide approaches and to analyse T-cell key cellular signaling pathways and functions [144, 150, 151, 152] . Indeed, we have found them particularly suited for prolongued in vitro culture in SILAC medium and subsequent isolation of their nucleolus followed by MS analysis, which requires up to 85 millions of cells. We fused Tat to the TAP tag to enable future downstream applications such as Tandem affinity purification or Chromatin IP analysis. Importantly, we have confirm that N-terminal TAP-tag did not interfere with Tat function nor its localisation in Jurkat cells, when compared to untagged-Tat. Of note, Tat subcellular distribution can vary according to the cell type employed. While Tat is known to accumulate in the nucleus and nucleolus in Jurkat cells and other transformed cell lines, in primary T-cells, Tat was described to primarily accumulate at the plasma membrane, while trafficking via the nucleus where it functions [32] . These differences remain to be characterised but could be related to different expression levels of transport factors in transformed cell lines versus primary cells, as recently described by Kuusisto et al. [39] . Furthermore, Stauber and Pavlakis have suggested that Tat nucleolar localisation could be the results of Tat overexpression [31] . Here, we have selected and employed a polyclonal population of Jurkat T-cells expressing Tat at different levels. We propose that this heterogeneity in Tat expression levels might reflect Tat stochastic expression described during viral replication [153] . Using a quantitative proteomic strategy based on an organellar approach, we quantified over 520 nucleolar proteins, including 49 proteins exhibiting a significant fold change. The extent to which the induced variations in the abundance of nucleolar proteins are biologically relevant and can affect cellular and/or viral processes remains to be determined. Nevertheless, the biological nature of the pathways and macromolecular complexes affected enable us to discuss their potential associations with HIV-1 pathogenesis. HIV-1 Tat is expressed early following HIV-1 genome integration and mediates the shift to the viral production phase, associated with robust proviral gene expression, viral proteins assembly and ultimately, virions budding and release. In this context and based on our results, we propose that Tat could participate in shaping the intracellular environment and metabolic profile of T cells to favor host biosynthetic activities supporting robust virions production. Indeed, we observed the distinct nucleolar enrichment of ribosomal proteins and enzymes associated with ribosomal biogenesis, which could be indicative of an increase in protein synthesis. With the notable exeption of RPL35A nucleolar depletion, ribosomal proteins and enzymes associated with ribosomal biogenesis were in the top 20 most enriched nucleolar proteins (NHP2L1, RLP14, RPL17, RPL27, RPS2, RPL13). Furthermore, this effect appears to be specific to HIV-1 Tat since transcription inhibition by Actinomycin D resulted in the overall depletion of ribosomal proteins in the nucleolus [9] . Moreover, quantitative proteomics analysis of the nucleous in adenovirus-infected cells showed a mild decrease in ribosomal proteins [24] . Whether this reflect a shift in ribosome biogenesis and/or a change in the composition of the ribosomal subunits remains to be determined. Nevertheless, the adapted need for elevated ribosome production is intuitive for a system that needs to support the increased demand for new viral proteins synthesis. In parralel, we observed the concordant modulation of pathways regulating protein homeostasis. We noted the significant nucleolar accumulation of multiple molecular chaperones including the HSPs, the TCP-1 complex, and CANX/CALR molecules and the disrupted nucleolar abundance of proteins belonging to the ubiquitin-proteasome pathway, which controls the supply of ribosomal proteins [104, 105] . These observations further support previous studies describibing the modulation of the proteasomal activity by Tat, which affect the expression, assembly, and localization of specific subunits of the proteasomal complexes [106, 107, 108, 109, 110, 111, 113] . We also observed the concomitant depletion of CASP10 in the nucleolus of Jurkat TAP-Tat. It has been suggested that CASP10 could be targeted to the nucleolus to inhibit protein synthesis [154] . Interestingly, the presence and potential roles of molecular chaperones in the nucleolus have been highlighted by Banski et al, who elaborate on how the chaperone network could regulate ribosome biogenesis, cell signaling, and stress response [97, 155] . As viral production progresses into the late phase and cellular stress increases, nucleolar enrichment of molecular chaperones by Tat could not only enable adequat folding of newly synthetised viral proteins but could also promote tolerance of infected cells to stress and maintain cell viability. Coincidentally, we observed the marked nucleolar enrichment of enzymes belonging to metabolic pathways including glycolysis, pentose phosphate, nucleotide and amino acid biosynthetic pathways. Similarly, these pathways are elevated in proliferative T-cells or in cancer cells following a metabolic shift to aerobic glycolysis, also known as the Warburg effect [156, 157, 158, 159] . There, glucose intermediates from the glycolysis pathway are not only commited to energy production and broke-down into pyruvate for the TCA cycle, but are redirected to alternative pathways, including the pentose phosphate pathway, and used as metabolic precursors to produce nucleotides, amino acids, acetyl CoA and NADPH for redox homeostasis. Consistently, we also noted the concomittant nucleolar enrichment of enzymes belonging to the nucleotide synthesis pathway, including IMPH2, a rate limiting enzyme known to control the pool of GTP. Similarly, we noted the nucleolar enrichment of PSAT1, an enzyme involved in serine and threonin metabolism, which is associated with cellular proliferation [160] . Collectively, we propose that by controlling protein homeostasis and metabolic pathways, Tat could meet both the energetic and biosynthetic demand of HIV-1 productive infection. Of note, while nucleotide metabolism enzymes are associated with the nucleus, glycolysis takes place in the cytoplasm. Nevertheless, glycolytic enzymes have been detected in both the nuclear and nucleolar fractions by proteomic analyses [8, 161] . Furthermore glycolytic enzymes, such as PKM2, LDH, phosphoglycerate kinase, GAPDH, and aldolase, also have been reported to display nuclear localization and bind to DNA [162] . More specifically, PKM2 is known to associate with promoter and participate in the regulation of gene expression as a transcriptional coactivator [163] . HIV-1 Tat has previously been described as an immunoregulator and more specifically, has been reported both to inhibit or to promote TCR signaling [164] . We have observed the nucleolar enrichment by Tat of key proximal or downstream components of T-cell signaling pathways, including ZAP70, ILF3 and STAT3, which play crucial roles in T-cell development and activation. We had previously identified them as T-cell specific components of the nucleolus, and IF studies suggested that their association with the nucleolus could be regulated by specific conditions [165] . Our results further support that Tat could contribute to the dysregulation of TCR-derived signals and that the nucleolus could represent an important spatial link for TCR signaling molecules. We observed the coordinated nucleolar enrichment of key components of the DNA replication, recombination and repair pathways by Tat. These include XRCC5 and XRCC6, HMGA1, APEX1, MCM2-7, SMC2, RFC1 and RFC2, while RFC4 was found to be significantly depleted. Interestingly, these cofactors have been associated with the efficiency of retroviral DNA integration into the host DNA or the integrity of integrated provirus [166] . Whether the increased abundance of these factors within the nucleolus could be associated with their potential participation in the integration and maintenance of provirus gene integrity, remains to be determined. The mechanisms of Tat-mediated segregation and compartimentalisation of proteins in or out of the nucleolus may depend on factor(s) inherent for each protein and the nature of their relationship with Tat, since subcellular fractionation combined with WB analysis showed that the pattern and extent of subcellular redistribution between proteins varied. We could observe cases where Tat upregulated the expression of proteins which resulted in a general increase of theses proteins throughout the cellular compartments including the nucleolus (DDX3, TNPO1). Alternatively, Tat could trigger the nucleolar translocation of proteins directly from the cytoplasm or the nucleoplasm (pRb). Additionally, we observed cytoplasmic proteins redistributed to both the nucleoplasm and nucleolus upon Tat expression (STAT3, ZAP70 and HSP90). Finally, we also noted protein depletion in the nucleolar fraction accompanied by an increase in the nucleoplasm (SSRP1). It remains difficult at this stage, to appreciate whether the accumulation of specific proteins would result in their activation or inhibition by sequestering them away from their site of action. Conversely, the depletion of a protein from the nucleolus could either result in the down-regulation of its activity in this location or could be the result of its mobilization from its storage site, the nucleolus, to the nucleoplasm or cytoplasm where it can perform its function. Remarkably, we identified several known HIV-1 Tat partners involved in HIV-1 pathogenesis, which suggests that Tat could physically modulate their nucleolar targeting or their recruitment to specific site in the nucleoplasm or cytoplasm. Tat could also promote post-translational modifications, which could mediate the targeting of specific proteins to the nucleolus. This is exemplified by the following enriched proteins, pRb, PP1 and STAT3, for which phosphorylation is induced by Tat. Importantly, their phosphorylation status determines their subcellular distribution, thus providing a potential mechanism for their redistribution by Tat. Moreover, our data indicates that serine/threonine kinases (CK2 a') and phosphatases (PP1) were significantly enriched in the nucleolar fractions of Jurkat TAP-Tat. These enzymes account for the majority of the phosphorylation/ dephosphorylation activity in the nucleolus and can act as regulators of nucleolar protein trafficking. In addition, Tat significantly decreased the levels of SUMO-2 in the nucleolus. Similarly, SUMO-mediated post-translational modifications are known to modulate nucleolar protein localization [104] . Given the potential importance of post-translational modifications, including phosphorylation in the Tat-mediated change of abundance of nucleolar proteins, a more targeted proteomic approach such as the enrichment for phosphopetides, would extend the resolution of our screening approach. The control of protein turnover is also an important mean to modulate the abundance of nucleolar proteins. Ribosomal proteins are degraded by the Ubiquitin-Proteasome pathway to ensure their abundance matches up with rRNA transcription levels. Conversely, heat shock proteins HSP90s protect them from degradation. Interestingly, our data showing that Tat modulation the abundance proteins associated with the Ubiquitin-proteasome and heat-shock pathway. This could contribute to the observed enrichment of ribosomal proteins by Tat. Nevertheless, we cannot exclude that the increased abundance of ribosomal proteins in the nucleolus could be the result of Tat-mediated prevention of their export to the cytoplasm. Interestingly, using a different cellular system, a drosophila melanogaster Tat transgenic strain, Ponti et al, analysed the effects of Tat on ribosome biogenesis, following 3 days heat shock treatment to induce Tat expression under the control of the hsp70 promoter [167] . Following Tat expression, they observed a defect in pre-rRNA processing associated with a decrease in the level of 80S ribosomes [167] . Nevertheless, the different cellular system employed combined with the 3 days heatshock induction make their results difficult to compare with ours. While previous system-level studies have monitored the effects of HIV-1 Tat expression on T cells, to our knowledge, we have presented here the first proteomic analysis of dynamic composition of the nucleolus in response to HIV-1 Tat expression. Using quantitative proteomics, we have underlined the changes in abundance of specific nucleolar proteins and have highlighted the extensive and coordinated nucleolar reorganization in response to Tat constitutive expression. Our findings underscore that Tat expressing T-cells exhibit a unique nucleolar proteomic profile, which may reflect a viral strategy to facilitate the progression to robust viral production. Importantly, we noted the functional relationship of nucleolar proteins of our dataset with HIV-1 pathogenesis and HIV-1 Tat in particular. This further increases our confidence in our experimental strategy and suggests a role for Tat in the spatial control and subcellular compartimentaliation of these cellular cofactors. Ultimatly, our study provides new insights on the importance of Tat in the cross talk between nucleolar functions and viral pathogenesis. Importantly, we have also identified changes in nucleolar protein abundance that were not previously associated with HIV-1 pathogenesis, including proteins associated with metabolic pathways, which provide new potential targets and cellular pathways for therapeutic intervention. Jurkat T-cells, clone E6.1 (ATCC), Jurkat NTAP-Tat and Jurkat NTAP were maintained in RPMI-1640 medium supplemented with 10% (v/v) foetal bovine serum (Gibco, EU approved), and antibiotics. Phoenix-GP cells (G.P. Nolan; www.stanford.edu/ group/nolan/), were maintained in DMEM medium supplemented with 10% (v/v) foetal bovine serum (GIBCO, EU approved). Cells were counted using Scepter TM 2.0 Cell Counter (Millipore). The sequence of HIV-1 Tat (HIV-1 HXB2, 86 amino acids) was sub-cloned into pENTR 2B vector (Invitrogen, A10463). Using the Gateway technology (Invitrogen), we introduced the HIV-1 Tat sequence into the plasmid pCeMM-NTAP(GS)-Gw [168] . Phoenix cells (G.P. Nolan; www.stanford.edu/group/ nolan/), were transfected using Fugene 6 (Roche) with 5 mg of the plasmid NTAP-Tat or NTAP and 3 mg of the pMDG-VSVG. Viral supernatants were collected after 48 h, filtered and used to transduce the Jurkat cell lines. The construct is termed NTAP-Tat, the empty vector was termed NTAP. Using retroviral gene delivery, we stably transduced Jurkat cells (clone E6.1 (ATCC)). The positive clones named Jurkat NTAP-Tat and Jurkat NTAP were sorted to enrich the population of cells expressing GFP using the BC MoFlo XDP cell sorter (Beckman Coulter). Sub-cellular fractions (10 mg) were resolved by SDS-PAGE and transferred onto BioTrace PVDF membranes (Pall corporation). The following primary antibodies were used: a-Tubulin (Sc 5286), C23 (Sc 6013), and Fibrillarin (Sc 25397) were from Santa Cruz Biotechnology, and PARP (AM30) from Calbiochem, mouse anti-ZAP 70 (05-253, Millipore), rabbit anti-STAT3 (06-596, Millipore), rabbit anti-ILF3 (ab92355, Abcam), rabbit anti-HSP90 beta (ab32568, Abcam), mouse anti-ADAR1 (ab88574, Abcam), rabbit anti-HDAC1 (ab19845, Abcam), rabbit anti-SSRP1 (ab21584, Abcam) rabbit anti-BOP1 (ab86982, Abcam), mouse anti-KpNB1 (ab10303, Abcam), rabbit anti-HIV-1 Tat (ab43014, Abcam), rabbit anti-CK2A (ab10466, Abcam), rabbit anti-DDX3X (ab37160, Abcam), mouse anti-TNPO1 (ab2811, Abcam), mouse anti-HSP90A (CA1023, MERCK), and rabbit-anti RB1 (sc-102, Santa Cruz).The following secondary antibodies were used ECL: Anti-mouse IgG and ECL Anti-rabbit IgG (GE Healthcare), and Donkey anti-goat IgG (Sc 2020) (Santa Cruz Biotechnology). For SILAC analysis SILAC-RPMI R0K0 and SILAC-RPMI R6K6 (Dundee cells) media supplemented with 10% dialyzed FBS (GIBCO, 26400-036) were used. The Jurkat cells expressing NTAP-Tat and NTAP were serially passaged and grown for five doublings to ensure full incorporation of the labelled amino acids. Cells viability was checked with Trypan Blue (0.4% solution, SIGMA) and further confirmed using PI staining and FACS analysis. Cells were mixed to the ratio 1:1 to obtain 140610 6 cells. Nucleoli were isolated from the mixed cell population as previously described in Jarboui et al., [165] . Nucleolar extracts (100 mg) were resuspended in 50 mM ammonium bicarbonate and in solution trypsin digested as previously described in Jarboui et al. [165] . Sample was run on a Thermo Scientific LTQ ORBITRAP XL mass spectrometer connected to an Eksigent NANO LC.1DPLUS chromatography system incorporating an auto-sampler. Sample was loaded onto a Biobasic C18 PicofritTM column (100 mm length, 75 mm ID) and was separated by an increasing acetonitrile gradient, using a 142 min reverse phase gradient (0-40% acetonitrile for 110 min) at a flow rate of 300 nL min-1. The mass spectrometer was operated in positive ion mode with a capillary temperature of 200uC, a capillary voltage of 46V, a tube lens voltage of 140V and with a potential of 1800 V applied to the frit. All data was acquired with the mass spectrometer operating in automatic data dependent switching mode. A high resolution MS scan was performed using the Orbitrap to select the 5 most intense ions prior to MS/MS analysis using the Ion trap. The incorporation efficiency of labelled amino-acids was determined by analysing the peptides identified in isolated nucleoli from cell population maintained in ''Heavy'' medium as described in [169] . Our analysis showed that we had an incorporation efficiency .95% (data not shown). The MS/MS spectra were searched for peptides identification and quantification using the MaxQuant software [170] (version 1.1.1.36), the Human IPI Database (version 3.83) and the Andromeda search engine associated to MaxQuant [171] . Standard settings were used for MaxQuant with the Acetyl (Protein N-term) as variable modification and Carbamidomethyl (Cys) as fixed modification, 2 missed cleavage were allowed, except that the filtering of labelled amino acids was prohibited. Initial mass deviation of precursor ion and fragment ions were 7 ppm and 0.5 Da, respectively. Each protein ratio was calculated as the intensity-weighted average of the individual peptides ratios. Proteins were identified with the minimum of one peptide with a false discovery rate less than 1%. Gene ontology, KEGG pathway and Pfam terms were extracted from UNIPROT entries using Perseus, a software from the MaxQuant Data analysis package (http://www.maxquant.org ), and the ToppGene suite tools [54] . The Jurkat NTAP-Tat and Jurkat NTAP were transfected using the Amaxa electroporation system (Amaxa biosystem) with the pGL3 (pGL3-LTR) (Promega) as recommended by Amaxa Biosystem. Dual-luciferase assays (Promega) were performed according to the manufacturer's instructions. Luciferase activity was measured and normalized against the total amount of proteins as quantified by the BCA protein quantification kit (Pierce, Thermo Scientific). To preserve their original shape, we performed immunostaining of Jurkat cells in suspension. Cells were fixed in 2% PFA for 10 min at RT, permeabilised in 0.5% Triton X-100 for 15 min at RT and blocked with 5% FCS. Cells were incubated with the rabbit HIV-1 Tat antibody (ab43014, Abcam) followed by the secondary antibody anti-Rabbit alexa fluor 647 (A-21246, Invitrogen). Cells were allowed to attach to Cell-Tak (BD) coated Silanised Slides (DaoCytomation), and stained with DAPI. Images were captured with a Carl Zeiss Confocal Microscope equipped with a Plan-Apochromat 63X/1.4 oil DIC objective. The proteomics RAW Data file from the mass spectrometry analysis was deposited to the Tranche repository(https:// proteomecommons.org/tranche/) [172] . The file can be accessed and downloaded using the following hash key: (R3O5SV5Z6HvWqrBNDhp21tXFetluDWYxvwMIfU-h6e1kMgarauCSq4dlNcxeUvFOHDEzLeDcg4X5Y8reSb6-MUA6wM1kIAAAAAAAAB/w = = ). Materials and Methods S1 Description of the methods employed to examine cell cycle, cell viability and cell proliferation analysis. (DOCX)

How long is the nuclear protein PARP-1?

Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus (SARS-CoV-Z) and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown (11th March), although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March [95% credible interval 21,000-120,000]. Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London (2020), doi: https://doi.org/10.25561/77731 1 Introduction Following the emergence of a novel coronavirus (SARS-CoV-Z) in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing (such as banning large gatherings and advising individuals not to socialize outside their households), border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase (dependent on how much greater than 1 the reproduction number is) until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups (e.g. contacts of cases). Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds (Bayesian credible intervals) of the total populations infected (attack rates), case detection probabilities, and the reproduction number over time (Rt). We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards (Figure 1). Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced (Figure 2- 12). Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected (Figure 2) than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 [1.9-15.2] million people have been infected as of March 28th, giving an attack rate of 9.8% [3.2%-25%] of the population (Table 1). Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% (7.0 [18-19] million people) have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 [240,000-1,500,000] people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected (mean [95% credible intervall) Austria 1.1% [0.36%-3.1%] Belgium 3.7% [1.3%-9.7%] Denmark 1.1% [0.40%-3.1%] France 3.0% [1.1%-7.4%] Germany 0.72% [0.28%-1.8%] Italy 9.8% [3.2%-26%] Norway 0.41% [0.09%-1.2%] Spain 15% [3.7%-41%] Sweden 3.1% [0.85%-8.4%] Switzerland 3.2% [1.3%-7.6%] United Kingdom 2.7% [1.2%-5.4%] 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 [3.01-4.66], which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers (Appendix 8.4.2, Appendix 8.4.6). The initial reproduction numbers are also uncertain due to (a) importation being the dominant source of new infections early in the epidemic, rather than local transmission (b) possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths (e.g. Italy, Spain), suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 [0.14-2.14] for Norway to a posterior mean of2.64 [1.40-4.18] for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 (posterior probability of being less than 1.0 is 44% on average across the countries). We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic (e.g. Germany, UK, Norway). Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well (Bayesian goodness of fit tests). We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths (Appendix 8.3). The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect (Figure 1, Figure 4). However, when doing a sensitivity analysis (Appendix 8.4.3) with uninformative prior distributions (where interventions can increase deaths) we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval (CI), light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model (assuming no intervention had taken place). Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented (and R, = R0 i.e. the initial reproduction number estimated before interventions). Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed (Figure 3). Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C). By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 [21,000-120,000] deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 [13,000- 84,000] and 16,000 [5,400-35,000] deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 [73-1,000] deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions (up to 28 March) and forecasts (after) for (a) Italy and (b) Spain from our model with interventions (blue) and from the no interventions counterfactual model (pink); credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% (no effect on transmissibility) (ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 (red) or was subsequent to other interventions (green). Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% [l.9%-ll%] with considerable variation between countries (Table 1). Our estimates imply that the populations in Europe are not close to herd immunity ("50-75% if R0 is 2-4). Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths (Italy, Spain). lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified (see Appendix 8.4 for sensitivity analysis). While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear (on the logarithmic scale) reduction in deaths (see Figure 10). The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher (38,000 deaths averted) than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions (e.g. Italy, Spain) strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths (e.g. Germany, UK). We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, (see the sensitivity analysis reported in Appendix 8.4.3) and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC (European Centre of Disease Control), where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing (for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely). In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed (ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards). Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals (e.g. UK) or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police (e.g. France). The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples (Norway/Denmark) have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 (details in Appendix 8.1 and 8.2). Replication code is available at https://github.com/|mperia|CollegeLondon/covid19model/releases/tag/vl.0 We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution (time from infection to the onset of symptoms), an onset-to-death distribution (time from the onset of symptoms to death), and the population-averaged infection fatality ratio (adjusted for the age structure and contact patterns of each country, see Appendix). Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution (the average time from infection of one person to the time at which they infect another) and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths (and get stronger as time progresses). To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust (Appendix 8.4). 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E(Dam) that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths (1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr (probability of death given infection)9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates (the unadjusted ifr, referred to here as ifr’) in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period (infection to onset of symptoms or infection- to-onset) distribution and the time between onset of symptoms and death (onset-to-death). The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ (Gamma(5.1,0.86) + Gamma(18.8,0.45)) Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution (mean 23.9 days). Right, survival probability of infected individuals per day given the infection fatality ratio (1%) and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g(T), (the time between when a person gets infected and when they subsequently infect another other people), which we choose to be Gamma distributed: g ~ Gamma (6.50.62). The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg(T)dT fors = 2,3, and 91 = fT=Og(T)dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures (k=l), self—isolating if ill (k=2), banning of public events (k=3), any government intervention in place (k=4), implementing a partial or complete lockdown (k=5) and encouraging social distancing and isolation (k=6). We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” (k=4) indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp(— 212:1 O(Rheum)- The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0(1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma(. 5,1). The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal(2.4, IKI) with K ~ Normal(0,0.5), Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential(T), where T~Exponential(0.03). These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo (HMC) sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed (see below). 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data (non-cumulative) and fit our model. We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo (MCMC) chains have converged to the equilibrium distribution (the correct posterior distribution). Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval (SI) distributions (means between 5 and 8 days). We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval (SI) distribution means between 5 and 8 days. We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below (compare with the main analysis in Figure 4). In this series of univariate analyses, we find (Figure 15) that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others (such as the UK). To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g(r) with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = (1 + %) .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 (red) vs R(FO) (black) Figure 18: Our estimated R0 (black) versus theoretically derived Ru(red) from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions (up to 28 March) and forecasts (after) for all countries except Italy and Spain from our model with interventions (blue) and from the no interventions counterfactual model (pink); credible intervals are shown one week into the future. DOI: https://doi.org/10.25561/77731 Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities (primary ordered schools also shut on 16th).26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states (kantons).54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV2). Science (2020) doi:10.1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11(2019) 3. Worldometers.info. Hong Kong: coronavirus cases. https://www.wo rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4. Ferguson, N. et al. 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Robert Koch Institut https://web.archive.org/web/20200312004624/https://www.rki.de/SharedDocs/FAQ/NCOV2019/F AQ_Liste.html (2020). 38. Ministero della Salute. Governo annuncia sospensione dell’attivita didattica dal 5 a|15 marzo. Ministero della Salute http://www.salute.gov.it/portale/nuovocoronavirus/dettaglioVideoNuovoCoronavirus.jsp?lingua=ita liano&menu=multimedia&p=video&id=2052 (2020). 39. CNN. Italy prohibits travel and cancels all public events in its northern region. CNN https://edition.cnn.com/2020/03/08/europe/italy-coronavirus-lockdown-europe-intl/index.html (2020). 40. Attualita. Coronavirus: stop a pub, cinema, teatro e discoteche anche a Roma. Ecco cosa prevede il nuovo decreto. Roma Today https://www.romatoday.it/attualita/coronavirus-pub-cinema- teatri-locali-chiusi-nuovo-decreto.html (2020). 41. Gazzetta Ufficiale. DECRETO DEL PRESIDENTE DEL CONSIGLIO DEl MINISTRI. Gazzetta Ufflclale https://www.gazzettaufficiale.it/eli/id/2020/03/08/20A01522/sg (2020). 42. Helsedirektoratet. The Norwegian Directorate of Health has issued a decision to close schools and other educational institutions. Helsedirektoratet https://www.helsedirektoratet.no/nyheter/the- norwegian-directorate-of—health-has-issued-a-decision-to-close-schools-and-other-educationa|- institutions (2020). 43. Krostensen, Mette; Hellem-Hansen, Viktoria L.; Tandstad, B. Folkehelseinstituttet mener 23.000 kan vaere smittet. NRK https://www.nrk.no/norge/folkehelseinstituttet-mener-23.000-kan- vaere-smittet-1.14958149 (2020). 44. Norweigen Government. The Government is establishing clear quarantine and isolation rules. regjeringen.no https://www.regjeringen.no/en/aktuelt/the-government-is-establishing-clear- quarantine-and-isolation-rules/id2693647/ (2020). 45. Wikipedia. 2020 coronavirus pandemic in Spain. Wikimedia Foundation https://en.wikipedia.org/wiki/2020_coronavirus_pandemic_in_Spain. 46. Gabinete de Prensa. El Gobierno anuncia nuevas medidas para evitar la extension del nuevo coronavirus COVID-19. Gobierno de Espana https://www.mscbs.gob.es/gabinete/notasPrensa.do?id=4807 (2020). 47. Gabinete de Prensa. El Consejo Interterritorial del SNS acuerda medidas concretas para zonas con transmision comunitaria significativa de coronavirus. Gobierno de Espana https://www.mscbs.gob.es/gabinete/notasPrensa.do?id=4806 (2020). 48. Folkhalsomyndigheten. Larosaten och gymnasieskolor uppmanas nu att bedriva distansundervisning. Folkhdlsomyndigheten https://www.folkhalsomyndigheten.se/nyheter-och- press/nyhetsarkiv/2020/mars/larosaten-och-gymnasieskolor-uppmanas-nu-att-bedriva- distansundervisning(2020). 49. The Local. Sweden bans large events to halt coronavirus spread. The Local https://www.theloca|.se/20200311/sweden-to-ban-large-public-gatherings-over-coronavirus (2020). 50. Radosevich. Stockholmers urged to work from home as COVID-19 community spread confirmed. Sveriges Radio https://sverigesradio.se/sida/artikel.aspx?programid=2054&artikel=7430511(2020). 51. Folkhalsomyndigheten. Flera tecken p\aa samhallsspridning av covid-19 i Sverige. Folkhdlsomyndigheten https://www.folkhalsomyndigheten.se/nyheter-och- press/nyhetsarkiv/2020/mars/flera-tecken-pa-samhallsspridning-av-covid-19-i-sverige/ (2020). 52. Bundesamt fur Gesendheit BAG. Bundesrat verscharft Massnahmen gegen das Coronavirus zum Schutz der Gesundheit und unterstUtzt betroffene Branchen. Schweizerische Eidgenossenschaft https://www.bag.admin.ch/bag/de/home/das-bag/aktuell/medienmitteilungen.msg-id-78437.html (20201 53. Bundesamt fur Gesundheit BAG. Coronavirus: Bundesrat verbietet Ansammlungen von mehr als fUnf Personen. Schweizerische Eidgenossenschaft https://www.bag.admin.ch/bag/de/home/das- bag/aktuell/medienmitteilungen.msg-id-78513.html (2020). 54. Bundesamt fur Gesundheit BAG. Coronavirus: Bundesrat erklart die «ausserordentliche Lage» und verscharft die Massnahmen. Schweizerische Eidgenossenschaft https://www.bag.admin.ch/bag/de/home/das-bag/aktuell/medienmitteilungen.msg-id-78454.html (20201 55. Bundesamt fur Gesundheit BAG. Neue Hygiene- und Verhaltensregeln zum Schutz gegen das neue Coronavirus. Schweizerische Eidgenossenschaft https://www.bag.admin.ch/bag/de/home/das- bag/a ktuell/medienmitteilungen.msg-id-78304.html (2020). 56. UK Government, D. for E. Schools, colleges and early years settings to close. UK Government https://www.gov.uk/government/news/schools-colleges-and-early-years-settings-to-close (2020). 57. UK Government. PM address to the nation on coronavirus: 23 March 2020. UK Government https://www.gov.uk/government/speeches/pm-address-to-the-nation-on-coronavirus-23-march- 2020(20201 58. Boycott-Owen, Mason; Bowman, Verity; Kelly-Linden, Jordan; Gartner, A. G. H. S. T. Coronavirus: Boris Johnson puts UK in lockdown as death tolls reaches 55. The Telegraph https://www.telegraph.co.uk/global-health/science-and-disease/coronavirus-news-uk-latest- update-covid-19-death-toll-cases/ (2020). 59. BBC News. Coronavirus: People with fever or ’continuous’ cough told to self—isolate. BBC News https://www.bbc.co.uk/news/uk-51857856 (2020).

What is the population-averaged infection fatality ratio?

MERS coronavirus: diagnostics, epidemiology and transmission https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4687373/ SHA: f6fcf1a99cbd073c5821d1c4ffa3f2c6daf8ae29 Authors: Mackay, Ian M.; Arden, Katherine E. Date: 2015-12-22 DOI: 10.1186/s12985-015-0439-5 License: cc-by Abstract: The first known cases of Middle East respiratory syndrome (MERS), associated with infection by a novel coronavirus (CoV), occurred in 2012 in Jordan but were reported retrospectively. The case first to be publicly reported was from Jeddah, in the Kingdom of Saudi Arabia (KSA). Since then, MERS-CoV sequences have been found in a bat and in many dromedary camels (DC). MERS-CoV is enzootic in DC across the Arabian Peninsula and in parts of Africa, causing mild upper respiratory tract illness in its camel reservoir and sporadic, but relatively rare human infections. Precisely how virus transmits to humans remains unknown but close and lengthy exposure appears to be a requirement. The KSA is the focal point of MERS, with the majority of human cases. In humans, MERS is mostly known as a lower respiratory tract (LRT) disease involving fever, cough, breathing difficulties and pneumonia that may progress to acute respiratory distress syndrome, multiorgan failure and death in 20 % to 40 % of those infected. However, MERS-CoV has also been detected in mild and influenza-like illnesses and in those with no signs or symptoms. Older males most obviously suffer severe disease and MERS patients often have comorbidities. Compared to severe acute respiratory syndrome (SARS), another sometimes- fatal zoonotic coronavirus disease that has since disappeared, MERS progresses more rapidly to respiratory failure and acute kidney injury (it also has an affinity for growth in kidney cells under laboratory conditions), is more frequently reported in patients with underlying disease and is more often fatal. Most human cases of MERS have been linked to lapses in infection prevention and control (IPC) in healthcare settings, with approximately 20 % of all virus detections reported among healthcare workers (HCWs) and higher exposures in those with occupations that bring them into close contact with camels. Sero-surveys have found widespread evidence of past infection in adult camels and limited past exposure among humans. Sensitive, validated reverse transcriptase real-time polymerase chain reaction (RT-rtPCR)-based diagnostics have been available almost from the start of the emergence of MERS. While the basic virology of MERS-CoV has advanced over the past three years, understanding of the interplay between camel, environment, and human remains limited. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12985-015-0439-5) contains supplementary material, which is available to authorized users. Text: An email from Dr Ali Mohamed Zaki, an Egyptian virologist working at the Dr Soliman Fakeeh Hospital in Jeddah in the Kingdom of Saudi Arabia (KSA) announced the first culture of a new coronavirus to the world. The email was published on the website of the professional emerging diseases (ProMED) network on 20 th September 2012 [1] (Fig. 1) and described the first reported case, a 60 year old man from Bisha in the KSA. This information led to the rapid discovery of a second case of the virus, this time in an ill patient in the United Kingdom, who had been transferred from Qatar for care [2] . The new virus was initially called novel coronavirus (nCoV) and subsequentlty entitled the Middle East respiratoy syndrome coronavirus (MERS-CoV). As of 2 nd of September 2015, there have been 1,493 detections of viral RNA or virus-specific antibodies across 26 countries (Additional file 1: Figure S1 ) confirmed by the World Health Organization (WHO), with over a third of the positive people dying (at least 527, 35 %) [3] . Since that first report, a slow discovery process over the following two to three years revealed a virus that had infected over 90 % of adult dromedary camels (DC; Camelus dromedarius) in the KSA [4] , also DCs across the Arabian Peninsula and parts of Africa that are a source of DC imports for the KSA [5] . To date, MERS-CoV has not been detected in DCs tested in zoos or herds from other parts of the world [6] [7] [8] [9] . Occasionally, virus is transmitted from infected DCs to exposed humans. Subsequent transmission to other humans requires relatively close and prolonged exposure [10] . The first viral isolate was patented and concerns were raised that this would restrict access to both the virus and to viral diagnostics [11, 12] . However, sensitive, validated reverse transcriptase real-time polymerase chain reaction (RT-rtPCR)-based diagnostics were quickly described and virus was made freely available subject to routine biosafety considerations [13] . Subsequent epidemiology and research has identified the cell receptor as exopeptidase dipeptidyl peptidase 4 (DPP4; also called CD26); that MERS-CoV has a broad tropism, replicating better in some cells lines and eliciting a more proinflammatory response than SARS-CoV; is widespread in DCs; has the potential to infect other animals and that MERS kills its human host more often than SARS did (20-40 % versus 9 % for SARS [14] ) [15] [16] [17] [18] [19] . In humans, overt disease was given the name Middle East respiratory syndrome, with the acronym MERS. From intermittent animal-to-human spill-over events, the MERS-CoV spreads sporadically among people, causing more severe disease among older adults, especially males, with pre-existing diseases. The spread of MERS-CoV among humans has often been associated with outbreaks in hospitals, with around 20 % of all cases to date involving healthcare workers (HCWs). Although DCs appear to suffer the equivalent of a 'common cold' from MERS-CoV infection, in humans, the virus can be a more serious and opportunistic pathogen associated with the death of up to 40 % of reported cases. It has yet to be established whether infections thought to have been acquired from an animal source produce a more severe outcome than those spread between humans [20] . Studies have established that the mean incubation period for MERS is five to six days, ranging from two to 16 days, with 13 to 14 days between when illness begins in one person and subsequently spreads to another [21] [22] [23] [24] . Among those with progressive illness, the median time to death is 11 to 13 days, ranging from five to 27 days [23, 24] . Fever and gastrointestinal symptoms may form a prodrome, after which symptoms decline, only to be followed by a more severe systemic and respiratory syndrome [25, 26] . The first WHO case definition [27] defined probable cases of MERS based on the presence of febrile illness, cough and requirement for hospitalization with suspicion of lower respiratory tract (LRT) involvement. It also included roles for contact with a probable or confirmed case or for travel or residence within the Arabian Peninsula. If strictly adhered to, only the severe syndrome would be subject to laboratory testing, which was the paradigm early on [21] . From July 2013, the revised WHO case definition included the importance of seeking out and understanding the role of asymptomatic cases and from June 2014, the WHO definition more clearly stated that a confirmed case included any person whose sample was RT-PCR positive for MERS-CoV, or who produced a seroconversion, irrespective of clinical signs and symptoms. [28] [29] [30] Apart from the WHO and the KSA Ministry of Health reports, asymptomatic or subclinical cases of MERS-CoV infection were documented in the scientific literature although not always as often as occurred early on [31, 32] . The KSA definition of a case became more strict on 13 th May 2014, relying on the presence of both clinical features and laboratory confirmation [33] . Testing of asymptomatic people was recommended against from December 2014 [34] , reinforced by a case definition released by the KSA Ministry of Health in June 2015 [35] . The KSA has been the source of 79 % of human cases. Severe MERS is notable for its impact among older men with comorbid diseases including diabetes mellitus, cirrhosis and various lung, renal and cardiac conditions [36] [37] [38] . Interestingly in June 2015, an outbreak in South Korea followed a similar distribution [39, 40] . Among laboratory confirmed cases, fever, cough and upper respiratory tract (URT) signs and symptoms usually occur first, followed within a week by progressive LRT distress and lymphopaenia [37] . Patients often present to a hospital with pneumonia, or worse, and secondary bacterial infections have been reported [37, 41] . Disease can progress to acute respiratory distress syndrome and multiorgan system failure [37] . MERS has reportedly killed approximately 35 % of all reported cases, 42 % of cases in the KSA, yet only 19 % of cases in South Korea, where mortality ranged from 7 % among younger age groups to 40 % among those aged 60 years and above [42] ; all may be inflated values with asymptomatic or mild infections sometimes not sought or not reported [34] . General supportive care is key to managing severe cases [43] . Children under the age of 14 years are rarely reported to be positive for MERS-CoV, comprising only 1.1 % (n = 16) of total reported cases. Between 1 st September 2012 and 2 nd December 2013, a study described the then tally of paediatric cases in the KSA, which stood at 11 (two to 16 years of age; median 13 years); nine were asymptomatic (72 %) and one infant died [44] . In Amman, Jordan, 1,005 samples from hospitalized children under the age of two years with fever and/or respiratory signs and symptoms were tested but none were positive for MERS-CoV RNA, despite being collected at a similar time to the first known outbreak of MERS-CoV in the neighbouring town of Al-Zarqa [45] . A second trimester stillbirth occurred in a pregnant woman during an acute respiratory illness and while not RT-rtPCR positive, the mother did subsequently develop antibodies to MERS-CoV, suggestive of recent infection [46] . Her exposure history to a MERS-CoV RT-rtPCR positive relative and an antibody-reactive husband, her incubation period and her symptom history met the WHO criteria for being a probable MERS-CoV case [46] . Diagnostic methods were published within days of the ProMED email announcing the first MERS case [47] , including several now gold standard in-house RT-rtPCR assays (Fig. 2 ) as well as virus culture in Vero and LLC-MK2 cells [18, 47, 48] . A colorectal adenocarcinoma (Caco-2) epithelial cell line has since been recommended for isolation of infections MERS-CoV [49] . We previously [18] .). Open reading frames are indicated as yellow rectangles bracketed by terminal untranslated regions (UTR; grey rectangles). FS-frame-shift. Predicted regions encompassing recombination break-points are indicated by orange pills. Created using Geneious v8.1 [211] and annotated using Adobe Illustrator. Beneath this is a schematic depicting the location of RT-PCR primers (blue arrows indicate direction) and oligoprobes (green rectangles) used in the earliest RT-rtPCR screening assays and conventional, semi-nested (three primers) RT-PCR confirmatory sequencing assays [47, 48] . Publication order is noted by first [27 th September 2012; red] and second [6 th December 2012; orange] coloured rectangles; both from Corman et al. [47, 48] Those assays recommended by the WHO are highlighted underneath by yellow dots [53] . The NSeq reverse primer has consistently contained one sequence mismatch with some MERS-CoV variants. An altered version of that from Mackay IM, Arden KE. Middle East respiratory syndrome: An emerging coronavirus infection tracked by the crowd. Virus Res 2015 Vol 202:60-88 with permission from Elsevier [5] reviewed the broad tropism of MERS-CoV [5] . However, as is well described, cell culture is a slow, specialised and insensitive method [50] while PCR-based techniques are the preferred method for MERS-CoV detection. The first open reading frames (ORF 1a and 1b; Fig. 2 ) have become a key diagnostic and taxonomic target for CoV species identification. With less than 80 % identity between the amino acid sequence of MERS ORF 1ab and betacoronavirus relatives, Tylonycteris bat HKU4 and Pipistrellus bat HKU5, it can be concluded that it is a novel and distinct virus. MERS-CoV is predicted to encode ten open reading frames with 5' and 3' untranslated regions [51] . The structural proteins include the spike (S), envelope (E), membrane (M) and nucleocapsid (N) [52] . The products of ORF1a and ORF1b are predicted to encode nonstructural proteins. The majority of specimen testing to date has employed validated RT-rtPCR assays shown to be sensitive and specific [47, 48, 53] . The RealStar® kit uses these WHOrecommended assays [54] . The target sequences of these screening assays have not changed among genomes examined until at least mid-2015 (IMM observation). Other RT-rtPCR assays have been developed and validated for use as laboratory-based diagnostic tools [55] [56] [57] . Additionally, loop-mediated [58, 59] or recombinase polymerase [60] isothermal assays have been designed for field deployment. The detection of MERS-CoV antigen has not been common to date but the combination of short turnaround time from test to result, high throughput and identification of viral proteins makes this an attractive option. Detection of viral proteins rather than viral RNA indicates the likely presence of infectious virus. The first rapid immunochromatographic tool described could detect recombinant MERS-CoV nucleocapsid protein from DC nasal swabs with 94 % sensitivity and 100 % specificity compared to RT-rtPCR [61] . A different approach used a monoclonal antibody-based capture ELISA targeting the MERS-CoV nucleocapsid protein with a sensitivity of 10 3 TCID 50 and 100 % specificity [62] . Demonstration of a seroconversion to a MERS-CoV infection meets the current WHO definition of a case so optimized and thoroughly validated sero-assays employed alongside good clinical histories are useful to both identify prior MERS-CoV infection and help support transmission studies. Because serology testing is, by its nature, retrospective, it is usual to detect a viral footprint, in the form of antibodies, in the absence of any signs or symptoms of disease and often in the absence of any viral RNA [63] . Strategic, widespread sero-surveys of humans using samples collected after 2012 are infrequent. Much of the Arabian Peninsula and all of the Horn of Africa lack baseline data describing the proportion of the community who may have been infected by a MERS-CoV. However, sero-surveys have had widespread use in elucidating the role of DCs as a transmission source for MERS-CoV. Because of the identity shared between DC and human MERS-CoV (see Molecular epidemiology: using genomes to understand outbreaks), serological assays for DC sero-surveys should be transferrable to human screening with minimal re-configuration. Also, no diagnostically relevant variation in neutralization activity have been found from among a range of circulating tested MERS-CoV isolates and sera, so whole virus or specific protein-based sero-assays should perform equivalently in detecting serological responses to the single MERS-CoV serotype [49] . The development of robust serological assays requires reliable panels of wellcharacterized animal or human sera, including those positive for antibodies specific to MERS-CoV, as well as to likely sources of cross-reaction [64] . Obtaining these materials was problematic and slowed the development and commercialization of antibody detection assays for human testing [64] . A number of commercial ELISA kits, immunofluorescent assays (IFA) kits, recombinant proteins and monoclonal antibodies have been released [31, [65] [66] [67] [68] . Initially, conventional IFAs were used for human sero-surveys. These relied on MERS-CoV-infected cell culture as an antigen source, detecting the presence of human anti-MERS-CoV IgG, IgM or neutralizing antibodies in human samples [18, 48, 69] . No sign of MERS-CoV antibodies was found among 2,400 sera from patients visiting Hospital in Jeddah, from 2010 through 2012, prior to the description of MERS-CoV [18] . Nor did IFA methods detect any sign of prior MERS-CoV infection among a small sample of 130 healthy blood donors from another Hospital in Jeddah (collected between Jan and Dec 2012) [70] . Of 226 slaughterhouse workers, only eight (3.5 %) were positive by IFA, and those sera could not be confirmed by virus neutralization (NT) test. The study indicated that HCoV-HKU1 was a likely source of crossreactive antigen in the whole virus IFA [70] . Whole virus MERS-CoV IFA also suffered from some cross-reactivity with convalescent SARS patient sera and this could not be resolved by an NT test which was also cross-reactive [71] . IFA using recombinant proteins instead of whole-virus IFA, has been shown to be a more specific tool [31] . Since asymptomatic zoonoses have been posited [72] , an absence of antibodies to MERS-CoV among some humans who have regular and close contact with camels may reflect the rarity of actively infected animals at butcheries, a limited transmission risk associated with slaughtering DCs [70] , a pre-existing cross-protective immune status or some other factor(s) resulting in a low risk of disease and concurrent seroconversion developing after exposure in this group. IFA using recombinant proteins instead. Some sero-assays have bypassed the risks of working with infectious virus by creating transfected cells expressing recombinant portions of the MERS-CoV nucleocapsid and spike proteins [48, 73] , or using a recombinant lentivirus expressing MERS-CoV spike protein and luciferase [74, 75] . A pseudo particle neutralization (ppNT) assay has seen widespread used in animal studies and was at least as sensitive as the traditional microneutralization (MNT) test. [10, 74, [76] [77] [78] ] Studies using small sample numbers and ppNT found no evidence of MERS-CoV neutralizing antibody in sera from 158 children with LRT infections between May 2010 and May 2011, 110 sera from 19 to 52 year old male blood donors and 300 selfidentified animal workers from the Jazan Region of the KSA during 2012 [79, 80] . Similarly, a study of four herdsmen in contact with an infected DC herd in Al-Ahsa, eight people who had intermittent contact with the herd, 30 veterinary surgeons and support staff who were not exposed to the herd, three unprotected abattoir workers in Al-Ahsa and 146 controls who were not exposed to DCs in any professional role, found none with serological evidence of past MERS-CoV infection using the ppNT assay [10] . A delay in the neutralizing antibody response to MERS-CoV infection was associated with increased disease severity in South Korea cases with most responses detectable by week three of illness while others, even though disease was severe, did not respond for four or more weeks [81] . The implications for our ability to detect any response in mild or asymptomatic cases was not explored but may be a signifcant factor in understanding exposure in the wider community. A Jordanian outbreak of acute LRT disease in a hospital in 2012 was retrospectively found to be associated with MERS-CoV infection, initially using RT-rtPCR, but subsequently, and on a larger scale, through positivity by ELISA and IFA or MNT test. [46, 82, 83] This outbreak predated the first case of MERS in the KSA. The ELISA used a recombinant nucleocapsid protein from the group 2 betacoronavirus bat-CoV HKU5 to identify antibodies against the equivalent crossreactive MERS-CoV protein [71] . It was validated using 545 sera collected from people with prior HCoV-OC43, HCoV-229E, SARS-CoV, HCoV-NL63, HRV, HMPV or influenza A(H1N1) infections but was reportedly less specific than the recombinant IFA discussed above. It was still considered an applicable tool for screening large sample numbers [82] . A protein microarray expressing the S1 protein subunit has also been validated and widely used for DC testing [5, 84] . Detection of MERS-CoV infection using ELISA or S1 subunit protein microarray [84] is usually followed by confirmatory IFA and/ or a plaque-reduction neutralization (PRNT) [69, 70, 85] or MNT test. [74, 85, 86] This confirmatory process aims toensure the antibodies detected are able to specifically neutralize the intended virus and are not more broadly reactive to other coronaviruses found in DCs (bovine CoV, BCoV) or humans (HCoV-OC43, HCoV-229E, HCoV-NL63, HCoV-HKU1, SARS-CoV). In the largest study of human sera, a tiered diagnostic process assigned both recombinant IFA and recombinant ELISA positive sera to 'stage 1' seropositivity. A stage 2 seropositive result additionally required a suitably titred PRNT result [87] . The study found 15 sera collected in 2012 to 2013 from 10,009 (0.2 %) people in 13 KSA provinces contained MERS-CoV antibodies, but significantly higher proportions in occurred in camel shepherds (two of 87; 2.3 %) and slaughterhouse workers (five of 140; 3.6 %) [87] . Contemporary surveys are needed. MERS-CoV does not appear to be easily transmitted from DCs to humans, or perhaps it is [72] , but generally does not trigger a detectable immune response if only mild disease or asymptomatic infection results. Serology assays are in need of further validation in this area so care is required when moving newly developed diagnostic serology algorithms from a research setting to one that informs public health decisions. This was reinforced when a false positive US case, purported to have been infected after a handshake and two face-to-face meetings, did not withstand further confirmatory analysis using a more specific, NT assay and was subsequently retracted [88, 89] . The WHO recommends sampling from the LRT for MERS-CoV RT-rtPCR testing, especially when sample collection is delayed by a week or more after onset of symptoms. [53] LRT samples are also best for attempting isolation of infectious virus, although the success of culture is reduced when disease persists [49] . Recommended sample types include bronchoalveolar lavage (BAL), tracheal/tracheobronchial aspirate, pleural fluid and sputum [53, 90] . Fresh samples yield better diagnostic results than refrigerated material [69] and if delays in testing of ≥72 h are likely, samples (except for blood) should be frozen at −70°C [90] . If available, lung biopsy or autopsy tissues can also be tested [53] . The URT is a less invasive and more convenient sampling site however, and an oropharyngeal and throat swab or a nasopharyngeal aspirate/wash are recommended when URT sampling is to be conducted [90] . Paired sera, collected two to three weeks apart are preferable for serological testing while a single sample is suggested to be sufficient if collected two weeks after onset of disease or a single serum collected during the first 10-12 days if conducting RT-rtPCR [53, 90] . Human urine and stool have been found to contain MERS-CoV RNA 12 to 26 days after symptom onset [25, 69, 91] and are listed as samples that should be considered [53, 90] . In two cases that arrived in the Netherlands, urine was RT-rtPCR negative but faeces was weakly positive and sera were RT-rtPCR positive for five days or more [25] . The finding of MERS-CoV viral RNA in serum provides an avenue for retrospective PCR-based studies if respiratory samples are unavailable [83] . RNAaemia may also correlate with disease severity; signs of virus were cleared from the serum of a recovered patient, yet lingered until the death of another [92] . Clinically suspected MERS cases may return negative results by RT-rtPCR. Data have shown one or more negative URT samples may be contradicted by further URT sampling or the use of LRT samples, which is preferred [2, 43, 93] . Higher viral loads occur in the LRT compared to the URT. [22, 69, 88, 94] This fits with the observation that the majority of disease symptoms are reported to manifest as systemic and LRT disease [21] . However, on occasion, even LRT specimens from MERS cases may initially be negative, only to later become positive by RT-PCR [95] . This may be due to poor sampling when a cough is absent or non-productive or because the viral load is low [95] . Despite this both the largest human MERS-CoV studies [32, [96] [97] [98] and smaller ones [22, 25, 99] , use samples from the URT. It is then noteworthy that one study reported an association between higher loads in the URT and worse clinical outcome including intensive care and death [94] . At writing, no human data exist to define whether the virus replicates solely or preferentially in the LRT or URT, or replicates in other human tissues in vivo although MERS-CoV RNA has been detected from both the URT and LRT in a macaque monkey model [100] .The distribution of DPP4 in the human upper airways is also not well described. Individual human case studies report long periods of viral shedding, sometimes intermittently and not necessarily linked to the presence of disease symptoms. [25, 69, 99, 101] In one instance, a HCW shed viral RNA for 42 days in the absence of disease [99] . It is an area of high priority to better understand whether such cases are able to infect others. Over three quarters of MERS cases shed viral RNA in their LRT specimens (tracheal aspirates and sputum) for at least 30 days, while only 30 % of contacts were still shedding RNA in their URT specimens [91, 102] . In the only study to examine the effect of sample type on molecular analysis, 64 nasopharyngeal aspirates (NPA; an URT sample), 30 tracheal aspirates, 13 sputa and three BAL were examined. The tracheal aspirates and BAL returned the highest viral load values followed by NPA and sputum. Unsurprisingly, higher viral loads generally paralleled whole genome sequencing and culture success and, in NPA testing, were significantly correlated with severe disease and death [49, 94, 103] . This study demonstrated the importance of LRT sampling for whole genome sequencing. When tested, samples positive for MERS-CoV are often negative for other pathogens [2, 25, 93, 104] . However, many studies make no mention of additional testing for endemic human respiratory viruses [21, 23, 73, 105] . When viruses are sought, they have included human herpesvirus (HHV), rhinoviruses (HRV), enteroviruses (EV), respiratory syncytial virus (RSV), parainfluenzavirus types 1, 2 and 3 (PIVs),influenzaviruses (IFVs), endemic HCoVs, adenoviruses (AdVs) metapneumovirus (MPV) and influenza A\H1N1 virus; co-detections with MERS-CoV have been found on occasion [2, 22, 37, 69, 97] . Bacterial testing is sometimes included (for example, for Legionella and Pneumococcus) but the impact of bacterial co-presence is also unclear [22, [104] [105] [106] . Further testing of the LRT sample from the first MERS case used IFA to screen for some viruses (negative for IFV, PIVs, RSV and AdVs) and RT-PCR for others (negative for AdV, EVs, MPV and HHVs) [18] . RT-PCR also detected MERS-CoV. The WHO strongly recommends testing for other respiratory pathogens [53] but with this recommendation often discounted, there are limited data to address the occurrence and impact of co-infections or alternative viral diagnoses among both MERS cases and their contacts. Little is known of other causes of MERS-like pneumonia in the KSA or of the general burden of disease due to the known classical respiratory viruses. Testing of adult pilgrims performing the Hajj in 2012 to 2014 has not detected any MERS-CoV. In 2012, nasal swabs from 154 pilgrims collected prior to leaving for or departing from the KSA were tested [47] . In 2013, testing was significantly scaled up with 5,235 nasopharyngeal swabs from 3,210 incoming pilgrims and 2,025 swabs from outgoing pilgrims tested [98] . It should be noted that most pilgrims arrived from MERS-free countries. A further 114 swabs were taken from pilgrims with influenza-like illness [96, 107] . In earlier Hajj gatherings, it was found that influenza viruses circulated widely, whilst other viruses, often rhinoviruses, circulated more selectively, interpreted as indicating their importation along with foreign pilgrims. [107] [108] [109] Over time, increased influenza vaccination has been credited for a fall in the prevalence of influenza like illnesses among Hajj pilgrims. [110] A LRT sample is often not collected for these studies [98, 107, 109] , so false negative findings are a possibility although little is known about the initial site of MERS-CoV infection and replication; it may have been assumed it was the LRT because disease was first noticed there but the URT may be the site of the earliest replication. In Jeddah between March and July 2014 (hereafter called the Jeddah-2014 outbreak; Fig. 3 ), there was a rapid increase in MERS cases, accompanied by intense screening; approximately 5,000 samples from in and around the region were tested in a month yielding around 140 MERS-CoV detections (~3 % prevalence) [111] . Among 5,065 individuals sampled and tested across the KSA between October 2012 and September 2013,108 (2.1 %) detections were made in a hospital-centric population which included hospitalized cases (n = 2,908; 57.4 %), their families (n = 462; 9.1 %) and associated HCWs (n = 1,695; 33.5 %) [32] . Among the detections, 19 (17.8 %) were HCWs and 10 (9.3 %) were family contacts [32] . The 2-3 % prevalence of active MERS-CoV infections is not dissimilar to the hospital-based prevalence of other human CoVs. [112] However, the proportion of deaths among those infected with MERS-CoV is much higher than that known for the HCoVs NL63, HKU1, 229E or OC43 in other countries, and even above that for SARS-CoV; it is not a virus that could reasonably be described as a "storm in a teacup". It is the low transmission rate that has prevented worldwide spread, despite many "opportunities". Very early in the MERS outbreak, some animals were highly regarded as either the reservoir or intermediate host(s) of MERS-CoV with three of the first five cases having contact with DCs [73, 113, 114] . Today, animal MERS-CoV infections must be reported to the world organization for animal health as an emerging disease [115] . A summary of the first MERS cases reported by the WHO defined animal contact with humans as being direct and within 10 days prior to symptom onset [20] . This definition made no specific allowance for acquisition from DCs through a droplet-based route, which is very likely route for acquisition of a virus that initially and predominantly causes respiratory disease [23] . Camels are known to produce high levels of MERS-CoV RNA in their URT and lungs [116] . Providing support for a droplet transmission route and perhaps indicating the presence of RNA in smaller, drier droplet nuclei, MERS-CoV RNA was identified in a high volume air sample collected from a barn housing an infected DC [117] . The precise source from which humans acquire MERS-CoV remains poorly studied but it seems likely that animal and human behavioural factors may play roles (Fig. 3) [118] . These factors may prove important for human cases who do not describe any DC contact [119] nor any contact with a confirmed case. Whether the WHO definition of animal contact is sufficient to identify exposure to this respiratory virus remains unclear. Wording focuses on consumption of DC products but does not specifically ascribe risk to a droplet route for acquisition of MERS-CoV from DC [120] . Some MERS patients are listed in WHO disease notices as being in proximity to DCs or farms, but the individuals have not described coming into contact with the animals. No alternative path for acquiring infection is reported in many of these instances. What constitutes a definition of "contact" during these interviews has been defined for one study [72] . Despite this lack of clarity, the WHO consider that evidence linking MERS-CoV transmission between DCs to humans is irrefutable (Fig. 4) [120] . The possibility that bats were an animal host of MERS-CoV was initially widely discussed because of the existing diversity of coronaviruses known to reside among them [121] [122] [123] [124] . Conclusive evidence supporting bats as a source for human infections by MERS-CoV has yet to be found, but bats do appear to host ancestral representatives [53, 125] . However, these are not variants of the same virus nor always within the same phylogenetic lineage as MERS-CoV; they are each a genetically distinct virus. Bat-to-human infection by MERS-CoV is a purely speculative event. The only piece of MERS-CoV-specific evidence pointing to bats originates from amplification of a 190 nt fragment of the RNAdependent RNA polymerase gene of the MERS-CoV genome, identified in a faecal pellet from an insectivorous Emballonuridae bat, Taphozous perforatus found in Bisha, the KSA [121] . While very short, the sequence of the fragment defined it as a diagnostic discovery. Subsequently a link to DCs was reported [85] and that link has matured into a verified association [38, 126] (Fig. 4) . (See figure on previous page.) Fig. 3 Monthly detections of MERS-CoV (blue bars) and of cases who died (red bars) with some dates of interest marked for 2012 to 4 th September 2015. An approximation of when DC calving season [128] and when recently born DCs are weaned is indicated. Spring (green) and summer (orange) in the Arabian Peninsula are also shaded. Note the left-hand y-axis scale for 2014 and 2015 which is greater than for 2012/13. Sources of these public data include the WHO, Ministries of Health and FluTrackers [207] [208] [209] . Earlier and subsequent versions of this chart are maintained on a personal blog [210] . Modified and reprinted from Mackay IM, Arden KE. Middle East respiratory syndrome: An emerging coronavirus infection tracked by the crowd. Virus Res 2015 Vol 202:60-88 with permission from Elsevier [5] DCs, which make up 95 % of all camels, have a central presence in the Arabian Peninsula where human-DC contact ranges from little to close [119] . Contact may be commonplace and could occur in variety of ways (Fig. 4a) . There are several large well-attended festivals, races, sales and parades which feature DCs and DCs are also kept and bred close to populated areas in the KSA [127, 128] . DC milk and meat are widely consumed and the older DC is an animal of ritual significance after the Hajj pilgrimage [129] . However, MERS-CoV infection frequency is reportedly much lower than is the widespread and frequent habit of eating, drinking and preparing DC products. Daily ingestion of fresh unpasteurized DC milk is common among the desert Bedouin and many others in the KSA. DC urine is also consumed or used for supposed health benefits. Despite camel butchery being a local occupation, neither butchers nor other at-risk groups are identifiable among MERS cases; this may simply be a reporting issue rather than an unexplainable absence of MERS. A small case-control study published in 2015 identified direct DC contact, and not ingestion of products, to be associated with onset of MERS [38] . The first sero-survey of livestock living in the Middle East region was conducted during 2012-2013 [85] . DCs were sampled from a mostly Canary Island-born herd and from Omani DCs (originally imported from the Horn of Africa) [85] . A neutralising antibody assay found only 10 % of strongly seropositive Canary Island [5] . b Camel-to-human infections appear to be infrequent, while human-to-human spread of infection is regularly facilitated by poor IPC in healthcare settings where transmission is amplified, accounting for the bulk of cases. There are human MERS cases that do not fall into either category of source and it is unclear if these acquired infection through some entirely separate route, or from cases that escaped diagnosis. c Hypothetical ways in which subclinical (when infection may not meet a previously defined clinical threshold of signs and/or symptoms) or asymptomatic (no obvious signs or measured, noticed or recalled symptoms of illness) MERS-CoV infection may be implicated in transmission DC sera could neutralise MERS-CoV while all Omani DC sera had high levels of specific MERS-CoV neutralizing antibody [85] . This indicated that DCs had in the past been infected by MERS-CoV, or a very similar virus. Since this study, a host of peer-reviewed reports have looked at both DCs and other animals, and the possibility that they may host MERS-CoV infection. Seropositive DCs have been found throughout the Arabian Peninsula including Oman, the KSA, Qatar, Jordan, the United Arab Emirates (UAE), Kuwait as well as Sudan, Somalia, Egypt, Tunisia, Nigeria, Kenya and Ethiopia in Africa and the Canary Islands [85, [130] [131] [132] [133] [134] . Other animals tested include sheep, cows, pigs, horses, donkeys, mules, birds, water buffalo, goats, Bactrian camels, llamas and guanaco (south American camelids) but none had detectable neutralising antibody against MERS-CoV [4, 74, 78, 85, 86, 135, 136] . No virology or serology studies of human samples from areas in Africa where there are camels with a history of MERS-CoV have been reported to date. However,an absence of unexplained pneumonia that may be attributable to MERS-CoV infection may not signal the absence of virus among humans in each country but simply reflect a lack of expensive epidemiology studies conducted by resource-poor countries. It is thus unclear whether MERS-CoV, or an antigenically related CoV, is an unrecognized pathogen in these regions, perhaps circulating for even longer than it has been known in the Arabian Peninsula [133] . MERS-CoV RNA has also been detected in DC samples, and recovery of infectious virus has also been achieved from DC samples [4, 77, 117, 132, [137] [138] [139] [140] [141] . From some of these, full or majority length genomes of MERS-CoV have been sequenced [77, 137, 138] . DC versions of MERS-CoV were found to be as similar to each other, as were variants detected from different humans over time and across distance. Antibody screening assays have also detected crossreactive antibodies in sera. These were identified as such by screening sera against similar viruses, for example BCoV or HCoV-OC43 (as an antigenic facsimile for BCoV). It is possible that other MERS-CoV-like viruses also reside within DCs, but this does not detract from the definitive finding of MERS-CoV genetic sequences in both DCs and humans [117, 142, 143] . Screening studies have shown that juvenile DCs are more often positive for virus or viral RNA while older DCs are more likely to be seropositive and RNA or virus negative [76, 77, 144] . In adult DCs, MERS-CoV RNA has been detected among animals with pre-existing antibody, suggesting re-infection is possible [77, 144] . Viral loads among positive DCs can be very high [4, 76, 77, 139, 144] and DCs have been found positive both when ill with URT respiratory signs [77, 117, 142, 145] or when apparently healthy [137] . These findings indicate DCs host natural MERS-CoV infections. Furthermore, stored DC sera have revealed signs of MERS-CoV in DCs which date back over three decades (the earliest collected in 1983) [4, 133, 135] . Older sera have not been tested and so precisely how long DCs have been afflicted by MERS-CoV, whether the virus is enzootic among them, introduced to them decades or centuries ago from bats in Africa or the Arabian Peninsula, or they are the subject of regular but short-lived viral incursions from an as yet unknown host, cannot be answered. Researchers sought to determine a direction for infection; were DCs transmitting virus to humans or were humans infecting DCs? At a Qatari site, a farm owner and his employee became ill in mid-October 2013 and tested positive for MERS-CoV RNA in a sputum and throat swab sample, respectively. RT-rtPCRs found MERS-CoV RNA in 11 of 14 positive DC nasal swabs at the farm; six (43 %) positive by two or more assays [138] . The results indicated a recent outbreak had occurred in this herd; the first indication of MERS-CoV RNA found within DCs with a temporal association to human infections. Three positive DC samples were confirmed by sequencing a 358 nt portion of the spike gene; these sequences were identical to each other, again with close homology to other human and DC MERS-CoV sequences [138] . The DCs and human contacts yielded ORF1a and ORF4b sequences differing by only a single nucleotide each, clustering closely with the Hafr-Al-Batin_1_2013 variant [138] . Subsequent case studies found evidence of a concurrent human and DC infection and the direction of that infection was inferred to be from the ill DCs and to their human owners [117, 142, 146] . Partial genome sequences indicated that a human and a MERS-CoV RT-rtPCR positive DC had been infected by a variant of the same virus, harbouring the same distinct pattern of nucleotide polymorphisms. [142] All nine DC in the owner's herd, serially sampled, reacted in a recombinant S1 antigen ELISA, with the two animals that had been RT-rtPCR positive showing a small, verifiable rise in antibody titre [142] . A rise in titre theoretically begins 10 to 21 days after DC infection [142] . The authors suggested that the rise in titre in DC sera which occurred alongside a declining RNA load, while the patient was actively ill and hospitalized, indicated that the DCs were infected first followed by the owner [117, 142] . BCoV antibodies were also present, and rising in one of the two RT-rtPCR positive animals but no animal's antibodies could neutralise BCoV infection [142] . Camel calving season occurs in the winter months (between late October and late February; Fig. 3 ) and this may be a time when there is increased risk to humans of spill-over due to new infections among naïve DC populations [128] . What role maternal camel antibody might play in delaying infection of calves remains unknown [128, 142] . Juvenile DCs appear to host active infection more often than adult DCs and thus the sacrificial slaughter of DCs, which must be five years of age or older (termed a thane), may not be accompanied by significant risk of exposure to infection. In contrast to earlier results, slaughterhouse workers who kill both younger and older DCs, may be an occupational group with significantly higher incidence of seropositivity to MERS-CoV when animals have active MERS-CoV infections [129, 139, [147] [148] [149] . Expanded virological investigations of African DCs may lead to more seropositive animals and geographic areas in which humans may be at risk. It is possible that there are areas where humans already harbour MERS-CoV infections that have not been identified because of an absence of laboratory surveillance. Virological investigations of bats may lead to findings of ancestral viruses and viral 'missing links' and identifying any other animal sources of zoonotic spread is important to inform options for reducing human exposures [56, 76] . Infectious MERS-CoV added to DC, goat or cow milk and stored at 4°C could be recovered at least 72 h later and, if stored at 22°C, recovery was possible for up to 48 h [150] . MERS-CoV titre decreased somewhat when recovered from milk at 22°C but pasteurization completely ablated MERS-CoV infectivity [150] . In a subsequent study, MERS-CoV RNA was identified in the milk, nasal secretion and faeces of DCs from Qatar [151] . A single study has examined the ability of MERS-CoV to survive in the environment [150] . Plastic or steel surfaces were inoculated with 10 6 TCID 50 of MERS-CoV at different temperature and relative humidity (RH) and virus recovery was attempted in cell culture. At high ambient temperature (30°C) and low RH (30 %) MERS-CoV remained viable for 24 h [150] . By comparison, a well known and efficently transmitted respiratory virus, influenza A virus, could not be recovered in culture beyond four hours under any conditions [150] . Aerosol experiments found MERS-CoV viability only decreased 7 % at low RH at 20°C. In comparison, influenza A virus decreased by 95 % [150] . MERS-CoV survival is inferior to that previously demonstrated for SARS-CoV [152] . For context, pathogenic bacteria can remain viable and airborne for 45 min in a coughed aerosol and can spread 4 m. MERS-CoV's ability to remain viable over long time periods gives it the capacity to thoroughly contaminate a room's surfaces when occupied by an infected and symptomatic patient [153] . Whether MERS-CoV can remain adrift and infectious for extended periods (truly airborne) remains unknown. Such findings expand our understanding of the possibilities for droplets to transmit respiratory viruses in many settings, including hospital waiting rooms, emergency departments, treatment rooms, open intensive care facilities and private patient rooms. The nature and quality of air exchange, circulation and filtration are important variables in risk measurement and reduction as is the use of negative pressure rooms to contain known cases. Droplet spread between humans is considered the mechanism of human-to-human transmission and the need for droplet precautions was emphasized after the Al-Ahsa hospital, the KSA and the South Korean outbreaks [21, 23, 154, 155] . By extrapolation, aerosol-generating events involving DCs (urination, defecation, and preparation and consumption of DC products) should be factored into risk measurement and reduction efforts and messaged using appropriate context. The provision of evidence supporting the best formulation of personal protective equipment to be worn by HCWs who receive, manage or conduct procedures on infectious cases remains a priority. MERS-CoV was found and characterized because of its apparent association with severe, and therefore more obvious, illness in humans; we were the canaries in the coal mine. Sero-assays and prospective cohort studies have yet to determine the extent to which milder or asymptomatic cases contribute to MERS-CoV transmission chains. However, transmission of MERS-CoV is defined as sporadic (not sustained), intra-familial, often healthcare associated, inefficient and requiring close and prolonged contact [22, 31, 63, 93, 97, 102, 156] In a household study, 14 of 280 (5 %) contacts of 26 MERS-CoV positive index patients were RNA or antibody positive; the rate of general transmission, even in outbreaks is around 3 % [31] . It seems that the majority of human cases of MERS-CoV, even when numbers appear to increase suddenly, do not readily transmit to more than one other human so to date, the localized epidemic of MERS-CoV has not been self-sustaining [157] [158] [159] [160] [161] . That is to say, the basic reproduction number (R 0 ) -the average number of infections caused by one infected individual in a fully susceptible populationhas been close to one throughout various clusters and outbreaks. If R 0 was greater than 1, a sustained increase in case numbers would be expected. Some R o calculations may be affected by incomplete case contact tracing, limited community testing and how a case is defined. That MERS has had a constant presence in the Arabian Peninsula since 2012 is due to ongoing, sporadic spill-over events from DCs amplified by poorly controlled hospital outbreaks. The first known MERS human-to-human transmission event was one characterized by acute LRT disease in a healthcare setting in Jordan. In stark contrast, a sero-survey of HCW who were sometimes in close and prolonged contact with the first, fatal MERS-CoV case in 2012 [162] , found none of the HCW had seroconverted four months later, despite an absence of eye protection and variable compliance with required PPE standards [162] . Early on in the MERS story, samples for testing were mostly collected from patients with severe illness and not those with milder acute respiratory tract infections. Contacts of confirmed MERS cases were often observed for clinical illness, but not tested. These omissions may have confounded our understanding of MERS-CoV transmission and biased early data towards higher numbers of seriously ill and hospitalized patients, inflating the apparent proportion of fatal cases. Case-control studies were not a focus. As testing paradigms changed and contacts were increasingly tested, more asymptomatic and mild infections were recognized [163] . A rise in the cases termed asymptomatic (which enlarge the denominator for calculations of the proportion of fatal cases, defined in [164] ) resulted in a drop in the proportion of fatal cases during the Jeddah-2014 outbreak. Historically, such rises are consistent with changing definitions and laboratory responses and clinical management of a newly discovered virus infection that was first noted only among the severely ill. Upon follow-up, over three-quarters of such MERS-CoV RNA positive people did recall having one or more symptoms at the time, despite being reported as asymptomatic [165] raising some question over the reliability of other reported data. The proportion of fatal MERS cases within the KSA compared to outside the KSA, as well as the age, and sex distribution change in different ways when comparing MERS outbreaks. Approximately 43 % of MERS cases (549 of 1277) in the KSA were fatal betwen 2012 and December 2015 while 21 % (72 of 330) died among those occurring outside of the KSA. The total number of male cases always outnumber females and the proportion of male deaths is always greater than the proportion of females who die. However the proportion of male deaths from total males with MERS is a similar figure to that for females. In the KSA, there is a greater proportion of younger males among cases and deaths than were observed from the 2015 South Korean or the Jeddah-2014 outbreaks (Additional file 2: Figure S2 ). Why these aspects have differed may be due to differences in the time to presentation and diagnosis, the nature and quality of supportive care, the way a person became infected (habits, exposure to a human or zoonotic source, viral load, route of infection) or the extent to which different populations are burdened by underlying diseases [40] . As a group, HCWs comprised 16 % of MERS cases in the KSA and South Korea. It is apparent that the weekly proportion of infected HCWs increases alongside each steep rise in overall detections (Fig. 5) . In May 2013, the WHO published guidelines for IPC during care of probable or confirmed cases of MERS-CoV infection in a healthcare setting [166] . This is explainable because to date, each case rise has been intimately associated with healthcare-facility related outbreaks [118] . These rises in MERS-CoV detections can decrease the average age during each event because HCWs are usually younger than inpatients with MERS. Healthcare facilities have been a regular target for suggested improvements aimed at improving infection prevention and control (IPC) procedures [115, 118] . Most of the analysis of MERS-CoV genetics has been performed using high throughput or "deep" sequencing methods for complete genome deduction [167] [168] [169] . MERS-CoV was the first subject of such widespread use of deep sequencing to study an emerging viral outbreak with global reach. The technique can produce genomic [207] [208] [209] . Earlier and subsequent versions of this chart are maintained on a personal blog [210] length coverage in a single experiment with highly repetitious measurement of each nucleotide position [52, 140] . Despite assays having been published early on, subgenomic sequencing, once the mainstay of viral outbreak studies, has less often been published during MERS-CoV characterization [48] . As more genomes from both humans and DCs have been characterized, two clades have become apparent; A and B (Fig. 6) . Clade A contains only human-derived MERS-CoV genomes from Jordan, while Clade B comprises the majority of human and camel genomes deduced thus far [168] . Two studies during 2015, one looking at Jeddah-2014 MERS-CoV variants and another looking at a variant exported from South Korea to China, have now identified signs of genetic recombination among MERS-CoV variants. While human and camel whole genome sequences have retained >99 % identity with each other, members of genetically distinct lineages can and do swap genetic material when suitable conditions and coinfections co-occur [170] [171] [172] . Shared identity implies that the major source for human acquisition is the DC, rather than another animal, although more testing of other animal species is needed to confirm that conclusion. Over a month, a DC virus sequenced on different occasions did not change at all indicating a degree of genomic stability in its host, supporting that DCs are the natural, rather than intermediate, host for the MERS-CoV we know today [77] . To date, recombination has been localised to breakpoints near the boundary between ORF1a and ORF1b regions, within the spike gene [170] and in the ORF1b region (Fig. 2) [172] . It is not unexpected that recombination should occur since it is well known among other CoVs [124] and because the majority of MERS-CoV whole genomes collected from samples spanning three years (2012-2015) and from humans, camels and different countries have shown close genetic identity to each other, with just enough subtle variation to support outbreak investigations so long as whole genome sequencing is applied [52, 77, 135, 138, 168, [173] [174] [175] . Changes in genome sequence may herald alterations to virus transmissibility, replication, persistence, lethality or response to future drugs. If we have prior knowledge of the impact of genetic changes because of thorough characterization studies, we can closely Fig. 6 The genetic relationship between MERS-CoV nucleotide sequences (downloaded from GenBank using the listed accession numbers and from virological.org [212] ). This neighbour joining tree was created in MEGA v6 using an alignment of human and DCderived MERS-CoV sequences (Geneious v8.1 [211] ). Clades are indicated next to dark (Clade A) or pale (Clade B) blue vertical bars. Camel icons denote genomes from DCs. Healthcare or community outbreaks are boxed and labelled using previously described schemes [212, 213] monitor the genomic regions and better understand any changes in transmission or disease patterns as they occur. Genetic mutations noted during the largest of human outbreaks, Jeddah-2014, did not impart any major replicative or immunomodulatory changes when compared to earlier viral variants in vitro [156, 176] . However, we understand very little of the phenotypic outcomes that result from subtle genetic change in MERS-CoV genomes. To date no clinical relevance or obvious in vivo changes to viral replication, shedding or transmission has been reported or attributed to mutations or to new recombinant viruses [156] . But vigilance and larger, more contemporary and in vivo studies are needed. Genome sequence located to a distinct clade were identified from an Egyptian DC that was probably imported from Sudan. This does not fit into either of the current clades [125, 168, 177] . A virus sequenced from a Neoromicia capensis bat was more closely related to MERS-CoV than other large bat-derived sequences had been to that point, but the genome of a variant of a MERS-CoV has yet to be discovered and deduced from any bat [125] . Analyses of MERS-CoV genomes have shown that most single nucleotide differences among variants were located in the last third of the genome (Fig. 2) , which encodes the spike protein and accessory proteins [168] . At least nine MERS-CoV genomes contained amino acid substitutions in the receptor binding domain (RBD) of the spike protein and codons 158 (N-terminal region), 460 (RBD), 1020 (in heptad repeat 1), 1202 and 1208 bear investigation as markers of adaptive change [140, 169] . The spike protein had not changed in the recombinant MERS-CoV genome identified in China in 2015 but was reported to have varied at a higher rate than that for complete MERS-CoV genomes, among South Korean variants [172, 178] . This highlights that subgenomic regions may not always contain enough genetic diversity to prove useful for differentiating viral variants. Despite this, one assay amplifying a 615 nucleotide fragment of the spike S2 domain gene for Sanger sequencing agreed with the results generated by the sequencing of a some full genomes and was useful to define additional sequence groupings [177] . Genomic sequence can also be used to define the geographic boundaries of a cluster or outbreak and monitor its progress, based on the similarity of the variants found among infected humans and animals when occurring together, or between different sites and times (Fig. 6 ) [169] . This approach was employed when defining the geographically constrained MERS hospital outbreak in Al-Ahsa, which occurred between 1 st April and 23 rd May 2013, as well as clusters in Buraidah and a community outbreak in Hafr Al-Batin, the KSA. Genomic sequencing identified that approximately 12 MERS-CoV detections from a community outbreak in Hafr Al-Batin between June and August 2013 may have been triggered by an index case becoming infected through DC contact [175] . Sequencing MERS-CoV genomes from the 2013 Al-Ahsa hospital outbreak indicated that multiple viral variants contributed to the cases but that most were similar enough to each other to be consistent with human-tohuman transmission. Molecular epidemiology has revealed otherwise hidden links in transmission chains encompassing a period of up to five months [179] . However, most outbreaks have not continued for longer than two to three months and so opportunities for the virus to adapt further to humans through co-infection and sustained serial passage have been rare [169] . In Riyadh-2014, genetic evidence supported the likelihood of multiple external introductions of virus, implicating a range of healthcare facilities in an event that otherwise looked contiguous [23, 168, 179] . Riyadh is a nexus for camel and human travel and has had more MERS cases than any other region of the KSA to date but also harbours a wide range of MERS-CoV variants [128, 167, 179] . However the South Korean outbreak originated from a single infected person, resulting in three to four generations of cases [180, 181] . Studies of this apparently recombinant viral variant did not find an increased evolutionary rate and no sign of virus adaptation thus the outbreak seems to have been driven by circumstance rather than circumstance together with mutation [181] . For many MERS cases detected outside the Arabian Peninsula, extensive contact tracing has been performed and the results described in detail. Contact tracing is essential to contain the emergence and transmission of a new virus and today it is supported by molecular epidemiology. Although it is an expensive and time consuming process, contact tracing can identify potential new infections and through active or passive monitoring, react more rapidly if disease does develop. Results of contact tracing to date have found that onward transmission among humans is an infrequent event. For example, there were 83 contacts, both symptomatic and asymptomatic, of a case treated in Germany who travelled from the UAE but no sign of virus or antibody were found in any of them [73] . The very first MERS case had made contact with 56 HCWs and 48 others, but none developed any indication of infection [162] . In a study of 123 contacts of a case treated in France, only seven matched the definition for a possible case and were tested; one who had shared a 20 m 2 hospital room while in a bed 1.5 m away from the index case for a prolonged period was positive [26] . None of the contacts of the first two MERS cases imported into the USA in 2014 contained any MERS-CoV footprint [182] and none of the 131 contacts of two travellers returning to the Netherlands developed MERS-CoV antibodies or tested RNA positive [25, 183] . Analyses of public data reveal many likely instances of nosocomial acquisition of infection in the Arabian Peninsula and these data may be accompanied by some details noting contact with a known case or facility. One example identified the likely role of a patient with a subclinical infection, present in a hospital during their admission for other reasons, as the likeliest index case triggering a family cluster [93] . Contact tracing was a significant factor in the termination of a 2015 outbreak involving multiple South Korean hospitals [184] . Such studies demonstrate the necessity of finding and understanding a role for mild and asymptomatic cases, together with restricting close contact or prolonged exposure of infected people to others, especially older family members and friends with underlying disease (Fig. 4c) . The hospital-associated outbreak in Jeddah in 2014 was the largest and most rapid accumulation of MERS-CoV detections to date. The greatest number of MERS-CoV detections of any month on record occurred in Jeddah in April. The outbreak was mostly (>60 % of cases) associated with human-to-human spread within hospital environments and resulted from a lack of, or breakdown in, infection prevention and control [37, 185, 186] . A rise in fatalities followed the rapid increase in case numbers. In 2015 two large outbreaks occurred. South Korea was the site of the first large scale outbreak outside the Arabian Peninsula and produced the first cases in both South Korea and China, occurring between May and July 2015. This was closely followed by a distinct outbreak in Ar Riyad province in the KSA which appeared to come under control in early November. After staying in Bahrain for two weeks, a 68 year old male (68 M) travelled home to South Korea via Qatar, arriving free of symptoms on the 4 th May 2015 [187] . He developed fever, myalgia and a cough nearly a week later (11 th ). He visited a clinic as an outpatient between the 12 th and 15 th of May and was admitted to Hospital A on the 15 th [188] . He was discharged from Hospital A on the 17 th then visited and was admitted to the emergency department of Hospital B on the 18 th . During this second stay, a sputum sample was taken and tested positive for MERS-CoV on the 20 th [187, 188] , triggering transfer to the designated isolation treatment facility. Over a period of 10 days, the index case was seen at three different hospitals, demonstrating a key feature of "hospital shopping" that shaped the South Korean outbreak. Approximately 34 people were infected during this time [187] . In total 186 cases were generated in this outbreak, all linked through a single transmission chain to 68 M; 37 cases died [189] . In South Korea, the national health insurance system provides for relatively low cost medical care, defraying some costs by making family members responsible for a portion of the ministration of the sick, resulting in them sometimes staying for long periods in the rooms that often have more than four beds in them [24] . Other factors thought to have enabled this outbreak included unfamiliarity of local clinicians with MERS, ease with which the public can visit and be treated by tertiary hospitals, the custom of visiting sick friends and relatives in hospitals, the hierarchical nature of Korean society, crowded emergency rooms, poor IPC measures, a lack of negative pressure isolation rooms and poor inter-hospital communication of patient disease histories [24, [190] [191] [192] . All of the reported transmission occurred across three or four generations and apart from one unknown source, were all hospital-acquired [24, 120, 181, [193] [194] [195] . Few clinical details about these cases have been reported to date and detail on transmission and contact tracing is minimal. The hospitals involved were initially not identified, governmental guidance and actions produced confusing messages and there was very limited communication at all early on which resulted in unnecessary concern, distrust and a distinct economic impact [191, [196] [197] [198] . Early in the outbreak, a infected traveller, the son of an identified case in South Korea, passed through Hong Kong on his way to China where he was located, isolated and cared for in China [91, 199, 200] . No contacts became ill. The outbreak was brought under control in late July/ early August [201] after improved IPC measures were employed, strong contact tracing monitoring and quarantine, expanded laboratory testing, hospitals were better secured, specialized personnel were dispatched to manage cases and international cooperation increased [202, 203] . A review of public data showed that, as for MERS in the KSA, older age and the presence of underlying disease were significantly associated with a fatal outcome in South Korea. [40] Even though R 0 is <1, super-spreading events facilitated by circumstances created in healthcare settings and characterized by cluster sizes over 150, such as this one, are not unexpected from MERS-CoV infection [204] . The dynamic of an outbreak depends on the R 0 and an individual's viral shedding patterns, contact type and frequency, hospital procedures and population structure and density [204] . In the region of Ar Riyad, including the capital city of Riyadh, a hospital based cluster began, within a single hospital, from late June 2015 [205] . By mid-September there had been approximately170 cases reported but the outbreak appeared to been brought under control in November. It became apparent early on that MERS-CoV spread relatively ineffectively from human-to-human. Despite ongoing and possibly seasonal introduction of virus to the human population via infected DCs and perhaps other animals yet to be identified, the vast majority of MERS-CoV transmission has occurred from infected to uninfected humans in close and prolonged contact through circumstances created by poor infection control in health care settings. This opportunistic virus has had its greatest impact on those with underlying diseases and such vulnerable people, sometimes suffering multiple comorbidities, have been most often associated with hospitals, creating a perfect storm of exposure, transmission and mortality. It remains unclear if this group are uniquely affected by MERS-CoV or if other respiratory virus infections, including those from HCoVs, produce a similarly serious impact. In South Korea, a single imported case created an outbreak of 185 cases and 36 deaths that had a disproportionate impact on economic performance, community behaviour and trust in government and the health care system. Household human-to human transmission occurs but is also limited. Educational programs will be essential tools for combatting the spread of MERS-CoV both within urban and regional communities and for the health care setting. Vigilance remains important for containment since MERS-CoV is a virus with a genetic makeup that has been observed for only three years and is not stable. Among all humans reported to be infected, nearly 40 % have died. Continued laboratory testing, sequencing, analysis, timely data sharing and clear communication are essential for such vigilance to be effective. Global alignment of case definitions would further aid accurate calculation of a case fatality ratio by including subclinical case numbers. Whole genome sequencing has been used extensively to study MERS-CoV travel and variation and although it remains a tool for experts, it appears to be the best tool for the job. MERS and SARS have some clinical similarities but they also diverge significantly [206] . Defining characteristics include the higher PFC among MERS cases (above 50 % in 2013 and currently at 30-40 %; well above the 9 % of SARS) and the higher association between fatal MERS and older males with underlying comorbidities. For the viruses, MERS-CoV has a broader tropism, grows more rapidly in vitro, more rapidly induces cytopathogenic change, triggers distinct transcriptional responses, makes use of a different receptor, induces a more proinflammatory state and has a delayed innate antiviral response compared to SARS-CoV. There appears to be a 2-3 % prevalence of MERS-CoV in the KSA with a 5 % chance of secondary transmission within the household. There is an increased risk of infection through certain occupations at certain times and a much greater chance for spread to other humans during circumstances created by humans, which drives more effective transmission than any R 0 would predict on face value. Nonetheless, despite multiple mass gatherings that have afforded the virus many millions of opportunities to spread, there have remarkably been no reported outbreaks of MERS or MERS-CoV during or immediately after these events. There is no evidence that MERS-CoV is a virus of pandemic concern. Nonetheless, hospital settings continue to describe MERS cases and outbreaks in the Arabian Peninsula. As long as we facilitate the spread of MERS-CoV among our most vulnerable populations, the world must remain on alert for cases which may be exported more frequently when a host country with infected camel reservoirs is experiencing human clusters or outbreaks. The MERS-CoV appears to be an enzootic virus infecting the DC URT with evidence of recent genetic recombination. It may once have had its origins among bats, but evidence is lacking and the relevance of that to today's ongoing epidemic is academic. Thanks to quick action, the sensitive and rapid molecular diagnostic tools required to achieve rapid and sensitive detection goal have been in place and made widely available since the virus was reported in 2012. RT-PCR testing of LRT samples remains the gold standard for MERS-CoV confirmation. Serological tools continue to emerge but they are in need of further validation using samples from mild and asymptomatic infections and a densely sampled cohort study to follow contacts of new cases may address this need. Similarly, the important question of whether those who do shed MERS-CoV RNA for extended periods are infectious while appearing well, continues to go unanswered. It is even unclear just how many 'asymptomatic' infections have been described and reported correctly which in turn raises questions about the reliability of other clinical data collection to date. While the basic virology of MERS-CoV has advanced over the course of the past three years, understanding what is happening in, and the interplay between, camel, environment and human is still in its infancy. Additional file 1: Figure S1 . The

How was the first culture of the new Coronavirus announced?

Evidence for the Convergence Model: The Emergence of Highly Pathogenic Avian Influenza (H5N1) in Viet Nam https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4580613/ SHA: ee5b43d20a640664510cb7a540caaae4a8e19933 Authors: Saksena, Sumeet; Fox, Jefferson; Epprecht, Michael; Tran, Chinh C.; Nong, Duong H.; Spencer, James H.; Nguyen, Lam; Finucane, Melissa L.; Tran, Vien D.; Wilcox, Bruce A. Date: 2015-09-23 DOI: 10.1371/journal.pone.0138138 License: cc-by Abstract: Building on a series of ground breaking reviews that first defined and drew attention to emerging infectious diseases (EID), the ‘convergence model’ was proposed to explain the multifactorial causality of disease emergence. The model broadly hypothesizes disease emergence is driven by the co-incidence of genetic, physical environmental, ecological, and social factors. We developed and tested a model of the emergence of highly pathogenic avian influenza (HPAI) H5N1 based on suspected convergence factors that are mainly associated with land-use change. Building on previous geospatial statistical studies that identified natural and human risk factors associated with urbanization, we added new factors to test whether causal mechanisms and pathogenic landscapes could be more specifically identified. Our findings suggest that urbanization spatially combines risk factors to produce particular types of peri-urban landscapes with significantly higher HPAI H5N1 emergence risk. The work highlights that peri-urban areas of Viet Nam have higher levels of chicken densities, duck and geese flock size diversities, and fraction of land under rice or aquaculture than rural and urban areas. We also found that land-use diversity, a surrogate measure for potential mixing of host populations and other factors that likely influence viral transmission, significantly improves the model’s predictability. Similarly, landscapes where intensive and extensive forms of poultry production overlap were found at greater risk. These results support the convergence hypothesis in general and demonstrate the potential to improve EID prevention and control by combing geospatial monitoring of these factors along with pathogen surveillance programs. Text: Two decades after the Institute of Medicine's seminal report [1] recognized novel and reemerging diseases as a new category of microbial threats, the perpetual and unexpected nature of the emergence of infectious diseases remains a challenge in spite of significant clinical and biomedical research advances [2] . Highly Pathogenic Avian Influenza (HPAI) (subtype H5N1) is the most significant newly emerging pandemic disease since HIV/AIDS. Its eruption in Southeast Asia in 2003-4 and subsequent spread globally to more than 60 countries fits the complex systems definition of "surprise" [3] . In this same year that IOM had published its final report on microbial threats which highlighted H5N1's successful containment in Hong Kong in 1997 [4] , massive outbreaks occurred in Southeast Asia where it remains endemic, along with Egypt's Nile Delta. Since 2003, HPAI H5N1 has killed millions of poultry in countries throughout Asia, Europe, and Africa, and 402 humans have died from it in sixteen countries according to WHO data as of January 2015. The threat of a pandemic resulting in millions of human cases worldwide remains a possibility [5] . Lederberg et al. [1] first pointed to the multiplicity of factors driving disease emergence, which later were elaborated and described in terms of 'the convergence model' [6] . The model proposes emergence events are precipitated by the intensifying of biological, environmental, ecological, and socioeconomic drivers. Microbial "adaptation and change," along with "changing ecosystems" and "economic development and land use" form major themes. Joshua Lederberg, the major intellectual force behind the studies summed-up saying "Ecological instabilities arise from the ways we alter the physical and biological environment, the microbial and animal tenants (humans included) of these environments, and our interactions (including hygienic and therapeutic interventions) with the parasites" [6] . Combining such disparate factors and associated concepts from biomedicine, ecology, and social sciences in a single framework remains elusive. One approach suggested has been to employ social-ecological systems theory that attempts to capture the behavior of so-called 'coupled natural-human systems', including the inevitable unexpected appearance of new diseases, themselves one of the "emerging properties" of complex adaptive systems (CAS) [7, 8] . The convergence model can be so adapted by incorporating the dynamics of urban, agricultural, and natural ecosystem transformations proposed with this framework. These associated multifaceted interactions including feedbacks that affect ecological communities, hosts and pathogen populations, are the proximate drivers of disease emergence. The initial HPAI H5N1 outbreaks in Vietnam represent an ideal opportunity to adapt and test a CAS-convergence model. Emergence risk should be highest in the most rapidly transforming urban areas, peri-urban zones where mixes of urban-rural, modern-traditional land uses and poultry husbandry coincide most intensely. Specifically we hypothesized a positive association between the presence of HPAI outbreaks in poultry at the commune level and: 1) peri-urban areas, as defined by Saksena et al. [9] , 2) land-use diversity, and 3) co-location of intensive and extensive systems of poultry. We used the presence or absence at the commune level of HPAI H5N1 outbreaks in poultry as the dependent variable. Vietnam experienced its first HPAI H5N1 outbreak in late 2003, since then, there have been five waves and sporadic outbreaks recorded over the years [10, 11] . We chose to study the first wave (Wave 1) that ended in February 2004 and the second wave (Wave 2) that occurred between December 2004 and April 2005. We used data from the Viet Nam 2006 Agricultural Census to develop an urbanicity classification that used data collected at a single point in time (2006) but across space (10,820 communes) to infer processes of change (urbanization, land-use diversification, and poultry intensification) [9] . The 58 provinces in Vietnam (not counting the 5 urban provinces that are governed centrally) are divided into rural districts, provincial towns, and provincial cities. Rural districts are further divided into communes (rural areas) and towns, and provincial towns and cities are divided into wards (urban subdistricts) and communes. A commune in Viet Nam is thus the third level administrative subdivision, consisting of villages/hamlets. For the purpose of simplicity we will henceforth use the term "commune" to refer to the smallest administrative unit whether it is a commune, town, or ward. We included risk factors documented in previous work. We also aimed to understand the differences, if any, in risk dynamics at different scales; comparing risks at the national scale to those at two sub-national agro-ecological zones. For this purpose we chose to study the Red River and Mekong River deltas, well known hot spots of the disease. Hence we conducted two sets of analyses (waves 1 and 2) for three places (nation, Red River Delta, and Mekong Delta) producing a total of 6 wave-place analyses. Data on outbreaks were obtained from the publicly available database of Viet Nam's Department of Animal Health. Given the highly complex dynamics of the epidemics and in keeping with recent methodological trends, we used multiple modeling approaches-parametric and non-parametric-with a focus on spatial analysis. We used both 'place' oriented models that can take into account variations in factors such as policies and administration as well as 'space' oriented models that recognize the importance of physical proximity in natural phenomenon [12] . Very few empirical studies have attempted to determine whether urbanization is related to EID outbreaks or whether urbanization is associated primarily with other factors related to EID outbreaks. One immediate problem researchers face is defining what is rural, urban, and transitional (i.e., peri-urban). Some studies have used official administrative definitions of urban and rural areas, but this approach is limited in its bluntness [13] . Other studies prioritized human population density as a satisfactory surrogate [11, [14] [15] [16] [17] [18] [19] [20] , but this approach ignores the important fact that density is not a risk factor if it is accompanied by sufficient infrastructure to handle the population. Spencer [21] examined urbanization as a non-linear characteristic, using household-level variables such as water and sanitation services. He found evidence that increased diversity in water supply sources and sanitation infrastructure were associated with higher incidences of HPAI. These studies employed a limited definition of urbanization that lacked a well-defined characterization of peri-urbanization. Still other studies have mapped the relative urban nature of a place, a broad concept that is often referred to as 'urbanicity' [22] [23] [24] [25] . While these studies show differences in the rural/ urban nature of communities across space and time, they have been limited to small-to medium-scale observational studies; and they have failed to distinguish between different levels of "ruralness". Perhaps the best known model of peri-urbanization is McGee's concept of desakota (Indonesian for "village-town") [26] . McGee identified six characteristics of desakota regions: 1) a large population of smallholder cultivators; 2) an increase in non-agricultural activities; 3) extreme fluidity and mobility of population; 4) a mixture of land uses, agriculture, cottage industries, suburban development; 5) increased participation of the female labor force; and 6) "grey-zones", where informal and illegal activities group [26] . Saksena et al. [9] built on McGee's desakota concepts and data from the 2006 Viet Nam Agricultural Census to establish an urbanicity classification. That study identified and mapped the 10,820 communes, the smallest administrative unit for which data are collected, as being rural, peri-urban, urban, or urban core. This project used the Saksena classification to assess associations between urbanicity classes, other risks factors, and HPAI outbreaks. Researchers have estimated that almost 75% of zoonotic diseases are associated with landcover and land-use changes (LCLUC) [27, 28] . LCLUC such as peri-urbanization and agricultural diversification frequently result in more diverse and fragmented landscapes (number of land covers or land uses per unit of land). The importance of landscape pattern, including diversity and associated processes, which equate to host species' habitat size and distribution, and thus pathogen transmission dynamics is axiomatic though the specific mechanisms depend on the disease [29, 30] . Landscape fragmentation produces ecotones, defined as abrupt edges or transitions zones between different ecological systems, thought to facilitate disease emergence by increasing the intensity and frequency of contact between host species [31] Furthermore, fragmentation of natural habitat tends to interrupt and degrade natural processes, including interspecies interactions that regulate densities of otherwise opportunistic species that may serve as competent hosts [32] , although it is not clear if reduced species diversity necessarily increases pathogen transmission [33] . Rarely has research connected land-use diversification to final health endpoints in humans or livestock; this study attempts to link land-use diversity with HPAI H5N1 outbreaks. Human populations in the rapidly urbanizing cities of the developing world require access to vegetables, fruits, meat, etc. typically produced elsewhere. As theorized by von Thünen in 1826 [34] , much of this demand is met by farms near cities [35] , many in areas undergoing processes of peri-urbanization [26] . Due to the globalization of poultry trade, large-scale chicken farms raising thousands of birds have expanded rapidly in Southeast Asia and compete with existing small backyard farmers [36] . Large, enterprise-scale (15,000-100,000 birds) operations are still rare in Viet Nam (only 33 communes have such a facility). On the other hand, domestic and multinational companies frequently contract farmers to raise between 2,000 and 15,000 birds. Recent studies have examined the relative role of extensive (backyard) systems and intensive systems [15, [17] [18] [19] 37] . In much of Asia there is often a mix of commercial and backyard farming at any one location [36] . Experts have suggested that from a biosecurity perspective the co-location of extensive and intensive systems is a potential risk factor [38] . Intensive systems allow for virus evolution (e.g. Low Pathogenic Avian Influenza to HPAI) and transformation, while extensive systems allow for environmental persistence and circulation [39] . Previous studies of chicken populations as a risk factor have distinguished between production systems-native chickens, backyard chickens; flock density; commercial chickens, broilers and layers density, etc. [15, [17] [18] [19] 37] . In isolation, however, none of these number and/or density based poultry metrics adequately measures the extent of co-location of intensive and extensive systems in any given place. Intensive and extensive systems in Viet Nam have their own fairly well defined flock sizes. A diversity index of the relative number of intensive and extensive systems of poultry-raising can better estimate the effect of such co-location; this study attempts to link a livestock diversity index with the presence or absence of HPAI H5N1 outbreaks at the commune level. This study investigated for the 10,820 communes of Viet Nam a wide suite of socio-economic, agricultural, climatic and ecological variables relevant to poultry management and the transmission and persistence of the HPAI virus. Many of these variables were identified based on earlier studies of HPAI (as reviewed in Gilbert and Pfeiffer [40] ). Three novel variables were included based on hypotheses generated by this project. All variables were measured or aggregated to the commune level. The novel variables were: • Degree of urbanization: We used the urbanicity classification developed by Saksena et al. [9] to define the urban character of each commune. The classification framework is based on four characteristics: 1) percentage of households whose main income is from agriculture, aquaculture and forestry, 2) percentage of households with modern forms of toilets, 3) percentage of land under agriculture, aquaculture and forestry and 4) the Normalized Differentiated Vegetation Index (NDVI). The three-way classification enabled testing for non-linear and non-monotonous responses. • Land-use diversity: We measured land-use diversity using the Gini-Simpson Diversity Index [41] . The Gini-Simpson Diversity Index is given by 1-λ, where λ equals the probability that two entities taken at random from the dataset of interest represent the same type. In situations with only one class (complete homogeneity) the Gini-Simpson index would have a value equal to zero. Such diversity indices have been used to measure land-use diversity [42] . We used the following five land-use classes: annual crops, perennial crops, forests, aquaculture and built-up land (including miscellaneous uses) for which data were collected in the 2006 Agricultural Census. The area under the last class was calculated as the difference between the total area and the sum of the first four classes. The following variables are listed according to their role in disease introduction, transmission and persistence, though some of these factors may have multiple roles. • Human population related transmission. Human population density [11, 14-16, 18, 19, 44, 45] . • Poultry trade and market. Towns and cities were assumed to be active trading places [10, 18, 37, 44, 46] . So, the distance to the nearest town/city was used as indicator of poultry trade. Trade is facilitated by access to transportation infrastructure [37, 47, 48] . So, the distance to the nearest a) national highway and b) provincial highway was used as indicator of transportation infrastructure. • Disease introduction and amplification. The densities of chicken were calculated based on commune area [15, 19, 37, 49] . • Intermediate hosts. Duck and geese densities were calculated using total commune area [11, 19, 49] . As previous studies have shown a link between scavenging in rice fields by ducks and outbreaks, we also calculated duck density using only the area under rice. • Agro-ecological and environmental risk factors. Previous studies have shown that the extent of rice cultivation is a risk factor, mainly due its association with free ranging ducks acting as scavengers [10] . We used percentage of land under rice cultivation as a measure of extent. Rice cropping intensity is also a known risk factor [11, 17, 37] . We used the mean number of rice crops per year as a measure of intensity. The extent of aquaculture is a known risk factor [10] , possibly because water bodies offer routes for transmission and persistence of the virus. The percentage of land under aquaculture was used as a metric. Proximity to water bodies increases the risk of outbreaks [47, [50] [51] [52] , possibly by increasing the chance of contact between wild water birds and domestic poultry. We measured the distance between the commune and the nearest: a) lake and b) river. Climatic variables-annual mean temperature and annual precipitation-have been associated with significant changes in risk [48, 53] . Elevation, which is associated with types of land cover and agriculture, has been shown to be a significant risk factor in Vietnam [10] . Compound Topographical Index (CTI, also known as Topographical Wetness Index) is a measure of the tendency for water to pool. Studies in Thailand and elsewhere [54] have shown that the extent of surface water is a strong risk factor, possibly due to the role of water in long-range transmission and persistence of the virus. In the absence of reliable and inexpensive data on the extent of surface water we used CTI as a proxy. CTI has been used in Ecological Niche Models (ENM) of HPAI H5N1 [55, 56] . However, given the nature of ENM studies, the effect of CTI as a risk factor has been unknown so far. CTI has been used as a risk factor in the study of other infectious and non-infectious diseases [57] . Some studies have shown that at local scales, the slope of the terrain (a component of CTI) was significantly correlated with reservoir species dominance [58] . CTI is a function of both the slope and the upstream contributing area per unit width orthogonal to the flow direction. CTI is computed as follows: CTI = ln (A s / (tan (β)) where; A s = Area Value calculated as ((flow accumulation + 1) Ã (pixel area in m 2 )) and β is the slope expressed in radians [59] . Though previous studies have indicated that Normalized Difference Vegetation Index (NDVI) is a risk factor [10, 20, 55, 60, 61], we did not include it explicitly in our models, as the urban classification index we used included NDVI [9] . We obtained commune level data on HPAI H5N1 outbreaks from the publicly available database of the Department of Animal Health [10] . Viet Nam experienced its first major epidemic waves between December 2003 and February 2006 [10] . We chose to study the first wave (Wave 1) that ended in February 2004 and the second wave (Wave 2) that occurred between December 2004 and April 2005. In Wave 1, 21% of the communes and in Wave 2, 6% of the communes experienced outbreaks. We used data from the 1999 Population Census of Viet Nam to estimate human population per commune. We relied on data from two Agriculture Censuses of Viet Nam. This survey is conducted every five years covering all rural households and those peri-urban households that own farms. Thus about three-fourths of all of the country's households are included. The contents of the survey include number of households in major production activities, population, labor classified by sex, age, qualification, employment and major income source; agriculture, forestry and aquaculture land used by households classified by source, type, cultivation area for by crop type; and farming equipment by purpose. Commune level surveys include information on rural infrastructure, namely electricity, transportation, medical stations, schools; fresh water source, communication, markets, etc. Detailed economic data are collected for large farms. We used the 2006 Agriculture Census for most variables because the first three epidemic waves occurred between the Agricultural Censuses of 2001 and 2006 but were closer in time to the 2006 census [10] . However, for data on poultry numbers we used the 2001 Agriculture Census data set because between 1991 and 2003 the poultry population grew at an average rate of 7% annually. However, in 2004, after the first wave of the H5N1 epidemic, the poultry population fell 15%. Only by mid-2008 did the poultry population return close to pre-epidemic levels. Thus, we considered the poultry population data from the 2001 census to be more representative. We aggregated census household data to the commune level. A three-way classification of the rural-to-urban transition was based on a related study [9] . Raster data on annual mean temperature and precipitation were obtained from the World-Clim database and converted to commune level data. The bioclimatic variables were compiled from the monthly temperature and precipitation values and interpolated to surfaces at 90m spatial resolution [62] . This public database provides data on the average climatic conditions of the period 1950-2000. Elevation was generated from SRTM 90 meter Digital Elevation Models (DEM) acquired from the Consortium for Spatial Information (CGIAR-CSI). Compound Topographical Index (CTI) data were generated using the Geomorphometry and Gradient Metrics Toolbox for Arc-GIS 10.1. Prior to risk factor analysis we cleaned the data by identifying illogical values for all variables and then either assigning a missing value to them or adjusting the values. Illogical values occurred mainly (less than 1% of the cases) for land-related variables such as percentage of commune land under a particular type of land use. Next we tested each variable for normality using the BestFit software (Palisade Corporation). Most of the variables were found to follow a log-normal distribution and a log-transform was used on them. We then examined the bi-variate correlations between all the risk factors (or their log-transform, as the case may be). Correlations were analyzed separately for each place. Certain risk factors were then eliminated from consideration when |r| ! 0.5 (r is the Pearson correlation coefficient). When two risk factors were highly correlated, we chose to include the one which had not been adequately studied explicitly in previously published risk models. Notably, we excluded a) elevation (correlated with human population density, chicken density, duck density, percentage land under paddy, annual temperature and compound topographical index), b) human population density (correlated with elevation and CTI), c) chicken density (only at national level, correlated with CTI), d) duck and goose density (correlated with elevation, chicken density, percentage land under paddy, land use diversity index and CTI), e) annual temperature (correlated with elevation and CTI) and f) cropping intensity (correlated with percentage land under paddy). Considering the importance of spatial autocorrelation in such epidemics, we used two modeling approaches: 1) multi-level Generalized Linear Mixed Model (GLMM) and 2) Boosted Regression trees (BRT) [63, 64] with an autoregressive term [65] . GLMM is a 'place' oriented approach that is well suited to analyzing the effect of administrative groupings, while BRT is a 'space' oriented approach that accounts for the effects of physical proximity. We began by deriving an autoregressive term by averaging the presence/absence among a set of neighbors defined by the limit of autocorrelation, weighted by the inverse of the Euclidean distance [65] . The limit of the autocorrelation of the response variable was obtained from the range of the spatial correlogram ρ (h) [66] . To determine which predictor variables to include in the two models, we conducted logistic regression modeling separately for each of them one by one but included the autoregressive term each time. We finally included only those variables whose coefficient had a significance value p 0.2 (in at least one wave-place combination) and we noted the sign of the coefficient. This choice of p value for screening risk factors is common in similar studies [15, 18, 45, 67] . We used a two-level GLMM (communes nested under districts) to take account of random effects for an area influenced by its neighbors, and thus, we studied the effect of spatial autocorrelation. We used robust standard errors for tests of fixed effects. Boosted regression trees, also known as stochastic gradient boosting, was performed to predict the probability of HPAI H5N1 occurrence and determine the relative influence of each risk factor to the HPAI H5N1 occurrence. This method was developed recently and applied widely for distribution prediction in various fields of ecology [63, 64] . It is widely used for species distribution modeling where only the sites of occurrence of the species are known [68] . The method has been applied in numerous studies for predicting the distribution of HPAI H5N1 disease [16, 51, [69] [70] [71] . BRT utilizes regression trees and boosting algorithms to fit several models and combines them for improving prediction by performing iterative loop throughout the model [63, 64] . The advantage of BRT is that it applies stochastic processes that include probabilistic components to improve predictive performance. We used regression trees to select relevant predictor variables and boosting to improve accuracy in a single tree. The sequential process allows trees to be fitted iteratively through a forward stage-wise procedure in the boosting model. Two important parameters specified in the BRT model are learning rate (lr) and tree complexity (tc) to determine the number of trees for optimal prediction [63, 64] . In our model we used 10 sets of training and test points for cross-validation, a tree complexity of 5, a learning rate of 0.01, and a bag fraction of 0.5. Other advantages of BRT include its insensitivity to co-linearity and non-linear responses. However, for the sake of consistency with the GLMM method, we chose to eliminate predictors that were highly correlated with other predictors and to make log-transforms where needed. In the GLMM models we used p 0.05 to identify significant risk factors. The predictive performances of the models were assessed by the area under the curve (AUC) of the receiver operation characteristic (ROC) curve. AUC is a measure of the overall fit of the model that varies from 0.5 (chance event) to 1.0 (perfect fit) [72] . A comparison of AUC with other accuracy metrics concluded that it is the most robust measure of model performance because it remained constant over a wide range of prevalence rates [73] . We used the corrected Akaike Information Criteria (AICc) to compare each GLMM model with and without its respective suite of fixed predictors. We used SPSS version 21 (IBM Corp., New York, 2012) for GLMM and R version 3.1.0 (The R Foundation for Statistical Computing, 2014) for the BRT. For calculating the spatial correlogram we used the spdep package of R. The fourteen predictor variables we modeled (see tables) were all found to be significantly associated with HPAI H5N1 outbreaks (p 0.2) in at least one wave-place combination based on univariate analysis (but including the autoregressive term) ( Table 1) . Land-use diversity, chicken density, poultry flock size diversity and distance to national highway were found to have significant associations across five of the six wave-place combinations. power of the GLMM models, as measured by the AUC, is very good with AUC values ranging from 0.802 to 0.952 (Tables 2-7 ). The predictive power of the national models was higher than that of the delta models. The predictive power of the BRT models is good, with AUCs ranging from 0.737 to 0.914. The BRT models also had a better predictive power at the national level than at the delta level. These values are higher than those reported for Wave 1 (AUC = 0.69) and Wave 2 (AUC = 0.77) by Gilbert et al. [11] . Both Gilbert et al. [11] and this study found that at the national level the predictive performance for Wave 2 was higher than that for Wave 1. Wave 2 mainly affected the Mekong River Delta. Previous studies indicated the duck density was an important predictor [11] ; our results, however, indicated that the diversity of duck flock size was a more important predictor than duck density. Both the GLMM and BRT models found annual precipitation to be a significant factor. The GLMM model indicated a negative association; similar to what was found by studies in China [51] and in the Red River Delta [53] . A global study of human cases also found occurrence to be higher under drier conditions [74] . Generally, the role of precipitation was found to be far more significant in the deltas than for the country as a whole. The unadjusted Relative Risk (RR) of peri-urban areas in comparison with non-peri-urban areas was 1.41 and 1.60 for Waves 1 and 2, respectively. In terms of urbanicity, we found that chicken density, percentage of land under rice, percentage of land under aquaculture, flock size diversity for duck and geese, and the Compound Topographical Index (CTI) to be highest in peri-urban areas (Fig 1a-1e) . We also found that land-use diversity was higher in rural areas, but peri-urban areas had diversity levels only marginally lower (Fig 1f) . The urbanicity variable alone, however, was not found to be significantly associated with HPAI H5N1 in any place according to the GLMM model except for the urban level in Red River Delta for Wave 2 and in the Mekong River Delta for Wave 1. The BRT model ranked urbanicity as one of the least influential variables. Land-use diversity was found to be significantly associated with HPAI H5N1 in both waves for Viet Nam according to the GLMM model, but at the delta level the association was significant only for Wave 2 in the Mekong River Delta. The BRT model indicated that land-use diversity highly influenced HPAI H5N1 at the national level in Wave 2. For the remaining waveplace combinations land-use diversity had middle to below-middle rank of influence. Both the GLMM and BRT models indicated that the diversity of chicken flock-size had a strong association with HPAI H5N1 for both waves at the national level. This was generally found to be true at the delta levels with some exceptions. The diversity of duck and goose flock size was also significantly associated with HPAI H5N1 in all places, but the associations were much stronger in Wave 2 than in Wave 1. The GLMM model indicated that the CTI had a very strong association with HPAI H5N1 at the national level in both waves although this was not true in the two deltas. The CTI is a steady state wetness index commonly used to quantify topographic control on hydrological processes. Accumulation numbers in flat areas, like deltas, are very large; hence the CTI was not a relevant variable in the GLMM model in these areas. The BRT model however indicated that CTI had middle to low influence in all waves and places. We found very high spatial clustering effects as indicated by the fact that in all waves and places the BRT model found the spatial autocorrelation term to have the highest rank of influence. As expected, the relative influence of the autocorrelation term at the national level was higher (60-78%) than at the delta levels (14-35%). In the GLMM models we found the Akaike Information Criterion (AIC) using the entire set of 14 variables to be much lower than the AICs of a GLMM model without fixed effects. This indicated that though clustering effects were significant, our theory driven predictor variables improved model performance. A limitation of using surveillance methods for the dependent variable (poultry outbreaks) is that the data may have reporting/detection biases [11] . Under-reporting/detection in rural areas as compared to peri-urban areas is possible. We believe that the urbanicity and the shortest distance to nearest town risk factors serve as rough proxies for reporting/detection efficiency. Previous studies have tended to use human population density as a proxy for this purpose. In our study we found a strong association between human population density and urbanicity. But we acknowledge that a categorical variable such as urbanicity may provide less sensitivity than a continuous variable such as human population density in this specific context. This study explored the validity of a general model for disease emergence that combined the IOM 'convergence model' [6] and the social-ecological systems model [7, 8] , for investigating the specific case of HPAI in Vietnam. We sought to test the hypotheses that measures of urbanization, land-use diversification, and poultry intensification are correlated with outbreaks in poultry. Our results generally support the hypothesis that social-ecological system transformations are associated with H5NI outbreaks in poultry. The results presented here highlight three main findings: 1) when relevant risk factors are taken into account, urbanization is generally not a significant independent risk factor; but in peri-urban landscapes emergence factors converge, including higher levels of chicken densities, duck and geese flock size diversities, and fraction of land under rice or aquaculture; 2) high land-use diversity landscapes, a variable not previously considered in spatial studies of HPAI H5N1, are at significantly greater risk for HPAI H5N1 outbreaks; as are 3) landscapes where intensive and extensive forms of poultry production are co-located. Only one other study has explicitly examined urbanicity in the context of HPAI H5N1. Loth et al. [17] found peri-urban areas in Indonesia were significantly associated with HPAI H5N1 cases, even based on multivariate models. Our study, however, attempted both to associate HPAI H5N1 with degree of urbanicity and to determine the features of peri-urban areas that place them at risk. When those features (i.e., chicken densities, duck and geese flock size diversities, and the fraction of land under rice or aquaculture) are included in multivariate models, the role of the urbanization variable per se diminishes. We found in the main river deltas in Viet Nam (Red River and Mekong), urbanization had no significant association with HPAI H5N1. This may be due to the fact that the deltas are more homogenous, in terms of urbanization, than the country as a whole. This is the first study to examine land-use diversity as a risk factor for HPAI H5N1. Measured by the Gini-Simpson Diversity Index of the five land-use classes on which data were collected in the 2006 Viet Nam Agricultural Census, and the presence or absence of HPAI outbreaks at the commune level, our results indicate a strong association between land-use diversity and HPAI H5N1 at the national level and in the Mekong River Delta. This metric captures both the variety of habitats and of the complexity of geospatial patterning likely associated with transmission intensity. Our results are similar to what has been observed by studies of other EIDs using fragmentation metrics (e.g. [75] [76] [77] . This is one of the few studies, however, to link landscape fragmentation to an EID disease in poultry and not just to the vector and/or hosts of the EID. Previous studies have focused on poultry production factors such as type of species, size of flocks, and extent of commercialization (e.g. [15, [17] [18] [19] . This study expands on those findings by providing evidence that when intensive and extensive systems of chicken and/or duck and geese production co-exist in the same commune, the commune experiences higher risk of disease outbreak. Future studies need to examine the biological causal mechanisms in this context. We suggest that national census data (particularly agricultural censuses) compiled at local levels of administration provide valuable information that are not available from remotely sensed data (such as poultry densities) or require a large amount of labor to map at national to larger scales (land-use diversity). Mapping land-use classes at the national scale for local administrative units (i.e., the 10,820 communes in Viet Nam) is not an insignificant task. Future studies, however, could examine the correlation between a census-based metric with metrics derived from remote sensing used to measure proportional abundance of each landcover type within a landscape [78] . Vietnam is relatively advanced in making digital national population and agricultural census data available in a format that can be linked to administrative boundaries. While other nations are beginning to develop similar capacities, in the short term the application of this method to other countries may be limited. Ultimately, both census and remotely sensed data can be used independently to map the urban transition and diversity of land use; these tools, however, may provide their greatest insights when used together. Another important contribution of this study was the discovery of the importance of CTI. So far CTI had been used only in ecological niche modeling studies of HPAI H5N1; the specific role and direction of influence of CTI had has so far been unknown. Our study, the first to use CTI as a risk factor, found it had a large positive influence on HPAI H5N1 risk at the national level. Previous studies have highlighted the role of surface water extent in the persistence and transmission of the HPAI H5N1 virus. These studies measured surface water extent as area covered by water, magnitude of seasonal flooding, distance to the nearest body of water, or other variables that are often difficult to map using remotely sensed data, especially for large area studies. CTI on the other hand has the potential to serve as an excellent surrogate which can easily be measured in a GIS database. The national and regional (delta) models differed quite considerably, both in terms of performance and significant risk factors. In the deltas we commonly found only chicken density, duck flock size diversity and annual precipitation to be significant. This suggests dynamics of risk at the commune level are strongly dependent on the spatial range of analysis, consistent with another study in the Mekong Delta [61] . Though that study's model initially included three dozen commonly known risk factors, the significant risk factors were limited to poultry flock density, proportion households with electricity, re-scaled NDVI median May-October, buffalo density and sweet potato yield. Another study in the Red River Delta [79] found that in addition to the typical poultry density metrics, only the presence of poultry traders was significant. We speculate that for smaller regions, especially for known hot-spots, the relevant risk factors are those that reflect short-range, short-term driving forces such as poultry trading, presence of live bird markets and wet markets etc. Improving model performance for smaller regions would require highly refined and nuanced metrics for poultry trading, road infrastructure, water bodies, etc.-data that are typically not available through census surveys. The differences between the national and regional models suggest that our results can inform planners making decisions at different hierarchical levels of jurisdiction: national, region and local. Our study has the potential to inform the design of future research related to the epidemiology of other EIDs in Viet Nam and elsewhere. For example, we speculate that in Southeast Asia, Japanese encephalitis, the transmission of which is associated with rice cultivation and flood irrigation [80] , may also show a strong association with peri-urbanization. In some areas of Asia these ecological conditions occur near, or occasionally within, urban centers. Likewise, Hantaan virus, the cause of Korean hemorrhagic fever, is associated with the field mouse Apodemus agrarius and rice harvesting in fields where the rodents are present [80] . Our work has demonstrated that the percentage of land under rice in peri-urban areas and rural areas is similar. Hence diseases associated with rice production are likely to peak in peri-urban areas given other risk factors such as land-use diversity, CTI, and distance to infrastructure. Our poultry flock-size diversity findings may also be relevant to understanding the dynamics of other poultry related infections such as Newcastle disease. Finally, these results suggest the validity of a general model of zoonotic disease emergence that integrates IOM's convergence model with the subsequently proposed social-ecological systems and EID framework. Thus, convergence represents the coalescence in time and space of processes associated with land-cover and land-use changes. Project results question whether the urban/rural land-use dichotomy is useful when large areas and parts of the population are caught between the two. Planners need better tools for mapping the rural-urban transition, and for understanding how the specific nature of peri-urban environments creates elevated health risk that require adaptation of existing planning, land use, and development practices.

What is the relationship between HIN1 viral transmission and poultry production.

Hantaviruses in the Americas and Their Role as Emerging Pathogens https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3185593/ SHA: efe13a8d42b60ef9f7387ea539a1b2eeb5f80101 Authors: Hjelle, Brian; Torres-Pérez, Fernando Date: 2010-11-25 DOI: 10.3390/v2122559 License: cc-by Abstract: The continued emergence and re-emergence of pathogens represent an ongoing, sometimes major, threat to populations. Hantaviruses (family Bunyaviridae) and their associated human diseases were considered to be confined to Eurasia, but the occurrence of an outbreak in 1993–94 in the southwestern United States led to a great increase in their study among virologists worldwide. Well over 40 hantaviral genotypes have been described, the large majority since 1993, and nearly half of them pathogenic for humans. Hantaviruses cause persistent infections in their reservoir hosts, and in the Americas, human disease is manifest as a cardiopulmonary compromise, hantavirus cardiopulmonary syndrome (HCPS), with case-fatality ratios, for the most common viral serotypes, between 30% and 40%. Habitat disturbance and larger-scale ecological disturbances, perhaps including climate change, are among the factors that may have increased the human caseload of HCPS between 1993 and the present. We consider here the features that influence the structure of host population dynamics that may lead to viral outbreaks, as well as the macromolecular determinants of hantaviruses that have been regarded as having potential contribution to pathogenicity. Text: Emerging pathogens cause new or previously unrecognized diseases, and among them, emerging zoonotic diseases are a major concern among scientists studying infectious diseases at different spatial and temporal scales [1, 2] . Changes in biotic and abiotic conditions may alter population disease dynamics and lead to the emergence of zoonotic infections [3] [4] [5] [6] . During the last decades, several outbreaks of emerging and re-emerging viral pathogens have occurred, affecting both purely-local and worldwide/pandemic involvement of human populations. Among the conspicuous examples are influenza A, Ebola virus, hepatitis C virus, severe adult respiratory distress (SARS), coronavirus, and human immunodeficiency virus, which challenge prevention and control measures of public health systems [7] . In the Americas, the recent outbreak of pandemic influenza A subtype H1N1 became a major target for control due to its rapid spread, and uncertainties in virulence and transmissibility, yet vaccine availability was limited when significant activity occurred in advance of the traditional influenza season [8] . However, in the last century outbreaks of several viral-related diseases have emerged or re-emerged involving arenaviruses and dengue viruses, and more recently, hantaviruses, and the expansion of the geographic range of West Nile virus. Among zoonotic diseases, small mammals are hosts of several pathogenic RNA viruses, especially Arenaviridae and Bunyaviridae: Hantavirus [9] [10] [11] . Hantavirus infections became a concern in the Americas after the description of an outbreak of acute respiratory distress occurred in the Four Corners area in 1993 [12] . The newly recognized disease, hantavirus cardiopulmonary syndrome, HCPS (or hantavirus pulmonary syndrome), was linked to infection by the newly-discovered Sin Nombre virus (SNV), and the rodent Peromyscus maniculatus (deer mouse) was identified as the reservoir [13] . However, hantavirus infections have a much longer history. A review of ancient Chinese writings, dating back to approximately 960 AD, revealed descriptions closely resembling hemorrhagic fever with renal syndrome (HFRS), the syndrome caused by Old World hantaviruses [14] . During the twentieth century, cases of acute febrile disease with renal compromise were described from several Eurasian countries and Japan, often in association with military engagements [15] . HFRS as a distinct syndrome, however, was first brought to the attention of western medicine in association with an outbreak that occurred among United Nations troops during the Korean conflict between 1951 and 1954, where more than 3,200 soldiers were afflicted [16] . It took more than two decades until the etiologic agent, Hantaan virus (HTNV), was isolated from the striped field mouse Apodemus agrarius, detected in part by the binding of antibodies from patient serum samples to the lung tissues of healthy, wild-caught field mice [17, 18] . The virus was later found to represent the type species of a new genus Hantavirus of the family Bunyaviridae, although it was later apparent that the first hantavirus to be isolated was the shrew-borne Thottapalayam virus [19] . The categorization of hantaviruses as belonging to the family Bunyaviridae is due in part to the consistent presence of three RNA genomes that are circularized in vivo as a result of the presence of terminal complementary nucleotides that help fold the genome into a -hairpin‖ morphology, first described for the Uukuniemi phlebovirus [19, 20] . Table 1 is a list of the predominant, serologically distinct pathogenic hantaviruses. Many other named genotypes are described, but such other pathogenic forms are generally closely related to Andes or, in some cases, Sin Nombre virus. During virus maturation, the precursor form GPC is processed using a membrane -bound protease into Gn and Gc, a cleavage that occurs, and appears to be signaled, after the conserved peptide signal WAASA at the C-terminal of Gn [24] . Although the two proteins can be expressed independently through transfection, they can be retained in the wrong cellular compartment (ER or aggresome); they thus must be co-expressed to allow them stability so that the two can be assembled correctly in the Golgi [25, [27] [28] [29] . A number of activities and properties have been identified for the hantavirus envelope glycoproteins, including some features that are suspected to be involved in the pathogenicity of the disease-causing serotypes, a possibility that has engendered experimental attention. The glycoproteins are the known or presumed ligands for at least two distinct cellular receptors, the 3 integrin chain and decay accelerating factor, or DAF [30, 31] ; with gC1qR/p32 also identified as another potential entry receptor [32] . Comparisons with the tick-borne encephalitis virus E protein, led Tischler et al. to consider the Gc glycoprotein as a potential class II fusion protein, perhaps imparting fusion activity to the virion, and this hypothesis has gained support in other studies [33, 34] . Additional activities have been identified with, or claimed to be related to, Gn. For many of these studies, an underlying premise has held that there are differences between the glycoproteins of -pathogenic‖ hantaviruses relative to viruses in the genus that are dubbed to be -non-pathogenic‖. While it is true that it has not yet been possible to link Prospect Hill virus (PHV) to human disease, the absence of evidence for its pathogenicity should perhaps not be equated with the evidence of its absence. One might only consider that the level of disease (e.g., lethargy, fever, proteinuria, and azotemia) associated with infection of nonhuman primates by PHV is not significantly different from that recorded for nonhuman primate models using the known-pathogen Puumala virus (PUUV) [35, 36] . For the purpose of this discussion we will presume that apathogenic hantaviruses are indeed apathogenic. While some studies have suggested that Gn glycoproteins are directed more rapidly into the ubiquitin-proteosome pathway than are apathogenic forms, others have interpreted differences in the handling of Gn glycoproteins across hantavirus species by the ubiquitin-proteosomal system as independent of pathogenicity [37] [38] [39] . Some investigators have directed their efforts toward identifying a differential capacity, either kinetic or in absolute magnitude, in the ability of pathogenic and apathogenic hantaviruses to elicit an interferon response in cells. One premise that emerges is that apathogenic forms would tend to induce an earlier innate response that would render it more likely that the virus would be quickly cleared or rendered less competent in its replication so as to blunt any pathological response in the host [40] [41] [42] . The anti-hantavirus innate response can in some cases be attributed to viral interaction as a ligand of TLR-3, but not in others, and in endothelial cells, it appears not to require more than the viral particle itself, even when introduced in replication-incompetent form [43, 44] . Proteins and mRNAs prominently induced by hantaviruses include MxA and IFIT-1 (ISG-56) and others including some with known or suspected anti-viral activity. Those hantaviruses, often highly pathogenic strains, that fail to induce a potent antiviral response, are suspected or presumed to have a (more) potent interferon-pathway antagonism mechanism relative to other viruses, a mechanism that acts positively to prevent an effective innate response from forming, at least early in infection [42, 45] . Yet some instances are reported wherein highly pathogenic hantaviruses, such as SNV, are also able to induce expression of interferon-stimulated gene mRNAs, even very early in infection, with ISG proteins, as expected, taking longer to appear in the cell [44] . Anti-interferon activities have also been attributed to the NSs protein that may be elaborated in cells infected by serotypes that encode this protein [46] . Other investigators have examined the activities of hantavirus glycoproteins and other proteins that might themselves directly affect some aspects of the pathogenic progression associated with hantavirus infection of humans, such as vascular permeability changes. While early attempts to directly cause increases in permeability of endothelial monolayers with viral particles or viral infection were largely disappointing, hantaviruses have been identified as adversely affecting endothelial migration over substrata and in potentiating VEG-F-induced endothelial permeability [47, 48] . The shorter (50-kD) nucleocapsid or N protein is a structural component of the viral nucleocapsid, along with the genomic viral RNA segments. As an RNA-binding protein that engages the hairpin termini of the genomic segments with high affinity [49, 50] , it limits the access of the RNA to host nucleases and helps to render viral replication a closed process within the cytoplasm. It also acts as a peripheral membrane protein, as does the L protein [51] , an activity that could play a role in its presumed, but not yet demonstrated function as matrix [52] . Until recently, it had not been appreciated that N has a wide variety of other activities, some of which can be linked, not only to fundamental requirements of replication, but also to the interference with an array of the intracellular processes of the normal cell. Thus, an interaction between the amino terminus of the hantavirus N protein and the cellular protein Daxx has been proposed, with the suggestion of potential pro-apoptotic consequences [51] . N is also reported to interact with actin microfilaments, and the SUMO-1 protein [53, 54] . Using reporter-gene based assays, Connie Schmaljohn and her colleagues have reported that Hantaan virus' nucleocapsid protein has an inhibitory role in inflammatory responses mediated by NF kappa B (NF-B). The effects on NF-B expression appeared to be confined to prevention of its nuclear translocation after its attempted activation with lipopolysaccharide, LPS [55] . In the cytoplasm of infected cells, N protein can be found in cellular P bodies where it sequesters and protects 5' caps. It may locate the caps through its interaction with DCP1, a key constituent of P bodies. During hantavirus infection, the viral RNAs become concentrated in P bodies, through their interaction with N and DCP1. The N protein demonstrates preferential protection of mRNAs engineered to prematurely terminate their encoded protein in comparison to native mRNAs [56] . N protein has been increasingly linked to viral replication and translation, sometimes in previously unanticipated ways. It is among a growing family of diverse viral proteins that can serve as a nonspecific -RNA chaperone‖, an activity that should facilitate the L polymerase's access to vRNA for transcription and replication, in that it can transiently dissociate misfolded RNA structures [57] . Some of N protein's effects on translation might not immediately be recognized to be adaptive in nature. It can replace the entire EIF4F translational initiation complex, simultaneously presenting the ribosome with a replacement for the cap-binding activity of eIF 4E, binding to the 43S pre-initiation complex as does eIF 4G, while replacing the helicase activity of eIF 4A, which is presumed to be needed to dissociate higher-order RNA structure [56, 58] . These three factors normally work together to achieve translational initiation. In P bodies, N protein's ability to bind at high affinity to capped native cellular oligoribonucleotides, along with its activity in protecting capped RNAs from degradation likely facilitates the access of capped oligonucleotides for use in transcriptional initiation by L polymerase (-cap snatching‖). Trafficking of N for viral assembly: Classically, N protein in infected cells appears to be clustered or particulate in nature, with a heavy concentration at a single perinuclear location, widely considered to be the Golgi [27] . The N proteins of hantaviruses are found in association with particulate fractions, and confocal microscopy and biochemical-inhibitor studies have shown that N tracks along microtubules but not with actin filaments [52] . The ultimate destination for N, for its assembly into viral particles is the Golgi, and it traffics there via the endoplasmic reticulum-Golgi intermediate complex (ERGIC), also known as vesicular-tubular cluster [52] . A dominant negative inhibitor, dynamitin, associated with dynein-mediated transport, reduced N's accumulation in the Golgi. Later studies suggested that the specific dependence on microtubular transport is specific to Old World hantaviruses such as HTNV, but that the New World hantavirus ANDV is instead associated with actin filaments [59] . However, recent data indicates that microtubular transport is indeed utilized for the New World hantavirus SNV [60] . Hantavirus diseases of man have long been suspected of having an immunopathogenic basis in part because of their relatively long incubation period of 2-3 weeks and the observed temporal association between immunologic derangements and the first appearance of signs and symptoms of hantavirus illness. HFRS and HCPS share many clinical features, leading many investigators to consider them to be, in essence, different manifestations of a similar pathogenic process, differing mainly in the primary target organs of disease expression ( Table 2 ). The pathogenesis of hantavirus infections is the topic of a continuously-updated review in the series UpToDate [61] . By the time symptoms appear in HCPS, both strong antiviral responses, and, for the more virulent viral genotypes, viral RNA can be detected in blood plasma or nucleated blood cells respectively [63, 64] . At least three studies have correlated plasma viral RNA with disease severity for HCPS and HFRS, suggesting that the replication of the virus plays an ongoing and real-time role in viral pathogenesis [65] [66] [67] . Several hallmark pathologic changes have been identified that occur in both HFRS and HCPS. A critical feature of both is a transient (~ 1-5 days) capillary leak involving the kidney and retroperitoneal space in HFRS and the lungs in HCPS. The resulting leakage is exudative in character, with chemical composition high in protein and resembling plasma. The continued experience indicating the strong tissue tropism for endothelial cells, specifically, is among the several factors that make β3 integrin an especially attractive candidate as an important in vivo receptor for hantaviruses. It is likely that hantaviruses arrive at their target tissues through uptake by regional lymph nodes, perhaps with or within an escorting lung histiocyte. The virus seeds local endothelium, where the first few infected cells give rise, ultimately, to a primary viremia, a process that appears to take a long time for hantavirus infections [62, 63] . By the time that secondary viremia emerges, the agents of the more severe forms of HFRS and HCPS have begun to achieve sufficient mass as to induce, through PAMP-PRR interactions and other means, the expression of proinflammatory cytokines [64] . For HCPS, that expression favors the pulmonary bed and lymphoid organs, yet, for unknown reasons, spares the retroperitoneum and, in general, the kidney. In HFRS the situation is reversed, and yet it is often not appreciated that the expected preferential tissue tropism of HFRS-associated viruses and their HCPS-associated counterparts for the renal and pulmonary beds, respectively, is not as one would predict through the manifestations of the two diseases. Local elaboration of inflammatory and chemotactic mediators is considered to be a requirement for the development of systemic disease symptoms, with those abnormalities sometimes culminating in shock and death. Yet it is not hypoxemia, due to the prominent pulmonary edema, that leads to death in most fatal cases of HCPS, but rather intoxication of the heart by as-yet-undefined mediators that leads to the low cardiac output state and the associated shock syndrome [64, 65] . It is tempting to speculate that mediators produced in the lung in connection with the inflammatory infiltrate can percolate through the coronary circulation with minimal dilution in HCPS, a disadvantageous consequence of the close anatomic juxtaposition of the two organs. Thus, at least three classes of potential mechanisms, some overlapping and all certainly nonexclusive of the others, could be presumed to underlie the pathogenesis of HCPS. These include: (1) Innate immune mechanisms. The nature of interactions between hantavirus pathogen-associated molecular patterns (PAMP) with the pattern recognition receptors (PRR) of susceptible endothelial cells are beginning to be clarified. The prototypical HTNV appears to be recognized by TLR-3 [43] . Such an infection has consequences such as increased expression of HLA-DR in dendritic cells [66] and differentiation of monocytes toward dendritic cells [67] . (2) Direct viral effects. The observed correlation between viral load and disease severity leaves the possibility open that hantavirus particles or RNA can themselves have toxic effects on cells or on signaling. Some investigators have favored direct viral toxicity, acting through the inhibition of endothelial cell barrier function, as an explanation for much of the capillary leak, although there is widespread agreement that multiple mechanisms that mediate pathogenesis likely operate simultaneously in the affected patient [68] . A potentially important clue toward the mechanism by which hantavirus infections deplete blood platelets and, in some cases cause hemorrhagic manifestations, was advanced by the recent discovery that pathogenic hantaviruses are able to recruit platelets to adhere to endothelial cell surfaces, with β3 integrin used as a critical binding element [69] . (3) Pathogenic effects caused by the activities of specific viral macromolecules. We have reviewed some of the activities associated with the Gn, Gc and N, virally-encoded polypeptides in previous sections. Testing models of pathogenesis can be done more effectively when there is an animal model that mimics key aspects of the disease. There is no such model that closely mimics HFRS, but animal models exist for both the asymptomatic carriage of PUUV and SNV by their native carrier rodents, the bank vole Myodes glareolus and the deer mouse P. maniculatus; as well as a Syrian hamster model using ANDV or the related Maporal virus from Venezuela, for which an HCPS-mimetic disease is observed [70] [71] [72] [73] . The ANDV-Syrian hamster model has a number of features in common with the human disease, as well as some differences. Unlike the neurologic diseases that have been possible to elicit with HTNV, the hamster model for HCPS appears to be caused by capillary leak that results in pulmonary edema and the production of a pleural effusion with exudative characteristics. Typically the hamsters die between 11 and 14-d post-inoculation, reflecting a slightly accelerated incubation period in comparison to human infections. As with human HCPS, the microscopic examination of the lung reveals abundant fibrin deposition, thickened alveolar septa, and viral antigen expressed abundantly in the microvascular endothelium. ANDV-infected hamsters fitted with physiologic monitoring devices exhibited diminished pulse pressures, tachycardia, and hypotension that appear to closely mimic the shock that is believed to be the proximate cause of demise in patients who succumb to HCPS [65, 74] . Compared to the human disease, ANDV-infected hamsters exhibit exceptionally high titers of live ANDV in their tissues, with much of the viral replication occurring in hepatocytes, which are spared in the human disease. Titers of live ANDV in some cases exceed 10 8 /g, whereas hantavirus isolates from human tissues have been notoriously difficult to obtain. Despite the universal occurrence of mildly-elevated hepatic enzymes in patients with HCPS, hepatic enzymes do not appear to be present at elevated levels in the blood of diseased hamsters even immediately before death [75] . The protracted incubation period associated with hantavirus disease gives the host considerable time to mount a mature immune response against the virus. Thus, in contradistinction to infections of comparable severity and related symptomatology associated with arenaviruses and filoviruses, hantavirus infections of humans are associated with antibody responses of significant titer by the time symptoms commence. Despite this observation, it appears to be possible that natural variation in individual neutralizing antibody responses among patients with SNV infections can be linked to disease severity, suggesting that administration of antiviral antibodies could prove effective therapeutically [76] . In the case of ANDV infection, new evidence has emerged indicating that the apparent clearance of the virus from the blood does not result in the complete removal of antigenic stimulus by the virus, suggesting that the virus may persist, perhaps in some as-yet undetermined immunologically privileged site [77] . A role for T cell-mediated pathological responses in HFRS and HCPS has been the source of speculation for a variety of reasons. The severity of SNV-associated HCPS may have made it more apparent that the onset of pulmonary edema, tachycardia and hypertension seemed to be all but universally temporally associated with the appearance of a spectrum of highly-activated cells of the lymphoid lineage in the peripheral blood. Cells with a close morphologic similarity to these -immunoblasts‖ were detected in the congested, heavy lungs of patients who came to autopsy, as well as in lymphoid organs and in the portal triads [63, [78] [79] [80] . These observations led to speculation that some component of hantavirus pathogenesis could be linked to the appearance of antiviral T cells that could stimulate or contribute to the appearance of a -storm‖ of mediators and the associated capillary leak phenotype. Subsequent studies have borne out the expectation that a significant fraction of the immunoblast population in patients with HCPS are T cells with specificity for specific class I HLA-presented epitopes of viral antigens, including Gn, Gc and N [77, [81] [82] [83] . Presumably, the antiviral activities of such cells, manifested in part through their elaboration of mediators in the affected interstitium, can contribute to the endothelial/capillary leak that lies at the heart of hantavirus pathogenesis. Because early cases of HCPS often came to autopsy, it became possible to examine necropsied tissues for expression of cytokines. The study by Mori et al. (1999) revealed high relative expression of proinflammatory cytokines including TNF, IL-1, IL-6, providing evidence in favor of a -cytokine storm‖ model for pathogenesis [64] . The authors believed, based on the morphology of cytokine-secreting cells, that both monocytes and lymphocytes were contributing to the production of cytokines. That proinflammatory mediators are found in elevated levels in the plasma as well as the renal interstitium of patients with acute hantaviral illness has been recognized for some time as well [84, 85] . While diagnosis of HCPS as well as HFRS is best accomplished with IgM serology, in the acute stage of SNV infection, RT-PCR can also be used if blood cells or blood clot are used instead of plasma or serum, where sensitivity even using nested PCR primers drops to about 70% [86] [87] [88] . In a facility at which many cases of HCPS are treated, the University of New Mexico medical center in Albuquerque, a diagnostic service has long been offered in which the patient's hematologic findings are analyzed to establish the probability that a patient has HCPS. The combination of thrombocytopenia, elevated abundance of -immunoblast‖ lymphocytes, left-shifted polymorphonuclear cell population without strong morphologic evidence for their activation, and elevated hemoglobin or hematocrit values is highly specific for HCPS and allows clinicians the ability to put presumptive-HCPS patients on extracorporeal membrane oxygenation (ECMO), which is believed to have saved many patients from a lethal outcome [89] . Human infection by hantaviruses is thought to follow contact with secretions or excretions produced by infected rodents. In the United States, 538 human infections by hantavirus were reported through late December 2009 [90] , with New Mexico, Arizona and Colorado exhibiting the highest case-loads. While the prototypical central American hantavirus in central America was Rio Segundo virus of Reithrodontomys mexicanus from Costa Rica, the first human disease appeared some years later in Panama, where Choclo virus (CHOV) arose as the etiologic agent and is believed to be responsible for all known cases of HCPS. The fulvous pygmy rice rat Oligoryzomys fulvescens has been identified as the rodent reservoir [91] . In Panama, the first cases of HCPS, albeit with little or no evident cardiac involvement, were reported in 1999, and since then, 106 human infections have occurred with a 26% mortality rate [92] . Serosurveys of mammals in Mexico and Costa Rica have found anti-hantavirus antibodies [93] [94] [95] [96] , and seroprevalences ranging between 0.6 to 1.6% in human populations were reported despite the absence of known HCPS cases [97] . In South America, HCPS cases have been indentified in Argentina, Bolivia, Brazil, Chile, Paraguay and Uruguay, and evidence for human exposure to hantaviruses have also been reported in Venezuela [98] and Perú [99] . In southern South America, ANDV is the main etiologic agent with cases in Chile and Argentina reported since 1995. In Chile, 671 cases of HCPS due to ANDV have occurred during the period 2001-2009 [100] . Since 1995, more than 1,000 HCPS cases have been reported in Argentina [101] ; in Brazil, approximately 1,100 HCPS cases have been identified between 1993 and 2008 [102] . Case-fatality ratios in those three countries have been similar, ranging from 30% (Argentina), 36% (Chile) and 39% (Brazil). Hantavirus infections occur more frequently in men than women, although the male/female ratio is highly variable. For example, Panamanian communities showed a ratio of 55 men to 45 women [103] , while in Chile the ratio is more biased to males (71%) [104] . In the Paraguayan Chaco the male-female ratio approaches 50% [105] . In North America, by December 2009 63% of case-patients were males [90] . All ethnic and racial groups seem to be susceptible to hantavirus infections, and the differences between certain groups (as indigenous and non-indigenous) are more likely correlated with the type habitat where the population resides (e.g., rural versus urban areas). In fact, rural communities account for the highest hantavirus incidences overall and are therefore at higher risk [92, [105] [106] [107] [108] [109] [110] [111] , although the importance of peridomestic settings as a major area of exposure has also been emphasized [112, 113] . The main mechanism by which humans acquire hantavirus infection is by exposure to aerosols of contaminated rodent feces, urine, and saliva [114, 115] . This can occur when humans reside in areas in close proximity to those that rodents inhabit, live in areas infested with rodents, or when rodents invade human settings, which are more frequent in rural habitats. There is a long history of human co-existence with rodents, raising questions about the apparent recent increases in hantavirus-related illnesses, especially HCPS. Other than an apparent association with El Niño southern oscillation (ENSO) events in some regions [116, 117] , the recent increases in incidence of HCPS do not seem to follow a readily-defined temporal or spatial pattern. However, some landscape features such as habitat fragmentation or human-disturbed areas may influence rodent population dynamics and impact viral incidence [118] [119] [120] [121] . Despite the stochasticity associated with contraction of hantavirus infection, certain scenarios have been recognized as posing higher risk. Human activities in poorly ventilated buildings that aerosolize particulates that are then inhaled (i.e., cleaning, shaking rugs, dusting) are frequently identified among patients admitted for HCPS [11, 122] . Outdoor activities are thought to convey lower risk due to lability of hantaviruses to UV radiation and the presumed tendency to be dispersed in wind, although certain environmental conditions seem to maintain the virus for longer periods outside its natural host allowing for indirect transmission [123] . An alternative but uncommon route of virus transmission is by rodent bites [124] [125] [126] . Field workers handling mammals are potentially at higher risk of exposure with hantavirus infections, although when quantified through serosurveys the absolute risk appears rather slight [127] . A new study in Colorado suggests the possibility that a rodent bite may have been the proximate vehicle for outdoor transmission of SNV [128] , which re-emphasizes the use of personal protective equipment during field work activities [129] . As a particular case within hantaviruses, person-to-person transmission has exclusively been documented for the South American Andes virus [130] [131] [132] [133] [134] [135] . The identification of this transmission route has been made using both molecular tools and epidemiological surveys, but the mechanism of interpersonal transmission is not well established. Recent findings show that family clusters and specifically sexual partners share the greater risk of interpersonal transmission, although sexual transmission per se can be neither inferred nor refuted presently [130, 135] . Interestingly, ANDV may also be shed by humans through other biological fluids such as urine [136] , illustrating the particular properties that differentiate this virus from other hantaviruses. Although interpersonal transmission seems to be unique for ANDV, viral RNA of PUUV has been detected in saliva of patients with HFRS, and some patients with SNV-HCPS have viral RNA in tracheal secretions [88, 137] . Hantaviruses in the Americas are naturally hosted by rodents (Muridae and Cricetidae) as well as shrews (Soricidae) and moles (Talpidae) (Figure 1) . Three shrew and one mole species have been reported to host hantaviruses and their pathogenicity for humans remains unknown [22, 138, 139] . At least 15 rodent species have been identified as carriers of different pathogenic hantaviruses, with some South American genotypes such as Castelo do Sonhos (CDSV) or Hu39694 only identified after human infections (Figure 1 ). Hantaviruses typically show high species-specificity and no intermediate host [140] . However, some hantavirus genotypes have been described in the same rodent species. Such is the case of Playa de Oro (OROV) and Catacamas (CATV) identified in Oryzomys couesi [141, 142] , or Maporal (MAPV) and Choclo (CHOV) hosted by O. fulvescens [91, 143] . In North America both Muleshoe and Black Creek Canal hantaviruses have been detected in geographically-distant Sigmodon hispidus [144, 145] . Also, one hantavirus genotype (e.g., Juquitiba-like virus) may be carried by more than one rodent species (O. nigripes, Oxymycterus judex, Akodon montesis). Another example is Laguna Negra virus (LANV) which after being identified in Calomys laucha [146] has also been reported in C. callosus [147] . The rapid increase in the discovery of new hantaviruses and the identification of their hosts does not seem likely to end soon as new small mammal species are screened [95] . This subject is complicated by continued controversy in the criteria for the classification of distinct hantaviruses [148, 149] , which is also tied to host taxonomic classification and taxonomic rearrangements. Cross-species transmission is a major process during spread, emergence, and evolution of RNA viruses [6, 150] . Particularly within hantaviruses, spillover to secondary hosts are increasingly identified as more extensive studies are performed [151] [152] [153] [154] [155] [156] . For example, ANDV is the predominant etiologic agent of HCPS in South America, and O. longicaudatus the main rodent reservoir. Spillover in at least four other rodent species that co-occur with the reservoir have been identified, with Abrothrix longipilis showing the second higher prevalence to ANDV-antibodies, and there is presently no question that the virus is extremely similar genetically between the two host rodents [157, 158] . In North America, spillover of Bayou virus (BAYV) may have occurred from the main reservoir O. palustris to S. hispidus, R. fulvescens, P. leucopus, and B. taylori [159] [160] [161] . Hantavirus spillover is more likely to occur with host populations inhabiting sympatric or syntopic regions [151, 162] , and cross-species transmission would presumably have greater chances of success if the host species are closely related [163] . An interesting exception is found between Oxbow virus (OXBV) and Asama virus (ASAV) in which a host-switch process seemed to have occurred between mammals belonging to two families (Talpidae and Soricidae), likely as a result of alternating and recurrent co-divergence of certain taxa through evolutionary time [138] . Hantaviruses are horizontally transmitted between rodents and are not transmitted by arthropods (unlike other viruses of the family Bunyaviridae). Spillover infection to nonhuman mammals usually results in no onward (or -dead-end‖) transmission, but if humans are infected may result in high morbidity and mortality [122, 164] . During the spring of 1993, an outbreak of patients with HCPS due to SNV occurred in the Four Corners states resulting in more than 60% case-fatality among the initial cases, many involving members of the Navajo tribe [12, 121] . In Panama, an outbreak was reported during 1999-2000 in Los Santos, and 12 cases where identified with three fatalities [165, 166] . This represented the first report of human hantavirus infections in Central America. In South America, the first largest identified outbreak occurred in the Chaco region in northwestern Paraguay during 1995-1996. Seventeen individuals were identified with SNV antibody (ELISA) or were antigen (IHC) positive out of 52 suspected cases [167] . Major outbreaks due to ANDV occurred in 1996 in southern Argentina [131, 134] ; in southern Chile clusters of patients presented with hantavirus illness in 1997 [158] . In Brazil, the first outbreak was identified in the Brazilian Amazon (Maranhão State) in 2000, and involved small villages that resulted in a 13.3% prevalence of those tested (398 total residents) [168] . The factors that trigger hantavirus outbreaks are still poorly understood, probably because they result from several interacting biotic and abiotic features whose key parameters are difficult to model. However, the use of new modeling approaches that involve geographical and environmental features seem to be promising in predicting potential hantavirus outbreaks and/or areas of higher risk [169] [170] [171] [172] . Because hantaviruses are known to be directly transmitted from infected to susceptible hosts, the first natural approach is to relate outbreaks to the ecology of the viral hosts. Hantavirus transmission and persistence in rodent populations depends on several factors that interact to affect ecological dynamics of the host, which in turn is strongly influenced by the behavioral characteristics of individual rodent species, to landscape structure, and environmental features [173, 174] . Viral transmission depends on contact rates among susceptible hosts, and despite the prevailing notion that a higher density increases encounters and hence secondary infected hosts, contrasting patterns relating rodent population size and virus prevalence can be found [175] . In addition, it has been shown that SNV transmission follows a contact heterogeneity pattern, where individuals in the population have different probability of transmitting the infection [176] . The understanding of viral transmission proves to be far more complex when species other than the main reservoir host are incorporated in the model. In fact, recent studies have shown that higher hosts species diversity is correlated with lower infection prevalence in North America for P. maniculatus [177] , in Central America for O. fulvescens (reservoir of Choclo virus) and Zygodontomys brevicauda (reservoir of Calabazo virus) [178] , and in South America for Akodon montensis (reservoir of Jabora virus) [162] . Contact rates vary according to the spatial distribution of populations and seem to be strongly influenced by landscape structure. For example, SNV prevalence in P. maniculatus was higher in landscapes with a higher level of fragmentation of the preferred habitat [179] . In addition, certain properties of the landscape such as elevation, slope, and land cover seem to be useful in detecting areas with persistent SNV infections, and therefore thought to be refugial areas where the virus can be maintained for years [169] . Changes in the natural environment of reservoir species, such as forest fragmentation and habitat loss, may alter population abundance and distribution and lead to hantavirus outbreaks, as observed in the Azurero Peninsula of Panama [118, 119] . Also, differences in the microhabitat, including overstory cover, may lead to differences in the ecological dynamics within populations and affect the rate of exposure to the virus [180] . Differences in hantavirus infections through contrasting landscapes in the latitudinal span have been found in rodent populations of O. longicaudatus in Chile, suggesting that humans are differentially exposed to the virus [107, 181] . Rodent population dynamics are affected by seasonal changes of weather and climate [182, 183] . In the case of the ENSO-associated outbreaks, a complex cascade of events triggered by highly unusual rains in the precedent year have been postulated to result in an increase of primary production and rodent densities, also increasing the likelihood of transmission of the virus to humans, but it has proved difficult to precisely demonstrate the suggested intermediate events such as increased rodent densities in the increased caseload [116, 121, 184] . In South America, effects of climate change and hantavirus outbreaks have not been well studied, despite the knowledge that several rodents species that are reservoirs of emerging diseases have dramatically been affected by events like El Niño [185] . Changes in host population dynamics are also affected by seasonality, which may lead to disease outbreaks when processes that equilibrate rodent populations from season to season are interrupted [186] . Viral emergence may continue to be promoted as human-introduced changes continue to increase in the environment at different geographical scales. Human incursions into previously uncultivated environments may lead to new contacts between rodent reservoirs and humans, increasing the likelihood of contracting infections [187] . These changes may also alter rodent's population structure and dynamics and interspecies interactions creating conditions that may lead to viral outbreaks, viral establishment in new hosts, and emergence of HCPS [102, 162] , even with seemingly slight ecological disturbance to the virus-host system [188] . Certain pathophysiologic characteristics, including thrombocytopenia and shock, of hantavirus diseases of humans, bear substantial similarity to the hemorrhagic fevers induced by other viruses such arenaviruses, filoviruses and flaviviruses, despite sharing essentially no sequence similarities therewith. Such observations raise questions about whether such commonalities in pathogenesis are chance similarities of phenotype, or instead report the presence of common molecular mechanisms among the viruses. In this review we discuss the general properties, discoveries and epidemiology/ecology of the New World forms of pathogenic hantaviruses, and also seek to identify some of the characteristics of the viral macromolecules and immunologic mechanisms that have been proposed as potential direct mediators of the pathogenic events that characterize the human disease HCPS. While it is unlikely that expression of any particular viral protein or RNAs in isolation can be relied upon to replicate key phenotypes of infection by the complete virus, some of the findings have been sufficiently consistent with what is known of the pathogenesis in vivo that they offer plausible first-pass leads in the search for therapeutic targets. We look forward to the mechanistic revelations that will follow the inevitably expanded usage of powerful methods such as deep sequencing, ever-more advanced imaging, and microscopic methods, and animal models that can at last be said to be close mimics of human hantavirus disease.

What is N-protein?

Haunted with and hunting for viruses https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7089303/ SHA: c51c4f6146d0c636bc4dc3839c16b9e3ef52849a Authors: Gao, George Fu; Wu, Ying Date: 2013-08-07 DOI: 10.1007/s11427-013-4525-x License: cc-by Abstract: nan Text: pecially with next-generation sequencing (NGS) for new virus genome discovery, e.g., Ruben Donis et al. [10] sequenced a bat-derived influenza virus genome by using NGS in 2012, raising a serious question as to whether or not our seasonal or pandemic flu might have another reservoir host. Chen and colleagues [11] confirmed the SFTSV independently by using NGS. Indeed, metagenomics analysis has yielded a great deal of new viruses, especially from the environment. Our actively hunting for new viruses has made some significant contributions for our understanding of virus ecology, pathogenesis and interspecies transmission. Science China Life Sciences has focused on this hot topic in the event of the H7N9 outbreak after a comprehensive overview of the topic addressing HPAIV H5N1 in 2009 in the journal [12] [13] [14] . In this issue, six groups have been invited to present their recent findings on the emerging viruses, in addition to a previous report on H7N9 [3] . Shi [15] reviewed recent discoveries of new viruses or virus genomes from bat. Bat is believed to harbor many more viruses than we ever thought as a reservoir host or even a susceptible host [16] . After the SARS-CoV virus, we have been actively seeking for new coronaviruses from bat and have yielded many of them, including potential human infecting HKU-1, 4, 5 and 9 [17, 18] . Recent MERS-CoV infection is another example for severe disease caused by used-to-be-less pathogenic coronaviruses. Shi and colleagues [19] by using NGS have discovered many unknown animal viruses from bat, especially some important paramyxoviruses and reoviruses. Filovirus has also been identified in bat with potential severe outcomes. Lyssaviruses (with many genotypes, including rabies virus) in the Rhabdoviridae family have been linked with severe fatal human cases, even in the developed countries, including Australia, with the bites of bats in the city [20, 21] . The potential roles of these viruses in bats for interspecies transmission are yet to be elucidated. Tan and colleagues [22] specifically focused on the newly-emerged MERS-CoV. The virus was identified in 2012 in the Middle East with some exported cases to Europe. In 2013 the virus has been re-emerging and expanding its borders to more European countries. In the initial diagnosis, the pan-coronavirus real-time reverse transcription polymerase chain reaction (RT-PCR) assay played a very important role for the identification of the causative agents. By using this method, scientists detected an expected-size PCR fragment for the corresponding conserved region of ORF1b of the replicase gene of a coronavirus. This is another example that molecular biology methods played for the discovery of new pathogens. Soon the receptor used by MERS-CoV to enter the host cells was identified [23] and the molecular basis of the receptor binding to the virus was also elucidated recently [8] . Enterovirus has been known as serious human pathogens for a long time but their significance to the public health has been emphasized by the emergence of enterovirus 71 in 1998 as a serious pathogenic agents for children in Taiwan [24] and re-emerged in mainland China in 2008 [25] . In this issue, Duan and colleagues [26] summarized the findings of new enteroviruses by using NGS. Because of the application of new NGS technology they also challenged the Koch's postulates. A new model of Koch's postulates, named the metagenomic Koch's postulates, has provided guidance for the study of the pathogenicity of novel viruses. The review also provided a detailed description of the NGS and related molecular methods for the virus discovery followed by a list of new enteroviruses found in human feces. These include viruses in the family of Piconaviridae, Parvoviridae, Circoviridae, Astroviridae and Polyomaviridae. Yu Xue-Jie and colleagues [27] reviewed the new bunyavirus, SFTSV, identified in China. As the virus discoverers, they have overviewed the whole process of the discovery, which is helpful and meaningful for the new virus discoveries in the future. The disease caused by SFTSV, with a CFR of 12%, had been in China for a couple of years before the causative agent was finally identified. There are still a lot of questions remained unknown for this new virus and vigorous studies are in great need. The transmission route of the virus has not been clarified but tick as vector is suspected. Domestic and wild animals, e.g., goats, boars, cattle and dogs, are believed to be the virus-amplifying hosts. Therefore the effective control measures are still under evaluation. Vaccines protecting the SFTSV infection are under its way in Chinese Center for Disease Control and Prevention. Recently a similar virus has been identified in both Japan and USA (a new name of Heartland virus was proposed for the US virus) [9] . In addition to new viruses infecting human beings, some new viruses infecting animals but their public health significance needing to be further evaluated, have also been discovered. The new flavivirus, duck egg-drop syndrome virus (DEDSV), is a good example. Su and colleagues [28] reviewed the characterization of the DEDSV and its disease form in this issue. The virus was found closely-related to a long-time-known virus, Tembusu virus [29, 30] . Initially, the disease was only found in egg-raising ducks but soon it was found in pigeons, chickens and geese [31, 32] . Yet the transmission vector, though mosquitoes are suspected, has not been identified. Due to the public health concerns of its related viruses, potential human infection of DEDSV should be evaluated. Research on insect viruses is reviving in recent years. In this issue, Zhou and colleagues [33] reviewed the newly-identified insect viruses in China. Insects are the largest group of animals on the Earth therefore they also carry many more viruses. Studies on these viruses can provide useful knowledge for our understanding about animal or human infecting viruses. More importantly, modification and application of insect-infecting viruses can be used as effective biologicals for the control of insect pest. The new viruses identified include Wuhan nodavirus (WhNV), a member of family Nodaviridae; Dendrolimus punctatus tetravirus (DpTV), a new member of the genus Omegatetravirus of the family Alphatetravirida; Ectropis obliqua picorna-like virus (EoV), a positive-strand RNA virus causing a lethal granulosis infection in the larvae of the tea looper (Ectropis obliqua), the virus a member of the Flaviridae family. While we are enjoying ourselves with the civilization of modern societies, the ecology has ever been changing. Human beings encounter more ecology-climate-changing problems, including the zoonotic pathogens. We have to face some unknown pathogenic agents passively. To get ourselves well prepared we also ought to actively hunt for unknown pathogens. Prediction and pre-warning can only be realized by knowing more about the unknown. This is especially true for infectious agents.

What assay played an important role?

MERS coronavirus: diagnostics, epidemiology and transmission https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4687373/ SHA: f6fcf1a99cbd073c5821d1c4ffa3f2c6daf8ae29 Authors: Mackay, Ian M.; Arden, Katherine E. Date: 2015-12-22 DOI: 10.1186/s12985-015-0439-5 License: cc-by Abstract: The first known cases of Middle East respiratory syndrome (MERS), associated with infection by a novel coronavirus (CoV), occurred in 2012 in Jordan but were reported retrospectively. The case first to be publicly reported was from Jeddah, in the Kingdom of Saudi Arabia (KSA). Since then, MERS-CoV sequences have been found in a bat and in many dromedary camels (DC). MERS-CoV is enzootic in DC across the Arabian Peninsula and in parts of Africa, causing mild upper respiratory tract illness in its camel reservoir and sporadic, but relatively rare human infections. Precisely how virus transmits to humans remains unknown but close and lengthy exposure appears to be a requirement. The KSA is the focal point of MERS, with the majority of human cases. In humans, MERS is mostly known as a lower respiratory tract (LRT) disease involving fever, cough, breathing difficulties and pneumonia that may progress to acute respiratory distress syndrome, multiorgan failure and death in 20 % to 40 % of those infected. However, MERS-CoV has also been detected in mild and influenza-like illnesses and in those with no signs or symptoms. Older males most obviously suffer severe disease and MERS patients often have comorbidities. Compared to severe acute respiratory syndrome (SARS), another sometimes- fatal zoonotic coronavirus disease that has since disappeared, MERS progresses more rapidly to respiratory failure and acute kidney injury (it also has an affinity for growth in kidney cells under laboratory conditions), is more frequently reported in patients with underlying disease and is more often fatal. Most human cases of MERS have been linked to lapses in infection prevention and control (IPC) in healthcare settings, with approximately 20 % of all virus detections reported among healthcare workers (HCWs) and higher exposures in those with occupations that bring them into close contact with camels. Sero-surveys have found widespread evidence of past infection in adult camels and limited past exposure among humans. Sensitive, validated reverse transcriptase real-time polymerase chain reaction (RT-rtPCR)-based diagnostics have been available almost from the start of the emergence of MERS. While the basic virology of MERS-CoV has advanced over the past three years, understanding of the interplay between camel, environment, and human remains limited. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12985-015-0439-5) contains supplementary material, which is available to authorized users. Text: An email from Dr Ali Mohamed Zaki, an Egyptian virologist working at the Dr Soliman Fakeeh Hospital in Jeddah in the Kingdom of Saudi Arabia (KSA) announced the first culture of a new coronavirus to the world. The email was published on the website of the professional emerging diseases (ProMED) network on 20 th September 2012 [1] (Fig. 1) and described the first reported case, a 60 year old man from Bisha in the KSA. This information led to the rapid discovery of a second case of the virus, this time in an ill patient in the United Kingdom, who had been transferred from Qatar for care [2] . The new virus was initially called novel coronavirus (nCoV) and subsequentlty entitled the Middle East respiratoy syndrome coronavirus (MERS-CoV). As of 2 nd of September 2015, there have been 1,493 detections of viral RNA or virus-specific antibodies across 26 countries (Additional file 1: Figure S1 ) confirmed by the World Health Organization (WHO), with over a third of the positive people dying (at least 527, 35 %) [3] . Since that first report, a slow discovery process over the following two to three years revealed a virus that had infected over 90 % of adult dromedary camels (DC; Camelus dromedarius) in the KSA [4] , also DCs across the Arabian Peninsula and parts of Africa that are a source of DC imports for the KSA [5] . To date, MERS-CoV has not been detected in DCs tested in zoos or herds from other parts of the world [6] [7] [8] [9] . Occasionally, virus is transmitted from infected DCs to exposed humans. Subsequent transmission to other humans requires relatively close and prolonged exposure [10] . The first viral isolate was patented and concerns were raised that this would restrict access to both the virus and to viral diagnostics [11, 12] . However, sensitive, validated reverse transcriptase real-time polymerase chain reaction (RT-rtPCR)-based diagnostics were quickly described and virus was made freely available subject to routine biosafety considerations [13] . Subsequent epidemiology and research has identified the cell receptor as exopeptidase dipeptidyl peptidase 4 (DPP4; also called CD26); that MERS-CoV has a broad tropism, replicating better in some cells lines and eliciting a more proinflammatory response than SARS-CoV; is widespread in DCs; has the potential to infect other animals and that MERS kills its human host more often than SARS did (20-40 % versus 9 % for SARS [14] ) [15] [16] [17] [18] [19] . In humans, overt disease was given the name Middle East respiratory syndrome, with the acronym MERS. From intermittent animal-to-human spill-over events, the MERS-CoV spreads sporadically among people, causing more severe disease among older adults, especially males, with pre-existing diseases. The spread of MERS-CoV among humans has often been associated with outbreaks in hospitals, with around 20 % of all cases to date involving healthcare workers (HCWs). Although DCs appear to suffer the equivalent of a 'common cold' from MERS-CoV infection, in humans, the virus can be a more serious and opportunistic pathogen associated with the death of up to 40 % of reported cases. It has yet to be established whether infections thought to have been acquired from an animal source produce a more severe outcome than those spread between humans [20] . Studies have established that the mean incubation period for MERS is five to six days, ranging from two to 16 days, with 13 to 14 days between when illness begins in one person and subsequently spreads to another [21] [22] [23] [24] . Among those with progressive illness, the median time to death is 11 to 13 days, ranging from five to 27 days [23, 24] . Fever and gastrointestinal symptoms may form a prodrome, after which symptoms decline, only to be followed by a more severe systemic and respiratory syndrome [25, 26] . The first WHO case definition [27] defined probable cases of MERS based on the presence of febrile illness, cough and requirement for hospitalization with suspicion of lower respiratory tract (LRT) involvement. It also included roles for contact with a probable or confirmed case or for travel or residence within the Arabian Peninsula. If strictly adhered to, only the severe syndrome would be subject to laboratory testing, which was the paradigm early on [21] . From July 2013, the revised WHO case definition included the importance of seeking out and understanding the role of asymptomatic cases and from June 2014, the WHO definition more clearly stated that a confirmed case included any person whose sample was RT-PCR positive for MERS-CoV, or who produced a seroconversion, irrespective of clinical signs and symptoms. [28] [29] [30] Apart from the WHO and the KSA Ministry of Health reports, asymptomatic or subclinical cases of MERS-CoV infection were documented in the scientific literature although not always as often as occurred early on [31, 32] . The KSA definition of a case became more strict on 13 th May 2014, relying on the presence of both clinical features and laboratory confirmation [33] . Testing of asymptomatic people was recommended against from December 2014 [34] , reinforced by a case definition released by the KSA Ministry of Health in June 2015 [35] . The KSA has been the source of 79 % of human cases. Severe MERS is notable for its impact among older men with comorbid diseases including diabetes mellitus, cirrhosis and various lung, renal and cardiac conditions [36] [37] [38] . Interestingly in June 2015, an outbreak in South Korea followed a similar distribution [39, 40] . Among laboratory confirmed cases, fever, cough and upper respiratory tract (URT) signs and symptoms usually occur first, followed within a week by progressive LRT distress and lymphopaenia [37] . Patients often present to a hospital with pneumonia, or worse, and secondary bacterial infections have been reported [37, 41] . Disease can progress to acute respiratory distress syndrome and multiorgan system failure [37] . MERS has reportedly killed approximately 35 % of all reported cases, 42 % of cases in the KSA, yet only 19 % of cases in South Korea, where mortality ranged from 7 % among younger age groups to 40 % among those aged 60 years and above [42] ; all may be inflated values with asymptomatic or mild infections sometimes not sought or not reported [34] . General supportive care is key to managing severe cases [43] . Children under the age of 14 years are rarely reported to be positive for MERS-CoV, comprising only 1.1 % (n = 16) of total reported cases. Between 1 st September 2012 and 2 nd December 2013, a study described the then tally of paediatric cases in the KSA, which stood at 11 (two to 16 years of age; median 13 years); nine were asymptomatic (72 %) and one infant died [44] . In Amman, Jordan, 1,005 samples from hospitalized children under the age of two years with fever and/or respiratory signs and symptoms were tested but none were positive for MERS-CoV RNA, despite being collected at a similar time to the first known outbreak of MERS-CoV in the neighbouring town of Al-Zarqa [45] . A second trimester stillbirth occurred in a pregnant woman during an acute respiratory illness and while not RT-rtPCR positive, the mother did subsequently develop antibodies to MERS-CoV, suggestive of recent infection [46] . Her exposure history to a MERS-CoV RT-rtPCR positive relative and an antibody-reactive husband, her incubation period and her symptom history met the WHO criteria for being a probable MERS-CoV case [46] . Diagnostic methods were published within days of the ProMED email announcing the first MERS case [47] , including several now gold standard in-house RT-rtPCR assays (Fig. 2 ) as well as virus culture in Vero and LLC-MK2 cells [18, 47, 48] . A colorectal adenocarcinoma (Caco-2) epithelial cell line has since been recommended for isolation of infections MERS-CoV [49] . We previously [18] .). Open reading frames are indicated as yellow rectangles bracketed by terminal untranslated regions (UTR; grey rectangles). FS-frame-shift. Predicted regions encompassing recombination break-points are indicated by orange pills. Created using Geneious v8.1 [211] and annotated using Adobe Illustrator. Beneath this is a schematic depicting the location of RT-PCR primers (blue arrows indicate direction) and oligoprobes (green rectangles) used in the earliest RT-rtPCR screening assays and conventional, semi-nested (three primers) RT-PCR confirmatory sequencing assays [47, 48] . Publication order is noted by first [27 th September 2012; red] and second [6 th December 2012; orange] coloured rectangles; both from Corman et al. [47, 48] Those assays recommended by the WHO are highlighted underneath by yellow dots [53] . The NSeq reverse primer has consistently contained one sequence mismatch with some MERS-CoV variants. An altered version of that from Mackay IM, Arden KE. Middle East respiratory syndrome: An emerging coronavirus infection tracked by the crowd. Virus Res 2015 Vol 202:60-88 with permission from Elsevier [5] reviewed the broad tropism of MERS-CoV [5] . However, as is well described, cell culture is a slow, specialised and insensitive method [50] while PCR-based techniques are the preferred method for MERS-CoV detection. The first open reading frames (ORF 1a and 1b; Fig. 2 ) have become a key diagnostic and taxonomic target for CoV species identification. With less than 80 % identity between the amino acid sequence of MERS ORF 1ab and betacoronavirus relatives, Tylonycteris bat HKU4 and Pipistrellus bat HKU5, it can be concluded that it is a novel and distinct virus. MERS-CoV is predicted to encode ten open reading frames with 5' and 3' untranslated regions [51] . The structural proteins include the spike (S), envelope (E), membrane (M) and nucleocapsid (N) [52] . The products of ORF1a and ORF1b are predicted to encode nonstructural proteins. The majority of specimen testing to date has employed validated RT-rtPCR assays shown to be sensitive and specific [47, 48, 53] . The RealStar® kit uses these WHOrecommended assays [54] . The target sequences of these screening assays have not changed among genomes examined until at least mid-2015 (IMM observation). Other RT-rtPCR assays have been developed and validated for use as laboratory-based diagnostic tools [55] [56] [57] . Additionally, loop-mediated [58, 59] or recombinase polymerase [60] isothermal assays have been designed for field deployment. The detection of MERS-CoV antigen has not been common to date but the combination of short turnaround time from test to result, high throughput and identification of viral proteins makes this an attractive option. Detection of viral proteins rather than viral RNA indicates the likely presence of infectious virus. The first rapid immunochromatographic tool described could detect recombinant MERS-CoV nucleocapsid protein from DC nasal swabs with 94 % sensitivity and 100 % specificity compared to RT-rtPCR [61] . A different approach used a monoclonal antibody-based capture ELISA targeting the MERS-CoV nucleocapsid protein with a sensitivity of 10 3 TCID 50 and 100 % specificity [62] . Demonstration of a seroconversion to a MERS-CoV infection meets the current WHO definition of a case so optimized and thoroughly validated sero-assays employed alongside good clinical histories are useful to both identify prior MERS-CoV infection and help support transmission studies. Because serology testing is, by its nature, retrospective, it is usual to detect a viral footprint, in the form of antibodies, in the absence of any signs or symptoms of disease and often in the absence of any viral RNA [63] . Strategic, widespread sero-surveys of humans using samples collected after 2012 are infrequent. Much of the Arabian Peninsula and all of the Horn of Africa lack baseline data describing the proportion of the community who may have been infected by a MERS-CoV. However, sero-surveys have had widespread use in elucidating the role of DCs as a transmission source for MERS-CoV. Because of the identity shared between DC and human MERS-CoV (see Molecular epidemiology: using genomes to understand outbreaks), serological assays for DC sero-surveys should be transferrable to human screening with minimal re-configuration. Also, no diagnostically relevant variation in neutralization activity have been found from among a range of circulating tested MERS-CoV isolates and sera, so whole virus or specific protein-based sero-assays should perform equivalently in detecting serological responses to the single MERS-CoV serotype [49] . The development of robust serological assays requires reliable panels of wellcharacterized animal or human sera, including those positive for antibodies specific to MERS-CoV, as well as to likely sources of cross-reaction [64] . Obtaining these materials was problematic and slowed the development and commercialization of antibody detection assays for human testing [64] . A number of commercial ELISA kits, immunofluorescent assays (IFA) kits, recombinant proteins and monoclonal antibodies have been released [31, [65] [66] [67] [68] . Initially, conventional IFAs were used for human sero-surveys. These relied on MERS-CoV-infected cell culture as an antigen source, detecting the presence of human anti-MERS-CoV IgG, IgM or neutralizing antibodies in human samples [18, 48, 69] . No sign of MERS-CoV antibodies was found among 2,400 sera from patients visiting Hospital in Jeddah, from 2010 through 2012, prior to the description of MERS-CoV [18] . Nor did IFA methods detect any sign of prior MERS-CoV infection among a small sample of 130 healthy blood donors from another Hospital in Jeddah (collected between Jan and Dec 2012) [70] . Of 226 slaughterhouse workers, only eight (3.5 %) were positive by IFA, and those sera could not be confirmed by virus neutralization (NT) test. The study indicated that HCoV-HKU1 was a likely source of crossreactive antigen in the whole virus IFA [70] . Whole virus MERS-CoV IFA also suffered from some cross-reactivity with convalescent SARS patient sera and this could not be resolved by an NT test which was also cross-reactive [71] . IFA using recombinant proteins instead of whole-virus IFA, has been shown to be a more specific tool [31] . Since asymptomatic zoonoses have been posited [72] , an absence of antibodies to MERS-CoV among some humans who have regular and close contact with camels may reflect the rarity of actively infected animals at butcheries, a limited transmission risk associated with slaughtering DCs [70] , a pre-existing cross-protective immune status or some other factor(s) resulting in a low risk of disease and concurrent seroconversion developing after exposure in this group. IFA using recombinant proteins instead. Some sero-assays have bypassed the risks of working with infectious virus by creating transfected cells expressing recombinant portions of the MERS-CoV nucleocapsid and spike proteins [48, 73] , or using a recombinant lentivirus expressing MERS-CoV spike protein and luciferase [74, 75] . A pseudo particle neutralization (ppNT) assay has seen widespread used in animal studies and was at least as sensitive as the traditional microneutralization (MNT) test. [10, 74, [76] [77] [78] ] Studies using small sample numbers and ppNT found no evidence of MERS-CoV neutralizing antibody in sera from 158 children with LRT infections between May 2010 and May 2011, 110 sera from 19 to 52 year old male blood donors and 300 selfidentified animal workers from the Jazan Region of the KSA during 2012 [79, 80] . Similarly, a study of four herdsmen in contact with an infected DC herd in Al-Ahsa, eight people who had intermittent contact with the herd, 30 veterinary surgeons and support staff who were not exposed to the herd, three unprotected abattoir workers in Al-Ahsa and 146 controls who were not exposed to DCs in any professional role, found none with serological evidence of past MERS-CoV infection using the ppNT assay [10] . A delay in the neutralizing antibody response to MERS-CoV infection was associated with increased disease severity in South Korea cases with most responses detectable by week three of illness while others, even though disease was severe, did not respond for four or more weeks [81] . The implications for our ability to detect any response in mild or asymptomatic cases was not explored but may be a signifcant factor in understanding exposure in the wider community. A Jordanian outbreak of acute LRT disease in a hospital in 2012 was retrospectively found to be associated with MERS-CoV infection, initially using RT-rtPCR, but subsequently, and on a larger scale, through positivity by ELISA and IFA or MNT test. [46, 82, 83] This outbreak predated the first case of MERS in the KSA. The ELISA used a recombinant nucleocapsid protein from the group 2 betacoronavirus bat-CoV HKU5 to identify antibodies against the equivalent crossreactive MERS-CoV protein [71] . It was validated using 545 sera collected from people with prior HCoV-OC43, HCoV-229E, SARS-CoV, HCoV-NL63, HRV, HMPV or influenza A(H1N1) infections but was reportedly less specific than the recombinant IFA discussed above. It was still considered an applicable tool for screening large sample numbers [82] . A protein microarray expressing the S1 protein subunit has also been validated and widely used for DC testing [5, 84] . Detection of MERS-CoV infection using ELISA or S1 subunit protein microarray [84] is usually followed by confirmatory IFA and/ or a plaque-reduction neutralization (PRNT) [69, 70, 85] or MNT test. [74, 85, 86] This confirmatory process aims toensure the antibodies detected are able to specifically neutralize the intended virus and are not more broadly reactive to other coronaviruses found in DCs (bovine CoV, BCoV) or humans (HCoV-OC43, HCoV-229E, HCoV-NL63, HCoV-HKU1, SARS-CoV). In the largest study of human sera, a tiered diagnostic process assigned both recombinant IFA and recombinant ELISA positive sera to 'stage 1' seropositivity. A stage 2 seropositive result additionally required a suitably titred PRNT result [87] . The study found 15 sera collected in 2012 to 2013 from 10,009 (0.2 %) people in 13 KSA provinces contained MERS-CoV antibodies, but significantly higher proportions in occurred in camel shepherds (two of 87; 2.3 %) and slaughterhouse workers (five of 140; 3.6 %) [87] . Contemporary surveys are needed. MERS-CoV does not appear to be easily transmitted from DCs to humans, or perhaps it is [72] , but generally does not trigger a detectable immune response if only mild disease or asymptomatic infection results. Serology assays are in need of further validation in this area so care is required when moving newly developed diagnostic serology algorithms from a research setting to one that informs public health decisions. This was reinforced when a false positive US case, purported to have been infected after a handshake and two face-to-face meetings, did not withstand further confirmatory analysis using a more specific, NT assay and was subsequently retracted [88, 89] . The WHO recommends sampling from the LRT for MERS-CoV RT-rtPCR testing, especially when sample collection is delayed by a week or more after onset of symptoms. [53] LRT samples are also best for attempting isolation of infectious virus, although the success of culture is reduced when disease persists [49] . Recommended sample types include bronchoalveolar lavage (BAL), tracheal/tracheobronchial aspirate, pleural fluid and sputum [53, 90] . Fresh samples yield better diagnostic results than refrigerated material [69] and if delays in testing of ≥72 h are likely, samples (except for blood) should be frozen at −70°C [90] . If available, lung biopsy or autopsy tissues can also be tested [53] . The URT is a less invasive and more convenient sampling site however, and an oropharyngeal and throat swab or a nasopharyngeal aspirate/wash are recommended when URT sampling is to be conducted [90] . Paired sera, collected two to three weeks apart are preferable for serological testing while a single sample is suggested to be sufficient if collected two weeks after onset of disease or a single serum collected during the first 10-12 days if conducting RT-rtPCR [53, 90] . Human urine and stool have been found to contain MERS-CoV RNA 12 to 26 days after symptom onset [25, 69, 91] and are listed as samples that should be considered [53, 90] . In two cases that arrived in the Netherlands, urine was RT-rtPCR negative but faeces was weakly positive and sera were RT-rtPCR positive for five days or more [25] . The finding of MERS-CoV viral RNA in serum provides an avenue for retrospective PCR-based studies if respiratory samples are unavailable [83] . RNAaemia may also correlate with disease severity; signs of virus were cleared from the serum of a recovered patient, yet lingered until the death of another [92] . Clinically suspected MERS cases may return negative results by RT-rtPCR. Data have shown one or more negative URT samples may be contradicted by further URT sampling or the use of LRT samples, which is preferred [2, 43, 93] . Higher viral loads occur in the LRT compared to the URT. [22, 69, 88, 94] This fits with the observation that the majority of disease symptoms are reported to manifest as systemic and LRT disease [21] . However, on occasion, even LRT specimens from MERS cases may initially be negative, only to later become positive by RT-PCR [95] . This may be due to poor sampling when a cough is absent or non-productive or because the viral load is low [95] . Despite this both the largest human MERS-CoV studies [32, [96] [97] [98] and smaller ones [22, 25, 99] , use samples from the URT. It is then noteworthy that one study reported an association between higher loads in the URT and worse clinical outcome including intensive care and death [94] . At writing, no human data exist to define whether the virus replicates solely or preferentially in the LRT or URT, or replicates in other human tissues in vivo although MERS-CoV RNA has been detected from both the URT and LRT in a macaque monkey model [100] .The distribution of DPP4 in the human upper airways is also not well described. Individual human case studies report long periods of viral shedding, sometimes intermittently and not necessarily linked to the presence of disease symptoms. [25, 69, 99, 101] In one instance, a HCW shed viral RNA for 42 days in the absence of disease [99] . It is an area of high priority to better understand whether such cases are able to infect others. Over three quarters of MERS cases shed viral RNA in their LRT specimens (tracheal aspirates and sputum) for at least 30 days, while only 30 % of contacts were still shedding RNA in their URT specimens [91, 102] . In the only study to examine the effect of sample type on molecular analysis, 64 nasopharyngeal aspirates (NPA; an URT sample), 30 tracheal aspirates, 13 sputa and three BAL were examined. The tracheal aspirates and BAL returned the highest viral load values followed by NPA and sputum. Unsurprisingly, higher viral loads generally paralleled whole genome sequencing and culture success and, in NPA testing, were significantly correlated with severe disease and death [49, 94, 103] . This study demonstrated the importance of LRT sampling for whole genome sequencing. When tested, samples positive for MERS-CoV are often negative for other pathogens [2, 25, 93, 104] . However, many studies make no mention of additional testing for endemic human respiratory viruses [21, 23, 73, 105] . When viruses are sought, they have included human herpesvirus (HHV), rhinoviruses (HRV), enteroviruses (EV), respiratory syncytial virus (RSV), parainfluenzavirus types 1, 2 and 3 (PIVs),influenzaviruses (IFVs), endemic HCoVs, adenoviruses (AdVs) metapneumovirus (MPV) and influenza A\H1N1 virus; co-detections with MERS-CoV have been found on occasion [2, 22, 37, 69, 97] . Bacterial testing is sometimes included (for example, for Legionella and Pneumococcus) but the impact of bacterial co-presence is also unclear [22, [104] [105] [106] . Further testing of the LRT sample from the first MERS case used IFA to screen for some viruses (negative for IFV, PIVs, RSV and AdVs) and RT-PCR for others (negative for AdV, EVs, MPV and HHVs) [18] . RT-PCR also detected MERS-CoV. The WHO strongly recommends testing for other respiratory pathogens [53] but with this recommendation often discounted, there are limited data to address the occurrence and impact of co-infections or alternative viral diagnoses among both MERS cases and their contacts. Little is known of other causes of MERS-like pneumonia in the KSA or of the general burden of disease due to the known classical respiratory viruses. Testing of adult pilgrims performing the Hajj in 2012 to 2014 has not detected any MERS-CoV. In 2012, nasal swabs from 154 pilgrims collected prior to leaving for or departing from the KSA were tested [47] . In 2013, testing was significantly scaled up with 5,235 nasopharyngeal swabs from 3,210 incoming pilgrims and 2,025 swabs from outgoing pilgrims tested [98] . It should be noted that most pilgrims arrived from MERS-free countries. A further 114 swabs were taken from pilgrims with influenza-like illness [96, 107] . In earlier Hajj gatherings, it was found that influenza viruses circulated widely, whilst other viruses, often rhinoviruses, circulated more selectively, interpreted as indicating their importation along with foreign pilgrims. [107] [108] [109] Over time, increased influenza vaccination has been credited for a fall in the prevalence of influenza like illnesses among Hajj pilgrims. [110] A LRT sample is often not collected for these studies [98, 107, 109] , so false negative findings are a possibility although little is known about the initial site of MERS-CoV infection and replication; it may have been assumed it was the LRT because disease was first noticed there but the URT may be the site of the earliest replication. In Jeddah between March and July 2014 (hereafter called the Jeddah-2014 outbreak; Fig. 3 ), there was a rapid increase in MERS cases, accompanied by intense screening; approximately 5,000 samples from in and around the region were tested in a month yielding around 140 MERS-CoV detections (~3 % prevalence) [111] . Among 5,065 individuals sampled and tested across the KSA between October 2012 and September 2013,108 (2.1 %) detections were made in a hospital-centric population which included hospitalized cases (n = 2,908; 57.4 %), their families (n = 462; 9.1 %) and associated HCWs (n = 1,695; 33.5 %) [32] . Among the detections, 19 (17.8 %) were HCWs and 10 (9.3 %) were family contacts [32] . The 2-3 % prevalence of active MERS-CoV infections is not dissimilar to the hospital-based prevalence of other human CoVs. [112] However, the proportion of deaths among those infected with MERS-CoV is much higher than that known for the HCoVs NL63, HKU1, 229E or OC43 in other countries, and even above that for SARS-CoV; it is not a virus that could reasonably be described as a "storm in a teacup". It is the low transmission rate that has prevented worldwide spread, despite many "opportunities". Very early in the MERS outbreak, some animals were highly regarded as either the reservoir or intermediate host(s) of MERS-CoV with three of the first five cases having contact with DCs [73, 113, 114] . Today, animal MERS-CoV infections must be reported to the world organization for animal health as an emerging disease [115] . A summary of the first MERS cases reported by the WHO defined animal contact with humans as being direct and within 10 days prior to symptom onset [20] . This definition made no specific allowance for acquisition from DCs through a droplet-based route, which is very likely route for acquisition of a virus that initially and predominantly causes respiratory disease [23] . Camels are known to produce high levels of MERS-CoV RNA in their URT and lungs [116] . Providing support for a droplet transmission route and perhaps indicating the presence of RNA in smaller, drier droplet nuclei, MERS-CoV RNA was identified in a high volume air sample collected from a barn housing an infected DC [117] . The precise source from which humans acquire MERS-CoV remains poorly studied but it seems likely that animal and human behavioural factors may play roles (Fig. 3) [118] . These factors may prove important for human cases who do not describe any DC contact [119] nor any contact with a confirmed case. Whether the WHO definition of animal contact is sufficient to identify exposure to this respiratory virus remains unclear. Wording focuses on consumption of DC products but does not specifically ascribe risk to a droplet route for acquisition of MERS-CoV from DC [120] . Some MERS patients are listed in WHO disease notices as being in proximity to DCs or farms, but the individuals have not described coming into contact with the animals. No alternative path for acquiring infection is reported in many of these instances. What constitutes a definition of "contact" during these interviews has been defined for one study [72] . Despite this lack of clarity, the WHO consider that evidence linking MERS-CoV transmission between DCs to humans is irrefutable (Fig. 4) [120] . The possibility that bats were an animal host of MERS-CoV was initially widely discussed because of the existing diversity of coronaviruses known to reside among them [121] [122] [123] [124] . Conclusive evidence supporting bats as a source for human infections by MERS-CoV has yet to be found, but bats do appear to host ancestral representatives [53, 125] . However, these are not variants of the same virus nor always within the same phylogenetic lineage as MERS-CoV; they are each a genetically distinct virus. Bat-to-human infection by MERS-CoV is a purely speculative event. The only piece of MERS-CoV-specific evidence pointing to bats originates from amplification of a 190 nt fragment of the RNAdependent RNA polymerase gene of the MERS-CoV genome, identified in a faecal pellet from an insectivorous Emballonuridae bat, Taphozous perforatus found in Bisha, the KSA [121] . While very short, the sequence of the fragment defined it as a diagnostic discovery. Subsequently a link to DCs was reported [85] and that link has matured into a verified association [38, 126] (Fig. 4) . (See figure on previous page.) Fig. 3 Monthly detections of MERS-CoV (blue bars) and of cases who died (red bars) with some dates of interest marked for 2012 to 4 th September 2015. An approximation of when DC calving season [128] and when recently born DCs are weaned is indicated. Spring (green) and summer (orange) in the Arabian Peninsula are also shaded. Note the left-hand y-axis scale for 2014 and 2015 which is greater than for 2012/13. Sources of these public data include the WHO, Ministries of Health and FluTrackers [207] [208] [209] . Earlier and subsequent versions of this chart are maintained on a personal blog [210] . Modified and reprinted from Mackay IM, Arden KE. Middle East respiratory syndrome: An emerging coronavirus infection tracked by the crowd. Virus Res 2015 Vol 202:60-88 with permission from Elsevier [5] DCs, which make up 95 % of all camels, have a central presence in the Arabian Peninsula where human-DC contact ranges from little to close [119] . Contact may be commonplace and could occur in variety of ways (Fig. 4a) . There are several large well-attended festivals, races, sales and parades which feature DCs and DCs are also kept and bred close to populated areas in the KSA [127, 128] . DC milk and meat are widely consumed and the older DC is an animal of ritual significance after the Hajj pilgrimage [129] . However, MERS-CoV infection frequency is reportedly much lower than is the widespread and frequent habit of eating, drinking and preparing DC products. Daily ingestion of fresh unpasteurized DC milk is common among the desert Bedouin and many others in the KSA. DC urine is also consumed or used for supposed health benefits. Despite camel butchery being a local occupation, neither butchers nor other at-risk groups are identifiable among MERS cases; this may simply be a reporting issue rather than an unexplainable absence of MERS. A small case-control study published in 2015 identified direct DC contact, and not ingestion of products, to be associated with onset of MERS [38] . The first sero-survey of livestock living in the Middle East region was conducted during 2012-2013 [85] . DCs were sampled from a mostly Canary Island-born herd and from Omani DCs (originally imported from the Horn of Africa) [85] . A neutralising antibody assay found only 10 % of strongly seropositive Canary Island [5] . b Camel-to-human infections appear to be infrequent, while human-to-human spread of infection is regularly facilitated by poor IPC in healthcare settings where transmission is amplified, accounting for the bulk of cases. There are human MERS cases that do not fall into either category of source and it is unclear if these acquired infection through some entirely separate route, or from cases that escaped diagnosis. c Hypothetical ways in which subclinical (when infection may not meet a previously defined clinical threshold of signs and/or symptoms) or asymptomatic (no obvious signs or measured, noticed or recalled symptoms of illness) MERS-CoV infection may be implicated in transmission DC sera could neutralise MERS-CoV while all Omani DC sera had high levels of specific MERS-CoV neutralizing antibody [85] . This indicated that DCs had in the past been infected by MERS-CoV, or a very similar virus. Since this study, a host of peer-reviewed reports have looked at both DCs and other animals, and the possibility that they may host MERS-CoV infection. Seropositive DCs have been found throughout the Arabian Peninsula including Oman, the KSA, Qatar, Jordan, the United Arab Emirates (UAE), Kuwait as well as Sudan, Somalia, Egypt, Tunisia, Nigeria, Kenya and Ethiopia in Africa and the Canary Islands [85, [130] [131] [132] [133] [134] . Other animals tested include sheep, cows, pigs, horses, donkeys, mules, birds, water buffalo, goats, Bactrian camels, llamas and guanaco (south American camelids) but none had detectable neutralising antibody against MERS-CoV [4, 74, 78, 85, 86, 135, 136] . No virology or serology studies of human samples from areas in Africa where there are camels with a history of MERS-CoV have been reported to date. However,an absence of unexplained pneumonia that may be attributable to MERS-CoV infection may not signal the absence of virus among humans in each country but simply reflect a lack of expensive epidemiology studies conducted by resource-poor countries. It is thus unclear whether MERS-CoV, or an antigenically related CoV, is an unrecognized pathogen in these regions, perhaps circulating for even longer than it has been known in the Arabian Peninsula [133] . MERS-CoV RNA has also been detected in DC samples, and recovery of infectious virus has also been achieved from DC samples [4, 77, 117, 132, [137] [138] [139] [140] [141] . From some of these, full or majority length genomes of MERS-CoV have been sequenced [77, 137, 138] . DC versions of MERS-CoV were found to be as similar to each other, as were variants detected from different humans over time and across distance. Antibody screening assays have also detected crossreactive antibodies in sera. These were identified as such by screening sera against similar viruses, for example BCoV or HCoV-OC43 (as an antigenic facsimile for BCoV). It is possible that other MERS-CoV-like viruses also reside within DCs, but this does not detract from the definitive finding of MERS-CoV genetic sequences in both DCs and humans [117, 142, 143] . Screening studies have shown that juvenile DCs are more often positive for virus or viral RNA while older DCs are more likely to be seropositive and RNA or virus negative [76, 77, 144] . In adult DCs, MERS-CoV RNA has been detected among animals with pre-existing antibody, suggesting re-infection is possible [77, 144] . Viral loads among positive DCs can be very high [4, 76, 77, 139, 144] and DCs have been found positive both when ill with URT respiratory signs [77, 117, 142, 145] or when apparently healthy [137] . These findings indicate DCs host natural MERS-CoV infections. Furthermore, stored DC sera have revealed signs of MERS-CoV in DCs which date back over three decades (the earliest collected in 1983) [4, 133, 135] . Older sera have not been tested and so precisely how long DCs have been afflicted by MERS-CoV, whether the virus is enzootic among them, introduced to them decades or centuries ago from bats in Africa or the Arabian Peninsula, or they are the subject of regular but short-lived viral incursions from an as yet unknown host, cannot be answered. Researchers sought to determine a direction for infection; were DCs transmitting virus to humans or were humans infecting DCs? At a Qatari site, a farm owner and his employee became ill in mid-October 2013 and tested positive for MERS-CoV RNA in a sputum and throat swab sample, respectively. RT-rtPCRs found MERS-CoV RNA in 11 of 14 positive DC nasal swabs at the farm; six (43 %) positive by two or more assays [138] . The results indicated a recent outbreak had occurred in this herd; the first indication of MERS-CoV RNA found within DCs with a temporal association to human infections. Three positive DC samples were confirmed by sequencing a 358 nt portion of the spike gene; these sequences were identical to each other, again with close homology to other human and DC MERS-CoV sequences [138] . The DCs and human contacts yielded ORF1a and ORF4b sequences differing by only a single nucleotide each, clustering closely with the Hafr-Al-Batin_1_2013 variant [138] . Subsequent case studies found evidence of a concurrent human and DC infection and the direction of that infection was inferred to be from the ill DCs and to their human owners [117, 142, 146] . Partial genome sequences indicated that a human and a MERS-CoV RT-rtPCR positive DC had been infected by a variant of the same virus, harbouring the same distinct pattern of nucleotide polymorphisms. [142] All nine DC in the owner's herd, serially sampled, reacted in a recombinant S1 antigen ELISA, with the two animals that had been RT-rtPCR positive showing a small, verifiable rise in antibody titre [142] . A rise in titre theoretically begins 10 to 21 days after DC infection [142] . The authors suggested that the rise in titre in DC sera which occurred alongside a declining RNA load, while the patient was actively ill and hospitalized, indicated that the DCs were infected first followed by the owner [117, 142] . BCoV antibodies were also present, and rising in one of the two RT-rtPCR positive animals but no animal's antibodies could neutralise BCoV infection [142] . Camel calving season occurs in the winter months (between late October and late February; Fig. 3 ) and this may be a time when there is increased risk to humans of spill-over due to new infections among naïve DC populations [128] . What role maternal camel antibody might play in delaying infection of calves remains unknown [128, 142] . Juvenile DCs appear to host active infection more often than adult DCs and thus the sacrificial slaughter of DCs, which must be five years of age or older (termed a thane), may not be accompanied by significant risk of exposure to infection. In contrast to earlier results, slaughterhouse workers who kill both younger and older DCs, may be an occupational group with significantly higher incidence of seropositivity to MERS-CoV when animals have active MERS-CoV infections [129, 139, [147] [148] [149] . Expanded virological investigations of African DCs may lead to more seropositive animals and geographic areas in which humans may be at risk. It is possible that there are areas where humans already harbour MERS-CoV infections that have not been identified because of an absence of laboratory surveillance. Virological investigations of bats may lead to findings of ancestral viruses and viral 'missing links' and identifying any other animal sources of zoonotic spread is important to inform options for reducing human exposures [56, 76] . Infectious MERS-CoV added to DC, goat or cow milk and stored at 4°C could be recovered at least 72 h later and, if stored at 22°C, recovery was possible for up to 48 h [150] . MERS-CoV titre decreased somewhat when recovered from milk at 22°C but pasteurization completely ablated MERS-CoV infectivity [150] . In a subsequent study, MERS-CoV RNA was identified in the milk, nasal secretion and faeces of DCs from Qatar [151] . A single study has examined the ability of MERS-CoV to survive in the environment [150] . Plastic or steel surfaces were inoculated with 10 6 TCID 50 of MERS-CoV at different temperature and relative humidity (RH) and virus recovery was attempted in cell culture. At high ambient temperature (30°C) and low RH (30 %) MERS-CoV remained viable for 24 h [150] . By comparison, a well known and efficently transmitted respiratory virus, influenza A virus, could not be recovered in culture beyond four hours under any conditions [150] . Aerosol experiments found MERS-CoV viability only decreased 7 % at low RH at 20°C. In comparison, influenza A virus decreased by 95 % [150] . MERS-CoV survival is inferior to that previously demonstrated for SARS-CoV [152] . For context, pathogenic bacteria can remain viable and airborne for 45 min in a coughed aerosol and can spread 4 m. MERS-CoV's ability to remain viable over long time periods gives it the capacity to thoroughly contaminate a room's surfaces when occupied by an infected and symptomatic patient [153] . Whether MERS-CoV can remain adrift and infectious for extended periods (truly airborne) remains unknown. Such findings expand our understanding of the possibilities for droplets to transmit respiratory viruses in many settings, including hospital waiting rooms, emergency departments, treatment rooms, open intensive care facilities and private patient rooms. The nature and quality of air exchange, circulation and filtration are important variables in risk measurement and reduction as is the use of negative pressure rooms to contain known cases. Droplet spread between humans is considered the mechanism of human-to-human transmission and the need for droplet precautions was emphasized after the Al-Ahsa hospital, the KSA and the South Korean outbreaks [21, 23, 154, 155] . By extrapolation, aerosol-generating events involving DCs (urination, defecation, and preparation and consumption of DC products) should be factored into risk measurement and reduction efforts and messaged using appropriate context. The provision of evidence supporting the best formulation of personal protective equipment to be worn by HCWs who receive, manage or conduct procedures on infectious cases remains a priority. MERS-CoV was found and characterized because of its apparent association with severe, and therefore more obvious, illness in humans; we were the canaries in the coal mine. Sero-assays and prospective cohort studies have yet to determine the extent to which milder or asymptomatic cases contribute to MERS-CoV transmission chains. However, transmission of MERS-CoV is defined as sporadic (not sustained), intra-familial, often healthcare associated, inefficient and requiring close and prolonged contact [22, 31, 63, 93, 97, 102, 156] In a household study, 14 of 280 (5 %) contacts of 26 MERS-CoV positive index patients were RNA or antibody positive; the rate of general transmission, even in outbreaks is around 3 % [31] . It seems that the majority of human cases of MERS-CoV, even when numbers appear to increase suddenly, do not readily transmit to more than one other human so to date, the localized epidemic of MERS-CoV has not been self-sustaining [157] [158] [159] [160] [161] . That is to say, the basic reproduction number (R 0 ) -the average number of infections caused by one infected individual in a fully susceptible populationhas been close to one throughout various clusters and outbreaks. If R 0 was greater than 1, a sustained increase in case numbers would be expected. Some R o calculations may be affected by incomplete case contact tracing, limited community testing and how a case is defined. That MERS has had a constant presence in the Arabian Peninsula since 2012 is due to ongoing, sporadic spill-over events from DCs amplified by poorly controlled hospital outbreaks. The first known MERS human-to-human transmission event was one characterized by acute LRT disease in a healthcare setting in Jordan. In stark contrast, a sero-survey of HCW who were sometimes in close and prolonged contact with the first, fatal MERS-CoV case in 2012 [162] , found none of the HCW had seroconverted four months later, despite an absence of eye protection and variable compliance with required PPE standards [162] . Early on in the MERS story, samples for testing were mostly collected from patients with severe illness and not those with milder acute respiratory tract infections. Contacts of confirmed MERS cases were often observed for clinical illness, but not tested. These omissions may have confounded our understanding of MERS-CoV transmission and biased early data towards higher numbers of seriously ill and hospitalized patients, inflating the apparent proportion of fatal cases. Case-control studies were not a focus. As testing paradigms changed and contacts were increasingly tested, more asymptomatic and mild infections were recognized [163] . A rise in the cases termed asymptomatic (which enlarge the denominator for calculations of the proportion of fatal cases, defined in [164] ) resulted in a drop in the proportion of fatal cases during the Jeddah-2014 outbreak. Historically, such rises are consistent with changing definitions and laboratory responses and clinical management of a newly discovered virus infection that was first noted only among the severely ill. Upon follow-up, over three-quarters of such MERS-CoV RNA positive people did recall having one or more symptoms at the time, despite being reported as asymptomatic [165] raising some question over the reliability of other reported data. The proportion of fatal MERS cases within the KSA compared to outside the KSA, as well as the age, and sex distribution change in different ways when comparing MERS outbreaks. Approximately 43 % of MERS cases (549 of 1277) in the KSA were fatal betwen 2012 and December 2015 while 21 % (72 of 330) died among those occurring outside of the KSA. The total number of male cases always outnumber females and the proportion of male deaths is always greater than the proportion of females who die. However the proportion of male deaths from total males with MERS is a similar figure to that for females. In the KSA, there is a greater proportion of younger males among cases and deaths than were observed from the 2015 South Korean or the Jeddah-2014 outbreaks (Additional file 2: Figure S2 ). Why these aspects have differed may be due to differences in the time to presentation and diagnosis, the nature and quality of supportive care, the way a person became infected (habits, exposure to a human or zoonotic source, viral load, route of infection) or the extent to which different populations are burdened by underlying diseases [40] . As a group, HCWs comprised 16 % of MERS cases in the KSA and South Korea. It is apparent that the weekly proportion of infected HCWs increases alongside each steep rise in overall detections (Fig. 5) . In May 2013, the WHO published guidelines for IPC during care of probable or confirmed cases of MERS-CoV infection in a healthcare setting [166] . This is explainable because to date, each case rise has been intimately associated with healthcare-facility related outbreaks [118] . These rises in MERS-CoV detections can decrease the average age during each event because HCWs are usually younger than inpatients with MERS. Healthcare facilities have been a regular target for suggested improvements aimed at improving infection prevention and control (IPC) procedures [115, 118] . Most of the analysis of MERS-CoV genetics has been performed using high throughput or "deep" sequencing methods for complete genome deduction [167] [168] [169] . MERS-CoV was the first subject of such widespread use of deep sequencing to study an emerging viral outbreak with global reach. The technique can produce genomic [207] [208] [209] . Earlier and subsequent versions of this chart are maintained on a personal blog [210] length coverage in a single experiment with highly repetitious measurement of each nucleotide position [52, 140] . Despite assays having been published early on, subgenomic sequencing, once the mainstay of viral outbreak studies, has less often been published during MERS-CoV characterization [48] . As more genomes from both humans and DCs have been characterized, two clades have become apparent; A and B (Fig. 6) . Clade A contains only human-derived MERS-CoV genomes from Jordan, while Clade B comprises the majority of human and camel genomes deduced thus far [168] . Two studies during 2015, one looking at Jeddah-2014 MERS-CoV variants and another looking at a variant exported from South Korea to China, have now identified signs of genetic recombination among MERS-CoV variants. While human and camel whole genome sequences have retained >99 % identity with each other, members of genetically distinct lineages can and do swap genetic material when suitable conditions and coinfections co-occur [170] [171] [172] . Shared identity implies that the major source for human acquisition is the DC, rather than another animal, although more testing of other animal species is needed to confirm that conclusion. Over a month, a DC virus sequenced on different occasions did not change at all indicating a degree of genomic stability in its host, supporting that DCs are the natural, rather than intermediate, host for the MERS-CoV we know today [77] . To date, recombination has been localised to breakpoints near the boundary between ORF1a and ORF1b regions, within the spike gene [170] and in the ORF1b region (Fig. 2) [172] . It is not unexpected that recombination should occur since it is well known among other CoVs [124] and because the majority of MERS-CoV whole genomes collected from samples spanning three years (2012-2015) and from humans, camels and different countries have shown close genetic identity to each other, with just enough subtle variation to support outbreak investigations so long as whole genome sequencing is applied [52, 77, 135, 138, 168, [173] [174] [175] . Changes in genome sequence may herald alterations to virus transmissibility, replication, persistence, lethality or response to future drugs. If we have prior knowledge of the impact of genetic changes because of thorough characterization studies, we can closely Fig. 6 The genetic relationship between MERS-CoV nucleotide sequences (downloaded from GenBank using the listed accession numbers and from virological.org [212] ). This neighbour joining tree was created in MEGA v6 using an alignment of human and DCderived MERS-CoV sequences (Geneious v8.1 [211] ). Clades are indicated next to dark (Clade A) or pale (Clade B) blue vertical bars. Camel icons denote genomes from DCs. Healthcare or community outbreaks are boxed and labelled using previously described schemes [212, 213] monitor the genomic regions and better understand any changes in transmission or disease patterns as they occur. Genetic mutations noted during the largest of human outbreaks, Jeddah-2014, did not impart any major replicative or immunomodulatory changes when compared to earlier viral variants in vitro [156, 176] . However, we understand very little of the phenotypic outcomes that result from subtle genetic change in MERS-CoV genomes. To date no clinical relevance or obvious in vivo changes to viral replication, shedding or transmission has been reported or attributed to mutations or to new recombinant viruses [156] . But vigilance and larger, more contemporary and in vivo studies are needed. Genome sequence located to a distinct clade were identified from an Egyptian DC that was probably imported from Sudan. This does not fit into either of the current clades [125, 168, 177] . A virus sequenced from a Neoromicia capensis bat was more closely related to MERS-CoV than other large bat-derived sequences had been to that point, but the genome of a variant of a MERS-CoV has yet to be discovered and deduced from any bat [125] . Analyses of MERS-CoV genomes have shown that most single nucleotide differences among variants were located in the last third of the genome (Fig. 2) , which encodes the spike protein and accessory proteins [168] . At least nine MERS-CoV genomes contained amino acid substitutions in the receptor binding domain (RBD) of the spike protein and codons 158 (N-terminal region), 460 (RBD), 1020 (in heptad repeat 1), 1202 and 1208 bear investigation as markers of adaptive change [140, 169] . The spike protein had not changed in the recombinant MERS-CoV genome identified in China in 2015 but was reported to have varied at a higher rate than that for complete MERS-CoV genomes, among South Korean variants [172, 178] . This highlights that subgenomic regions may not always contain enough genetic diversity to prove useful for differentiating viral variants. Despite this, one assay amplifying a 615 nucleotide fragment of the spike S2 domain gene for Sanger sequencing agreed with the results generated by the sequencing of a some full genomes and was useful to define additional sequence groupings [177] . Genomic sequence can also be used to define the geographic boundaries of a cluster or outbreak and monitor its progress, based on the similarity of the variants found among infected humans and animals when occurring together, or between different sites and times (Fig. 6 ) [169] . This approach was employed when defining the geographically constrained MERS hospital outbreak in Al-Ahsa, which occurred between 1 st April and 23 rd May 2013, as well as clusters in Buraidah and a community outbreak in Hafr Al-Batin, the KSA. Genomic sequencing identified that approximately 12 MERS-CoV detections from a community outbreak in Hafr Al-Batin between June and August 2013 may have been triggered by an index case becoming infected through DC contact [175] . Sequencing MERS-CoV genomes from the 2013 Al-Ahsa hospital outbreak indicated that multiple viral variants contributed to the cases but that most were similar enough to each other to be consistent with human-tohuman transmission. Molecular epidemiology has revealed otherwise hidden links in transmission chains encompassing a period of up to five months [179] . However, most outbreaks have not continued for longer than two to three months and so opportunities for the virus to adapt further to humans through co-infection and sustained serial passage have been rare [169] . In Riyadh-2014, genetic evidence supported the likelihood of multiple external introductions of virus, implicating a range of healthcare facilities in an event that otherwise looked contiguous [23, 168, 179] . Riyadh is a nexus for camel and human travel and has had more MERS cases than any other region of the KSA to date but also harbours a wide range of MERS-CoV variants [128, 167, 179] . However the South Korean outbreak originated from a single infected person, resulting in three to four generations of cases [180, 181] . Studies of this apparently recombinant viral variant did not find an increased evolutionary rate and no sign of virus adaptation thus the outbreak seems to have been driven by circumstance rather than circumstance together with mutation [181] . For many MERS cases detected outside the Arabian Peninsula, extensive contact tracing has been performed and the results described in detail. Contact tracing is essential to contain the emergence and transmission of a new virus and today it is supported by molecular epidemiology. Although it is an expensive and time consuming process, contact tracing can identify potential new infections and through active or passive monitoring, react more rapidly if disease does develop. Results of contact tracing to date have found that onward transmission among humans is an infrequent event. For example, there were 83 contacts, both symptomatic and asymptomatic, of a case treated in Germany who travelled from the UAE but no sign of virus or antibody were found in any of them [73] . The very first MERS case had made contact with 56 HCWs and 48 others, but none developed any indication of infection [162] . In a study of 123 contacts of a case treated in France, only seven matched the definition for a possible case and were tested; one who had shared a 20 m 2 hospital room while in a bed 1.5 m away from the index case for a prolonged period was positive [26] . None of the contacts of the first two MERS cases imported into the USA in 2014 contained any MERS-CoV footprint [182] and none of the 131 contacts of two travellers returning to the Netherlands developed MERS-CoV antibodies or tested RNA positive [25, 183] . Analyses of public data reveal many likely instances of nosocomial acquisition of infection in the Arabian Peninsula and these data may be accompanied by some details noting contact with a known case or facility. One example identified the likely role of a patient with a subclinical infection, present in a hospital during their admission for other reasons, as the likeliest index case triggering a family cluster [93] . Contact tracing was a significant factor in the termination of a 2015 outbreak involving multiple South Korean hospitals [184] . Such studies demonstrate the necessity of finding and understanding a role for mild and asymptomatic cases, together with restricting close contact or prolonged exposure of infected people to others, especially older family members and friends with underlying disease (Fig. 4c) . The hospital-associated outbreak in Jeddah in 2014 was the largest and most rapid accumulation of MERS-CoV detections to date. The greatest number of MERS-CoV detections of any month on record occurred in Jeddah in April. The outbreak was mostly (>60 % of cases) associated with human-to-human spread within hospital environments and resulted from a lack of, or breakdown in, infection prevention and control [37, 185, 186] . A rise in fatalities followed the rapid increase in case numbers. In 2015 two large outbreaks occurred. South Korea was the site of the first large scale outbreak outside the Arabian Peninsula and produced the first cases in both South Korea and China, occurring between May and July 2015. This was closely followed by a distinct outbreak in Ar Riyad province in the KSA which appeared to come under control in early November. After staying in Bahrain for two weeks, a 68 year old male (68 M) travelled home to South Korea via Qatar, arriving free of symptoms on the 4 th May 2015 [187] . He developed fever, myalgia and a cough nearly a week later (11 th ). He visited a clinic as an outpatient between the 12 th and 15 th of May and was admitted to Hospital A on the 15 th [188] . He was discharged from Hospital A on the 17 th then visited and was admitted to the emergency department of Hospital B on the 18 th . During this second stay, a sputum sample was taken and tested positive for MERS-CoV on the 20 th [187, 188] , triggering transfer to the designated isolation treatment facility. Over a period of 10 days, the index case was seen at three different hospitals, demonstrating a key feature of "hospital shopping" that shaped the South Korean outbreak. Approximately 34 people were infected during this time [187] . In total 186 cases were generated in this outbreak, all linked through a single transmission chain to 68 M; 37 cases died [189] . In South Korea, the national health insurance system provides for relatively low cost medical care, defraying some costs by making family members responsible for a portion of the ministration of the sick, resulting in them sometimes staying for long periods in the rooms that often have more than four beds in them [24] . Other factors thought to have enabled this outbreak included unfamiliarity of local clinicians with MERS, ease with which the public can visit and be treated by tertiary hospitals, the custom of visiting sick friends and relatives in hospitals, the hierarchical nature of Korean society, crowded emergency rooms, poor IPC measures, a lack of negative pressure isolation rooms and poor inter-hospital communication of patient disease histories [24, [190] [191] [192] . All of the reported transmission occurred across three or four generations and apart from one unknown source, were all hospital-acquired [24, 120, 181, [193] [194] [195] . Few clinical details about these cases have been reported to date and detail on transmission and contact tracing is minimal. The hospitals involved were initially not identified, governmental guidance and actions produced confusing messages and there was very limited communication at all early on which resulted in unnecessary concern, distrust and a distinct economic impact [191, [196] [197] [198] . Early in the outbreak, a infected traveller, the son of an identified case in South Korea, passed through Hong Kong on his way to China where he was located, isolated and cared for in China [91, 199, 200] . No contacts became ill. The outbreak was brought under control in late July/ early August [201] after improved IPC measures were employed, strong contact tracing monitoring and quarantine, expanded laboratory testing, hospitals were better secured, specialized personnel were dispatched to manage cases and international cooperation increased [202, 203] . A review of public data showed that, as for MERS in the KSA, older age and the presence of underlying disease were significantly associated with a fatal outcome in South Korea. [40] Even though R 0 is <1, super-spreading events facilitated by circumstances created in healthcare settings and characterized by cluster sizes over 150, such as this one, are not unexpected from MERS-CoV infection [204] . The dynamic of an outbreak depends on the R 0 and an individual's viral shedding patterns, contact type and frequency, hospital procedures and population structure and density [204] . In the region of Ar Riyad, including the capital city of Riyadh, a hospital based cluster began, within a single hospital, from late June 2015 [205] . By mid-September there had been approximately170 cases reported but the outbreak appeared to been brought under control in November. It became apparent early on that MERS-CoV spread relatively ineffectively from human-to-human. Despite ongoing and possibly seasonal introduction of virus to the human population via infected DCs and perhaps other animals yet to be identified, the vast majority of MERS-CoV transmission has occurred from infected to uninfected humans in close and prolonged contact through circumstances created by poor infection control in health care settings. This opportunistic virus has had its greatest impact on those with underlying diseases and such vulnerable people, sometimes suffering multiple comorbidities, have been most often associated with hospitals, creating a perfect storm of exposure, transmission and mortality. It remains unclear if this group are uniquely affected by MERS-CoV or if other respiratory virus infections, including those from HCoVs, produce a similarly serious impact. In South Korea, a single imported case created an outbreak of 185 cases and 36 deaths that had a disproportionate impact on economic performance, community behaviour and trust in government and the health care system. Household human-to human transmission occurs but is also limited. Educational programs will be essential tools for combatting the spread of MERS-CoV both within urban and regional communities and for the health care setting. Vigilance remains important for containment since MERS-CoV is a virus with a genetic makeup that has been observed for only three years and is not stable. Among all humans reported to be infected, nearly 40 % have died. Continued laboratory testing, sequencing, analysis, timely data sharing and clear communication are essential for such vigilance to be effective. Global alignment of case definitions would further aid accurate calculation of a case fatality ratio by including subclinical case numbers. Whole genome sequencing has been used extensively to study MERS-CoV travel and variation and although it remains a tool for experts, it appears to be the best tool for the job. MERS and SARS have some clinical similarities but they also diverge significantly [206] . Defining characteristics include the higher PFC among MERS cases (above 50 % in 2013 and currently at 30-40 %; well above the 9 % of SARS) and the higher association between fatal MERS and older males with underlying comorbidities. For the viruses, MERS-CoV has a broader tropism, grows more rapidly in vitro, more rapidly induces cytopathogenic change, triggers distinct transcriptional responses, makes use of a different receptor, induces a more proinflammatory state and has a delayed innate antiviral response compared to SARS-CoV. There appears to be a 2-3 % prevalence of MERS-CoV in the KSA with a 5 % chance of secondary transmission within the household. There is an increased risk of infection through certain occupations at certain times and a much greater chance for spread to other humans during circumstances created by humans, which drives more effective transmission than any R 0 would predict on face value. Nonetheless, despite multiple mass gatherings that have afforded the virus many millions of opportunities to spread, there have remarkably been no reported outbreaks of MERS or MERS-CoV during or immediately after these events. There is no evidence that MERS-CoV is a virus of pandemic concern. Nonetheless, hospital settings continue to describe MERS cases and outbreaks in the Arabian Peninsula. As long as we facilitate the spread of MERS-CoV among our most vulnerable populations, the world must remain on alert for cases which may be exported more frequently when a host country with infected camel reservoirs is experiencing human clusters or outbreaks. The MERS-CoV appears to be an enzootic virus infecting the DC URT with evidence of recent genetic recombination. It may once have had its origins among bats, but evidence is lacking and the relevance of that to today's ongoing epidemic is academic. Thanks to quick action, the sensitive and rapid molecular diagnostic tools required to achieve rapid and sensitive detection goal have been in place and made widely available since the virus was reported in 2012. RT-PCR testing of LRT samples remains the gold standard for MERS-CoV confirmation. Serological tools continue to emerge but they are in need of further validation using samples from mild and asymptomatic infections and a densely sampled cohort study to follow contacts of new cases may address this need. Similarly, the important question of whether those who do shed MERS-CoV RNA for extended periods are infectious while appearing well, continues to go unanswered. It is even unclear just how many 'asymptomatic' infections have been described and reported correctly which in turn raises questions about the reliability of other clinical data collection to date. While the basic virology of MERS-CoV has advanced over the course of the past three years, understanding what is happening in, and the interplay between, camel, environment and human is still in its infancy. Additional file 1: Figure S1 . The

How has the vast majority of MERS-CoV transmission occurred?

Responding to the COVID-19 pandemic in complex humanitarian crises https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7085188/ SHA: d013e42811c6442b184da3b9bbfd9e334031a975 Authors: Poole, Danielle N.; Escudero, Daniel J.; Gostin, Lawrence O.; Leblang, David; Talbot, Elizabeth A. Date: 2020-03-21 DOI: 10.1186/s12939-020-01162-y License: cc-by Abstract: nan Text: Over 168 million people across 50 countries are estimated to need humanitarian assistance in 2020 [1] . Response to epidemics in complex humanitarian crisessuch as the recent cholera epidemic in Yemen and the Ebola epidemic in the Democratic Republic of Congois a global health challenge of increasing scale [2] . The thousands of Yemeni and Congolese who have died in these years-long epidemics demonstrate the difficulty of combatting even well-known pathogens in humanitarian settings. The novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) may represent a still greater threat to those in complex humanitarian crises, which lack the infrastructure, support, and health systems to mount a comprehensive response. Poor governance, public distrust, and political violence may further undermine interventions in these settings. Populations affected by humanitarian crises are expected to be particularly susceptible to COVID-19, the disease caused by SARS-CoV-2, due to displacement, crowded housing, malnutrition, inadequate water, sanitation, and hygiene (WASH) tools, and stigmatization. Disease outbreaks further reduce access to limited healthcare, which is increasingly disrupted by attacks on health facilities and the persistent overburdening of health systems. These situations escalate both the necessity and the difficulty of delivering accurate and actionable information to potentially affected populations [3] . As the international community responds to SARS-CoV-2, public health authorities in humanitarian crises begin at a disadvantage to enact appropriate infection control to prevent transmission in healthcare settings, identify infectious cases, administer supportive care and novel treatments for the seriously ill, and trace contacts. These standard public health measures are particularly difficult to perform in humanitarian settings. For example, limited public health, laboratory, and primary care services represent a barrier to testing. Providing the limited healthcare worker cadre with appropriate training and personal protective equipment, and ensuring a continuous supply chain for such, is a challenge in all settings, exacerbated in complex humanitarian crises. Frequent displacement and limited contact information may prevent effective contact tracing. Finally, intractable structural challenges such as overcrowding limit the implementation of both quarantine of those exposed and isolation of those who are ill. Given these increased vulnerabilities, humanitarian crises should be viewed as a priority for national and international bodies that seek to combat this unfolding pandemic. Resources must be identified to protect healthcare workers, develop and deploy rapid testing, improve surveillance, and enact quarantine and isolation of contacts and cases. To mitigate the impact of COVID-19 on crisesaffected populations, governments and agencies will implement the familiar, global evidence-based approaches for combatting respiratory viruses. Respiratory hygiene is a highly effective public health intervention, supported by evidence demonstrating that the spread of respiratory viruses, such as SARS-CoV-2, can be prevented by hand hygiene, safe cough practice, and social distancing [4] . Hand hygiene is a readily implemented behavior: the distribution of soap to households in humanitarian settings has been shown to increase handwashing by over 30% [5] . Furthermore, hand hygiene is an avenue of agency for protecting one's own health, consistent with the rights to dignity and to fully participate in decisions related to assistance in humanitarian crises. Widespread introduction of alcohol-based hand rubs is also possible in many resource-limited settings, with published protocols for local production [6] . The Sphere Handbook, a collection of rights-based guidelines for humanitarian response, is the foremost authority on minimum standards for humanitarian assistance [7] . However, despite the indisputable evidence for the efficacy of hand hygiene for reducing both bacterial and viral pathogen transmission, humanitarian WASH standards are based on evidence pertaining to the prevention of illnesses transmitted by the faecal-oral route, with the focus on hand hygiene proximate to latrines [5, 8] . And yet, latrines in crisis settings are often shared and distant from residential shelters, conferring a high risk of gender-based violence [9] . Gender-based violence around latrines is an important deterrent for accessing latrine-adjacent handwashing stations, particularly for hand hygiene to prevent respiratory pathogen transmission. Evidence-based guidelines alone in complex humanitarian crises may not suffice during the emergence of the current SARS-CoV-2 pandemic. Without the adaptation of existing standards, mitigation plans will fall short of health and human rights obligations in outbreak response. Crisis-affected community engagement is integral in pandemic planning, in order to maximize the real-world effectiveness of efficacious interventions. Transparent and credible information-sharing mechanisms are increasingly essential when pandemics threaten vulnerable populations [10] . Diplomacy bridging long-standing mistrust of public health and biomedical interventions and facilitating engagement with contentious actors is a necessary component of effective health governance in complex crisis settings [2] . Interventions tailored to the needs of crisis-affected populations, delivered with transparent information, in the context of inclusive governance practices, are urgently needed in the global response to the COVID-19 pandemic.

What is essential when pandemics threaten vulnerable populations?

Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus (SARS-CoV-Z) and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown (11th March), although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March [95% credible interval 21,000-120,000]. Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London (2020), doi: https://doi.org/10.25561/77731 1 Introduction Following the emergence of a novel coronavirus (SARS-CoV-Z) in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing (such as banning large gatherings and advising individuals not to socialize outside their households), border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase (dependent on how much greater than 1 the reproduction number is) until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups (e.g. contacts of cases). Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds (Bayesian credible intervals) of the total populations infected (attack rates), case detection probabilities, and the reproduction number over time (Rt). We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards (Figure 1). Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced (Figure 2- 12). Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected (Figure 2) than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 [1.9-15.2] million people have been infected as of March 28th, giving an attack rate of 9.8% [3.2%-25%] of the population (Table 1). Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% (7.0 [18-19] million people) have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 [240,000-1,500,000] people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected (mean [95% credible intervall) Austria 1.1% [0.36%-3.1%] Belgium 3.7% [1.3%-9.7%] Denmark 1.1% [0.40%-3.1%] France 3.0% [1.1%-7.4%] Germany 0.72% [0.28%-1.8%] Italy 9.8% [3.2%-26%] Norway 0.41% [0.09%-1.2%] Spain 15% [3.7%-41%] Sweden 3.1% [0.85%-8.4%] Switzerland 3.2% [1.3%-7.6%] United Kingdom 2.7% [1.2%-5.4%] 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 [3.01-4.66], which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers (Appendix 8.4.2, Appendix 8.4.6). The initial reproduction numbers are also uncertain due to (a) importation being the dominant source of new infections early in the epidemic, rather than local transmission (b) possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths (e.g. Italy, Spain), suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 [0.14-2.14] for Norway to a posterior mean of2.64 [1.40-4.18] for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 (posterior probability of being less than 1.0 is 44% on average across the countries). We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic (e.g. Germany, UK, Norway). Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well (Bayesian goodness of fit tests). We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths (Appendix 8.3). The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect (Figure 1, Figure 4). However, when doing a sensitivity analysis (Appendix 8.4.3) with uninformative prior distributions (where interventions can increase deaths) we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval (CI), light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model (assuming no intervention had taken place). Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented (and R, = R0 i.e. the initial reproduction number estimated before interventions). Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed (Figure 3). Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C). By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 [21,000-120,000] deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 [13,000- 84,000] and 16,000 [5,400-35,000] deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 [73-1,000] deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions (up to 28 March) and forecasts (after) for (a) Italy and (b) Spain from our model with interventions (blue) and from the no interventions counterfactual model (pink); credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% (no effect on transmissibility) (ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 (red) or was subsequent to other interventions (green). Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% [l.9%-ll%] with considerable variation between countries (Table 1). Our estimates imply that the populations in Europe are not close to herd immunity ("50-75% if R0 is 2-4). Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths (Italy, Spain). lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified (see Appendix 8.4 for sensitivity analysis). While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear (on the logarithmic scale) reduction in deaths (see Figure 10). The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher (38,000 deaths averted) than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions (e.g. Italy, Spain) strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths (e.g. Germany, UK). We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, (see the sensitivity analysis reported in Appendix 8.4.3) and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC (European Centre of Disease Control), where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing (for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely). In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed (ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards). Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals (e.g. UK) or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police (e.g. France). The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples (Norway/Denmark) have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 (details in Appendix 8.1 and 8.2). Replication code is available at https://github.com/|mperia|CollegeLondon/covid19model/releases/tag/vl.0 We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution (time from infection to the onset of symptoms), an onset-to-death distribution (time from the onset of symptoms to death), and the population-averaged infection fatality ratio (adjusted for the age structure and contact patterns of each country, see Appendix). Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution (the average time from infection of one person to the time at which they infect another) and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths (and get stronger as time progresses). To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust (Appendix 8.4). 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E(Dam) that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths (1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr (probability of death given infection)9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates (the unadjusted ifr, referred to here as ifr’) in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period (infection to onset of symptoms or infection- to-onset) distribution and the time between onset of symptoms and death (onset-to-death). The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ (Gamma(5.1,0.86) + Gamma(18.8,0.45)) Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution (mean 23.9 days). Right, survival probability of infected individuals per day given the infection fatality ratio (1%) and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g(T), (the time between when a person gets infected and when they subsequently infect another other people), which we choose to be Gamma distributed: g ~ Gamma (6.50.62). The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg(T)dT fors = 2,3, and 91 = fT=Og(T)dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures (k=l), self—isolating if ill (k=2), banning of public events (k=3), any government intervention in place (k=4), implementing a partial or complete lockdown (k=5) and encouraging social distancing and isolation (k=6). We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” (k=4) indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp(— 212:1 O(Rheum)- The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0(1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma(. 5,1). The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal(2.4, IKI) with K ~ Normal(0,0.5), Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential(T), where T~Exponential(0.03). These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo (HMC) sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed (see below). 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data (non-cumulative) and fit our model. We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo (MCMC) chains have converged to the equilibrium distribution (the correct posterior distribution). Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval (SI) distributions (means between 5 and 8 days). We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval (SI) distribution means between 5 and 8 days. We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below (compare with the main analysis in Figure 4). In this series of univariate analyses, we find (Figure 15) that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others (such as the UK). To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g(r) with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = (1 + %) .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 (red) vs R(FO) (black) Figure 18: Our estimated R0 (black) versus theoretically derived Ru(red) from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions (up to 28 March) and forecasts (after) for all countries except Italy and Spain from our model with interventions (blue) and from the no interventions counterfactual model (pink); credible intervals are shown one week into the future. DOI: https://doi.org/10.25561/77731 Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities (primary ordered schools also shut on 16th).26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states (kantons).54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV2). Science (2020) doi:10.1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11(2019) 3. Worldometers.info. Hong Kong: coronavirus cases. https://www.wo rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4. Ferguson, N. et al. Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand (Report 9). https://www.imperial.ac.uk/mrc-global-infectious- disease-analysis/news--wuhan-coronavirus/. 5. Cereda, D. et al. The early phase of the COVID-19 outbreak in Lombardy, Italy. arXiv (2020). 6. Zhao, A. J. et al. Title: Antibody responses to SARS-CoV-2 in patients of novel coronavirus disease 2019 Brief Title : Antibody responses in COVID-19 patients. (2020). 7. Jombart, T. et al. Inferring the number of COVID-19 cases from recently reported deaths. medRXiV 2020.03.10.20033761(2020)doi:10.1101/2020.03.10.20033761. 8. Zhang, J. et al. Age profile of susceptibility, mixing, and social distancing shape the dynamics of the novel coronavirus disease 2019 outbreak in China. (2020) doi:10.1101/2020.03.19.20039107. 9. Lourenco, J. et al. 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Which Western European country is estimated to have the lowest attack rate?

Inhibitory Effect and Possible Mechanism of Action of Patchouli Alcohol against Influenza A (H2N2) Virus https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6264369/ SHA: f2d842780b9928cc70f38a4458553f2431877603 Authors: Wu, Huaxing; Li, Beili; Wang, Xue; Jin, Mingyuan; Wang, Guonian Date: 2011-08-03 DOI: 10.3390/molecules16086489 License: cc-by Abstract: In the present study, the anti-influenza A (H2N2) virus activity of patchouli alcohol was studied in vitro, in vivo and in silico. The CC(50) of patchouli alcohol was above 20 µM. Patchouli alcohol could inhibit influenza virus with an IC(50) of 4.03 ± 0.23 µM. MTT assay showed that the inhibition by patchouli alcohol appears strongly after penetration of the virus into the cell. In the influenza mouse model, patchouli alcohol showed obvious protection against the viral infection at a dose of 5 mg/kg/day. Flexible docking and molecular dynamic simulations indicated that patchouli alcohol was bound to the neuraminidase protein of influenza virus, with an interaction energy of –40.38 kcal mol(–1). The invariant key active-site residues Asp151, Arg152, Glu119, Glu276 and Tyr406 played important roles during the binding process. Based on spatial and energetic criteria, patchouli alcohol interfered with the NA functions. Results presented here suggest that patchouli alcohol possesses anti-influenza A (H2N2) virus properties, and therefore is a potential source of anti-influenza agents for the pharmaceutical industry. Text: The influenza virus, which is one of the main causes of acute respiratory infections in humans, can lead to annual epidemics and infrequent pandemics. The two influenza pandemics of the 20 th century, "Asian Influenza (1957/H2N2)" and "Hong Kong Influenza (1968/H3N2)" resulted in the deaths of an estimated 2-3 million people globally [1, 2] . Today, their descendants continue to cause the majority of influenza infections in humans [3] . So far as it is learned that the most effective antiviral drug is the neuraminidase (NA) inhibitor, which target the NA glycoproteins of influenza A and B virus [4, 5] . The release of new virions from the infected cell is a key step in the influenza life cycle and need neuraminidase (NA) to cleave the α-ketosidic linkage between terminal sialic acid and an adjacent sugar residue [6] . The NA inhibitors were designed to prevent the key step by blocking the active site of enzyme and thus allow sufficient time for the host immune systems to remove infected viruses [7] . Consistent efforts have been devoted to the development of NA inhibitors, using the crystal structure of the N2 sub-type NA protein [8] [9] [10] [11] [12] [13] [14] [15] . Indeed, oseltamivir (Tamiflu) is the representative NA inhibitor that has proven to be uniquely applicable oral drug in clinical practice for the treatment of influenza infection [4, 8, 9] . However, with an increase in medical use, the oseltamivir-resistant strains have been found and probably lead to a large scale outbreak of novel pandemic flu [16, 17] . Patchouli alcohol ( Figure 1 ) has been well known for over a century. It is a major constituent of the pungent oil from the East Indian shrub Pogostemon cablin (Blanco) Benth, and widely used in fragrances. Patchouli oil is an important essential oil in the perfume industry, used to give a base and lasting character to a fragrance [16, 17] . The essential oil is very appreciated for its characteristic pleasant and long lasting woody, earthy, and camphoraceous odor, as well as for its fixative properties, being suitable for use in soaps and cosmetic products [16, 17] . The aerial part of Pogostemon cablin has wildly been used for the treatment of the common cold and as an antifungal agent in China [16, 17] . Moreover, the plant is widely used in Traditional Chinese Medicine as it presents various types of pharmacological activity according to the composition of the oil [16, 17] . Patchouli alcohol, as the major volatile constituent of patchouli oil, has been found to strongly inhibit H1N1 replication and weakly inhibit B/Ibaraki/2/85 replication [18] . To the best of our knowledge, the anti-influenza virus (H2N2) activities of patchouli alcohol have not been evaluated yet. Therefore, the aim of the present study was to evaluate the anti-influenza A virus (H2N2) activity of patchouli alcohol by MTT assay and mouse influenza model. On such basis, explicitly solvated docking and molecular dynamic (MD) methods were applied to investigative the binding mode involving patchouli alcohol with influenza virus NA protein. We anticipate that the insight into the understanding of inhibiting mechanism will be of value in the rational design of novel anti-influenza drugs. First the efficacy of patchouli alcohol on influenza A (H2N2) virus replication and cell viability were examined. CC 50 was used to express the cytotoxicity of patchouli alcohol on MDCK. The CC 50 of patchouli alcohol was above 20 mM, which indicated that patchouli alcohol did not affect the growth of MDCK (Table 1) . Thus, it seems that the antiviral effects of patchouli alcohol were not due to the cytotoxicity. Moreover, patchouli alcohol was found to inhibit influenza A (H2N2) virus with an IC 50 of 4.03 ± 0.23 µM. Based on the IC 50 and CC 50 values, the selectivity index (SI) was calculated as >4.96. It is reported that a SI of 4 or more is appropriate for an antiviral agent [18] , suggesting that patchouli alcohol can be judged to have anti-influenza A (H2N2) virus activity. Until now, it has been found that patchouli alcohol showed dose-dependent anti-influenza virus (A/PR/8/34, H1N1) activity, with an IC 50 value of 2.635 µM. Furthermore, it showed weak activity against B/Ibaraki/2/85 (IC 50 = 40.82 µM) [19] . With the addition of the above H2N2 inhibitory activity, we have a comprehensively view of the anti-influenza activity of patchouli alcohol. Cells were pretreated with patchouli alcohol prior to virus infection (pretreatment cells), viruses were pretreated prior to infection (pretreatment virus), and patchouli alcohol was added during the adsorption period (adsorption) or after penetration of the viruses into cells (replication). Experiments were repeated independently three times and data presented are the average of three experiments. The symbols * indicated very significant difference p < 0.01 with respect to other mode (pretreatment virus, adsorption and pretreatment cell). As shown in Figure 2 , patchouli alcohol showed anti-influenza A (H2N2) virus activity in a timedependent manner. It showed best antiviral activity when added at a concentration of 8 µM during the replication period with inhibition of the viral replication of 97.68% ± 2.09% for influenza A (H2N2) at 72 h. However, no significant effect was detected when patchouli alcohol was used for pretreatment of cells or viruses or when patchouli alcohol was only added during the adsorption phase. These results suggested that the inhibition of influenza A (H2N2) virus by patchouli alcohol appears to occur much more strongly after penetration of the virus into the cell. Besides, biochemical studies have indicated that the bioactivity of NA protein is essential determinant after the replication of influenza A (H2N2) virus [20] [21] [22] . Hence, we conclude that the function of NA protein may be suppressed by patchouli alcohol. To evaluate the toxicity of patchouli alcohol, the mean value of body weight of mice in each group was statistically analyzed. The mean weights of mice administered at the 2 mg/kg/dose oseltamivir, 2 mg/kg/dose patchouli alcohol and 10 mg/kg/dose of patchouli alcohol one time daily for 7 days were not significantly different compared with the normal control mice, showing no toxicity of patchouli alcohol and oseltamivir within the testing concentration (P > 0.05). Physiological status was observed in virus infection mice. Three days after viral infection, some mice, especially mice in the H2N2 infected control group showed changes in behavior, such as a tendency to huddle, diminished vitality, and ruffled fur, etc. In the mouse influenza model, viral infection leads to loss of body weight and high mortality. Therefore, the efficacy of patchouli alcohol and oseltamivir were evaluated on the basis of survival rate measured for 15 days post-infection, for treated infected animals relative to untreated infected (control) animals. A comparison of efficacy of patchouli alcohol and oseltamivir in vivo mouse influenza model (oral treatment) showed that at a dose of 5 mg/kg/day, patchouli alcohol showed obvious protection against the influenza virus, as the mean day to death was detected as 11.8 ± 1.1 (Table 2) . When the dose was lowered to 1 mg/kg/day, patchouli alcohol showed weaker protection (measured by Survivors/total) than that of 5 mg/kg/day, the mean day to death was 7.5 ± 1.8. Whereas oseltamivir at this dose level (1 mg/kg/day) showed 50% protection (measured by survivors/total) against the influenza virus. In the H2N2 infected control group, there were no survivors. In view of both in vitro and in vivo data, we conclude that patchouli alcohol could be used in the treatment of human influenza virus infections. Based on the above experiment data, patchouli alcohol is determined to be bound within NA protein. As the total energies and backbone root-mean-square-deviations (RMSD) in Figure 3 indicate, the energy-minimized patchouli alcohol-NA complex has been in equilibrium since about 0.5 ns, and then retains quite stable in the last 19.5 ns. It is consistent with the previous MD results of other NA inhibitors [23] [24] [25] [26] [27] [28] . Accordingly, the geometric and energetic analyses were made on the average structures of 0.5~20.0 ns MD trajectories, where the system has been already at equilibrium. The interaction energy (E inter ) of patchouli alcohol with NA was calculated at −40.38 kcal mol −1 , where the vdW rather than electrostatic interactions were found to play a dominant role, contribute to about 72% (−29.18 kcal mol −1 ). As shown in Figure 4 , the patchouli alcohol was bound at the active site which also bound to oseltamivir and zanamivir [28] . As Figure 5 shows, the oxygen atom of patchouli alcohol was oriented towards the sidechains of residues Glu119 and Tyr406, with one H-bond formed with each residue. The values of distances in Figure 6 further reveal that the docked complex remains rather stable throughout the simulation, with the average distances of Glu119:OE2patchouli alcohol:O and Tyr406:OH -patchouli alcohol:O less than 2.8 Å. The sum contributions (E sum ) of residues Glu119 and Tyr406 amounted to −8.46 and −7.37 kcal mol −1 , respectively (Table 3) . Besides, patchouli alcohol was stabilized by residues Arg118, Asp151, Arg152, Trp178, Ala246, Glu276, Arg292, Asn294 and Gln347, especially residues Asp151, Arg152 and Glu276 ( Figure 5 and Table 3 ). As a matter of fact, residues Asp151, Arg152, Glu119, Glu276 and Tyr406 of the NA protein have already received enough attention from rational drug designs [14, 30, 31] . The catalytic residues Asp151, Arg152 and Glu276 are crucial to the NA functions and the residues Glu119 and Tyr406 are important to stabilize the NA active sites [32, 33] . It suggests that the NA functions will be affected by the presence of patchouli alcohol, consistent with the above experiments. Patchouli alcohol matches with the NA active site and has an acceptable interaction energy. Considering the obvious structure discrepancies against current NA inhibitors, it represents an ideal lead compound for the designs of novel anti-influenza agents. Patchouli alcohol and oseltamivir were obtained from Sigma Chemical Co. (St. Louis, MO, USA, purity > 99%) and was stored in glass vials with Teflon sealed caps at −20 ± 0.5 °C in the absence of light. MDCK (Madin-Darby canine kidney) was purchased from Harbin Veterinary Research Institute (Harbin, Heilongjiang, China). The cells were grown in monolayer culture with Eagle's minimum essential medium (EMEM) supplemented with 10% fetal calf serum (FCS), 100 U/mL penicillin and 100 μg/mL streptomycin. The monolayers were removed from their plastic surfaces and serially passaged whenever they became confluent. Cells were plated out onto 96-well culture plates for cytotoxicity and anti-influenza assays, and propagated at 37 °C in an atmosphere of 5% CO 2 . The influenza strain A/Leningrad/134/17/1957 H2N2) was purchased from National Control Institute of Veterinary Bioproducts and Pharmaceuticals (Beijing, China). Virus was routinely grown on MDCK cells. The stock cultures were prepared from supernatants of infected cells and stored at −80 °C. The cellular toxicity of patchouli alcohol on MDCK cells was assessed by the MTT method. Briefly, cells were seeded on a microtiter plate in the absence or presence of various concentrations (20 µM -0.0098 µM) of patchouli alcohol (eight replicates) and incubated at 37 °C in a humidified atmosphere of 5% CO 2 for 72 h. The supernatants were discarded, washed with PBS twice and MTT reagent (5 mg/mL in PBS) was added to each well. After incubation at 37 °C for 4 h, the supernatants were removed, then 200 μL DMSO was added and incubated at 37 °C for another 30 min. After that the plates were read on an ELISA reader (Thermo Molecular Devices Co., Union City, USA) at 570/630 nm. The mean OD of the cell control wells was assigned a value of 100%. The maximal non-toxic concentration (TD 0 ) and 50% cytotoxic concentration (CC 50 ) were calculated by linear regression analysis of the dose-response curves generated from the data. Inhibition of virus replication was measured by the MTT method. Serial dilution of the treated virus was adsorbed to the cells for 1 h at 37 °C. The residual inoculum was discared and infected cells were added with EMEM containing 2% FCS. Each assay was performed in eight replicates. After incubation for 72 h at 37 °C, the cultures were measured by MTT method as described above. The concentration of patchouli alcohol and oseltamivir which inhibited virus numbers by 50% (IC 50 ) was determined from dose-response curves. Cells and viruses were incubated with patchouli alcohol at different stages during the viral infection cycle in order to determine the mode of antiviral action. Cells were pretreated with patchouli alcohol before viral infection, viruses were incubated with patchouli alcohol before infection and cells and viruses were incubated together with patchouli alcohol during adsorption or after penetration of the virus into the host cells. Patchouli alcohol was always used at the nontoxic concentration. Cell monolayers were pretreated with patchouli alcohol prior to inoculation with virus by adding patchouli alcohol to the culture medium and incubation for 1 h at 37 °C. The compound was aspirated and cells were washed immediately before the influenza A (H2N2) inoculum was added. For pretreatment virus, Influenza A (H2N2) was incubated in medium containing patchouli alcohol for 1h at room temperature prior to infection of MDCK cells. For analyzing the anti-influenza A (H2N2) inhibition during the adsorption period, the same amount of influenza A (H2N2) was mixed with the drug and added to the cells immediately. After 1 h of adsorption at 37 °C, the inoculum was removed and DMEM supplemented with 2 % FCS were added to the cells. The effect of patchouli alcohol against influenza A (H2N2) was also tested during the replication period by adding it after adsorption, as typical performed in anti-influenza A (H2N2) susceptibility studies. Each assay was run in eight replicates. Kunming mice, weighing 18-22 g (6 weeks of age) were purchased from Harbin Veterinary Research Institute Animal Co., Ltd. (Harbin, Heilongjiang, China) . First, the toxicity of patchouli alcohol and oseltamivir was assessed in the healthy mice by the loss of body weight compared with the control group (2% DMSO in physiological saline). The mice were orally administered with 10 mg/kg/dose patchouli alcohol, 2 mg/kg/dose patchouli alcohol or 2 mg/kg/dose oseltamivir (dissolved in 2% DMSO in physiological saline) one time daily for 7 days. The weight of mice was determined daily. We conducted procedures according to Principle of Laboratory Animal Care (NIH Publication No. 85 -23, revised 1985) and the guidelines of the Peking University Animal Research Committee. Kunming mice were anesthetized with isoflurane and exposed to virus (A/Leningrad/134/17/1957) by intranasal instillation. Drugs were prepared in 2% DMSO in physiological saline and administered 4 h prior to virus exposure and continued daily for 5 days. All mice were observed daily for changes in weight and for any deaths. Parameters for evaluation of antiviral activity included weight loss, reduction in mortality and/or increase in mean day to death (MDD) determined through 15 days. The N2 sub-type neuraminidase crystal structure (PDB code 1IVD) was obtained from the RCSB Protein Data Bank [34] . For convenience, the structure is named as NA hereafter. Geometry and partial atomic charges of the patchouli alcohol ( Figure 1) were calculated with the Discover 3.0 module (Insight II 2005) [35] by applying the BFGS algorithm [36] and the consistent-valence force-field (CVFF), with a convergence criterion of 0.01 kcal mol −1 Å −1 . The docking and molecular dynamics (MD) simulations were performed by the general protocols in the Insight II 2005 software packages, consistent with the previous literatures [24, 26, 28, 35, [37] [38] [39] . During the MD simulations, the canonical ensemble (NVT) was employed at normal temperature (300 K). The MD temperature was controlled by the velocity scaling thermostat [40] . Integrations of the classical equations of motion were achieved using the Verlet algorithm. The systems were solvated in a large sphere of TIP3P water molecules [40] with the radius of 35.0 Å, which is enough to hold the ensembles [40] . The MD trajectories were generated using a 1.0-fs time step for a total of 20.0 ns, saved at 5.0-ps intervals. The interaction energies of patchouli alcohol with NA and the respective residues at the NA active site were calculated by the Docking module [35], over the 0.5~20.0 ns MD trajectories. All results are expressed as mean values ± standard deviations (SDs) (n = 3). The significance of difference was calculated by one-way analysis of variance, and values p < 0.001 were considered to be significant. In conclusion, patchouli alcohol possesses anti-influenza A (H2N2) virus activity via interference with the NA function that cleaves the α-glycosidic bond between sialic acid and glycoconjugate. Our results provide the promising information for the potential use of patchouli alcohol in the treatment of influenza A (H2N2) virus infectious disease. Further mechanistic studies on the anti-influenza A virus activity are needed to support this point of view.

What was the conclusion of this study?

2019-nCoV: The Identify-Isolate-Inform (3I) Tool Applied to a Novel Emerging Coronavirus https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7081861/ SHA: f323af9a07cc54faf9bdbabadaacb0e8b46f99a2 Authors: Koenig, Kristi L.; Beÿ, Christian K.; McDonald, Eric C. Date: 2020-01-31 DOI: 10.5811/westjem.2020.1.46760 License: cc-by Abstract: 2019 Novel Coronavirus (2019-nCoV) is an emerging infectious disease closely related to MERS-CoV and SARS-CoV that was first reported in Wuhan City, Hubei Province, China in December 2019. As of January 2020, cases of 2019-nCoV are continuing to be reported in other Eastern Asian countries as well as in the United States, Europe, Australia, and numerous other countries. An unusually high volume of domestic and international travel corresponding to the beginning of the 2020 Chinese New Year complicated initial identification and containment of infected persons. Due to the rapidly rising number of cases and reported deaths, all countries should be considered at risk of imported 2019-nCoV. Therefore, it is essential for prehospital, clinic, and emergency department personnel to be able to rapidly assess 2019-nCoV risk and take immediate actions if indicated. The Identify-Isolate-Inform (3I) Tool, originally conceived for the initial detection and management of Ebola virus and later adjusted for other infectious agents, can be adapted for any emerging infectious disease. This paper reports a modification of the 3I Tool for use in the initial detection and management of patients under investigation for 2019-nCoV. After initial assessment for symptoms and epidemiological risk factors, including travel to affected areas and exposure to confirmed 2019-nCoV patients within 14 days, patients are classified in a risk-stratified system. Upon confirmation of a suspected 2019-nCoV case, affected persons must immediately be placed in airborne infection isolation and the appropriate public health agencies notified. This modified 3I Tool will assist emergency and primary care clinicians, as well as out-of-hospital providers, in effectively managing persons with suspected or confirmed 2019-nCoV. Text: 2019 Novel Coronavirus (2019-nCoV) is a novel respiratory disease first reported in Wuhan, Hubei Province, China in December 2019. 1 Chinese health officials were originally investigating a sudden increase in cases of pneumonia which were later determined to be linked to 2019-nCoV. While most cases originated within mainland China, the disease spread to neighboring countries including Taiwan, Thailand, South Korea, and Japan, and later to the United States, Europe, and Australia. A near real-time updated tracking website for cases and locations worldwide, along with reported deaths is available. 2 Chinese health authorities have sequenced 2019-nCoV and freely shared its genetic profile online. 3, 4 Additionally, on January 28, 2020, an Australian laboratory reported growing the virus from a patient sample. As of January 30, 2020, there have been at least 9,776 persons infected and 213 verified deaths. 2 These numbers are likely underestimates due to the limited information available regarding incubation time, transmissibility, and virus origin. The What was the research question? Investigators adapted the "Identify, Isolate, Inform" (3I) Tool for use in suspected cases of 2019-nCoV. What was the major finding of the study? A novel 2019-nCoV 3I Tool is designed for frontline clinicians in the management of suspected patients. This 2019-nCoV 3I adaptation will aid healthcare providers most likely to encounter the disease in the containment and effective treatment of patients. age distribution of these verified deaths is currently not available. One preliminary, small-scale study of 41 patients in Wuhan China, reported 6 deaths (15% mortality) with a median age of 49.0 years. 5 Additionally, transmission of the virus has reportedly occurred in healthcare facilities in Wuhan City, raising concerns of spread to healthcare workers, as was seen during prior outbreaks of the novel coronaviruses, Middle Eastern Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). Due to the dynamic nature of the outbreak, exposure criteria may change depending on where new cases of 2019-nCoV are detected, the degree of transmissibility, and when additional information regarding the origin of the virus is discovered and reported. On January 15, 2020, the Centers for Disease Control and Prevention (CDC) confirmed the first known imported case of 2019-nCoV in the US state of Washington. The patient had recently returned from Wuhan City, where he likely contracted the disease. Chicago health authorities reported a second US case on January 24, 2020. This was quickly followed by additional imported cases reported in Orange and Los Angeles Counties, California on January 26, 2020. Additional suspected cases continue to be evaluated. On January 30, 2020, the CDC reported the first local transmission in the US between members in a household. On the same day, the World Health Organization declared 2019-nCoV to be a Public Health Emergency of International Concern (PHEIC). 6 On January 31, 2020, the US Department of Health and Human Services declared coronavirus a public health emergency. 7 Healthy individuals and those with mild illness may be asymptomatic, while others may have more pronounced symptoms of fever or lower respiratory illness. Upon identification of a suspected patient, that individual should immediately be isolated with airborne precautions. Further workup and laboratory confirmation can then proceed. Emergency physicians (EPs), emergency medical services (EMS) personnel, and other healthcare workers who encounter patients with suspected 2019-nCoV infection must inform the appropriate authorities, including but not limited to hospital infection control and local or state public health agencies. Healthcare workers must follow on-going developments related to the outbreak, especially new information concerning detection and management. 8, 9 The 3I Tool outlined in this paper is consistent with current US CDC guidelines and can be applied in a variety of settings such as those in emergency departments, urgent-care clinics, physicians' offices, and prehospital settings. This paper will first briefly review 2019-nCoV and then present the novel 2019-nCoV 3I Tool as modified from its initial conception for Ebola virus disease 10,11 and later adapted for measles, 12 MERS, 13 mumps, 14 Zika virus disease, 15 hepatitis A, 16 pertussis, 17 and scabies. 18 Coronavirus 2019-nCoV infection commonly presents with signs and symptoms of pneumonia or as a nonspecific lower respiratory illness, with coughing or difficulty breathing accompanied by fever. 5, 19, 20 Fever and cough constitute the most common presentations. However, patients may have other respiratory symptoms, sore throat, nasal congestion, malaise, myalgia, and headache. Bilateral infiltrates may be seen on chest X-ray. Severe cases may present with sepsis and even shock. Conversely, some patients may present as only mildly ill or asymptomatic altogether. 21 To date, patients with underlying medical conditions and the elderly are more likely to become severely ill, require hospitalization, and ultimately die. 22 Early predictions for incubation time are between 2 and 14 days, based on data from similar coronaviruses. The 14-day criterion for epidemiological risk assumes the longest estimated incubation time. 23 In addition, the World Health Organization (WHO) has created its own interim case definition. 24 By definition, the main features of a novel virus, for example, how it is transmitted, will not be immediately known. However, as with the development of any 3I Tool, it is essential to understand specific characteristics of the disease. In the case of a novel virus such as 2019-CoV, this is challenging since information is rapidly evolving and the science is not yet fully understood. It is possible that the virus will undergo mutations over time that could substantially change its The Identify-Isolate-Inform (3I) Tool Applied to a Novel Emerging Coronavirus Koenig et al. features. Nevertheless, an appreciation of the key concepts that drive evidence-based management is beneficial (Table 1) . Management guidance will likely change over time. With the initial discovery of a new potential public health threat, it will likely be unclear how patients become sick. For example, rather than a contagion, there could be a contaminant or a toxin responsible for signs and symptoms. In this case, the possibility of an environmental toxin in the Wuhan Market was a consideration early on when limited to no human-tohuman transmission was reported. The mode of transmission has implications for the types of personal protective equipment (PPE) needed to protect healthcare providers in the prehospital, clinic, and hospital settings. 25 In addition, patients may need decontamination after exposure to certain toxins. 26 Another important consideration for application of the 3I Tool is whether the disease is contagious prior to symptom onset (like measles) or only after symptoms develop (like Ebola). A January 30, 2020 letter to the New England Journal of Medicine describes a purported confirmed instance of transmission from an asymptomatic individual. Researchers state that, before symptom onset, the primary case infected two individuals, one of which infected two additional colleagues. 27 Subsequent investigation suggested that the source patient did have mild symptoms and had taken an antipyretic, calling this reported asymptomatic transmission into question. While quarantine may not be feasible and can have unintended consequences, 28, 29, 30 it is a public health tool that can be considered in cases when disease is transmissible before symptom onset. 30 Conversely, if a disease is known not to be transmissible prior to symptom onset, asymptomatic exposed patients must be monitored, but do not require quarantine or isolation unless they develop symptoms. Initially, it may be unclear whether an infectious agent occurred naturally or was deliberately or accidentally released. In this case, a BSL-4 laboratory studying coronaviruses was located approximately 32 kilometers away from the market where initial exposures were felt to occur. 31 Recall that in 2001, the anthrax letter attacks were initially thought to be naturally occurring. Once determined to be bioterrorism, management of the event was similar to that for a chemical exposure with a sudden impact, defined scene, and need for a rapid response and decontamination on site. This differed from the WHO's modeling predicting an aerosolized release that would result in an incubation period with 100,000 or more persons exposed rather than the 22 people who contracted anthrax in 2001. 32 By understanding the key features of a novel disease, healthcare workers can take evidence-based measures to protect themselves, optimize individual patient management, and prevent further disease spread. It is currently unclear how 2019-nCoV is spread, but it is suspected to be transmitted through contact with infected respiratory secretions, like other known coronaviruses. There are instances of sustained human-to-human transmission across generations of cases, especially near the epicenter in Wuhan City. 21 Current evidence suggests that close contact with an infected person is a major factor in disease transmission. CDC defines "close contact" 33 as being in or within two meters of an area with a confirmed patient or being directly exposed to infectious secretions without appropriate PPE. Healthcare facilities in China have reported spread from person to person. In addition, some mildly ill or potentially even asymptomatic patients may have a higher chance of spreading the disease to others as they may be less likely to seek medical care. 34 The possibility that patients may be infectious prior to symptom onset further compounds the difficulty of containing the virus and effectively preventing transmission. The current majority of 2019-nCoV cases have been within China and its bordering countries. 2 Persons with recent travel (within 14 days) to Wuhan City or another region with widespread disease, or exposure to a patient under investigation, are considered to have an epidemiologic risk factor and should be assessed for signs and symptoms of a viral illness such as fever and respiratory symptoms. Coronavirus is a zoonotic virus The Identify-Isolate-Inform (3I) Tool Applied to a Novel Emerging Coronavirus that is transmitted to humans via contact with infected animals. Preliminary reports suggest the disease may have originated in a seafood and live animal market in Wuhan City, but it is still unknown how or whether such transmission occurred. Clinicians working with local public health departments must arrange to have specimens from patients under investigation (PUIs) sent to the CDC laboratory. At this time, the CDC has the only laboratory that can definitively test for 2019-nCoV, though laboratory testing capacity is being rapidly expanded. Polymerase chain reaction (PCR) assays conducted on samples from a patient's upper and lower respiratory tracts will be used to confirm potential cases. In addition, serum antibody titers can be analyzed for confirmation of infection or evidence of immunity. Up-to-date information about the needed specimens and handling requirements to test for 2019-nCoV are available on the CDC website. 35 Like other related coronaviruses, patients with 2019-nCoV frequently present with non-specific symptoms resembling that of influenza. Physicians may consider differential diagnoses related to a wide variety of respiratory infections. In order to relate these symptoms to 2019-nCoV, it is imperative that the identification of a potential exposure event (epidemiologic risk factor) within 14 days of symptom onset is made so that a more focused work-up for 2019-nCoV can be completed. Although the likelihood of coinfection of 2019-nCoV and another respiratory virus is thought to be low, a positive finding of another respiratory pathogen does not exclude the diagnosis of 2019-nCoV. Many commercially available respiratory panels include "coronavirus" in the results, but neither a positive nor a negative finding on these panels should be used to include or exclude a diagnosis of 2019-nCoV. Supportive care with appropriate infection control is the mainstay of current CDC treatment guidelines for 2019-nCoV. There are not yet any approved antiviral treatments for 2019-nCoV. Emergency Use Authorizations (EUA) for compassionate use cases may be forthcoming from the US federal government for normally unapproved treatments. Supportive treatment predominantly includes respiratory support, hydration, and antipyretics. General treatment for severe cases should focus on the preservation of vital organ function. In the future, antiviral medications may be available. If a secondary bacterial infection such as pneumonia develops, targeted antibiotics are indicated. Prevention of 2019-nCoV transmission, like any other infectious agent, involves minimizing risk of exposure. Vaccines are under accelerated development and may be useful in the future for post-exposure prophylaxis. Healthcare personnel are at increased risk and should practice standard, droplet, and airborne precautions when encountering an infected person, a PUI, or any symptomatic close contacts. Healthcare workers handling specimens should also adhere to CDC guidelines and should not attempt to perform any virus isolation or characterization. Fever screening has been implemented at numerous airports, including major international hubs within Asia and the US. The efficacy of this intervention is not well documented, however, as some infected persons may be afebrile and disease transmission might occur prior to symptom onset. 27 In addition, people can artificially lower their temperature readings, e.g., by applying ice to their foreheads. As outlined above, admission criteria for 2019-nCoV are similar to that of other patients. If patients do not meet medical criteria for hospitalization, they may be discharged home with isolation precautions and continued observation. EPs must notify local public health authorities so appropriate monitoring and community protective measures can be instituted. The Identify-Isolate-Inform (3I) Tool was initially developed for Ebola virus disease 10,11 and later adapted for measles, 12 MERS, 13 mumps, 14 Zika virus disease, 15 hepatitis A, 16 pertussis, 17 and scabies. 18 This novel tool for suspected 2019-nCoV patients ( Figure 1 ) provides frontline clinicians with a simple algorithm to manage an emerging disease. Identification of exposed patients with an epidemiologic risk factor within 14 days of symptom onset is a crucial first step. An automatic prompt in the electronic health record can be useful in assisting clinicians with early identification of patients at risk. Case definitions promulgated by the WHO 24 and CDC 33 provide useful comprehensive definitions that have been incorporated into the 3I Tool. The 2019-nCoV Tool provides an accurate, summarized algorithm to immediately, and effectively manage suspected patients until additional resources can be consulted. Patients who do not have an exposure risk or any symptoms may be triaged normally. However, before making patient contact, providers must first apply the Vital Sign Zero concept. 36 Vital Sign Zero is a preliminary, non-contact assessment (i.e., performed prior to touching a patient to take traditional vital signs) to first determine whether specific PPE is indicated before the examination commences. By taking the additional time to complete this assessment, risk of exposure and further transmission can be minimized. while in the treatment facility should be started and maintained to assist with the possibility of contact tracing. Following isolation, physicians should immediately inform the appropriate authorities. Patients who do not meet medical criteria for admission can be isolated at home during the evaluation phase. 37 Health department officials can help prevent transmission in isolated patients by providing in-home monitoring and implementing appropriate exposure-control measures. Providers in the prehospital setting who have a high likelihood of encountering 2019-nCoV patients, such as those near international ports of entry, should adhere to established exposure control guidelines. 38 Along with appropriate PPE, providers should also carry thermometers to quantify any fever. In the US, providers should contact the appropriate CDC quarantine station upon isolation of infected or suspected patients, especially those from Wuhan, China or other regions with widespread disease, who report symptoms in the last 14 days. As for other infectious diseases, assessing travel history is essential. Dispatch protocols have been instituted to facilitate identification of callers to 911 or the country-equivalent emergency number prior to prehospital personnel arrival. 39 In addition, CDC has promulgated EMS guidelines for prehospital PPE, transportation of PUIs, vehicle decontamination, and 911 Public Safety Answering Points (PSAPs) for 2019-nCoV. 40 2019-nCoV is an emerging infectious disease with rapidly evolving features, the full scope of which will be defined over time. Prior outbreaks of coronaviruses can help inform needed actions in the short term to assist with both treatment of individual patients and prevention of global disease spread. This adaptation of the Identify-Isolate-Inform Tool serves as a resource for healthcare workers who need to make clear, rapid assessments when confronted with potential patients. The concise nature of the 2019-nCoV 3I Tool allows for the rapid and effective management of a novel disease by healthcare providers.

When was the second COVID19 case reported in the US?

The Intranasal Application of Zanamivir and Carrageenan Is Synergistically Active against Influenza A Virus in the Murine Model https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4459876/ SHA: f0b1fa4036434b57c8307d43c39a4193f7e8053a Authors: Morokutti-Kurz, Martina; König-Schuster, Marielle; Koller, Christiane; Graf, Christine; Graf, Philipp; Kirchoff, Norman; Reutterer, Benjamin; Seifert, Jan-Marcus; Unger, Hermann; Grassauer, Andreas; Prieschl-Grassauer, Eva; Nakowitsch, Sabine Date: 2015-06-08 DOI: 10.1371/journal.pone.0128794 License: cc-by Abstract: BACKGROUND: Carrageenan is a clinically proven and marketed compound for the treatment of viral upper respiratory tract infections. As infections caused by influenza virus are often accompanied by infections with other respiratory viruses the combination of a specific anti-influenza compound with the broadly active antiviral polymer has huge potential for the treatment of respiratory infections. Thus, the combination of the specific anti-influenza drug Zanamivir together with carrageenan in a formulation suitable for intranasal application was evaluated in-vitro and in-vivo. PRINCIPAL FINDINGS: We show in-vitro that carrageenan and Zanamivir act synergistically against several influenza A virus strains (H1N1(09)pdm, H3N2, H5N1, H7N7). Moreover, we demonstrate in a lethal influenza model with a low pathogenic H7N7 virus (HA closely related to the avian influenza A(H7N9) virus) and a H1N1(09)pdm influenza virus in C57BL/6 mice that the combined use of both compounds significantly increases survival of infected animals in comparison with both mono-therapies or placebo. Remarkably, this benefit is maintained even when the treatment starts up to 72 hours post infection. CONCLUSION: A nasal spray containing carrageenan and Zanamivir should therefore be tested for prevention and treatment of uncomplicated influenza in clinical trials. Text: The periodic appearance of new influenza variants poses a worldwide pandemic threat. Since the emergence of the new A(H7N9) virus, more than 400 human cases were reported to the WHO with a mortality rate of more than 35%. Most patients with A(H7N9) infections had contact with poultry or visited live animal markets. However, some sporadic cases seemed to be a result of human to human transmissions [1, 2] . In contrast to pandemic viruses which fulminantly enter the human population and cause high mortality rates, seasonal influenza viruses generally cause uncomplicated and transient infections in humans, with virus replication localized to the upper respiratory tract [3, 4] . However, in its fully developed form influenza is an acute respiratory disease resulting in hospitalizations and deaths mainly among high-risk groups. Worldwide, annual epidemics result in about three to five million cases of severe illness, and about 250,000 to 500,000 deaths [5] . For this reason WHO [6] and CDC [7] recommend antiviral treatment for any patient with suspected influenza who is at risk for influenza complications without previous laboratory confirmation. It is known that influenza virus infections are often accompanied by other viral pathogens [8] . Depending on the detection method (qRT-PCR or immunofluorescence) different ratios of co-infections have been found. Analysis by qRT-PCR revealed that 54.5-83.3% of influenza A or B positive patients were found to have at least one concomitant respiratory viral infection [9] [10] [11] [12] . The detection frequency with immunofluorescence was found to be even higher (90-100%) [13, 14] . Potential concomitant viral pathogens of influenza virus infections include human rhinovirus (hRV), respiratory syncytial virus, adenovirus, human coronavirus, human metapneumovirus and parainfluenza virus [14, 15] . As a result of the multiple infections, a specific anti-influenza mono-therapy treats the influenza virus infection only, but not the infection with the concomitant viral pathogen. Hence, the therapy often fails to sufficiently resolve symptoms. This is also reflected by the fact that neuraminidase inhibitors (NI) are highly efficacious in animal models investigating influenza mono-infections [16, 17] but show lower efficacy against influenza symptoms in clinical trials in adults with natural infections [18] . Therefore, there is a high medical need for a broadly acting antiviral therapy in combination with a specific anti-influenza therapy for treatment of patients suffering from upper respiratory tract symptoms. Ideally, the substances present in the combination complement each other by different modes of action, leading to a treatment that provides full protection against a broad range of different respiratory viruses as well as different influenza strains with a low probability to induce escape mutations. One approach for a broad antiviral therapy is the creation of a protective physical barrier in the nasal cavity using carrageenan. Carrageenan is a high molecular weight sulfated polymer derived from red seaweed (Rhodophyceae) that has been extensively used in food, cosmetic and pharmaceutical industry and is generally recognized as safe by the FDA (GRAS) (reviewed in [19] ). Three main forms of carrageenans are commercially used: kappa, iota and lambda. They differ from each other in the degree of sulfation, solubility and gelling properties [20] . The antiviral mechanism of carrageenan is based on the interference with viral attachment; as a consequence, viral entry is inhibited [21, 22] . Its antiviral activity is dependent on the type of polymer as well as the virus and the host cells [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] and has been reviewed in [33] [34] [35] . We published that iota-carrageenan is a potent inhibitor of hRV [36] and influenza A [37] replication and demonstrated the antiviral efficacy of iota-carrageenan against common cold viruses by intranasal application in several randomized, double-blind, parallel group, placebo-controlled clinical trials [38] [39] [40] . The pooled analysis of two studies conducted in 153 children and 203 adults revealed that patients infected with any respiratory virus, who were intranasally treated with iota-carrageenan showed a 1.9 day faster recovery from common cold symptoms than placebo treated patients in the intention-to-treat population [41, 42] . The anti-influenza activity was shown by subgroup analysis of 49 influenza infected patients who benefited from a 3.3 days faster recovery from symptoms. The use of carrageenan nasal spray was associated with a significant reduction of the influenza viral load in nasal fluids and a significant increase in the number of virus free patients within the treatment period of 7 days. In good accordance Prieschl-Grassauer are co-founders of Marinomed Biotechnologie GmbH. Marinomed Biotechnologie GmbH had a role in study design, data collection and analysis, decision to publish, preparation of the manuscript and is financing the processing charge of the manuscript. with the literature [9] [10] [11] [12] [13] [14] we observed that the majority of influenza virus infected patients suffered from a concomitant respiratory viral infection (66%) as determined by real-time PCR. Carrageenan containing nasal sprays are already marketed for the treatment of respiratory viral infections under different brand names in 18 countries. At present the only available effective drugs for treatment and post exposure prevention of influenza are the NI (Oseltamivir and Zanamivir worldwide; Peramivir in Japan and South Korea). Since the large-scale use of M2 blockers for prophylaxis and treatment in humans [43] and farming [44] , the currently circulating influenza viruses already lack sensitivity to this drug group [45] . We have already shown an additive therapeutic effect of a combination therapy with intranasally applied iota-carrageenan and orally administered Oseltamivir in lethally H1N1 A/PR/ 8/34 infected mice and a treatment start 48 hours post infection (hpi) [37] . Due to these very promising results we further developed the concept of combining carrageenan with an NI therapy. In contrast to Oseltamivir, which needs to be activated by metabolic conversion, Zanamivir is directly applied as active drug and can also be administered intranasally [46] [47] [48] [49] [50] [51] [52] . The potential of an intranasal administration of Zanamivir was investigated by GlaxoSmithKline. In seven clinical challenge trials 66 volunteers were infected with influenza B/Yamagata/16/88 and 213 with influenza A/Texas/36/91 (H1N1). 156 of these participants got intranasally applied Zanamivir at different doses (daily dose levels from 6.4 mg to 96 mg) for prophylaxis or therapy [46, 47, 53, 54] . These challenge trials showed that treatment starting before and up to 36 hours post virus inoculation was associated with prevention of laboratory confirmed influenza and febrile illness as well as a reduction in viral titers, duration of shedding and symptoms. In total, safety data from 1092 patients after intranasal application of Zanamivir were published and no evidence for Zanamivir induced adverse events or increased frequencies of local nasal intolerance in comparison to placebo groups was found [46, 49, 52] . Taken together, the combination of a carrageenan nasal spray that provides broad antiviral activity against upper respiratory infections-including influenza-with Zanamivir, a specific anti-influenza drug, meets the existing medical need to treat multiple viral infections. In the present work we investigate the therapeutic effect of a combination of carrageenan and Zanamivir in-vitro and in an animal model. Kappa-carrageenan and iota-carrageenan were purchased from FMC Biopolymers (Philadelphia, PA). The identity, purity (>95%) of carrageenan subtypes and the molecular weight (>100,000) was confirmed by NMR analysis as described elsewhere [55] and the presence of lambda-carrageenan was below the detection limit of 3%. The dry polymer powders were dissolved in aqua bidest (Fresenius Kabi, Austria) to a final concentration of 2.4 mg/ml iota-and 0.8 mg/ml kappa-carrageenan. This 2x stock solution was sterile filtered through a 0.22 μm filter (PAA, Switzerland) and stored at room temperature until use. For further testing the stock solution was diluted to a mixture containing 1.2 mg/ml iota-carrageenan and 0.4 mg/ml kappa-carrageenan (hereinafter referred to as "carrageenan"). Zanamivir was purchased as powder (Haosun Pharma, China) and the identity and purity was confirmed by NMR analysis. Zanamivir was either dissolved in carrageenan or placebo solutions, followed by sterile filtration through a 0.22 μm filter (Sarstedt, Germany). For in-vivo studies all Zanamivir containing solutions were freshly prepared. Madin-Darby canine kidney (MDCK) cells were obtained from the American Type Culture Collection (ATCC, Manassas, VA) and cultivated in a 37°C incubator (Sanyo, Japan; CO 2 : 5%, relative humidity: >95%). MDCK cells were grown in Dulbecco's minimal essential (DMEM) high glucose medium (PAA, Austria) supplemented with 10% fetal bovine serum (FBS; PAA, Austria; heat inactivated). Influenza virus A/Hansa Hamburg/01/09 (H1N1(09)pdm) was kindly provided by Peter Staeheli Department of Virology, University of Freiburg, Germany and previously described in [56] ; A/Teal/Germany/Wv632/05 (H5N1) previously published in [57] (accession numbers CY061882-9) and A/Turkey/Germany/R11/01 (H7N7) (taxonomy ID 278191, accession number AEZ68716) were supplied by courtesy of Martin Beer, Institute of Diagnostic Virology, Friedrich-Loeffler-Institute, Riems, Germany; A/Aichi/2/68 (H3N2) was purchased from the ATCC. All influenza viruses were propagated in MDCK cells at 37°C and 5% CO 2 in influenza medium [Opti-Pro serum free medium (Gibco, Austria) supplemented with 4 mM L-glutamine (PAA, Austria), 1% antibiotic-antimycotic mix (PAA, Austria) and 5 μg/ml trypsin (Sigma Aldrich, Austria)]. To determine the 50% inhibitory concentration (IC 50 ) and the combination effect of carrageenan and Zanamivir, a semi-liquid plaque assay was developed. Into 96 well tissue culture plates 1.7x10 4 MDCK cells/well were seeded and infected at 90% confluence (24-28 hours later). Serial dilutions of carrageenan and Zanamivir were prepared in assay medium (influenza medium without trypsin). For infection, viruses were diluted to an MOI of 0.003 (H1N1(09)pdm and H3N2 Aichi), 0.015 (H5N1) or 0.004 (H7N7), respectively, in assay medium and incubated at room temperature (RT) for 10 min with the serial dilutions of carrageenan and/or Zanamivir, respectively. For evaluation of the combination effect of carrageenan and Zanamivir, viruses were diluted in assay medium containing constant concentrations of either carrageenan or Zanamivir. The other substance was serially diluted and used for virus incubation. Cells were infected in 6 replicates/compound dilution, respectively, and incubated at RT for 45 min before inoculum removal. Cells were further incubated with the respective concentration of the investigated substances present in the overlay [influenza medium with 2.25% Carboxymethylcellulose (CMC, Fluka, Austria)] for 30-42 hours at 37°C. Evolving plaques were evaluated after methanol/acetone cell fixation by immune staining with antibodies either directed against the influenza A nucleoprotein (AbD Serotec, Germany) (for H1N1(09)pdm, H5N1 and H7N7) or the hemagglutinin (AbD Serotec, Germany) (for H3N2). Analysis was done with a HRP labeled detection antibody (Thermo Scientific, Germany) using TMB (Biolegend, Germany) as substrate and a microplate reader at 450 nm. The reduction of detected signal represents a reduction in the number and size of plaques and indicates suppression of viral replication during infection and cultivation. After the immunostaining cells were stained with 0.005% crystal violet solution to assess the condition of the cell layer and the toxicity of the compounds. IC 50 values and standard deviations were calculated for a sigmoidal dose response model using XLfit Excel add-in version 5.3.1.3. All animal experiments were carried out according to the guidelines of the "European Convention for the Protection of Vertebrate Animals used for Experimental and other Scientific Purposes" and the Austrian law for animal experiments. All animal experiments were approved by the Veterinary University of Vienna institutional ethics committee and performed under the Austrian Federal Ministry of Science and Research experimental animal license numbers BMWF-68.205/0262-II/3b/2011 and BMWF-68.205/0142-II/3b2012. C57BL/6 mice were purchased from Janvier Labs, France and maintained under standard laboratory conditions in the animal facilities of the Veterinary University of Vienna. For euthanasia and anesthesia asphyxiation through CO 2 was used and all efforts were made to minimize suffering. For infection experiments, 3-5 weeks old female mice were intranasally inoculated with 50 μl influenza virus solution (25 μl/nostril) containing 2.27x10 3 or 1.65x10 3 plaque-forming unit of H1N1(09)pdm or H7N7, respectively. Subsequently, treatment started 24, 48 or 72 hpi, as indicated for the different experiments. Treatment was performed intranasally either with 50 μl therapeutic solution or placebo twice per day for 5 days. As therapy either carrageenan (containing 1.2 mg/ml iota-carrageenan and 0.4 mg/ml kappa-carrageenan to provide a daily dose of 12 mg/kg body weight (BW)), Zanamivir (containing either 130 μg/ml or 390 μg/ml Zanamivir, to provide a daily dose of 1 or 3 mg/kg BW, respectively) or a combination of carrageenan and Zanamivir were used. Carrageenan and Zanamivir are used at non-toxic concentrations as shown by [58] and [59] . Mice were monitored twice daily for 15 days for survival and weight loss. Mortality also includes mice that were sacrificed for ethical considerations when they had lost more than 25% of their initial body weight. We confirm the viral infection in these animals by necropsy and scoring of the lung inflammation. As the mechanisms underlying the antiviral activity of NI and carrageenans are fundamentally distinct, they are likely to exhibit different activities towards the individual influenza virus strains. As a result, in combination they could complement each other to provide protection against a broader spectrum of influenza virus strains than the individual compounds. To test this hypothesis, we investigated the sensitivity of various influenza virus strains to Zanamivir and carrageenan in an adapted plaque reduction assay with semi-liquid overlay in MDCK cells [60, 61] . Using this method, we determined the IC 50 of Zanamivir and carrageenan against influenza A viruses of human and animal origin, namely H1N1(09)pdm (A/Hansa Hamburg/01/09), H3N2 (A/Aichi/2/68), low pathogenic (LP) H5N1 (A/Teal/Germany/ Wv632/05) and LP H7N7 (A/Turkey/Germany/R11/01) ( Table 1) . Both substances were nontoxic at the highest tested concentration (400 μM Zanamivir and 533 μg/ml carrageenan), neither was their combination. Furthermore, CMC in the overlay did not show any virus inhibitory effect (data not shown). Inhibition of viral replication of all tested influenza strains was achieved with both substances. However, the IC 50 values varied widely depending on the influenza virus strain. The IC 50 values of Zanamivir ranged between 0.18 μM for H5N1 and 22.97 μM for H7N7 and that of carrageenan from 0.39 μg/ml to 118.48 μg/ml for H1N1(09)pdm and H7N7, respectively (see Table 1 ). These results demonstrate that carrageenan and Zanamivir target individual influenza strains to different extents so that they may complement each other to provide broader anti-influenza activity. The type of compound interaction was characterized by employing isobolograms (Fig 1) . As described in [62] , isobolograms graphically compare the doses of two compounds needed to reach 50% inhibition to the predicted doses calculated based on a model of drug additivity. A curve linearity of~1 is expected for an additive compound interaction whereas a curve progression <1 argue for synergistic and >1 for an antagonistic compound interaction. Two virus strains were selected for those experiments, one being the most sensitive to carrageenan (H1N1(09)pdm) and one being the least sensitive (H7N7). In both cases the isobolograms show a synergistic interaction of carrageenan and Zanamivir (Fig 1) . Thus, it was shown that Zanamivir and carrageenan target individual influenza viruses with different efficiencies, most probably due to their different antiviral strategies. As a result, the combination provides synergistic activity with higher protection against a broader spectrum of influenza virus strains than the individual compounds. In the influenza animal model, C57Bl/6 mice are challenged with a lethal dose of the respective virus and treated with different regimens in comparison to a vehicle control (placebo). Infection and treatment (twice a day for 5 days) are done intranasally without anesthesia. We investigated whether the combination of Zanamivir and carrageenan is more efficacious in reducing mortality than the corresponding mono-therapies. First, we determined the minimal effective dose of a Zanamivir mono-therapy that significantly improved survival time of H1N1 and H7N7 infected mice. For the H7N7 lethal infection the minimal effective dose of Zanamivir as mono-therapy ranged between 1 and 3 mg/kg BW/ day (data not shown). Next, we compared the antiviral activity of carrageenan (12 mg/kg BW/ day) and Zanamivir (1 and 3 mg/kg BW/day) mono-therapies with the respective combination versus placebo treatment. Survival rates of mice with treatment starting 24 hpi are shown in Fig 2A. All placebo treated mice died between day 7 and 9 and also in all mono-therapy groups 100% lethality was observed until day 15. In contrast, the combination therapies led to 50% and 90% survival, depending on the Zanamivir concentration. Statistical analysis showed that the Zanamivir mono-therapy 1 mg/kg BW/day did not show a significant benefit (p = 0.1810), whereas the mono-therapy with 3 mg/kg BW/day significantly increased the survival rate compared with placebo treated mice (p = 0.0016). Both Zanamivir concentrations experienced significant benefit in survival by the combination with carrageenan (p<0.0001). Similarly, the combination therapies resulted in remarkably increased survival (p = 0.0421 for 1 mg and p<0.0001 for 3 mg/kg BW/day) when compared to the carrageenan mono-therapy. No statistically significant difference was observed between the combination containing 3 mg/kg BW/day Zanamivir and that containing 1 mg/kg BW/day (p = 0.0525). However, a trend for an increased survival rate with the higher Zanamivir concentration was evident. Therefore, for further investigation the combination therapy containing 3 mg/kg BW/day Zanamivir was evaluated in lethally H7N7 infected mice. Next, the therapeutic potential of the combination with a delayed therapy start 48 or 72 hpi versus placebo treatment was explored. The survival rates of mice are shown in Fig 2B. All placebo treated mice died until day 10 and also in the group with the treatment start 72 hpi 100% lethality was found. In contrast, the combination therapy starting 48 hpi provided a statistically significant enhanced survival rate in comparison to placebo-treated mice (p = 0.0010). In summary, the combination of two effective, established mono-therapies resulted in a significantly enhanced survival in lethally H7N7 infected mice. Additionally, the combination therapy was highly efficient in comparison to placebo treatment even after a treatment onset up to 48 hpi. Intranasal therapy with carrageenan and Zanamivir starting 72 hpi significantly protects lethally influenza H1N1(09)pdm infected mice Next, the minimal effective dose of Zanamivir used as mono-therapy was evaluated in a lethal H1N1(09)pdm mouse model, following the same scheme as described in the H7N7 experiments. The lowest effective dose of Zanamivir after a treatment start 24 hpi was 1 mg/kg BW/ day and its combination with carrageenan was highly effective (data not shown). In the following experiment the therapeutic potential of the combination with a therapy start 48 or 72 hpi was investigated in comparison with the respective placebo treatment. As shown in Fig 3, the survival rates of mice treated with the combination therapy were highly significantly increased in comparison to the placebo group (p<0.0001). There was no difference in survival between the two therapy starting points, 48 or 72 hpi, which both resulted We investigated the antiviral effect of a combination of carrageenan with the NI Zanamivir in cell culture studies and in mouse influenza infection models. We have previously shown that a combined therapy of iota-carrageenan with the NI Oseltamivir led to significantly enhanced survival in mice infected with H1N1 PR/8/34 in comparison with the respective mono-therapies [37] . However, Oseltamivir is an orally administered prodrug, which has to be converted into its active form by metabolic processing. Therefore, a further development of a combination nasal spray was not possible with Oseltamivir. Instead Zanamivir-a NI that is applied as active drug-was chosen for the development of a compound combination. During the evaluation process we found that the binding efficiency of different carrageenan subtypes on different influenza strains varies. The combined use of iota-and kappa-carrageenan for the treatment of lethally influenza infected C57Bl/6 mice revealed a better therapeutic effect than the use of iota-carrageenan alone (S1 Fig). Thus, to provide a broader spectrum of activity against different influenza virus strains, a mixture of iota-and kappa-carrageenan (designated as carrageenan) was used for further evaluation. For investigation of the effect of a compound combination of carrageenan and Zanamivir, we examined their inhibition efficiency, individually and in combination, against influenza viruses in an adapted plaque reduction assay with semi-liquid overlay in MDCK cells. The combination showed a synergistic inhibition of virus replication in in-vitro assays with all tested influenza viruses (Fig 1) . This indicates that the physical interaction of the polymer with the virus does not disturb the inhibition of the neuraminidase by Zanamivir. This was confirmed in in-vitro tests examining a potential influence of the polymer on the neuraminidase inhibiting activity of Zanamivir (data not shown). Hence, the observed synergistic effect is based on the combination of two distinct underlying mechanisms. As a result, in the proposed combination both mechanisms would complement each other to provide more efficient protection against a broader spectrum of influenza virus strains than the individual compounds. The synergistic effect was also shown in lethal mice models (Fig 2 and Fig 3) . The pathogenicity of influenza viruses in mice varies and is dependent on the strain and its adaptation to the host. Depending on virus dose and strain, influenza viruses can induce lethal infections in certain mouse strains usually within two weeks [37, 63] . In our model, C57Bl/6 mice are challenged intranasally with a lethal dose of the respective virus and treated with different regimens in comparison to a vehicle control (placebo). In such a model, early virus replication takes place in the upper respiratory tract. From there, virus spreads to the lung and causes lethal pneumonia. The effect of the treatment on mortality is assessed in comparison to placebotreated control mice. Of all in-vitro tested influenza strains the H1N1(09)pdm and the LP H7N7 are particularly interesting for two reasons. First, they are highly relevant pathogens, as placebo or with the mono-therapies consisting of carrageenan (12 mg/kg BW/day) or Zanamivir (1 and 3 mg/ kg BW/day) or a combination thereof. Treatment started 24 hpi and continued for 5 days. (B) Mice (n = 20 per group) were lethally intranasally infected without anesthesia on day 0 and intranasally treated twice per day either with placebo or a combination of carrageenan with Zanamivir (3 mg/kg BW/day). Treatment started either 48 hpi or 72 hpi and continued for 5 days. On the y-axis the survival of mice [%] and on the x-axis the time post infection [days] is given. Placebo treated uninfected control mice showed 100% survival in both experiments (data not shown). Statistical analyses were conducted using log rank test and are shown beneath the graphs. Values of p<0.05 were considered statistically significant; non-significance (n.s.) was obtained with p-values >0.05. both are involved in recent influenza outbreaks. The H1N1(09)pdm is associated with more than 18,400 deaths in the season 2009/2010 while the LP H7N7 carries an HA closely related to that of the avian influenza H7N9 virus which has caused more than 175 deaths until October 2014 [64] . Second, they are of special interest for the carrageenan/Zanamivir combination approach. They have shown to differ in in-vitro susceptibility to carrageenan, Zanamivir (Table 1 ) and the combination thereof (Fig 1) . While H1N1(09)pdm was highly sensitive to inhibition by both substances alone, H7N7 required much higher concentrations of carrageenan and Zanamivir, respectively, to achieve similar inhibition efficiencies. Therefore, both virus strains were chosen to further explore the efficiency of the combination therapy in a mouse model. We established lethal mouse models with both viruses that resulted in 6.8 and 8.5 mean survival days for LP H7N7 and H1N1(09)pdm, respectively. These results are in good accordance to similar already published lethal influenza models [65] [66] [67] . In our models the lowest effective dose for Zanamivir at a treatment start 24 hpi was found to be between 1 to 3 mg/kg BW/day for both viruses. This concentration range is relatively high in comparison to other published studies. However, these studies were done under anesthesia with different viruses and a prophylactic therapy start [65, 66] . The fact that a higher dose of NI is needed for an effective treatment when the therapy starts 24 hpi is already known for Oseltamivir [68] . Nonetheless, also data with much higher effective concentrations (10 mg/kg BW/day [69] ) and with similar concentrations of Zanamivir (2.5 mg/kg BW/day [67] ) were published as well. We found that the combination of carrageenan with 3 mg/kg BW/day Zanamivir used for treatment of H7N7 infected mice resulted in significantly enhanced survival of mice in comparison to both mono-therapies (Fig 2) . The significantly enhanced survival compared to the placebo treated group was also found after a delayed treatment start 48 hpi. Furthermore, in the H1N1(09)pdm model the combination of carrageenan with 1 mg/kg BW/day Zanamivir showed statistically significant enhanced survival in comparison to placebo treatment even after a treatment start 72 hpi. This is a remarkable finding since NIs are normally not effective when applied 72 hpi. The finding supports the development of the Zanamivir and carrageenan combination approach. As the intranasal treatment regime is incapable to effectively treat virus infections of the lung, the primary target of such a product is the prophylaxis and therapy of uncomplicated influenza. Since the majority of influenza infections causes uncomplicated illnesses and practically all cases of influenza start with an infection of the nasal cavity or the upper respiratory tract, the therapeutic potential is huge. However, clinical studies are required to elucidate and demonstrate the potential of the proposed combination therapy. Combination of antiviral strategies has led to impressive achievements in the combat against other viral disease like HIV. In particular the problem of antiviral resistance could be addressed with this strategy. In the last decade concerns have been raised about the increased emergence of Oseltamivir resistant influenza viruses. The augmented appearance of viruses carrying the mutation H275Y in the neuraminidase of H1N1(09)pdm viruses that confers resistance to Oseltamivir left Zanamivir as only treatment option for symptomatic patients infected with an Oseltamivir resistant influenza strain [70] . In contrast to Oseltamivir, resistance to Zanamivir is less frequent. To date, Zanamivir resistant influenza has been detected only once, in an immunocompromised patient [71, 72] . However, lessons should be learned from previous anti-influenza interventions which resulted in occurrence of resistance against currently approved drugs [73] . Therefore, concerns are comprehensible that an increased Zanamivir use may also lead to the rapid emergence of resistances [74] . To overcome this threat, a combination of antivirals which inhibits virus replication by distinct mechanisms is a valid strategy. We checked for the possibility of generating double compound escape mutant viruses while passaging viruses in the presence of increasing concentrations of compound combinations. After 10 passages in MDCK cells no resistance to the compound combination for any tested influenza virus could be found (data not shown). However, this finding does not guarantee that emergence of Zanamivir escape mutants can be completely halted. In summary, we demonstrated that the anti-influenza mechanisms of both single compounds complement each other. The combination provides synergistically better protection against a broader spectrum of influenza viruses than the individual compounds. A nasal spray containing carrageenan together with Zanamivir provides an easy to apply treatment of upper respiratory tract infections in patients under suspicion to be influenza infected. Patients would benefit from the fast and efficient treatment of uncomplicated influenza in the upper respiratory tract. Due to the faster influenza virus clearance from the upper respiratory tract and the independent antiviral mechanism of carrageenan and Zanamivir the likelihood to develop escape mutations against Zanamivir will be reduced. Both individual compounds are able to reduce severity and/or duration of the influenza illness and a combination is expected to work similarly. Additionally, due to the broad antiviral effectiveness of carrageenan, patients will receive in parallel a treatment of concomitant viral infections. Therefore, patients will benefit from a decreased probability to develop complications. In consideration of the complications known to accompany an influenza virus illness this combinational therapy meets an urgent medical need. A second scope of this combination is the protection against newly emerging pandemic viruses during the time until identification of the virus followed by manufacturing and distribution of vaccines [43] . Even if, due to new reverse genetic techniques, less time for production of vaccines is needed, it still takes months before large quantities of vaccine are available [75] . During this time the human population should be protected to decelerate viral spread. At the moment the only available opportunities for personal protection are hygiene measures and the use of Tamiflu (brand name of Oseltamivir). Novel protection and treatment options for influenza are desperately needed. Based on our encouraging results in mice we suggest testing a nasal spray containing carrageenan in combination with the neuraminidase inhibitor Zanamivir in clinical trials for prevention or treatment of uncomplicated influenza infections. Supporting Information S1 Fig. Therapeutic efficacy of iota-carrageenan solely or together with kappa-carrageenan in influenza H7N7 lethal infected mice. Mice (n = 20 per group) were lethally intranasally infected without anesthesia on day 0 and accordingly intranasally treated twice per day either with placebo or with iota-carrageenan or with a mixture of iota-and kappa-carrageenan. Treatment started 24 hpi and continued for 5 days. On the y-axis the survival of mice [%] and on the x-axis the time post infection [days] is given. Placebo treated, uninfected control mice showed 100% survival (data not shown). Statistical analyses were conducted using log rank test and are shown beneath the graphs. Values of p<0.05 were considered statistically significant; non-significance (n.s.) was obtained with p-values >0.05. (TIFF)

What percentage of people infected with influenza have a viral coinfection?

The Intranasal Application of Zanamivir and Carrageenan Is Synergistically Active against Influenza A Virus in the Murine Model https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4459876/ SHA: f0b1fa4036434b57c8307d43c39a4193f7e8053a Authors: Morokutti-Kurz, Martina; König-Schuster, Marielle; Koller, Christiane; Graf, Christine; Graf, Philipp; Kirchoff, Norman; Reutterer, Benjamin; Seifert, Jan-Marcus; Unger, Hermann; Grassauer, Andreas; Prieschl-Grassauer, Eva; Nakowitsch, Sabine Date: 2015-06-08 DOI: 10.1371/journal.pone.0128794 License: cc-by Abstract: BACKGROUND: Carrageenan is a clinically proven and marketed compound for the treatment of viral upper respiratory tract infections. As infections caused by influenza virus are often accompanied by infections with other respiratory viruses the combination of a specific anti-influenza compound with the broadly active antiviral polymer has huge potential for the treatment of respiratory infections. Thus, the combination of the specific anti-influenza drug Zanamivir together with carrageenan in a formulation suitable for intranasal application was evaluated in-vitro and in-vivo. PRINCIPAL FINDINGS: We show in-vitro that carrageenan and Zanamivir act synergistically against several influenza A virus strains (H1N1(09)pdm, H3N2, H5N1, H7N7). Moreover, we demonstrate in a lethal influenza model with a low pathogenic H7N7 virus (HA closely related to the avian influenza A(H7N9) virus) and a H1N1(09)pdm influenza virus in C57BL/6 mice that the combined use of both compounds significantly increases survival of infected animals in comparison with both mono-therapies or placebo. Remarkably, this benefit is maintained even when the treatment starts up to 72 hours post infection. CONCLUSION: A nasal spray containing carrageenan and Zanamivir should therefore be tested for prevention and treatment of uncomplicated influenza in clinical trials. Text: The periodic appearance of new influenza variants poses a worldwide pandemic threat. Since the emergence of the new A(H7N9) virus, more than 400 human cases were reported to the WHO with a mortality rate of more than 35%. Most patients with A(H7N9) infections had contact with poultry or visited live animal markets. However, some sporadic cases seemed to be a result of human to human transmissions [1, 2] . In contrast to pandemic viruses which fulminantly enter the human population and cause high mortality rates, seasonal influenza viruses generally cause uncomplicated and transient infections in humans, with virus replication localized to the upper respiratory tract [3, 4] . However, in its fully developed form influenza is an acute respiratory disease resulting in hospitalizations and deaths mainly among high-risk groups. Worldwide, annual epidemics result in about three to five million cases of severe illness, and about 250,000 to 500,000 deaths [5] . For this reason WHO [6] and CDC [7] recommend antiviral treatment for any patient with suspected influenza who is at risk for influenza complications without previous laboratory confirmation. It is known that influenza virus infections are often accompanied by other viral pathogens [8] . Depending on the detection method (qRT-PCR or immunofluorescence) different ratios of co-infections have been found. Analysis by qRT-PCR revealed that 54.5-83.3% of influenza A or B positive patients were found to have at least one concomitant respiratory viral infection [9] [10] [11] [12] . The detection frequency with immunofluorescence was found to be even higher (90-100%) [13, 14] . Potential concomitant viral pathogens of influenza virus infections include human rhinovirus (hRV), respiratory syncytial virus, adenovirus, human coronavirus, human metapneumovirus and parainfluenza virus [14, 15] . As a result of the multiple infections, a specific anti-influenza mono-therapy treats the influenza virus infection only, but not the infection with the concomitant viral pathogen. Hence, the therapy often fails to sufficiently resolve symptoms. This is also reflected by the fact that neuraminidase inhibitors (NI) are highly efficacious in animal models investigating influenza mono-infections [16, 17] but show lower efficacy against influenza symptoms in clinical trials in adults with natural infections [18] . Therefore, there is a high medical need for a broadly acting antiviral therapy in combination with a specific anti-influenza therapy for treatment of patients suffering from upper respiratory tract symptoms. Ideally, the substances present in the combination complement each other by different modes of action, leading to a treatment that provides full protection against a broad range of different respiratory viruses as well as different influenza strains with a low probability to induce escape mutations. One approach for a broad antiviral therapy is the creation of a protective physical barrier in the nasal cavity using carrageenan. Carrageenan is a high molecular weight sulfated polymer derived from red seaweed (Rhodophyceae) that has been extensively used in food, cosmetic and pharmaceutical industry and is generally recognized as safe by the FDA (GRAS) (reviewed in [19] ). Three main forms of carrageenans are commercially used: kappa, iota and lambda. They differ from each other in the degree of sulfation, solubility and gelling properties [20] . The antiviral mechanism of carrageenan is based on the interference with viral attachment; as a consequence, viral entry is inhibited [21, 22] . Its antiviral activity is dependent on the type of polymer as well as the virus and the host cells [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] and has been reviewed in [33] [34] [35] . We published that iota-carrageenan is a potent inhibitor of hRV [36] and influenza A [37] replication and demonstrated the antiviral efficacy of iota-carrageenan against common cold viruses by intranasal application in several randomized, double-blind, parallel group, placebo-controlled clinical trials [38] [39] [40] . The pooled analysis of two studies conducted in 153 children and 203 adults revealed that patients infected with any respiratory virus, who were intranasally treated with iota-carrageenan showed a 1.9 day faster recovery from common cold symptoms than placebo treated patients in the intention-to-treat population [41, 42] . The anti-influenza activity was shown by subgroup analysis of 49 influenza infected patients who benefited from a 3.3 days faster recovery from symptoms. The use of carrageenan nasal spray was associated with a significant reduction of the influenza viral load in nasal fluids and a significant increase in the number of virus free patients within the treatment period of 7 days. In good accordance Prieschl-Grassauer are co-founders of Marinomed Biotechnologie GmbH. Marinomed Biotechnologie GmbH had a role in study design, data collection and analysis, decision to publish, preparation of the manuscript and is financing the processing charge of the manuscript. with the literature [9] [10] [11] [12] [13] [14] we observed that the majority of influenza virus infected patients suffered from a concomitant respiratory viral infection (66%) as determined by real-time PCR. Carrageenan containing nasal sprays are already marketed for the treatment of respiratory viral infections under different brand names in 18 countries. At present the only available effective drugs for treatment and post exposure prevention of influenza are the NI (Oseltamivir and Zanamivir worldwide; Peramivir in Japan and South Korea). Since the large-scale use of M2 blockers for prophylaxis and treatment in humans [43] and farming [44] , the currently circulating influenza viruses already lack sensitivity to this drug group [45] . We have already shown an additive therapeutic effect of a combination therapy with intranasally applied iota-carrageenan and orally administered Oseltamivir in lethally H1N1 A/PR/ 8/34 infected mice and a treatment start 48 hours post infection (hpi) [37] . Due to these very promising results we further developed the concept of combining carrageenan with an NI therapy. In contrast to Oseltamivir, which needs to be activated by metabolic conversion, Zanamivir is directly applied as active drug and can also be administered intranasally [46] [47] [48] [49] [50] [51] [52] . The potential of an intranasal administration of Zanamivir was investigated by GlaxoSmithKline. In seven clinical challenge trials 66 volunteers were infected with influenza B/Yamagata/16/88 and 213 with influenza A/Texas/36/91 (H1N1). 156 of these participants got intranasally applied Zanamivir at different doses (daily dose levels from 6.4 mg to 96 mg) for prophylaxis or therapy [46, 47, 53, 54] . These challenge trials showed that treatment starting before and up to 36 hours post virus inoculation was associated with prevention of laboratory confirmed influenza and febrile illness as well as a reduction in viral titers, duration of shedding and symptoms. In total, safety data from 1092 patients after intranasal application of Zanamivir were published and no evidence for Zanamivir induced adverse events or increased frequencies of local nasal intolerance in comparison to placebo groups was found [46, 49, 52] . Taken together, the combination of a carrageenan nasal spray that provides broad antiviral activity against upper respiratory infections-including influenza-with Zanamivir, a specific anti-influenza drug, meets the existing medical need to treat multiple viral infections. In the present work we investigate the therapeutic effect of a combination of carrageenan and Zanamivir in-vitro and in an animal model. Kappa-carrageenan and iota-carrageenan were purchased from FMC Biopolymers (Philadelphia, PA). The identity, purity (>95%) of carrageenan subtypes and the molecular weight (>100,000) was confirmed by NMR analysis as described elsewhere [55] and the presence of lambda-carrageenan was below the detection limit of 3%. The dry polymer powders were dissolved in aqua bidest (Fresenius Kabi, Austria) to a final concentration of 2.4 mg/ml iota-and 0.8 mg/ml kappa-carrageenan. This 2x stock solution was sterile filtered through a 0.22 μm filter (PAA, Switzerland) and stored at room temperature until use. For further testing the stock solution was diluted to a mixture containing 1.2 mg/ml iota-carrageenan and 0.4 mg/ml kappa-carrageenan (hereinafter referred to as "carrageenan"). Zanamivir was purchased as powder (Haosun Pharma, China) and the identity and purity was confirmed by NMR analysis. Zanamivir was either dissolved in carrageenan or placebo solutions, followed by sterile filtration through a 0.22 μm filter (Sarstedt, Germany). For in-vivo studies all Zanamivir containing solutions were freshly prepared. Madin-Darby canine kidney (MDCK) cells were obtained from the American Type Culture Collection (ATCC, Manassas, VA) and cultivated in a 37°C incubator (Sanyo, Japan; CO 2 : 5%, relative humidity: >95%). MDCK cells were grown in Dulbecco's minimal essential (DMEM) high glucose medium (PAA, Austria) supplemented with 10% fetal bovine serum (FBS; PAA, Austria; heat inactivated). Influenza virus A/Hansa Hamburg/01/09 (H1N1(09)pdm) was kindly provided by Peter Staeheli Department of Virology, University of Freiburg, Germany and previously described in [56] ; A/Teal/Germany/Wv632/05 (H5N1) previously published in [57] (accession numbers CY061882-9) and A/Turkey/Germany/R11/01 (H7N7) (taxonomy ID 278191, accession number AEZ68716) were supplied by courtesy of Martin Beer, Institute of Diagnostic Virology, Friedrich-Loeffler-Institute, Riems, Germany; A/Aichi/2/68 (H3N2) was purchased from the ATCC. All influenza viruses were propagated in MDCK cells at 37°C and 5% CO 2 in influenza medium [Opti-Pro serum free medium (Gibco, Austria) supplemented with 4 mM L-glutamine (PAA, Austria), 1% antibiotic-antimycotic mix (PAA, Austria) and 5 μg/ml trypsin (Sigma Aldrich, Austria)]. To determine the 50% inhibitory concentration (IC 50 ) and the combination effect of carrageenan and Zanamivir, a semi-liquid plaque assay was developed. Into 96 well tissue culture plates 1.7x10 4 MDCK cells/well were seeded and infected at 90% confluence (24-28 hours later). Serial dilutions of carrageenan and Zanamivir were prepared in assay medium (influenza medium without trypsin). For infection, viruses were diluted to an MOI of 0.003 (H1N1(09)pdm and H3N2 Aichi), 0.015 (H5N1) or 0.004 (H7N7), respectively, in assay medium and incubated at room temperature (RT) for 10 min with the serial dilutions of carrageenan and/or Zanamivir, respectively. For evaluation of the combination effect of carrageenan and Zanamivir, viruses were diluted in assay medium containing constant concentrations of either carrageenan or Zanamivir. The other substance was serially diluted and used for virus incubation. Cells were infected in 6 replicates/compound dilution, respectively, and incubated at RT for 45 min before inoculum removal. Cells were further incubated with the respective concentration of the investigated substances present in the overlay [influenza medium with 2.25% Carboxymethylcellulose (CMC, Fluka, Austria)] for 30-42 hours at 37°C. Evolving plaques were evaluated after methanol/acetone cell fixation by immune staining with antibodies either directed against the influenza A nucleoprotein (AbD Serotec, Germany) (for H1N1(09)pdm, H5N1 and H7N7) or the hemagglutinin (AbD Serotec, Germany) (for H3N2). Analysis was done with a HRP labeled detection antibody (Thermo Scientific, Germany) using TMB (Biolegend, Germany) as substrate and a microplate reader at 450 nm. The reduction of detected signal represents a reduction in the number and size of plaques and indicates suppression of viral replication during infection and cultivation. After the immunostaining cells were stained with 0.005% crystal violet solution to assess the condition of the cell layer and the toxicity of the compounds. IC 50 values and standard deviations were calculated for a sigmoidal dose response model using XLfit Excel add-in version 5.3.1.3. All animal experiments were carried out according to the guidelines of the "European Convention for the Protection of Vertebrate Animals used for Experimental and other Scientific Purposes" and the Austrian law for animal experiments. All animal experiments were approved by the Veterinary University of Vienna institutional ethics committee and performed under the Austrian Federal Ministry of Science and Research experimental animal license numbers BMWF-68.205/0262-II/3b/2011 and BMWF-68.205/0142-II/3b2012. C57BL/6 mice were purchased from Janvier Labs, France and maintained under standard laboratory conditions in the animal facilities of the Veterinary University of Vienna. For euthanasia and anesthesia asphyxiation through CO 2 was used and all efforts were made to minimize suffering. For infection experiments, 3-5 weeks old female mice were intranasally inoculated with 50 μl influenza virus solution (25 μl/nostril) containing 2.27x10 3 or 1.65x10 3 plaque-forming unit of H1N1(09)pdm or H7N7, respectively. Subsequently, treatment started 24, 48 or 72 hpi, as indicated for the different experiments. Treatment was performed intranasally either with 50 μl therapeutic solution or placebo twice per day for 5 days. As therapy either carrageenan (containing 1.2 mg/ml iota-carrageenan and 0.4 mg/ml kappa-carrageenan to provide a daily dose of 12 mg/kg body weight (BW)), Zanamivir (containing either 130 μg/ml or 390 μg/ml Zanamivir, to provide a daily dose of 1 or 3 mg/kg BW, respectively) or a combination of carrageenan and Zanamivir were used. Carrageenan and Zanamivir are used at non-toxic concentrations as shown by [58] and [59] . Mice were monitored twice daily for 15 days for survival and weight loss. Mortality also includes mice that were sacrificed for ethical considerations when they had lost more than 25% of their initial body weight. We confirm the viral infection in these animals by necropsy and scoring of the lung inflammation. As the mechanisms underlying the antiviral activity of NI and carrageenans are fundamentally distinct, they are likely to exhibit different activities towards the individual influenza virus strains. As a result, in combination they could complement each other to provide protection against a broader spectrum of influenza virus strains than the individual compounds. To test this hypothesis, we investigated the sensitivity of various influenza virus strains to Zanamivir and carrageenan in an adapted plaque reduction assay with semi-liquid overlay in MDCK cells [60, 61] . Using this method, we determined the IC 50 of Zanamivir and carrageenan against influenza A viruses of human and animal origin, namely H1N1(09)pdm (A/Hansa Hamburg/01/09), H3N2 (A/Aichi/2/68), low pathogenic (LP) H5N1 (A/Teal/Germany/ Wv632/05) and LP H7N7 (A/Turkey/Germany/R11/01) ( Table 1) . Both substances were nontoxic at the highest tested concentration (400 μM Zanamivir and 533 μg/ml carrageenan), neither was their combination. Furthermore, CMC in the overlay did not show any virus inhibitory effect (data not shown). Inhibition of viral replication of all tested influenza strains was achieved with both substances. However, the IC 50 values varied widely depending on the influenza virus strain. The IC 50 values of Zanamivir ranged between 0.18 μM for H5N1 and 22.97 μM for H7N7 and that of carrageenan from 0.39 μg/ml to 118.48 μg/ml for H1N1(09)pdm and H7N7, respectively (see Table 1 ). These results demonstrate that carrageenan and Zanamivir target individual influenza strains to different extents so that they may complement each other to provide broader anti-influenza activity. The type of compound interaction was characterized by employing isobolograms (Fig 1) . As described in [62] , isobolograms graphically compare the doses of two compounds needed to reach 50% inhibition to the predicted doses calculated based on a model of drug additivity. A curve linearity of~1 is expected for an additive compound interaction whereas a curve progression <1 argue for synergistic and >1 for an antagonistic compound interaction. Two virus strains were selected for those experiments, one being the most sensitive to carrageenan (H1N1(09)pdm) and one being the least sensitive (H7N7). In both cases the isobolograms show a synergistic interaction of carrageenan and Zanamivir (Fig 1) . Thus, it was shown that Zanamivir and carrageenan target individual influenza viruses with different efficiencies, most probably due to their different antiviral strategies. As a result, the combination provides synergistic activity with higher protection against a broader spectrum of influenza virus strains than the individual compounds. In the influenza animal model, C57Bl/6 mice are challenged with a lethal dose of the respective virus and treated with different regimens in comparison to a vehicle control (placebo). Infection and treatment (twice a day for 5 days) are done intranasally without anesthesia. We investigated whether the combination of Zanamivir and carrageenan is more efficacious in reducing mortality than the corresponding mono-therapies. First, we determined the minimal effective dose of a Zanamivir mono-therapy that significantly improved survival time of H1N1 and H7N7 infected mice. For the H7N7 lethal infection the minimal effective dose of Zanamivir as mono-therapy ranged between 1 and 3 mg/kg BW/ day (data not shown). Next, we compared the antiviral activity of carrageenan (12 mg/kg BW/ day) and Zanamivir (1 and 3 mg/kg BW/day) mono-therapies with the respective combination versus placebo treatment. Survival rates of mice with treatment starting 24 hpi are shown in Fig 2A. All placebo treated mice died between day 7 and 9 and also in all mono-therapy groups 100% lethality was observed until day 15. In contrast, the combination therapies led to 50% and 90% survival, depending on the Zanamivir concentration. Statistical analysis showed that the Zanamivir mono-therapy 1 mg/kg BW/day did not show a significant benefit (p = 0.1810), whereas the mono-therapy with 3 mg/kg BW/day significantly increased the survival rate compared with placebo treated mice (p = 0.0016). Both Zanamivir concentrations experienced significant benefit in survival by the combination with carrageenan (p<0.0001). Similarly, the combination therapies resulted in remarkably increased survival (p = 0.0421 for 1 mg and p<0.0001 for 3 mg/kg BW/day) when compared to the carrageenan mono-therapy. No statistically significant difference was observed between the combination containing 3 mg/kg BW/day Zanamivir and that containing 1 mg/kg BW/day (p = 0.0525). However, a trend for an increased survival rate with the higher Zanamivir concentration was evident. Therefore, for further investigation the combination therapy containing 3 mg/kg BW/day Zanamivir was evaluated in lethally H7N7 infected mice. Next, the therapeutic potential of the combination with a delayed therapy start 48 or 72 hpi versus placebo treatment was explored. The survival rates of mice are shown in Fig 2B. All placebo treated mice died until day 10 and also in the group with the treatment start 72 hpi 100% lethality was found. In contrast, the combination therapy starting 48 hpi provided a statistically significant enhanced survival rate in comparison to placebo-treated mice (p = 0.0010). In summary, the combination of two effective, established mono-therapies resulted in a significantly enhanced survival in lethally H7N7 infected mice. Additionally, the combination therapy was highly efficient in comparison to placebo treatment even after a treatment onset up to 48 hpi. Intranasal therapy with carrageenan and Zanamivir starting 72 hpi significantly protects lethally influenza H1N1(09)pdm infected mice Next, the minimal effective dose of Zanamivir used as mono-therapy was evaluated in a lethal H1N1(09)pdm mouse model, following the same scheme as described in the H7N7 experiments. The lowest effective dose of Zanamivir after a treatment start 24 hpi was 1 mg/kg BW/ day and its combination with carrageenan was highly effective (data not shown). In the following experiment the therapeutic potential of the combination with a therapy start 48 or 72 hpi was investigated in comparison with the respective placebo treatment. As shown in Fig 3, the survival rates of mice treated with the combination therapy were highly significantly increased in comparison to the placebo group (p<0.0001). There was no difference in survival between the two therapy starting points, 48 or 72 hpi, which both resulted We investigated the antiviral effect of a combination of carrageenan with the NI Zanamivir in cell culture studies and in mouse influenza infection models. We have previously shown that a combined therapy of iota-carrageenan with the NI Oseltamivir led to significantly enhanced survival in mice infected with H1N1 PR/8/34 in comparison with the respective mono-therapies [37] . However, Oseltamivir is an orally administered prodrug, which has to be converted into its active form by metabolic processing. Therefore, a further development of a combination nasal spray was not possible with Oseltamivir. Instead Zanamivir-a NI that is applied as active drug-was chosen for the development of a compound combination. During the evaluation process we found that the binding efficiency of different carrageenan subtypes on different influenza strains varies. The combined use of iota-and kappa-carrageenan for the treatment of lethally influenza infected C57Bl/6 mice revealed a better therapeutic effect than the use of iota-carrageenan alone (S1 Fig). Thus, to provide a broader spectrum of activity against different influenza virus strains, a mixture of iota-and kappa-carrageenan (designated as carrageenan) was used for further evaluation. For investigation of the effect of a compound combination of carrageenan and Zanamivir, we examined their inhibition efficiency, individually and in combination, against influenza viruses in an adapted plaque reduction assay with semi-liquid overlay in MDCK cells. The combination showed a synergistic inhibition of virus replication in in-vitro assays with all tested influenza viruses (Fig 1) . This indicates that the physical interaction of the polymer with the virus does not disturb the inhibition of the neuraminidase by Zanamivir. This was confirmed in in-vitro tests examining a potential influence of the polymer on the neuraminidase inhibiting activity of Zanamivir (data not shown). Hence, the observed synergistic effect is based on the combination of two distinct underlying mechanisms. As a result, in the proposed combination both mechanisms would complement each other to provide more efficient protection against a broader spectrum of influenza virus strains than the individual compounds. The synergistic effect was also shown in lethal mice models (Fig 2 and Fig 3) . The pathogenicity of influenza viruses in mice varies and is dependent on the strain and its adaptation to the host. Depending on virus dose and strain, influenza viruses can induce lethal infections in certain mouse strains usually within two weeks [37, 63] . In our model, C57Bl/6 mice are challenged intranasally with a lethal dose of the respective virus and treated with different regimens in comparison to a vehicle control (placebo). In such a model, early virus replication takes place in the upper respiratory tract. From there, virus spreads to the lung and causes lethal pneumonia. The effect of the treatment on mortality is assessed in comparison to placebotreated control mice. Of all in-vitro tested influenza strains the H1N1(09)pdm and the LP H7N7 are particularly interesting for two reasons. First, they are highly relevant pathogens, as placebo or with the mono-therapies consisting of carrageenan (12 mg/kg BW/day) or Zanamivir (1 and 3 mg/ kg BW/day) or a combination thereof. Treatment started 24 hpi and continued for 5 days. (B) Mice (n = 20 per group) were lethally intranasally infected without anesthesia on day 0 and intranasally treated twice per day either with placebo or a combination of carrageenan with Zanamivir (3 mg/kg BW/day). Treatment started either 48 hpi or 72 hpi and continued for 5 days. On the y-axis the survival of mice [%] and on the x-axis the time post infection [days] is given. Placebo treated uninfected control mice showed 100% survival in both experiments (data not shown). Statistical analyses were conducted using log rank test and are shown beneath the graphs. Values of p<0.05 were considered statistically significant; non-significance (n.s.) was obtained with p-values >0.05. both are involved in recent influenza outbreaks. The H1N1(09)pdm is associated with more than 18,400 deaths in the season 2009/2010 while the LP H7N7 carries an HA closely related to that of the avian influenza H7N9 virus which has caused more than 175 deaths until October 2014 [64] . Second, they are of special interest for the carrageenan/Zanamivir combination approach. They have shown to differ in in-vitro susceptibility to carrageenan, Zanamivir (Table 1 ) and the combination thereof (Fig 1) . While H1N1(09)pdm was highly sensitive to inhibition by both substances alone, H7N7 required much higher concentrations of carrageenan and Zanamivir, respectively, to achieve similar inhibition efficiencies. Therefore, both virus strains were chosen to further explore the efficiency of the combination therapy in a mouse model. We established lethal mouse models with both viruses that resulted in 6.8 and 8.5 mean survival days for LP H7N7 and H1N1(09)pdm, respectively. These results are in good accordance to similar already published lethal influenza models [65] [66] [67] . In our models the lowest effective dose for Zanamivir at a treatment start 24 hpi was found to be between 1 to 3 mg/kg BW/day for both viruses. This concentration range is relatively high in comparison to other published studies. However, these studies were done under anesthesia with different viruses and a prophylactic therapy start [65, 66] . The fact that a higher dose of NI is needed for an effective treatment when the therapy starts 24 hpi is already known for Oseltamivir [68] . Nonetheless, also data with much higher effective concentrations (10 mg/kg BW/day [69] ) and with similar concentrations of Zanamivir (2.5 mg/kg BW/day [67] ) were published as well. We found that the combination of carrageenan with 3 mg/kg BW/day Zanamivir used for treatment of H7N7 infected mice resulted in significantly enhanced survival of mice in comparison to both mono-therapies (Fig 2) . The significantly enhanced survival compared to the placebo treated group was also found after a delayed treatment start 48 hpi. Furthermore, in the H1N1(09)pdm model the combination of carrageenan with 1 mg/kg BW/day Zanamivir showed statistically significant enhanced survival in comparison to placebo treatment even after a treatment start 72 hpi. This is a remarkable finding since NIs are normally not effective when applied 72 hpi. The finding supports the development of the Zanamivir and carrageenan combination approach. As the intranasal treatment regime is incapable to effectively treat virus infections of the lung, the primary target of such a product is the prophylaxis and therapy of uncomplicated influenza. Since the majority of influenza infections causes uncomplicated illnesses and practically all cases of influenza start with an infection of the nasal cavity or the upper respiratory tract, the therapeutic potential is huge. However, clinical studies are required to elucidate and demonstrate the potential of the proposed combination therapy. Combination of antiviral strategies has led to impressive achievements in the combat against other viral disease like HIV. In particular the problem of antiviral resistance could be addressed with this strategy. In the last decade concerns have been raised about the increased emergence of Oseltamivir resistant influenza viruses. The augmented appearance of viruses carrying the mutation H275Y in the neuraminidase of H1N1(09)pdm viruses that confers resistance to Oseltamivir left Zanamivir as only treatment option for symptomatic patients infected with an Oseltamivir resistant influenza strain [70] . In contrast to Oseltamivir, resistance to Zanamivir is less frequent. To date, Zanamivir resistant influenza has been detected only once, in an immunocompromised patient [71, 72] . However, lessons should be learned from previous anti-influenza interventions which resulted in occurrence of resistance against currently approved drugs [73] . Therefore, concerns are comprehensible that an increased Zanamivir use may also lead to the rapid emergence of resistances [74] . To overcome this threat, a combination of antivirals which inhibits virus replication by distinct mechanisms is a valid strategy. We checked for the possibility of generating double compound escape mutant viruses while passaging viruses in the presence of increasing concentrations of compound combinations. After 10 passages in MDCK cells no resistance to the compound combination for any tested influenza virus could be found (data not shown). However, this finding does not guarantee that emergence of Zanamivir escape mutants can be completely halted. In summary, we demonstrated that the anti-influenza mechanisms of both single compounds complement each other. The combination provides synergistically better protection against a broader spectrum of influenza viruses than the individual compounds. A nasal spray containing carrageenan together with Zanamivir provides an easy to apply treatment of upper respiratory tract infections in patients under suspicion to be influenza infected. Patients would benefit from the fast and efficient treatment of uncomplicated influenza in the upper respiratory tract. Due to the faster influenza virus clearance from the upper respiratory tract and the independent antiviral mechanism of carrageenan and Zanamivir the likelihood to develop escape mutations against Zanamivir will be reduced. Both individual compounds are able to reduce severity and/or duration of the influenza illness and a combination is expected to work similarly. Additionally, due to the broad antiviral effectiveness of carrageenan, patients will receive in parallel a treatment of concomitant viral infections. Therefore, patients will benefit from a decreased probability to develop complications. In consideration of the complications known to accompany an influenza virus illness this combinational therapy meets an urgent medical need. A second scope of this combination is the protection against newly emerging pandemic viruses during the time until identification of the virus followed by manufacturing and distribution of vaccines [43] . Even if, due to new reverse genetic techniques, less time for production of vaccines is needed, it still takes months before large quantities of vaccine are available [75] . During this time the human population should be protected to decelerate viral spread. At the moment the only available opportunities for personal protection are hygiene measures and the use of Tamiflu (brand name of Oseltamivir). Novel protection and treatment options for influenza are desperately needed. Based on our encouraging results in mice we suggest testing a nasal spray containing carrageenan in combination with the neuraminidase inhibitor Zanamivir in clinical trials for prevention or treatment of uncomplicated influenza infections. Supporting Information S1 Fig. Therapeutic efficacy of iota-carrageenan solely or together with kappa-carrageenan in influenza H7N7 lethal infected mice. Mice (n = 20 per group) were lethally intranasally infected without anesthesia on day 0 and accordingly intranasally treated twice per day either with placebo or with iota-carrageenan or with a mixture of iota-and kappa-carrageenan. Treatment started 24 hpi and continued for 5 days. On the y-axis the survival of mice [%] and on the x-axis the time post infection [days] is given. Placebo treated, uninfected control mice showed 100% survival (data not shown). Statistical analyses were conducted using log rank test and are shown beneath the graphs. Values of p<0.05 were considered statistically significant; non-significance (n.s.) was obtained with p-values >0.05. (TIFF)

What is the recovery benefit of carageenan in patients with any respiratory virus?

Functional Genetic Variants in DC-SIGNR Are Associated with Mother-to-Child Transmission of HIV-1 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2752805/ Boily-Larouche, Geneviève; Iscache, Anne-Laure; Zijenah, Lynn S.; Humphrey, Jean H.; Mouland, Andrew J.; Ward, Brian J.; Roger, Michel 2009-10-07 DOI:10.1371/journal.pone.0007211 License:cc-by Abstract: BACKGROUND: Mother-to-child transmission (MTCT) is the main cause of HIV-1 infection in children worldwide. Given that the C-type lectin receptor, dendritic cell-specific ICAM-grabbing non-integrin-related (DC-SIGNR, also known as CD209L or liver/lymph node–specific ICAM-grabbing non-integrin (L-SIGN)), can interact with pathogens including HIV-1 and is expressed at the maternal-fetal interface, we hypothesized that it could influence MTCT of HIV-1. METHODS AND FINDINGS: To investigate the potential role of DC-SIGNR in MTCT of HIV-1, we carried out a genetic association study of DC-SIGNR in a well-characterized cohort of 197 HIV-infected mothers and their infants recruited in Harare, Zimbabwe. Infants harbouring two copies of DC-SIGNR H1 and/or H3 haplotypes (H1-H1, H1-H3, H3-H3) had a 3.6-fold increased risk of in utero (IU) (P = 0.013) HIV-1 infection and a 5.7-fold increased risk of intrapartum (IP) (P = 0.025) HIV-1 infection after adjusting for a number of maternal factors. The implicated H1 and H3 haplotypes share two single nucleotide polymorphisms (SNPs) in promoter region (p-198A) and intron 2 (int2-180A) that were associated with increased risk of both IU (P = 0.045 and P = 0.003, respectively) and IP (P = 0.025, for int2-180A) HIV-1 infection. The promoter variant reduced transcriptional activity in vitro. In homozygous H1 infants bearing both the p-198A and int2-180A mutations, we observed a 4-fold decrease in the level of placental DC-SIGNR transcripts, disproportionately affecting the expression of membrane-bound isoforms compared to infant noncarriers (P = 0.011). CONCLUSION: These results suggest that DC-SIGNR plays a crucial role in MTCT of HIV-1 and that impaired placental DC-SIGNR expression increases risk of transmission. Text: Without specific interventions, the rate of HIV-1 mother-tochild transmission (MTCT) is approximately 15-45% [1] . UNAIDS estimates that last year alone, more than 400,000 children were infected worldwide, mostly through MTCT and 90% of them lived in sub-Saharan Africa. In the most heavilyaffected countries, such as Zimbabwe, HIV-1 is responsible for one third of all deaths among children under the age of five. MTCT of HIV-1 can occur during pregnancy (in utero, IU), delivery (intrapartum, IP) or breastfeeding (postpartum, PP). High maternal viral load, low CD4 cells count, vaginal delivery, low gestational age have all been identified as independent factors associated with MTCT of HIV-1 [1] . Although antiretrovirals can reduce MTCT to 2%, limited access to timely diagnostics and drugs in many developing world countries limits the potential impact of this strategy. A better understanding of the mechanisms acting at the maternal-fetal interface is crucial for the design of alternative interventions to antiretroviral therapy for transmission prevention. Dendritic cell-specific ICAM-grabbing non-integrin-related (DC-SIGNR, also known as CD209L or liver/lymph node-specific ICAM-grabbing non-integrin (L-SIGN)) can interact with a plethora of pathogens including HIV-1 and is expressed in placental capillary endothelial cells [2] . DC-SIGNR is organized in three distinct domains, an N-terminal cytoplasmic tail, a repeat region containing seven repeat of 23 amino acids and a C-terminal domain implicated in pathogen binding. Alternative splicing of DC-SIGNR gene leads to the production of a highly diversify isoforms repertoire which includes membrane-bound and soluble isoforms [3] . It has been proposed that interaction between DC-SIGNR and HIV-1 might enhance viral transfer to other susceptible cell types [2] but DC-SIGNR can also internalize and mediate proteasome-dependant degradation of viruses [4] that may differently affect the outcome of infection. Given the presence of DC-SIGNR at the maternal-fetal interface and its interaction with HIV-1, we hypothesized that it could influence MTCT of HIV-1. To investigate the potential role of DC-SIGNR in MTCT of HIV-1, we carried out a genetic association study of DC-SIGNR in a well-characterized cohort of HIV-infected mothers and their infants recruited in Zimbabwe, and identified specific DC-SIGNR variants associated with increased risks of HIV transmission. We further characterized the functional impact of these genetic variants on DC-SIGNR expression and show that they affect both the level and type of DC-SIGNR transcripts produced in the placenta. Samples consisted of stored DNA extracts obtained from 197 mother-child pairs co-enrolled immediately postpartum in the ZVITAMBO Vitamin A supplementation trial (Harare, Zimbabwe) and followed at 6 weeks, and 3-monthly intervals up to 24 months. The ZVITAMBO project was a randomized placebocontrolled clinical trial that enrolled 14,110 mother-child pairs, between November 1997 and January 2000, with the main objective of investigating the impact of immediate postpartum vitamin A supplementation on MTCT of HIV-1. The samples used in the present study were from mother-child pairs randomly assigned to the placebo group of the ZVITAMBO project. Antiretroviral prophylaxis for HIV-1-positive antenatal women was not available in the Harare public-sector during ZVITAMBO patient recruitment. The samples were consecutively drawn from two groups: 97 HIV-1-positive mother/HIV-1-positive child pairs and 100 HIV-1-positive mother/HIV-negative child pairs. Mother's serological status was determined by ELISA and confirmed by Western Blot. Infants were considered to be infected if they were HIV-1 seropositive at 18 months or older and had two or more positive HIV-1-DNA polymerase chain reaction (PCR) results at earlier ages. 100 infants were considered to be uninfected as they were ELISA negative at 18 months or older and had two DNA PCR negative results from samples collected at a younger age. Of the 97 HIV-1-infected infants, 57 were infected IU, 11 were infected IP, and 17 were infected PP as determined by PCR analyses of blood samples collected at birth, 6 weeks, 3 and 6 months of age and according to the following definitions adapted from Bryson and colleagues [5] . Briefly, infants who were DNA PCR positive at birth were infected IU. Infants with negative PCR results from sample obtained at birth but who become positive by 6 weeks of age were infected IP. Infants with negative PCR results at birth and 6 weeks of age but who subsequently became DNA PCR positive were considered to be infected during the PP period. In the analysis comparing the 3 different modes of MTCT, 12 HIV-1-infected infants were excluded because the PCR results were not available at 6 weeks of age. Full methods for recruitment, baseline characteristics collection, laboratory procedures have been described elsewhere [6] . The nucleotide sequence variation of the entire promoter, coding and part of 39-UTR regions of DC-SIGNR gene in the study population was determined previously [7] . Haplotype reconstruction was performed using Bayesian statistical method implemented in PHASE [8] , version 2.1.1, using single nucleotide polymorphism (SNP) with a minimum allele frequency (MAF) of 2%. We applied the algorithm five times, using different randomly generated seeds, and consistent results were obtained across runs ( Figure 1 ). Fifteen haplotype-tagged SNPs (htSNPs) were identified by the HaploBlockFinder software [9] with a MAF $5%. These htSNPs were genotyped in the 197 infants by direct PCR sequencing analysis as we have described previously [7] . The DC-SIGNR exon 4 repeat region genotype was determined by PCR amplification followed by migration in 1.5% agarose gels [10] . DNA sequences in the promoter region were analysed with the TESS interface (http//:www.cbil.upenn.edu/tess) for putative transcription factors binding sites using the TRANSFAC database. Luciferase reporter assays using pGL2-Basic vector were performed in order to investigate the functional effect of mutations on DC-SIGNR promoter activity. Genomic DNA from subjects homozygous for the promoter variants and WT was amplified from nucleotide position 2715 to 21 and cloned between the BglII and HindIII multiple cloning sites in the pGL2-Basic vector which harbours a reporter firefly luciferase gene downstream (Invitrogen Canada inc, Burlington, Canada). All recombinants clones were verified by DNA sequencing. The firefly luciferase test reporter vector was co-transfected at a ratio of 10:1 with the constitutive expressor of Renilla luciferase, phRL-CMV (Promega, Madison, WI, USA). We cultured HeLa cells in 6 wells plates (2610 5 cells) and transfected them the following day using lipofectamine (Invitrogen) according to the manufacturer. Cells were lysed and luciferase assays were performed using 20 mg of protein extract according to the manufacturer (Promega) at 44 h post-transfection. Firefly luciferase activity was normalized to Renilla luciferase activity. 0 mg, 0,5 mg or 1 mg CMV-Tat vector was transfected with LTR-Luc as a positive control in these experiments. We carried out lucierase assays in triplicate in three independent experiments. Results are expressed as mean6 standard error of the mean (S.E.M). First-term placental tissues were obtained from abortions following voluntary interruption of pregnancy at CHUM Hôpital Saint-Luc (Montreal, Canada). Tissues from 3 H1 (associated with MTCT of HIV-1) and 3 H15 (wild-type) homozygous haplotypes were used to analyse possible differences in isoform expression. Total placental RNAs were extracted by MasterPure DNA and RNA Extraction Kit (Epicentre Biotechnologies, Madison, WI, USA) according to the manufacturer. Fragments corresponding to the DC-SIGNR coding region were reversed transcribed (RT) and then amplified by nested PCR with the following primers; RT primers RR, first PCR RF and RR and second PCR RcF and RcR according to Liu and colleagues [11] . 1 mg of total RNA was reverse transcribed with Expand RT (Roche Applied Science, Indianapolis, IN, USA) according to the manufacturer and were PCR-amplified with DNA Platinum Taq Polymerase (Invitrogen). Major PCR products from the second PCR reaction were gel extracted with the Qiagen Gel Extraction Kit (Qiagen Canada inc, Mississauga, ON, Canada) and cloned using the TOPO TA Cloning Kit for sequencing (Invitrogen). For each placenta, 15 different clones were randomly selected and amplified with M13 primers and sequenced with ABI PRISM 3100 capillary automated sequencer (Applied Biosystems, Foster City, CA, USA). Sequences were analysed and aligned with GeneBank reference sequence NM_014257 using Lasergene software (DNA Stars, Madison, WI, USA). Quantitative expression of DC-SIGNR isoforms 1,5 mg of placental RNA was reverse transcribed using 2.5 mM of Oligo dT 20 and Expand RT in 20 ml volume according to the manufacturer (Roche Applied Science). 15 ng of total cDNA in a final volume of 20 ml was used to perform quantitative real-time PCR using Universal Express SYBR GreenER qPCR Supermix (Invitrogen) on a Rotor Gene Realtime Rotary Analyser (Corbett Life Science, Sydney, Australia). Samples from 2 subjects in each group were used because RNA quality of others was not suitable for a qRT-PCR analysis. Amplification of all DC-SIGNR isoforms was performed using an exon 5 specific primer pair (Table S1 ). Membrane-bound isoforms were amplified using primers specific for exon 3, corresponding to the common trans-membrane domain of DC-SIGNR. Primers were targeted to the exon-exon junction and RNA extracts were treated with DNase (Fermantas International inc, Burlington, ON, Canada) to avoid amplification of contaminant DNA. Standard curves (50-500 000 copies per reaction) were generated using serial dilution of a full-length DC-SIGNR or commercial GAPDH (Invitrogen) plasmid DNA. All qPCR reactions had efficiencies ranging from 99% to 100%, even in the presence of 20 ng of non-specific nucleic acids, and therefore could be compared. The copy number of unknown samples was estimated by placing the measured PCR cycle number (crossing threshold) on the standard curve. To correct for differences in both RNA quality and quantity between samples, the expression levels of transcripts were normalised to the reference GAPDH gene transcripts. GAPDH primer sequences were kindly provided by A. Mes-Masson at the CHUM. The results are presented as target gene copy number per 10 5 copies of GAPDH. The ratio of membrane-bound isoforms was calculated as E3/E5. Soluble isoforms were calculated by subtracting membrane-bound from total isoforms. We carried out qPCR assays in triplicate in three independent experiments. Results are expressed as mean6S.E.M. Statistical analysis was performed using the GraphPad PRISM 5.0 for Windows (GraphPad Software inc, San Diego, CA, USA). Differences in baseline characteristics and genotypic frequencies of haplotypes or htSNPs were compared between groups using the x 2 analysis or Fisher's exact test. Logistic regression analysis was used to estimate odds ratios (OR) for each genotype and baseline risk factors. Multiple logistic regression was used to define independent predictors identified as significant in the crude analysis. ORs and 95% confidence interval were calculated with the exact method. Comparisons of continuous variables between groups were assessed with the unpaired two-tailed Student's t test when variables were normally distributed and with the Mann-Whitney U test when otherwise. Differences were considered significant at P,0.05. Written informed consent was obtained from all mothers who participated in the study and the ZVITAMBO trial and the investigation reported in this paper were approved by The We carried out an association study of DC-SIGNR polymorphism in 197 infants born to untreated HIV-1-infected mothers recruited in Harare, Zimbabwe. Among them, 97 infants were HIV-1-infected and 100 infants remained uninfected. Of the 97 HIV-1-infected infants, 57 were infected IU, 11 were infected IP, and 17 were infected PP. Timing of infection was not determined for 12 HIV-1-infected infants. Baseline characteristics of mothers and infants are presented in Table 1 . Maternal age and CD4 cell count, child sex, mode of delivery, duration of membrane rupture and gestational age were similar among all groups. However, maternal viral load .29 000 copies/ml was associated with increased risk in both IU and PP with odds ratios (OR) of 3.64 (95% CI = 1.82-7.31, P = 0.0002) and 4.45 (95% CI = 1.50-13.2, P = 0.0045) for HIV-1 transmission, respectively. Fifteen haplotype-tagged SNPs (htSNPs) corresponding to the 15 major DC-SIGNR haplotypes ( Figure 1 ) described among Zimbabweans [7] were genotyped in our study samples (Tables S2 and S3 ). H1 (31%) and H3 (11%) were the most frequent haplotypes observed (Figure 1 ). Being homozygous for the H1 haplotype was associated with increased risk of both IU (OR: 4.42, P = 0.022) and PP (OR: 7.31, P = 0.016) HIV-1 transmission ( Table 2) . Infants harbouring two copy combinations of H1 and/ or H3 haplotypes (H1-H1, H1-H3 or H3-H3) had increased risk of IU (OR: 3.42, P = 0.007) and IP (OR: 5.71, P = 0.025) but not PP (P = 0.098) HIV-1 infection compared to infant noncarriers ( Table 2 ). The latter associations remained significant after adjustment was made for the maternal viral load for both IU (OR: 3.57, 95% CI = 1.30-9.82, P = 0.013) and IP (OR: 5.71, 95% CI = 1.40-23.3, P = 0.025) HIV-1 transmission. The H1 and H3 haplotypes share a cluster of mutations (p-198A, int2-391C, int2-180A, ex4RPT, int5+7C) ( Figure 1 ). Of these, the p-198A and int2-180A variants were significantly associated with MTCT of HIV-1 (Table S2 ). In the unadjusted regression analysis, homozygous infants for the p-198A and int2-180A variants had increased risk of IU (OR: 2.07 P = 0.045, OR: 3.78, P = 0.003, respectively) and IP (OR: 2.47, P = 0.17, O.R: 5.71, P = 0.025, respectively) HIV-1 infection compared to heterozygote infants or noncarriers (Table 3) . When adjustment was made for maternal factors, only the association with the int2-180A variant remained significant for IU (OR: 3.83, 95% CI = 1.42-10.4, P = 0.008) and IP (O.R: 5.71, 95% CI = 1.40-23.3, P = 0.025) HIV-1 transmission. Thus, infants homozygous for DC-SIGNR variant int2-180A contained in H1 and H3 haplotypes were 4-fold to 6-fold more likely to be infected by HIV-1 during pregnancy or at delivery, respectively. Alternative splicing of the DC-SIGNR gene in the placenta produces both membrane-bound and soluble isoform repertoires [3] . The relative proportion of membrane bound and soluble DC-SIGNR could plausibly influence the susceptibility to HIV-1 infection [11] . We therefore hypothesized that the DC-SIGNR mutations associated with MTCT of HIV-1 would have an impact on both the level of DC-SIGNR expression and in the isoform repertoire produced. We investigated DC-SIGNR transcript expression in first-term placentas obtained after elective abortion. We cloned DC-SIGNR from placental tissues by RT-PCR from 3 homozygous H1 samples containing both the DC-SIGNR p-198AA and int2-180AA variants associated with HIV-1 transmission and 3 homozygous wild-type (WT) (p-198CC, int2-180GG) samples. Fifteen clones per sample were randomly selected for sequencing. As expected, we found an extensive repertoire of DC-SIGNR transcripts in all samples with 9 to 16 different isoforms per individual. A total of 65 distinct transcripts were identified ( Figure S1 ), of which 3 were full-length transcripts. 64 of the sequenced clones contained a total of 69 amino acid substitutions with 3 new C termini and 2 premature stop codons. However, the diversity was mostly attributable to the entire deletion of exon 2 or exon 3 or to variations in the length of the neck region (exon 4) of DC-SIGNR. The deletion of exon 3 eliminates the trans-membrane domain of the protein and leads to the expression of soluble DC-SIGNR isoforms [3] . Interestingly, the abundance of membrane-bound isoforms in placental tissues of the H1 homozygotes appears to be lower than that observed in samples from WT individuals ( Figure S1 ). The deletion of exon 3 was confirmed by sequencing and we hypothesize that the skipping of exon 3, could be due to the presence of the int2-180A mutation observed in infants with the H1 haplotype. In fact, this intron mutation is located 180 bp downstream from exon 3 and potentially modifies splicing events (Figure 2A ). We confirmed that the variation in transcript proportions seen between the two groups was also reflected at the level of mRNA expression in the placenta. To quantify membrane-bound vs soluble isoforms in placental samples from homozygous H1 and WT infants, we amplified the exon 5 (E5) sequence present in all DC-SIGNR isoforms (total transcripts). We then amplified exon 3 (E3) which is deleted in the soluble forms and then calculated the E3:E5 ratio. We found that placental tissues from homozygous H1 infants express a significantly lower proportion of membrane-bound DC-SIGNR (18%) compared to that in WT individuals (36%) (P = 0.004) ( Figure 2B ) suggesting that exon 3 skipping happens more frequently in presence of the DC-SIGNR int2-180A variant associated with MTCT of HIV-1. The DC-SIGNR int2-180A variant is always transmitted with the promoter mutation p-198A (Figure 1 ). In the unadjusted regression analysis, the p-198A variant was significantly associated with IU but not with IP and PP HIV-1 transmission (Table 3) . Computational transcription factor binding site analysis predicts Table 1 . Baseline characteristics of mother and infants risk factors for intrauterine (IU), intrapartum (IP) and postpartum (PP) mother-to-child HIV-1 transmission. Figure 3A ). The luciferase activity of the p-198A variant construct was significantly lower than that of the WT p-198C promoter construct (p-198C/A ratio = 2, P = 0.006) ( Figure 3B ) suggesting that DC-SIGNR p-198A affects promoter activity. The other promoter mutants (p-577C and p-323A) observed in the Zimbabwean population did not affect DC-SIGNR transcription in this assay ( Figure S2 ). To determine the net impact of the DC-SIGNR p-198A mutation on DC-SIGNR expression in the placenta, we quantitated the absolute number of total and membrane-bound DC-SIGNR transcripts in the H1 homozygote and wild-type placental samples as described earlier. The total number of DC-SIGNR transcripts was determined to be 6856213 (DC-SIGNR copies6S.E.M per 10 5 GAPDH copies) in the placental samples from homozygous H1 infants and was 4-fold lower compared to that found in placentas from WT individuals (27816638, P = 0.011) ( Figure 3C ). As suggested earlier, the int2-180A mutation might induce exon 3 skipping leading to a lower production of membrane-bound DC-SIGNR. Although, the decrease in the total number of DC-SIGNR transcripts in H1 homozygous placental samples containing both the p-198AA and int2-180AA variants affected the proportion of membrane-bound and soluble isoforms, the effect of these mutations was more pronounced on the membrane-bound isoforms with an 8-fold decrease (H1 = 117636.2 vs WT = 9906220.6, P = 0.003) compared to a 3-fold decrease in total soluble isoforms (H1 = 5686181.9 vs WT = 19256495.3, P = 0.03) ( Figure 3C ). Therefore, DC-SIGNR p-198A and int2-180A mutations associated with MTCT of HIV-1 significantly decreased the level of total placental DC-SIGNR transcripts, disproportionately affecting the membrane-bound isoform production. Table 3 . Associations between infant DC-SIGNR promoter p-198 and intron 2 (int2)-180 variants and intrauterine (IU), intrapartum (IP) and postpartum (PP) mother-to-child HIV-1 transmission. Our genetic results, supported by expression assay in placenta, suggest the involvement of DC-SIGNR in MTCT of HIV-1. Homozygosity for the haplotype H1 was associated with IU transmission in the unadjusted regression analysis. However, the association disappeared after adjustment was made for the maternal factors presumably because of the small number of H1 homozygote infants analysed in each groups. H1 and H3 were the most frequent haplotypes observed in the study population and they share a cluster of mutations (Figure 1 ). Grouping haplotypes H1 and H3 increased the power of the study and permitted the identification of specific DC-SIGNR mutations associated with MTCT of HIV-1. Indeed, two mutations shared by haplotypes H1 and H3 were associated with vertical transmission of HIV-1. The int2-180A was associated with a 4-fold increased risk of IU and 6fold increased risk of IP after adjustment for the maternal factors. Although the p-198A variant was associated with IU transmission, the association disappeared after adjustment was made for the maternal viral load. Nevertheless, we showed that this mutation reduces DC-SIGNR transcriptional activity in vitro and produces lower level of DC-SIGNR transcripts in placental tissues in combination with the int2-180A variant. Since int2-180A is always transmitted with p-198A on the MTCT associated combined haplotypes H1/H3, whereas p-198A is carried on other nonassociated haplotypes (Figure 1) , we can speculate that the p-198A mutation alone may have a minor effect in vivo whereas in combination with the int2-180A variant, they both act to reduce the level of placental DC-SIGNR expression resulting in an increased risk of MTCT of HIV-1. The majority of IU transmission occurs during the last trimester of pregnancy (reviewed in [12] ). Full-term placenta samples were not available for the current study and the expression assays were performed on first-term placental tissues. A previous study looking at DC-SIGNR placental isoforms repertoire in full-term placenta samples demonstrated similar diversity of DC-SIGNR transcripts as in the first-term placental tissues studied herein [3] . However, since levels of DC-SIGNR expression have never been compared between the different terms of pregnancy, it is not known whether DC-SIGNR expression varies during the course of pregnancy. Nevertheless, it is reasonable to assume that the inter-individual differences in both DC-SIGNR isoform repertoire and transcript levels observed between the H1 and WT homozygous infants would be reflected throughout the pregnancy. To date, most studies have focused on the potential role of DC-SIGNR in trans infection of HIV-1 in vitro [2, 10] . However, the multiple mechanisms involved in trans infection and redundancy among C-type lectin functions make it difficult to determine the actual participation of DC-SIGNR in this mode of infection in vivo [13, 14] . The strong correlation we observed between MTCT of HIV-1 and DC-SIGNR genetic variants producing low levels of DC-SIGNR in the placenta suggested that mechanisms other than DC-SIGNR-mediated trans infection might operate during vertical transmission of HIV-1. For example, DC-SIGNR has also been shown to function as a HIV-1 antigen-capturing receptor [15] . Chan and colleagues recently demonstrated that DC-SIGNR transfected CHO cells diminish SARS-CoV titers by enhanced capture and degradation of the virus in a proteasome-dependent manner [4] . Since endothelial cells express MHC-I and II, degraded viral antigens could then be presented to immune cells to elicit an adaptive immune response [16, 17] . The HIV-1 coreceptor CCR5, but not CD4, is co-expressed with DC-SIGNR on placental and blood-brain barrier (BBB) endothelial cells [18, 19] . HIV-1 gp120 binding to CCR5 receptor on endothelial cells compromises BBB integrity and enhances monocytes adhesion and transmigration across the BBB [20, 21] . It is thus possible that reduced expression of DC-SIGNR, particularly the membranebound isoforms, on placental capillary endothelial cells might favour HIV-1 binding to CCR5 receptor, instead of DC-SIGNR receptor, facilitating the migration of maternal HIV-1-infected cells across the placental barrier resulting in IU transmission of HIV-1. The int2-180A variant contained in the H1 and H3 haplotypes was associated with IP transmission suggesting that DC-SIGNR also affect transmission of HIV-1 during delivery. Little is known about the mechanisms underlying transmission of HIV-1 during delivery. Passage through the birth canal could potentially expose infants through a mucosal portal entry (presumably ophthalmic, skin, or gastrointestinal), whereas placental insult during delivery (physical or inflammatory) may enhance transplacental passage of maternal HIV-1-infected cells into foetal circulation [22, 23] . Such process called microtransfusion has been proposed in regards to the results obtain in a Malawian cohort. Kweik and colleagues found a significant association between levels of maternal DNA in umbilical cord blood and IP transmission of HIV-1 suggesting that passage of maternal infected cells through the placenta is likely to occur during delivery [22] . Thus, in a similar fashion as suggested earlier for IU transmission, the relatively lower level of DC-SIGNR in the placenta of homozygous infants harbouring the int2-180A variant could promote HIV-1 binding to CCR5 receptor on endothelial cells affecting the placental barrier integrity and facilitating the passage of maternal infected cells in foetal circulation during delivery. Beside DC-SIGNR, other HIV-1 receptors are known to influence MTCT of HIV-1 (reviewed in [24] ). Genetic variants in CCR5 have been shown to influence vertical transmission of HIV-1. CCR5 promoter variants resulting in higher expression of the receptor were associated with increased risk of MTCT of HIV-1 among sub-Saharan Africans [25, 26] . The 32-pb deletion polymorphism in CCR5 has be shown to protect from vertical transmission of HIV-1 [27] , but this variant is virtually absent among African populations [28] . High copy numbers of CCL3L1, a potent HIV-1 suppressive ligand for CCR5, are associated with higher chemokine production and lower risk of MTCT of HIV-1 among South African infants [29, 30] . Mannose-binding lectin (MBL) is an innate immune receptor synthesised in the liver and secreted in the bloodstream in response to inflammation signal. MBL promotes pathogen elimination by opsonization and phagocytosis, and reduced expression of MBL resulting from polymorphism in coding and non-coding regions has been associated with an increased risk of MTCT of HIV-1 [31, 32] . In this study, we demonstrate for the first time, the potential functional impact of DC-SIGNR mutations on its expression in the placenta and in vertical transmission of HIV-1. We believe that the presence of DC-SIGNR at the placental endothelial cell surface may protect infants from HIV-1 infection by capturing virus and promoting its degradation/presentation. However, in placenta containing low levels of DC-SIGNR, HIV-1 would preferentially binds CCR5 on endothelial cells resulting in a loss of placental barrier integrity and enhanced passage of maternal HIV-1-infected cells in foetal circulation leading to MTCT of HIV-1. This mechanism may also apply to other vertically-transmitted pathogens known to interact with DC-SIGNR such as HIV-2, hepatitis C and dengue viruses and warrant further investigation. Associations between child DC-SIGNR exon 4 repeated region genotypes and mother-to-child HIV-1 transmission.CI, Confidence interval; N, number; NA; not applicable; OR, odds ratio a P-value as determined by the Chi-square test. b Comparison between genotype and all others. Found at: doi:10.1371/journal.pone.0007211.s003 (0.05 MB DOC) Figure S1 DC-SIGNR transcripts repertoire in placenta. Major RT-PCR products from RNA extract from 3 homozygous H1 and 3 homozygous WT placenta samples were purified, cloned and sequenced. Sequenced were analysed according to NCBI reference sequence NM_014257. CT; cytoplasmic tail, TM; trans-membrane domain; WT; wild-type Found at: doi:10.1371/journal.pone.0007211.s004 (0.11 MB DOC) Figure S2 Effect of DC-SIGNR promoter variant on transcriptional activity in luciferase reporter assay in vitro in transfected HeLa cells. Relative luciferase expression from pGL2-Basic, parental vector without promoter. Expression DC-SIGNR promoter constructs, spanning p-577C variant or p-323A variant were calculated relatively to this value. Data are presented in mean values6S.E.M of three independent experiments performed in triplicate. One-way ANOVA test followed by the Dunnett test for multiple comparison was used to compare the relative luciferase expression of the p-557C and p-323A variant reporters against the wild-type (WT) construct (not significant). 0 mg, 0,5 mg or 1 mg CMV-Tat vector was transfected with LTR-Luc as a positive control in these experiments.

Why do low levels of DC-SIGNR enhance Mother to Child Transmission of HIV-1?

Frontiers in antiviral therapy and immunotherapy https://doi.org/10.1002/cti2.1115 SHA: facbfdfa7189ca9ff83dc30e5d241ab22e962dbf Authors: Heaton, Steven M Date: 2020 DOI: 10.1002/cti2.1115 License: cc-by Abstract: nan Text: Globally, recent decades have witnessed a growing disjunction, a 'Valley of Death' 1,2 no less, between broadening strides in fundamental biomedical research and their incommensurate reach into the clinic. Plumbing work on research funding and development pipelines through recent changes in the structure of government funding, 2 new public and private joint ventures and specialist undergraduate and postgraduate courses now aim to incorporate pathways to translation at the earliest stages. Reflecting this shift, the number of biomedical research publications targeting 'translational' concepts has increased exponentially, up 1800% between 2003 and 2014 3 and continuing to rise rapidly up to the present day. Fuelled by the availability of new research technologies, as well as changing disease, cost and other pressing issues of our time, further growth in this exciting space will undoubtedly continue. Despite recent advances in the therapeutic control of immune function and viral infection, current therapies are often challenging to develop, expensive to deploy and readily select for resistance-conferring mutants. Shaped by the hostvirus immunological 'arms race' and tempered in the forge of deep time, the biodiversity of our world is increasingly being harnessed for new biotechnologies and therapeutics. Simultaneously, a shift towards host-oriented antiviral therapies is currently underway. In this Clinical & Translational Immunology Special Feature, I illustrate a strategic vision integrating these themes to create new, effective, economical and robust antiviral therapies and immunotherapies, with both the realities and the opportunities afforded to researchers working in our changing world squarely in mind. Opening this CTI Special Feature, I outline ways these issues may be solved by creatively leveraging the so-called 'strengths' of viruses. Viral RNA polymerisation and reverse transcription enable resistance to treatment by conferring extraordinary genetic diversity. However, these exact processes ultimately restrict viral infectivity by strongly limiting virus genome sizes and their incorporation of new information. I coin this evolutionary dilemma the 'information economy paradox'. Many viruses attempt to resolve this by manipulating multifunctional or multitasking host cell proteins (MMHPs), thereby maximising host subversion and viral infectivity at minimal informational cost. 4 I argue this exposes an 'Achilles Heel' that may be safely targeted via host-oriented therapies to impose devastating informational and fitness barriers on escape mutant selection. Furthermore, since MMHPs are often conserved targets within and between virus families, MMHP-targeting therapies may exhibit both robust and broadspectrum antiviral efficacy. Achieving this through drug repurposing will break the vicious cycle of escalating therapeutic development costs and trivial escape mutant selection, both quickly and in multiple places. I also discuss alternative posttranslational and RNA-based antiviral approaches, designer vaccines, immunotherapy and the emerging field of neo-virology. 4 I anticipate international efforts in these areas over the coming decade will enable the tapping of useful new biological functions and processes, methods for controlling infection, and the deployment of symbiotic or subclinical viruses in new therapies and biotechnologies that are so crucially needed. Upon infection, pathogens stimulate expression of numerous host inflammatory factors that support recruitment and activation of immune cells. On the flip side, this same process also causes immunopathology when prolonged or deregulated. 5 In their contribution to this Special Feature, Yoshinaga and Takeuchi review endogenous RNA-binding proteins (RBPs) that post-transcriptionally control expression of crucial inflammatory factors in various tissues and their potential therapeutic applications. 6 These RBPs include tristetraprolin and AUF1, which promote degradation of AU-rich element (ARE)-containing mRNA; members of the Roquin and Regnase families, which respectively promote or effect degradation of mRNAs harbouring stem-loop structures; and the increasingly apparent role of the RNA methylation machinery in controlling inflammatory mRNA stability. These activities take place in various subcellular compartments and are differentially regulated during infection. In this way, mRNA-destabilising RBPs constitute a 'brake' on the immune system, which may ultimately be toggled therapeutically. I anticipate continued efforts in this area will lead to new methods of regaining control over inflammation in autoimmunity, selectively enhancing immunity in immunotherapy, and modulating RNA synthesis and virus replication during infection. Another mRNA under post-transcriptional regulation by Regnase-1 and Roquin is Furin, which encodes a conserved proprotein convertase crucial in human health and disease. Furin, along with other PCSK family members, is widely implicated in immune regulation, cancer and the entry, maturation or release of a broad array of evolutionarily diverse viruses including human papillomavirus (HPV), influenza (IAV), Ebola (EboV), dengue (DenV) and human immunodeficiency virus (HIV). Here, Braun and Sauter review the roles of furin in these processes, as well as the history and future of furin-targeting therapeutics. 7 They also discuss their recent work revealing how two IFN-cinducible factors exhibit broad-spectrum inhibition of IAV, measles (MV), zika (ZikV) and HIV by suppressing furin activity. 8 Over the coming decade, I expect to see an ever-finer spatiotemporal resolution of host-oriented therapies to achieve safe, effective and broad-spectrum yet costeffective therapies for clinical use. The increasing abundance of affordable, sensitive, high-throughput genome sequencing technologies has led to a recent boom in metagenomics and the cataloguing of the microbiome of our world. The MinION nanopore sequencer is one of the latest innovations in this space, enabling direct sequencing in a miniature form factor with only minimal sample preparation and a consumer-grade laptop computer. Nakagawa and colleagues here report on their latest experiments using this system, further improving its performance for use in resource-poor contexts for meningitis diagnoses. 9 While direct sequencing of viral genomic RNA is challenging, this system was recently used to directly sequence an RNA virus genome (IAV) for the first time. 10 I anticipate further improvements in the performance of such devices over the coming decade will transform virus surveillance efforts, the importance of which was underscored by the recent EboV and novel coronavirus (nCoV / COVID-19) outbreaks, enabling rapid deployment of antiviral treatments that take resistance-conferring mutations into account. Decades of basic immunology research have provided a near-complete picture of the main armaments in the human antiviral arsenal. Nevertheless, this focus on mammalian defences and pathologies has sidelined examination of the types and roles of viruses and antiviral defences that exist throughout our biosphere. One case in point is the CRISPR/Cas antiviral immune system of prokaryotes, which is now repurposed as a revolutionary gene-editing biotechnology in plants and animals. 11 Another is the ancient lineage of nucleocytosolic large DNA viruses (NCLDVs), which are emerging human pathogens that possess enormous genomes of up to several megabases in size encoding hundreds of proteins with unique and unknown functions. 12 Moreover, hundreds of human-and avian-infective viruses such as IAV strain H5N1 are known, but recent efforts indicate the true number may be in the millions and many harbour zoonotic potential. 13 It is increasingly clear that host-virus interactions have generated truly vast yet poorly understood and untapped biodiversity. Closing this Special Feature, Watanabe and Kawaoka elaborate on neo-virology, an emerging field engaged in cataloguing and characterising this biodiversity through a global consortium. 14 I predict these efforts will unlock a vast wealth of currently unexplored biodiversity, leading to biotechnologies and treatments that leverage the host-virus interactions developed throughout evolution. When biomedical innovations fall into the 'Valley of Death', patients who are therefore not reached all too often fall with them. Being entrusted with the resources and expectation to conceive, deliver and communicate dividends to society is both cherished and eagerly pursued at every stage of our careers. Nevertheless, the road to research translation is winding and is built on a foundation of basic research. Supporting industry-academia collaboration and nurturing talent and skills in the Indo-Pacific region are two of the four pillars of the National Innovation and Science Agenda. 2 These frame Australia's Medical Research and Innovation Priorities, which include antimicrobial resistance, global health and health security, drug repurposing and translational research infrastructure, 15 capturing many of the key elements of this CTI Special Feature. Establishing durable international relationships that integrate diverse expertise is essential to delivering these outcomes. To this end, NHMRC has recently taken steps under the International Engagement Strategy 16 to increase cooperation with its counterparts overseas. These include the Japan Agency for Medical Research and Development (AMED), tasked with translating the biomedical research output of that country. Given the reciprocal efforts at accelerating bilateral engagement currently underway, 17 the prospects for new areas of international cooperation and mobility have never been more exciting nor urgent. With the above in mind, all contributions to this CTI Special Feature I have selected from research presented by fellow invitees to the 2018 Awaji International Forum on Infection and Immunity (AIFII) and 2017 Consortium of Biological Sciences (ConBio) conferences in Japan. Both Australia and Japan have strong traditions in immunology and related disciplines, and I predict that the quantity, quality and importance of our bilateral cooperation will accelerate rapidly over the short to medium term. By expanding and cooperatively leveraging our respective research strengths, our efforts may yet solve the many pressing disease, cost and other sustainability issues of our time.

What do Australia's Medical Research and Innovation Priorities include?

Serological Assays Based on Recombinant Viral Proteins for the Diagnosis of Arenavirus Hemorrhagic Fevers https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3497043/ SHA: f1d308db379b3c293bcfc8fe251c043fe8842358 Authors: Fukushi, Shuetsu; Tani, Hideki; Yoshikawa, Tomoki; Saijo, Masayuki; Morikawa, Shigeru Date: 2012-10-12 DOI: 10.3390/v4102097 License: cc-by Abstract: The family Arenaviridae, genus Arenavirus, consists of two phylogenetically independent groups: Old World (OW) and New World (NW) complexes. The Lassa and Lujo viruses in the OW complex and the Guanarito, Junin, Machupo, Sabia, and Chapare viruses in the NW complex cause viral hemorrhagic fever (VHF) in humans, leading to serious public health concerns. These viruses are also considered potential bioterrorism agents. Therefore, it is of great importance to detect these pathogens rapidly and specifically in order to minimize the risk and scale of arenavirus outbreaks. However, these arenaviruses are classified as BSL-4 pathogens, thus making it difficult to develop diagnostic techniques for these virus infections in institutes without BSL-4 facilities. To overcome these difficulties, antibody detection systems in the form of an enzyme-linked immunosorbent assay (ELISA) and an indirect immunofluorescence assay were developed using recombinant nucleoproteins (rNPs) derived from these viruses. Furthermore, several antigen-detection assays were developed. For example, novel monoclonal antibodies (mAbs) to the rNPs of Lassa and Junin viruses were generated. Sandwich antigen-capture (Ag-capture) ELISAs using these mAbs as capture antibodies were developed and confirmed to be sensitive and specific for detecting the respective arenavirus NPs. These rNP-based assays were proposed to be useful not only for an etiological diagnosis of VHFs, but also for seroepidemiological studies on VHFs. We recently developed arenavirus neutralization assays using vesicular stomatitis virus (VSV)-based pseudotypes bearing arenavirus recombinant glycoproteins. The goal of this article is to review the recent advances in developing laboratory diagnostic assays based on recombinant viral proteins for the diagnosis of VHFs and epidemiological studies on the VHFs caused by arenaviruses. Text: The virus family Arenaviridae consists of only one genus, but most viruses within this genus can be divided into two different groups: the Old World arenaviruses and the New World arenaviruses (also known as the Tacaribe complex) [1, 2] . The differences between the two groups have been established through the use of serological assays. Most of the arenaviruses cause persistent infection in rodents without any symptoms, and humans acquire a variety of diseases when zoonotically infected. Lymphocytic choriomeningitis virus (LCMV) is the only arenavirus to exhibit a worldwide distribution, and causes illnesses such as meningitis [3, 4] . Congenital LCMV infections have also been reported [4, 5] . Most importantly, viral hemorrhagic fever (VHF) can be caused by several arenaviruses. Lassa fever, caused by the Lassa virus (LASV), an Old World arenavirus, is one of the most devastating VHFs in humans [6] . Hemorrhaging and organ failure occur in a subset of patients infected with this virus, and it is associated with high mortality. Many cases of Lassa fever occur in Western Africa in countries such as Guinea, Sierra Leone, and Nigeria [7] [8] [9] [10] [11] [12] [13] . Tacaribe complex lineage B of the New World arenaviruses consists of the Junin virus (JUNV), Guanarito virus (GUNV), Sabia virus (SABV) and Machupo virus (MACV), the etiological agents of Argentine, Venezuelan, Brazilian, and Bolivian hemorrhagic fevers, respectively [14, 15] . Although genetically distinct from one another, they appear to produce similar symptoms, accompanied by hemorrhaging in humans [14, 15] . These pathogenic New World arenavirus species are closely associated with a specific rodent species [6] . Humans are usually infected with pathogenic arenaviruses through direct contact with tissue or blood, or after inhaling aerosolized particles from urine, feces, and saliva of infected rodents. After an incubation period of 1-3 weeks, infected individuals abruptly develop fever, retrosternal pain, sore throat, back pain, cough, abdominal pain, vomiting, diarrhea, conjunctivitis, facial swelling, proteinuria, and mucosal bleeding. Neurological problems have also been described, including hearing loss, tremors, and encephalitis. Because the symptoms of pathogenic arenavirus-related illness are varied and nonspecific, the clinical diagnosis is often difficult [14, 16] . Human-to-human transmission may occur via mucosal or cutaneous contact, or through nosocomial contamination [14, 16] . These viruses are also considered to be potential bioterrorism agents [2] . A number of arenavirus species have been recently discovered as a result of both rodent surveys and disease outbreaks [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] . A novel pathogenic New World arenavirus, Chapare virus (CHPV), has been isolated from a fatal case of VHF in Bolivia [20] . In addition, five cases of VHF have been reported in South Africa, and a novel arenavirus, named Lujo virus, was isolated from a patient [17] . The Lujo virus is most distantly related to the other Old World arenaviruses [17] . To date, there is no information concerning the vertebrate host for the Chapare and Lujo viruses. There is some evidence of endemicity of the Lassa virus in neighboring countries [27, 28] . However, as the magnitude of international trade and travel is continuously increasing, and the perturbation of the environment (due either to human activity or natural ecological changes) may result in behavioral changes of reservoir rodents, highly pathogenic arenaviruses could be introduced to virus-free countries from endemic areas. In fact, more than twenty cases of Lassa fever have been reported outside of the endemic region in areas such as the USA, Canada, Europe, and Japan [29] [30] [31] [32] [33] . It is of great importance to detect these pathogens rapidly and specifically in order to minimize the risk and scale of outbreaks of VHFs caused by arenaviruses. However, these arenaviruses are classified as biosafety level (BSL)-4 pathogens, making it difficult to develop diagnostic techniques for these virus infections in laboratories without BSL-4 facilities. To overcome these difficulties, we have established recombinant viral nucleoproteins (rNPs)-based serological assays, such as IgG-enzyme-linked immunosorbent assay (ELISA), indirect immunofluorescence assay (IFA), and antigen (Ag)-capture ELISA for the diagnosis of VHFs caused by highly pathogenic arenaviruses. Furthermore, virus neutralization assays using pseudotype virus-bearing arenavirus GPs have been developed. In this review, we describe the usefulness of such recombinant protein-based diagnostic assays for diagnosing VHFs caused by arenaviruses. In outbreaks of VHFs, infections are confirmed by various laboratory diagnostic methods. Virus detection is performed by virus isolation, reverse transcription-polymerase chain reaction (RT-PCR), and antigen-capture ELISA. It has been shown that monoclonal antibody panels against pathogenic arenaviruses are useful for detecting viral antigens on the virus-infected cells as well as for investigating of antigenic relationships of arenaviruses [34] [35] [36] . Detection of the virus genome is suitable for a rapid and sensitive diagnosis of VHF patients in the early stage of illness, and extensive reviews of such RT-PCR assays have been described [37, 38] . More recently, progress in the RT-PCR method covering genetic variations of the hemorrhagic fever viruses (HFVs) [39, 40] and a multiplexed oligonucleotide microarray for the differential diagnosis of VHFs have also been reported [41] . On the other hand, antibodies against these viruses can be detected by the indirect immunofluorescence assay (IFA), or IgG-and IgM-ELISA. An IFA detects the antibody in the serum, which is able to bind to the fixed monolayer of the virus-infected cells. Although the interpretation of immunofluorescence results requires experience, the assay has advantages over other methods, since each virus generates a characteristic fluorescence pattern that adds specificity to the assay compared to a simple ELISA readout. A serological diagnosis by the detection of specific IgM and IgG antibodies to the HFVs must be sensitive, specific and reliable, because a misdiagnosis can lead to panic in the general population. An IgM-specific ELISA is suitable for detecting recent infection, but the relevance of IgM testing for acute VHF depends on the virus and the duration of illness; specific IgM is not often present in the very early stage of illness, and patients who die of VHF often fail to seroconvert at all. An IgG-specific ELISA is efficacious, not only in the diagnosis of a large number of VHF cases, especially during convalescence, but also for epidemiological studies in the endemic regions. The detailed methods used for the IFA and IgG-and IgM-ELISAs for the diagnosis of VHF using authentic virus-antigens have been described in detail [42] [43] [44] [45] . Arenaviruses have a bisegmented, negative-sense, single stranded RNA genome with a unique ambisense coding strategy that produces just four known proteins: a glycoprotein, a nucleoprotein (NP), a matrix protein (Z), and a polymerase (L) [46] . Of these proteins, the NP is the most abundant in virus-infected cells. Recombinant protein technology could meet the demand for a simple and reliable VHF test system, and recombinant NP (rNP) has been shown to be useful for serological surveys of IgM-and IgG antibodies against arenaviruses [47] [48] [49] [50] . Recombinant baculoviruses that express the full-length rNP of arenaviruses have been generated [48, 50, 51] . The method used for the purification of arenavirus rNP from insect Tn5 cells infected with recombinant baculoviruses is effective and simple compared to those for Ebola, Marburg, and Crimean-Congo hemorrhagic fever virus rNPs [51] [52] [53] [54] [55] . Most of the arenavirus rNPs expressed in insect cells using the recombinant baculoviruses are crystallized [56] and are solubilized in PBS containing 8M urea. Since the majority of Tn5 cellular proteins are solubilized in PBS containing 2M urea, the arenavirus rNPs in the insoluble fraction in PBS containing 2M urea can be solubilized by sonication in PBS containing 8M urea. After a simple centrifugation of the lysates in PBS containing 8M urea, the supernatant fractions can be used as purified rNP antigens without further purification steps [51] . The control antigen is produced from Tn5 cells infected with baculovirus lacking the polyhedrin gene (ΔP) in the same manner as the arenavirus rNPs ( Figure 1 ). Purified rNPs. The expression and purification efficiency of arenavirus rNP were analyzed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) after staining the gels with Coomassie blue. Purified NP antigens with approximate molecular weights of 62 kDa from Luna, LCM, Lassa, Lujo, Junin, Machupo, Guanarito, Sabia, and Chapare viruses and the purified negative control antigen (ΔP) are shown. As described above, recombinant baculoviruses allow the delivery of rNP antigens without using infectious live arenaviruses. An ELISA plate coated with the predetermined optimal quantity of purified rNPs (approximately 100 ng/well) is used for the IgG-antibody detection assay. An advantage of using recombinant rNP for the IgG-ELISA is that it enables a direct comparison of antibody cross-reactivity among arenavirus rNPs, since antigen preparations of all arenavirus rNPs tested are performed using the same method [51] . Rabbit anti-sera raised against LCMV-rNP and LASV-rNP show cross-reactivity to LASV-rNP and LCMV-rNP, respectively, indicating that rabbit antibodies against rNPs of Old World arenaviruses cross-react with rNPs of other Old World arenaviruses (Table 1 ) [51] . Similarly, rabbit anti-sera generated against JUNV-NP show cross-reactivity to the LASV-rNP and LCMV-rNP, although the reaction is weak. However, rabbit anti-sera against LASV-NP and LCMV-NP show a negative reaction to the JUNV-rNP (Table 1 ) [51] , indicating that rabbit antibodies against JUNV (a pathogenic New World arenavirus) NP might cross-react with the Old World arenavirus NP, whereas antibodies against Old World arenavirus NPs may not be able to react with pathogenic New World arenavirus NPs. The rNP-based IgG-ELISA has also been used for the characterization of a mouse monoclonal antibody (MAb). Nakauchi et al. [50] have investigated the cross-reactivity of MAbs against JUNV rNP to pathogenic New World arenavirus rNPs, as well as LASV rNP. MAb C11-12 reacts at the same level with the rNPs of all of the pathogenic New World arenaviruses, including JUNV, GTOV, MACV, SABV, and CHPV, indicating that this MAb recognizes an epitope conserved among pathogenic New World arenaviruses. Another MAb, C6-9, reacts specifically with the rNP of JUNV, but does not react with those of the other pathogenic New World arenaviruses [50] . This indicates that MAb C6-9 recognizes a JUNV-specific epitope. None of these MAbs reacts with the rNP of the human pathogenic Old World arenavirus LASV. Thus, the MAb C11-12 is considered to be a broadly reactive MAb against New World arenaviruses, whereas MAb C6-9 is JUNV-specific. These findings have been confirmed by detailed epitope analyses using peptide mapping [50] . Similarly, the cross-reactivity of MAbs against LASV rNP has been analyzed [51] . MAb 4A5 cross-reacts with the Mopeia virus (MOPV) but not with the LCMV rNP. MAb 6C11 cross-reacts with LCMV rNP, while MAb 2-11 does not cross-react with LCMV rNP [51] . Table 1 . Anti-serum reactivity for rNPs of different arenaviruses in IgG ELISAs. Reactivity for rNP from LASV LCMV JUNV anti-LASV NP It is important to evaluate whether rNP-based ELISA is useful for the diagnosis of human VHF cases. The specificity of the LASV-rNP-based IgG ELISA has been confirmed by using sera obtained from Lassa fever patients [51] . The Lassa fever patients' sera show a highly positive reaction in the LASV-rNP-based IgG-ELISA, but sera from patients with Argentine hemorrhagic fever (AHF), which is caused by JUNV, do not. The serum from an AHF patient showed a highly positive reaction in the JUNV-rNP-based IgG-ELISA [49] . In addition, it was shown that, using sera obtained from AHF cases, the results of the JUNV rNP-based IgG ELISA correlate well with an authentic JUNV antigen-based IgG ELISA [49] . An IgM-capture ELISA using purified LASV-rNP as an antigen has been developed in the same way as in previous reports [54, 57] and detects an LASV-IgM antibody [58] . In addition, immunoblot assays based on N-terminally truncated LASV rNP have been developed for detecting IgG and IgM antibodies against LASV. These methods may provide a rapid and simple Lassa fever test for use under field conditions [47] . An IFA using virus-infected cells is a common antibody test for VHF viruses [59] [60] [61] [62] [63] . To avoid the use of highly pathogenic viruses for the antigen preparation, mammalian cells expressing recombinant rNP have been developed [51, 57, [64] [65] [66] [67] [68] . Lassa virus NP antigen for IFA can be prepared simply as described [51] . Briefly, the procedure involves (1) transfecting HeLa cells with a mammalian cell expression vector inserted with the cloned NP cDNA; (2) expanding the stable NP-expressing cells by antibiotic selection; (3) mixing the rNP-expressing cells with un-transfected HeLa cells (at a ratio of 1:1); (4) spotting the cell mixtures onto glass slides, then drying and fixing them in acetone. In the IFA specific for LASV-NP, antibody positive sera show characteristic granular staining patterns in the cytoplasm (Figure 2 ) [69] , thus making it easy to distinguish positive from negative samples. The specificity of the assay has also been confirmed by using sera obtained from Lassa fever patients [51] . In addition, an IFA using JUNV rNP-expressing HeLa cells has been developed to detect antibodies against JUNV, and the assay has been evaluated by using AHF patients' sera [70] . The LASV-rNP-based antibody detection systems such as ELISA and IFA are suggested to be useful not only for the diagnosis of Lassa fever, but also for seroepidemiological studies of LASV infection. In our preliminary study, approximately 15% of the sera collected from 334 Ghanaians and less than 3% of 280 Zambians showed positive reactions in the LASV-rNP-based IgG ELISA [58] . These results are in agreement with the fact that Lassa fever is endemic to the West African region, including Ghana, but less in the East African region. For the diagnosis of many viral infections, PCR assays have been shown to have an excellent analytical sensitivity, but the established techniques are limited by their requirement for expensive equipment and technical expertise. Moreover, the high degree of genetic variability of the RNA viruses, including arenavirus and bunyavirus, poses difficulties in selecting primers for RT-PCR assays that can detect all strains of the virus. Since the sensitivity of the Ag-capture ELISA is comparable to that of RT-PCR for several virus-mediated infectious diseases, including Lassa fever and filovirus hemorrhagic fever [51, [71] [72] [73] , the Ag-capture ELISA is a sophisticated approach that can be used for the diagnosis of viral infections. Ag-capture ELISAs detecting viral NP in viremic sera have been widely applied to detect various viruses, since they are the most abundant viral antigens and have highly conserved amino acid sequences [50, 51, 54, 71, 72, 74, 75] . Polyclonal anti-sera or a mixture of MAbs present in the ascetic fluids from animals immunized for HFVs have been used for capture-antibodies in the Ag-capture ELISA [36, [76] [77] [78] [79] . MAbs recognizing conserved epitopes of the rNP are also used as capture antibodies since they have a high specificity for the antigens, and an identification of the epitopes of these MAbs is of crucial importance for the assessment of the specificity and cross-reactivity of the assay system [50, 51, 53, 54, 71, 75] . In order to develop a sensitive diagnostic test for Lassa fever and AHF, rNPs of LASV and JUNV (see above) have been prepared, and newly established MAbs against them have been characterized and used for Ag-capture ELISAs [50, 51] . The Ag-capture ELISA using MAb 4A5 has been confirmed to be useful in the detection of authentic LASV antigen in sera serially collected from hamsters infected with LASV [51] . The sensitivity of the MAb 4A5-based Ag-capture ELISA was similar to that of conventional RT-PCR, suggesting that the Ag-capture ELISA can be efficiently used in the diagnosis of Lassa fever [51] . Therefore, the MAb 4A5-based Ag-capture ELISA is considered to be useful in the diagnosis of Lassa fever. Also, by using MAbs raised against the rNP of JUNV, Ag-capture ELISAs specific for JUNV and broadly reactive to human pathogenic New World arenaviruses have been developed [50] . The Ag-capture ELISA using MAb E4-2 and C11-12 detected the Ags of all of the pathogenic New World arenaviruses tested, including JUNV. On the other hand, the Ag-capture ELISA using MAb C6-9 detects only the JUNV Ag. Considering that the symptoms of JUNV infection in humans are indistinguishable from those due to other pathogenic New World arenaviruses, the Ag capture ELISA using MAb C6-9 may be a useful diagnostic tool, especially for AHF [50] . The virus neutralization assay is accepted as the "gold standard" serodiagnostic assay to quantify the antibody response to infection and vaccination of a wide variety of viruses associated with human diseases [80] [81] [82] [83] [84] [85] [86] . The presence of neutralizing antibodies is a reliable indicator of protective immunity against VHF [87] [88] [89] . The most direct method for detection of neutralizing antibodies against HFVs is by plaque reduction neutralization tests using infectious viruses. However, because of the high pathogenicity of HFVs to humans and the strict regulation of select agents, only a limited number of laboratories are able to perform such neutralization tests. For many HFVs, replication-incompetent pseudotyped virus particles bearing viral envelope protein (GP) have been shown to mimic the respective HFV infections, thus, neutralization assays using the pseudotypes may be advantageous in some laboratory settings for the detection of antibodies to HFVs without the need for heightened biocontainment requirements. The VSV-based vector has already been used to generate replication-competent recombinant VSVs to study of the role of GPs of various viruses [90] [91] [92] . Recent advances in producing pseudotype virus particles have enabled the investigation of the virus cell entry, viral tropism, and effect of entry inhibitors, as well as measurement of the neutralization titers, by using human immunodeficiency virus-, feline immunodeficiency virus-, murine leukemia virus-, or VSV-based vectors [86, [93] [94] [95] [96] [97] [98] [99] [100] [101] [102] [103] . Pseudotypes based on VSV have advantages compared with other pseudotypes based on retroviruses for the following reasons. First, the pseudotype virus titer obtained with the VSV system is generally higher than that of the pseudotyped retrovirus system [104] . Second, the infection of target cells with a VSV pseudotype can be readily detected as green fluorescent protein (GFP)-positive cells at 7-16 h post-infection because of the high level of GFP expression in the VSV system [104, 105] . In contrast, the time required for infection in the pseudotyped retrovirus system is 48 h [106, 107] , which is similar to the time required for infectious viruses to replicate to a level that results in plaque-forming or cytopathic effects in infected cells. A high-throughput assay for determining neutralizing antibody titers using VSV pseudotypes expressing secreted alkaline phosphatase [108, 109] or luciferase ( Figure 3 ) has also been developed. We have recently developed a VSV-based pseudotype bearing Lassa virus GP (VSV-LAS-GP) for the detection of neutralizing antibodies in the sera obtained from a Lassa fever patient. An example of the LASV neutralization assay using the VSV pseudotype is shown (Figure 4 ). In the presence of serum from Lassa fever patients, the number of GFP-positive cells (infectivity of VSV-LAS-GP) is significantly reduced compared with the number in the absence of the patient's serum ( Figure 4A ). The control VSV pseudotype bearing VSV GP (VSV-VSV-G) is not neutralized by any sera. When the cut-off serum dilution is set at 50% inhibition of infectivity compared with the infectivity in the absence of the test serum, the neutralization titer of this patient's serum for VSV-LAS-GP is calculated to be 75 ( Figure 4B ). Likewise, a VSV-based pseudotype bearing the Junin virus GP has been developed for the detection of neutralizing antibodies from AHF patients' sera. The accuracy of the results of VSV-based neutralization assays has been confirmed by comparison with the results of the neutralization assay using live Junin virus [70] . The Lujo virus is a new member of the hemorrhagic fever-associated arenavirus family from Zambia and southern Africa, and the virus is classified as a BSL-4 pathogen [17] . The genome sequence analysis of the Lujo virus suggests that the virus is genetically distinct from previously characterized arenaviruses. In order to study the infectivity of this newly identified arenavirus, we have recently developed a luciferase-expressing VSV pseudotype bearing Lujo virus GPC (VSV-Lujo-GP). As shown in Figure 3 , infection with VSV-Lujo-GPC is specifically neutralized by rabbit anti-Lujo GPC serum. Thus, the VSV-Lujo-GP may be a useful tool not only for determining the neutralizing antibody titer within the serum, but also for exploring yet-to-be-defined cellular receptor(s) for Lujo virus infection or for screening inhibitors of the Lujo virus GP-mediated cell entry. Hemorrhagic fever outbreaks caused by pathogenic arenaviruses result in high fatality rates. A rapid and accurate diagnosis is a critical first step in any outbreak. Serologic diagnostic methods for VHFs most often employ an ELISA, IFA, and/or virus neutralization assay. Diagnostic methods using recombinant viral proteins have been developed and their utilities for diagnosing of VHF have been reviewed. IgG-and IgM-ELISAs and IFAs using rNPs as antigens are useful for the detection of antibodies induced in the patients' sera. These methods are also useful for seroepidemiological surveys for HFVs. Ag-capture ELISAs using MAbs to the arenavirus rNPs are specific for the virus species or can be broadly reactive for New World arenaviruses, depending on the MAb used. Furthermore, the VSV-based pseudotype system provides a safe and rapid tool for measuring virus neutralizing antibody titers, as well as a model to analyze the entry of the respective arenavirus in susceptible cells without using live arenaviruses. Recent discoveries of novel arenavirus species [17, 26, 110] and their potential to evolve predominantly via host switching, rather than with their hosts [110, 111] , suggest that an unknown pathogenic arenavirus may emerge in the future, and that the diagnostic methods for VHF caused by arenaviruses should thus be further developed and improved.

What diagnostic test has been show to have excellent sensitivity in detecting viral infections?

RNAi Therapeutic Platforms for Lung Diseases https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3816685/ Fujita, Yu; Takeshita, Fumitaka; Kuwano, Kazuyoshi; Ochiya, Takahiro 2013-02-06 DOI:10.3390/ph6020223 License:cc-by Abstract: RNA interference (RNAi) is rapidly becoming an important method for analyzing gene functions in many eukaryotes and holds promise for the development of therapeutic gene silencing. The induction of RNAi relies on small silencing RNAs, which affect specific messenger RNA (mRNA) degradation. Two types of small RNA molecules, i.e. small interfering RNAs (siRNAs) and microRNAs (miRNAs), are central to RNAi. Drug discovery studies and novel treatments of siRNAs are currently targeting a wide range of diseases, including various viral infections and cancers. Lung diseases in general are attractive targets for siRNA therapeutics because of their lethality and prevalence. In addition, the lung is anatomically accessible to therapeutic agents via the intrapulmonary route. Recently, increasing evidence indicates that miRNAs play an important role in lung abnormalities, such as inflammation and oncogenesis. Therefore, miRNAs are being targeted for therapeutic purposes. In this review, we present strategies for RNAi delivery and discuss the current state-of-the-art RNAi-based therapeutics for various lung diseases. Text: traditional surgery, Bivas-Benita et al. reported that no mortality occurred as a result of the use of the endotracheal technique. Endotracheal applications are currently being used by many practitioners in the pulmonary field [22, 34] ; this is useful for studying pulmonary drug delivery in mice. However, the approach is more complex in humans because an artificial path for the delivery of drugs into the lungs is used. Therefore, the method is being used in animal models to test and evaluate its reliability for possible clinical applications. Intratracheal route: under anesthesia, the trachea is exposed surgically, and a tube or needle is inserted through an incision made between the tracheal rings. Complications, such as vascular injury and air leakage, are possible due to the tracheotomy. (b) Endotracheal route: siRNAs are sprayed directly from the mouth into the lungs using a MicroSprayer ® aerolizer (Penn-Century, Philadelphia, PA, USA) and a laryngoscope. It is important to maintain a clear view of the trachea during the procedure. Intranasal delivery is another common method of pulmonary drug application in animal studies. In many studies, in vivo success has been demonstrated in delivering siRNAs to the lungs intranasally [22, 35, 36 ]. An experimental setup of intranasal delivery by spray or droplet is simple and painless for the animal. Although the success in delivering siRNAs intranasally in rodents cannot be completely extrapolated to human use because of the significant differences in lung anatomy [37] , this approach has potential for the clinical application of siRNAs. Phase II clinical trials have been initiated for the treatment of respiratory syncytial virus (RSV) infection, making use of intranasal application of naked chemically modified siRNA molecules that target viral gene products [17, 38] (see Section 3.1.1. for details). Intranasal entry has long been used to administer small molecules, such as proteins, for systemic delivery. Because the nasal mucosa is highly vascularized, delivery of a thin epithelium of medication across the surface area can result in rapid absorption of the medication into the blood. Therefore, siRNAs administered intranasally might be deposited in the nose, and some of them may be unable to reach the lower respiratory tract. In fact, it has been reported that intranasal application of unformulated siRNAs resulted in lower delivery efficiency and homogeneous pulmonary distribution than that achieved with intratracheal application [31] . The intranasal method is suitable for some lung diseases, such as upper respiratory infection by RSV, and it also has potential for systemic delivery rather than pulmonary delivery of siRNAs. Therefore, it is important to consider the route of administration in animal studies when assessing the delivery and therapeutic efficacy of a formulation for pulmonary delivery. Careful choice of efficient delivery in response to the condition of lung diseases is necessary. The use of aerosols to deliver medication to the lungs has a long history. Administration by inhalation is a popular and non-invasive method of delivering agents into the lungs. There are several inhalation devices available for the delivery of drugs into the lungs. Metered dose inhalers (MDIs) and dry powder inhalers (DPIs) are the most common modes of inhaled delivery. MDIs are the most commonly used inhalers for several lung diseases, such as asthma, bronchitis, and chronic obstructive pulmonary disease (COPD), and a spacer is an external device that is attached to an MDI to allow for better drug delivery by enhanced actuation and inhalation coordination. For most MDIs, the propellant is one or more gases called chlorofluorocarbons (CFCs). Although CFCs in drugs are safe for patients to inhale, they are harmful to the environment. Therefore, further development of inhalable siRNAs may not be the best way forward. DPIs are devices that deliver medication to the lungs in the form of dry powder. The use of DPIs has already shown promise for the in vivo delivery of therapeutic macromolecules such as insulin [39] and low-molecular-weight heparin [40] ; thus, it could be a better device for delivering siRNAs to the lungs. The advantages of DPIs are improved stability and sterility of biomolecules over liquid aerosols and propellant-free formation. Although drugs are commonly delivered to the lungs by inhalation, most in vivo studies using siRNAs have relied on intratracheal or intranasal delivery. The reason could be the difficulty in formulating inhalable siRNAs and maintaining the stability during the delivery process. A suitable carrier is also needed to protect nucleic acids from degradation due to shear force and increased temperature during the drying process. The use of spray-drying as a technique for engineering dry powder formulations of siRNA nanoparticles, which might enable the local delivery of biologically active siRNA directly to the lung tissue, has been demonstrated [24, 25] . In the future, the technique is desirable to estimate the in vivo study on siRNA therapy for inhalation. In the long term, we anticipate that there will be more sophisticated devices for clinical use and that those currently being developed will be more suitable. There are two main barriers to efficient pulmonary siRNA delivery to the cells of the lung. The first is the complex, branched anatomy of the lungs and biomechanical barriers, such as the mucus layer covering the airway cells [41, 42] (Figure 2) . A remarkable feature of the respiratory tract is its high degree of branching. Airway consists of respiratory bronchioles, alveolar ducts, and alveolar sacs. All of these structures bear alveoli, the tiny air sacs in which the gas exchange takes place. It is generally acknowledged that the critical factor for efficient siRNA delivery depends on the properties of RNAi drug particles in terms of size, charge, shape, velocity and density. For efficient pulmonary siRNA delivery, the particles must be deposited in the lower respiratory tract. Deposition in the airway is affected by the particle size and patient's pulmonary function. A particle size between 1-5 μm is found to be the most appropriate for deposition at the lower respiratory tract [23] . In addition, the presence of mucus and surfactant proteins, the mucociliary clearance actions, and phagocytosis by macrophages present major barriers to targeted pulmonary delivery. Therefore, delivery systems usually require delivery vectors, and these vectors need to be designed in order to maximize the siRNA deposition to the diseased area of the respiratory tract. Besides, the extracellular barriers to siRNA delivery also depend on physiological features of the respiratory tract, which may change with the disease stage and characteristics of the patient. At the active stage of lung disease, the physiological conditions of the airways might change and have significant impact on the efficiency of the pulmonary delivery system. During infection, inflammation, and allergic reaction, there is an increase in mucus secretion along with the impaired mucociliary clearance [43] . Moreover, asthma and COPD are both chronic inflammatory conditions of the lung associated with structural "remodeling" that is inappropriate to the maintenance of normal lung function [44] . The airway wall thickness, the high viscosity, and the composition of the mucus layer might be altered in patients who have inflammatory lung diseases. Figure 2 . Extracellular barriers to pulmonary siRNA delivery. The anatomical feature of the respiratory tract is its high degree of branching. The mucus lines the respiratory epithelium from the nasal cavity to the terminal bronchioles. The deposited particles on the ciliated epithelial cells are rapidly cleared by the mucociliary clearance actions. Mucus and mucociliary clearance of mucus-trapped particles is a pulmonary defense mechanism as a physiological barrier. In the alveolar, clara cells and type II alveolar cells secrete on the surface of the alveolar epithelium, forming a thin layer of pulmonary surfactants. The surfactants act as the main barrier for siRNA delivery because they reduce the transfection efficiency. In addition, the macrophages located in the alveoli rapidly engulf the foreign particles by phagocytosis. The particles taken up into the macrophages are subsequently degraded inside the cells. These factors present major barriers to targeted pulmonary delivery. The second is the airway cell membrance and its intracellular barriers ( Figure 3 ). For efficient gene silencing in the lungs, siRNAs must be delivered to their site of action, be stable, enter the target cells, and be present in the cytoplasm at sufficient concentration. Once the siRNAs reach the target cells, they must be trafficked into the cytoplasm and taken up by Argonaute (Ago)2/RNA-induced silencing complex (RISC), which degrades mRNAs and, subsequently, suppresses the sequence-specific gene expression. For efficient endocytosis to occur, particles should be under 150 nm in size. Particles within this size range could also avoid macrophage uptake and delayed lung clearance [45] . The physicochemical properties of siRNAs also play a significant role in crossing the biological membrane. Despite their small size, the negative charge and chemical degradability of siRNA molecules prevent them from readily crossing biological membranes. Therefore, efficient siRNA delivery approaches need to overcome this limitation by facilitating cellular uptake. One of the main functions of a delivery vector is to facilitate the cellular uptake of siRNAs [46] . The electrostatic complexation of siRNA molecules with cationic lipids and polymers helps to mask their net negative charge. The positively charged siRNA carrier complex interacts with anionic proteoglycans on the cell membrance, forms an endocytic vesicle, and enters the cells by endocytosis [47] . After cellular internalization, the siRNA carrier complex in endocytic vesicles is transported along microtubules to lysosomes that are co-localized with the microtubule-organizing center. To avoid lysosomal degradation, siRNAs must escape from the endosome into the cytoplasm, where they can associate with the RNAi machinery. Endosomal escape is a major barrier for efficient siRNA delivery [48, 49] . The endosomal entrapment and lysosomal degradation of siRNA and carriers contribute to the low transfection efficiency and is a major difficulty for delivery vectors. An ideal delivery agent should protect siRNAs from enzymatic degradation, facilitate cellular uptake, and promote endosomal escape inside the cells with negligible toxicity. Multiple approaches for the delivery of siRNAs have been reported, ranging from the relatively simple direct administration of saline-formulated siRNAs to lipid-based and polymer-based nanoparticle approaches and siRNA conjugation and complexation approaches [50] . The negative charge and chemical degradability of siRNAs under physiologically relevant conditions make its delivery a major challenge. Accordingly, the delivery of siRNAs usually requires a vector or carriers for their transfection into the target cells. In general, both viral and non-viral vectors are being assessed for siRNA delivery to lung cells. Some viral vectors, such as retroviruses and adenoviruses, have been demonstrated to mediate gene silencing in an in vitro lung model [51] and to induce RNAi in a range of animal tissues [52] . Recently, Guo et al. showed that lentivirus-mediated siRNA was used to specifically knock down the expression of nuclear protein 1 (NUPR1) in vivo, which resulted in inhibited tumor growth [53] . However, viral-based delivery has several disadvantages. The immune response to viruses not only impedes gene delivery but also has the potential to cause severe complications [54] . Recent well-documented cases, such as the death of Jesse Gelsinger due to complications related with an adenoviral delivery vector, highlight this problem [55] . In addition, some viral vectors may insert their genome at random positions in the host chromosome, which eventually restrict the gene function [56] . . Intracellular barriers to pulmonary siRNA delivery. Barriers to cellular internalization are dependent on the surface properties of siRNA and carriers (e.g., charge and size). After siRNAs are successfully taken into the target cells by endocytosis, the main barriers for delivering siRNAs to its site of action are the endosomal entrapment and lysosomal degradation of siRNA and carriers. To direct target-gene silencing, the siRNAs need to escape from the endosome into the cytoplasm, where they associate with the Ago2/RNA-induced silencing complex (RISC) to direct the cleavage of mRNAs bearing complementary binding sites. As an alternative to viral vectors, non-viral vectors, including lipid and polymer-based vectors, have been generally used for the delivery of siRNAs to the lungs due to their reduced toxicity [57] . Ongoing research into the transfection of primary cells and whole organisms with siRNA using non-viral transfection agents has produced some promising results. Lipid-based delivery vectors are successfully used to deliver siRNA in vitro and in vivo [58] . Cationic lipids are composed of positively charged head, a linker and hydrophobic. In general, lipid-based complexes are easy to formulate and good transfection efficacy is achieved due to interaction with negative charged cell membrance. Many commercial siRNA transfection agents are lipid-based delivery system, some of which are also employed for pulmonary delivery-DharmFECT [30] , Oligofectamine [59] , Lipofectamine [60] and TransIT-TKO [35] . Similarly, cationic polymers have also been assessed for siRNA delivery to lung cells. Cationic polymer polyethylenimine (PEI) is widely used for siRNA delivery [13, 61] . PEI is considered as the gold standard for in vitro gene delivery and its transfection efficiency depends on the molecular weight and degree of branching. On the other hand, lipid-based vectors can also induce toxicity and non-specific activation of inflammatory cytokine and interferon responses [62, 63] . Although polymer-based vectors elicit a relatively less strong immune response than lipid-based vectors, effective siRNA delivery to a local area in lung diseases requires more attention to the development of non-toxic delivery vectors. An important point for siRNA-mediated inhibition of gene expression is whether the observed effects are specific rather than due to off-target effects and free from potential interferon responses [64, 65] . Interestingly, some studies have shown that it was possible to administer "naked siRNAs" to mice and down-regulate an endogenous or exogenous target without inducing an interferon response [66] . The term "naked siRNAs" refers to the delivery of siRNAs without any delivery vectors. Naked siRNAs are degraded by serum endonucleases and are usually removed by glomerular filtration, resulting in a short plasma half-life of < 10 min. Thus, some studies of systemic delivery of naked siRNAs have failed to achieve the downregulation of the targeted gene [67, 68] . In contrast, there have also been some successes of locally delivering naked siRNAs to the lungs [15, 16, 20, 31] . A few of them reported that the use of delivery vectors showed no significant difference in gene silencing efficiency compared to that of naked siRNAs [16, 35] . Indeed, in one clinical trial, the delivery of naked siRNAs for the treatment of RSV has been used [17, 38] . This successful evidence can be because that naked siRNAs for clinical applications are highly chemically modified to prevent nuclease-induced degradation and presumably minimize immune stimulatory effects. Although it is unclear how the naked siRNAs cross the cell membrane, gain access to the cytoplasm, and remain intact to perform their biological action, both animal and human trials have been conducted successfully, showing the efficacy of naked siRNAs (ALN-RSV01) that were administered intranasally. This explanation has not been confirmed, but the physiological damage of respiratory epithelial cells caused by viral infection may have possibly influenced the mystery. The active change in airway epithelial cell membrance caused by infectious disease might affect cellular internalization. Naked siRNA delivery has some advantages, such as simple formation and the absence of toxicity or inflammatory responses that are usually associated with delivery vectors. Nevertheless, the advantage of naked siRNAs over delivery vectors in the treatment of lung diseases is controversial [69, 70] . Further in vivo investigations about both naked siRNAs and non-viral vectors are required. Lung disease is a major cause of death, and diminished quality of life is responsible for the suffering of many patients. Various lung diseases make life extremely difficult for the patients, and severe cases of these lung diseases can result in death. The high death rates associated with lung cancer are partially due to the fact that it is unfortunately difficult to cure. Above all, COPD is the fourth-leading cause of death in most industrialized countries and is predicted to become third by 2020 [71] . Therefore, decisive action is needed to stem the rising health and economic burden this represents. Chronic lung diseases, such as COPD and asthma, are disorders of the airways largely related to the presence of persistent inflammation. The approval of inhaled corticosteroids pioneered a new generation of therapy in treating chronic inflammatory diseases. This was the first time that an anti-inflammatory product was available to reduce the characteristic lung inflammation in airways and the associated obstruction. Corticosteroids are still an important therapeutic intervention. However, they are used with limitations in COPD and moderate to severe asthma. Likewise, the treatment of various refractory lung diseases also depends on systemic corticosteroid therapy. Many of these patients also suffered various side effects from systemic corticosteroid use, such as weight gain and uncontrolled hyperglycemia. Treatment of lung disease using cell-specific targeting as well as RNAi techniques represents a novel strategy and could possibly provide new opportunities in nanomedicine. Pulmonary applications of siRNA in in vivo conditions are frequently studied and often result in clinical trials [57, 72] . The findings of recent clinical studies of pulmonary RNAi therapeutics are discussed. Since the discovery of RNAi, the therapeutic potential of siRNAs has been rapidly recognized. In 2004, the first human clinical trial of RNAi-based therapy was initiated for the treatment of age-related macular degeneration with a siRNA targeting VEGF-receptor 1 delivered intravitreally [73] . Many studies have been conducted over the past few years that involve the delivery of siRNAs to the lungs for the treatment of various lung diseases. Delivery to the lungs will be most important to moving siRNA technology into the clinic. A number of siRNA-based therapies are being evaluated in clinical trials for the treatment of different conditions, including lung diseases such as asthma and RSV infection. Table 1 is a summary of clinical trials of siRNA-based therapeutics [74] . SiRNA shows potential for the treatment of various pulmonary viral infections, and it has been reported that siRNA-based therapeutics can also be used in the treatment of influenza [13] , parainfluenza virus [35] , severe acute respiratory syndrome (SARS) [14] , and RSV [35] . Above all, RSV is the most promising therapeutic target of siRNAs. RSV is a common cause of serious respiratory infections in infants and children. It also produces significant morbidity and mortality in adult immunocompromised or elderly populations [75] . An RSV vaccine is not available, and the only approved antiviral therapy for RSV is undesirable for pediatric patients due to its potential teratogenicity and limited effectiveness. Thus, a safe and efficacious RSV therapy has long been awaited for both pediatric and adult patients. RNAi-based therapy has shown promising effects in murine models of RSV infection [35] . The siRNA, ALN-RSV01, is directed against the mRNA encoding the N-protein of RSV that exhibits specific in vitro and in vivo anti-RSV activity. It is delivered without a delivery vector as a nasal spray and targets the upper respiratory tract instead of the lower lung area. ALN-RSV01 has undergone complete phase I intranasal and inhalation studies in healthy adults and has been found to be generally well tolerated [38] . Additionally, ALN-RSV01 has been evaluated in a randomized, double-blind, placebo-controlled phase II trial in lung transplant patients with RSV respiratory tract infection [76] . The administration of ALN-RSV01 to RSV infected lung transplant patients was safe and well tolerated and associated with a statistically significant improvement in symptoms. Based on these results, a larger multinational, randomized, double-blind Phase IIb trial of ALN-RSV01 has been initiated in lung transplant patients to confirm and extend these findings. Cancer is a major target of RNAi-based therapy, as oncogenes, mutated tumor suppressor genes, and several other genes contributing to tumor progression are potentially important targets for gene silencing by RNAi. Lung cancer is one of the most frequent tumors worldwide with regard to incidence rates and mortality. Patients with lung cancer are commonly diagnosed at an advanced stage of the disease and have limited therapeutic options. Although the knowledge regarding the genetic and molecular basis of lung cancer has regularly increased, the median survival rates of individuals with advanced lung cancer are still poor. RNAi-based therapy is an attractive strategy for the development of more effective anticancer therapies with reduced treatment-related toxicity. The major advantage of RNAi therapeutics in cancer might be the simultaneous targeting of multiple genes belonging to different cellular pathways that are involved in tumor progression. The simultaneously inhibition of several genes would also minimize the risk of drug resistance normally encountered with small molecule-based therapies, involving siRNAs and miRNAs. There have already been significant improvements in siRNAs for primary or metastatic lung cancer treatment by targeting oncogenes such as Akt1 [9] , Wilms tumor 1 (WT1) [12] , overexpressed genes such as the insulin-like growth factor receptor 1 (IGF-1R) [77] , NUPR1 [53] and EZH2 [78] . Some of these studies have successfully shown the efficacy of RNAi-based therapy through intrapulmonary administration of siRNAs with non-viral vectors. Although strategies to minimize off-target and nonspecific immune stimulatory effects must be devised, these data suggest that the silencing of the target gene with siRNAs is an attractive strategy for the prevention and treatment of primary and metastatic lung cancer. There are currently some clinical trials in progress estimating the safety and efficacy of siRNA-based drugs for cancer treatment. Atu027, a siRNA-lipoplex targeted against protein kinase N3 (PKN3), prevented lung metastasis in a phase I trial of various cancer models [79] . PKN3 is a downstream effector of the phosphoinositide 3-kinase (PI3K) signaling pathway [80] , which regulates diverse cellular responses, including development, growth, and survival [81] . Recently, PKN3 has also been considered as a suitable therapeutic target for modulating tumor angiogenesis because loss of function analysis with Atu027 in cultured primary endothelial cells showed an essential role of PKN3 for endothelial tube formation and migration [79] . Atu027 can be considered as a potential siRNA for preventing lung metastasis and might be suitable for preventing hematogenous metastasis combined with conventional cancer therapy. Inflammatory lung disease, also called COPD, includes a wide range of lung ailments. These related diseases include asthma, pulmonary fibrosis, and chronic bronchitis. They are influenced by a combination of environmental, genetic, and epigenetic components [82] . COPD is a chronic inflammatory disease of the airways. This disease is hallmarked by airflow that is not fully reversible. Systemic and local airway inflammation has been implicated in the pathogenesis of COPD [83] . COPD is mainly associated with tobacco smoking, and recent studies investigating the pathophysiology of emphysema have demonstrated that cigarette smoke can cause cells to enter cellular senescence. Smoking might cause cells to senesce due to DNA damage through increased cell turnover, which in turn leads to accelerated telomere shortening [84] . Lately, a lot of studies have investigated the role of cellular senescence in the development and progression of COPD [85] . Although several medication classes, including inhaled corticosteroids, are used for COPD treatment, none of these medications have been shown to significantly improve long-term lung function during the progression of the disease. Current interventions that have been shown to improve mortality in COPD are cessation of smoking and delivery of supplemental oxygen when hypoxemia is present. Many people are developing COPD, and the cause of this condition is complicated and not thoroughly understood. One key factor is genetic susceptibility. Some studies have shown a large genetic contribution to the variability in pulmonary function and COPD [86, 87] . Polymorphisms in multiple genes have been reported to be associated with COPD [87] , such as transcription factor [e.g. nuclear factor-kappa B (NFκB)] [88] , extracellular matrix (e.g., matrix metalloproteinase-12 (MMP-12)) [89, 90] , cytokines [e.g. tumor necrosis factor (TNF)-α] [91] , chemokines [e.g. interleukins (IL)-8, IL-8 receptor and chemokine receptor (CCR)1] [92, 93] , and apoptosis (e.g., caspase-3 and vascular endothelial growth factor (VEGF)) [94, 95] . Many of these have been identified as possible targets for therapeutic intervention using molecule inhibitors or antagonists. Although several new treatments that target the inflammatory process are now in clinical development, such as TNF-α inhibitors and I-kappaB kinase complex 2 (IKK2) inhibitors [96, 97] , clinical trials with siRNAs have never been performed in COPD. The delay of drug development for COPD might be due to the relatively recent emergence of research addressing the molecular basis of COPD. Furthermore, more research is needed to understand the essential molecular mechanisms about the pathogenesis of COPD and to develop monitoring techniques to support the development of RNAi therapies. Currently, no available treatments reduce the progression of COPD or suppress the inflammation in small airways and lung parenchyma. The RNAi-based approach for the key molecules also has potential implications for the treatment of COPD. Asthma is also a chronic inflammatory disease of the airways characterized by variable and recurring symptoms and reversible airflow obstruction. The World Health Organization estimates that 300 million people are currently affected and that, by the year 2025, another 100 million will be affected by the disease [98] . Inhaled corticosteroids are very effective in mild asthma because they improve symptoms and decrease exacerbations. However, in moderate and severe asthma, inhaled corticosteroids have important therapeutic limitations. Although corticosteroids remain an important therapeutic intervention for inflammatory lung diseases, their use is not always completely effective and is associated with side effects. Due to such limitations, it is clear that there is a need for new types of medications that can treat and improve the prognosis of moderate to severe asthma. Many target genes have been identified that participate in the pathogenesis of asthma. The most promising targets include genes coding for cytokines (IL-4, IL5, and IL-13), cytokine and chemokine receptors (IL-4 receptor and CCR3), and tyrosine kinases [spleen tyrosine kinase (Syk) and LCK/YES-related novel tyrosine kinase (Lyn)], as well as for transcription factors [signal transducers and activators of transcription 1 (STAT1), STAT6, GATA3, and NFκB] that are involved in asthma [19, 99, 100] . The genes that have been assessed as siRNA targets for the treatment of asthma in preclinical models are reported [101] . Currently, in a clinical trial for asthma, Excellair TM (ZaBeCor, Bala Cynwyd, PA, USA), a siRNA that targets Syk, is being used. The kinase is involved in signaling from a B cell receptor and is a key regulator of downstream signaling cascades that ultimately lead to the activation of several pro-inflammatory transcription factors. It has been reported that antisense oligonucleotides administered by aerosol were potent to decrease Syk expression, mediator release from alveolar macrophages, and Syk-dependent pulmonary inflammation [102] . Moreover, inhibition of inflammatory mediators was shown in a study using siRNA targeting Syk in airway epithelial cells [103] . Following the successful results of the company's Phase I clinical trial, a Phase II trial for its asthma drug candidate Excellair TM has already been initiated. Some of the current treatments for asthma and other inflammatory conditions, such as TNF-α inhibitors or leukotriene inhibitors, inhibit only one of the mediators of inflammation. In contrast, siRNA targeting Syk seeks to inhibit an initial signaling step of inflammation and, thereby, prevent the release of multiple inflammatory mediators. Overall, recent progress of siRNAs to the lungs has also improved the therapeutic feasibility of RNAi for inflammatory lung diseases. The rapid progress will put siRNA-based therapeutics on a fast track to the clinic. MiRNAs are small endogenous noncoding RNAs that regulate gene expression by repressing translation or promoting the degradation of their target mRNA. MiRNAs regulate gene expression by binding to the 3′ untranslated region (UTR) of their target mRNAs and mediating mRNA degradation or translational inhibition. In the human genome, transcripts of approximately 60% of all mRNAs are estimated to be targeted by miRNAs [104] . According to their function, miRNAs play an important role in cellular processes as development, proliferation, and apoptosis of pulmonary pathologies [105] . A growing number of miRNAs have been shown to be involved in different lung diseases. This evidence makes miRNAs a promising technology for current and future therapeutic development. We discuss the role of some miRNAs in various lung diseases as well as the possible future of these discoveries in clinical applications. Table 2 shows the summary of miRNAs in therapeutic development. At this point, a miRNA-based therapy has already entered a phase II clinical trial. There is evidence that upregulation or downregulation of miRNAs is critical for lung homeostasis and, thus, may contribute to the development of pathological pulmonary conditions. Many studies have focused on the role of miRNAs in inflammatory lung diseases, such as COPD [116, 117] , pulmonary fibrosis [118] [119] [120] [121] , and asthma [122] [123] [124] [125] (Table 3) . [130] [117, 129] The pathogenesis of COPD is attributed to not only chronic inflammation in the airways but also systemic inflammation [131] . Cigarette smoking is the main risk factor for the development of COPD. Smoking has been shown to cause biological change in the gene expression of the lungs [132] , and there are some reports about smoking-related miRNAs [117, 129, 130] . However, there are few reports that focus on the miRNAs related to the pathogenesis of this disease with systemic inflammatory components. Recent study on pulmonary fibroblasts of COPD patients presents less expression of miR-146a after stimulation with proinflammatory cytokines when compared with non-COPD subjects with similar smoking histories [127] . The downregulation of miR-146a resulted in a prolonged mRNA half-life of cyclooxygenase-2, thus increasing prostaglandin E2 in fibroblasts from COPD subjects. Moreover, Ezzie et al. researched the difference of miRNA profiles expressed in the lungs of smokers with and without COPD. They concluded that miR-223 and miR-1274a were the most affected miRNAs in subjects with COPD [126] . Yet, COPD is a complex, multi-component, and heterogeneous disorder with a number of different pathological processes and subgroups with their own characteristics and natural history [133] . A better understanding of the complexity of the disease and potential clinical relevance of the identified miRNAs is needed. Pulmonary fibrosis can be caused by an identifiable irritation to the lungs, but, in many cases, the cause is unknown, and the therapeutic possibilities are limited. Cigarette smoking is one of the most recognized risk factors for the development of pulmonary fibrosis. This disorder is mainly accompanied by increased expression of the key fibrotic mediator transforming growth factor β (TGF-β) and other cytokines produced at the lesion of active fibrosis [128] . Recently, it was reported that miRNAs may play an important regulatory role in the pulmonary fibrotic change in the lungs. The downregulation of let-7d in idiopathic pulmonary fibrosis (IPF) resulted in increased collagen deposition and alveolar septal thickening [119] . In addition, Liu et al. reported that the oncogenic miR-21 was found to be upregulated in IPF patients and in the murine lungs with bleomycin-induced fibrosis [118] . Although these miRNAs may be potential therapeutic targets because their expression is related to the regulation of TGF-β, the factor is necessary but not sufficient for pathologic fibrosis of the lungs. Pulmonary fibrosis is also a complicated illness that can have many different causes. Focus on the role of miRNAs in asthma has recently increased. Asthma is an inflammatory disease of the airway that is characterized by an abnormal response of T helper-2 (Th2)-type CD4+T lymphocytes against inhaled allergens [134] . In a different asthmatic mouse model, there was an observed increase in the expression of miR-21 in the lungs [123] . This report might contribute to the understanding of the inflammatory mechanism in the airway through the inhibition of IL-12, favoring the Th2 lymphocyte response. A toll-like receptor 4 (TLR4)-induced Th2 lymphocyte induces high expression of miR-126, and selective blockade of miR-126 suppressed the asthmatic phenotype [124] . In addition, airway remodeling is a characteristic feature of asthma and has important functional implications. Rodriguez et al. have shown that miR-155 is related to the development of inflammatory infiltration into the lung and airway remodeling [122] . Thus, some studies present a functional connection between miRNA expression and asthma pathogenesis and suggest that targeting miRNAs in the airways may lead to anti-inflammatory treatments for allergic asthma. Despite the evidence from experimental models, the expression profiling of miRNAs in airway biopsies from patients with mild asthma before and after treatment with inhaled corticosteroids and in healthy volunteers revealed no differences in miRNA expression [135] . Further investigations about the role of miRNAs related to asthma pathogenesis are required. Although the basic evidence of miRNA biology is still providing new insights, applications of miRNA-based therapy for inflammatory lung diseases are less advanced than those for lung cancer [136] . One reason for this could be that the disease heterogeneity is caused by the effects of many environmental air pollutants, including smoke and volatile organic compounds. The presence of several risk factors makes the understanding of the pathogenesis of inflammatory lung diseases complicated. Understanding the role that miRNAs play in the modulation of gene expression, leading to sustain the pathogenesis of lung diseases, is important for the development of new therapies that focus on the prevention of disease progression and symptom relief. Given the significant roles that miRNAs play in multiple pathways of lung carcinogenesis, increasing efforts are dedicated to the research and development of miRNA-based therapies, including restoring functions of tumor suppressive miRNAs or inhibiting oncogenic miRNAs. The development of miRNA-based therapies for lung cancer is growing prosperously with the help of new RNAi technologies. Compared to siRNA-based therapies, which are already in clinical trials, miRNAs are less toxic and have the potential to target multiple genes. The difficulty associated with miRNA delivery is mainly equal to that of siRNAs. The critical problems for the development of this therapy are effective delivery into target sites, potency of the therapy, and elimination of off-target effects [137] . There are two strategies as the therapeutic applications of miRNAs for lung cancer [138] . One strategy is miRNA replacement therapy, which involves the re-introduction of a tumor suppressor miRNA mimic to restore a loss of the function. MiRNA mimics are synthetic RNA duplexes designed to mimic the endogenous functions of miRNAs with chemical modifications for stability and cellular uptake. The concept of miRNA replacement therapy is most exemplified by the let-7 miRNA. let-7 is a tumor-suppressor miRNA in non-small-cell lung cancer that inversely correlates with the expression of the RAS oncoprotein, a key cancer gene [139] . Intranasal administration of let-7 mimic into mouse models of lung cancer significantly reduced tumor growth, suggesting that miRNA replacement therapy is indeed promising [106, 140, 141] . Another miRNA that shows the value of miRNA replacement is provided by miR-34a [107, 142] . Local and/or systemic delivery of a synthetic miR-34a mimic led to accumulation of miR-34a in the tumor tissue and inhibition of lung tumor growth. Lately, Ling et al. also showed that tumor suppressor miR-22 exhibited anti-lung cancer activity through post-transcriptional regulation of ErbB3 [143] . Thus, therapeutic miRNA mimics have a powerful potential by attacking multiple genes relevant to several diseases. However, it is necessary to pay attention to the potential toxicity in normal tissues under conditions in which the therapeutic delivery of miRNA mimics will lead to an accumulation of exogenous miRNAs in normal cells [138] . Although the assumptions are well founded, there is still insufficient evidence for toxicity caused by miRNA mimics. Indeed, several in vivo studies failed to reveal side effects caused by the miRNA mimics and suggested that delivery of miRNA mimics to normal tissues was well tolerated [107, 141] . It will be important to research miRNA mimic-induced effects in normal cells and to carefully assess toxicity before using them in clinical practice. The second strategy is directed toward a gain of function and aims to inhibit oncomiRs by using anti-miRNAs. Chemical modifications, such as 2'-O-methyl-group and locked nucleic acid (LNA), would increase oligo stability against nucleases [144] . Antisense oligonucleotides contained in these modifications are termed antagomirs or "LNA-antimiRs" [144, 145] . They are oligonucleotides with sequences complementary to the endogenous miRNA and inhibit the specific miRNA function. An LNA-antimiR against miR-122 has been shown to effectively silence miR-122 in non-human primates [145] , and the findings support the potential of these compounds as a new class of therapeutics. Moreover, it has also been reported that anti-miR-150 delivered into lung tumor xenografts in mice led to inhibited tumor growth [146] . Relative to studies on miRNA mimics, studies with antisense oligonucleotides have shown effective evidence with naked oligonucleotides. This illustrates the potential of chemical modifications of oligonucleotides to improve their stability, resistance to RNase, and pharmacologic properties. Therefore, inhibition of miRNA function by chemically modified antimiR oligonucleotides has become an important and widely used approach. Recent data from the first phase II study in patients with chronic HCV infection treated with the LNA-modified antimiR-122 showed that this compound was well tolerated and provided continuing viral suppression. An increasing number of studies have examined the therapeutic potential of miRNAs. Recently, the evidence of roles for miRNAs in determining drug resistance has emerged [147] . Cytotoxic and molecular target drugs have been widely used in the treatment of advanced lung cancer; unfortunately, many cases are still refractory to chemotherapy. In this situation, combining miRNA mimics or antimiR with chemotherapy may potentiate the efficacy of the cancer treatment in the future. In addition, miRNAs related with cancer stem cells may significantly broaden the field of miRNA-based therapy and suggest that miRNAs can be potential tools to kill cancer cells associated with therapy resistance, recurrence, and metastasis [108, 148] . Hence, the main challenge is the successful delivery and chemical modifications of the therapeutic miRNAs to the target tissue without harming normal tissues. RNAi-based approaches provide a promising therapeutic modality for the treatment of various lung diseases. One of the greatest challenges in RNAi-based therapy continues to be the delivery method of the therapeutic siRNAs and miRNAs to the target cells. Pulmonary delivery applications are very attractive, since they tend to be non-invasive, are locally restricted, and can be administered by the patient. A realistic therapeutic intervention, such as aerosolization, can enhance drug delivery to the site of action and decrease systemic exposure of the patient to the therapy, thereby reducing off-target effects. The advancement of pulmonary siRNA delivery to the clinic illustrates that RNAi-based therapy holds a central place in the future treatment of lung diseases. On the other hand, miRNAs have the opportunity to target multiple genes in a fine-tuned manner, and the miRNA-based therapy will provide an attractive anti-tumor and anti-inflammatory approach for various lung diseases. In particular, anti-miRNA therapy by chemically modified antimiR oligonucleotides has become a potential therapy for lung diseases because the oligonucleotides can be successfully delivered without delivery vectors. Increased evidence has indicated that miRNAs fulfill causative roles in a variety of lung diseases and have prompted investigations into their potential as therapeutic targets. Further understanding of the detailed mechanisms of RNAi-based therapy and investigations of more effective delivery methods are required for future development. These novel approaches could open new avenues for various lung diseases and improve the clinical outcome of the patients.

What are the essential conditions in siRNA delivery to effectively produce gene silencing in the lungs?

Hantaviruses in the Americas and Their Role as Emerging Pathogens https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3185593/ SHA: efe13a8d42b60ef9f7387ea539a1b2eeb5f80101 Authors: Hjelle, Brian; Torres-Pérez, Fernando Date: 2010-11-25 DOI: 10.3390/v2122559 License: cc-by Abstract: The continued emergence and re-emergence of pathogens represent an ongoing, sometimes major, threat to populations. Hantaviruses (family Bunyaviridae) and their associated human diseases were considered to be confined to Eurasia, but the occurrence of an outbreak in 1993–94 in the southwestern United States led to a great increase in their study among virologists worldwide. Well over 40 hantaviral genotypes have been described, the large majority since 1993, and nearly half of them pathogenic for humans. Hantaviruses cause persistent infections in their reservoir hosts, and in the Americas, human disease is manifest as a cardiopulmonary compromise, hantavirus cardiopulmonary syndrome (HCPS), with case-fatality ratios, for the most common viral serotypes, between 30% and 40%. Habitat disturbance and larger-scale ecological disturbances, perhaps including climate change, are among the factors that may have increased the human caseload of HCPS between 1993 and the present. We consider here the features that influence the structure of host population dynamics that may lead to viral outbreaks, as well as the macromolecular determinants of hantaviruses that have been regarded as having potential contribution to pathogenicity. Text: Emerging pathogens cause new or previously unrecognized diseases, and among them, emerging zoonotic diseases are a major concern among scientists studying infectious diseases at different spatial and temporal scales [1, 2] . Changes in biotic and abiotic conditions may alter population disease dynamics and lead to the emergence of zoonotic infections [3] [4] [5] [6] . During the last decades, several outbreaks of emerging and re-emerging viral pathogens have occurred, affecting both purely-local and worldwide/pandemic involvement of human populations. Among the conspicuous examples are influenza A, Ebola virus, hepatitis C virus, severe adult respiratory distress (SARS), coronavirus, and human immunodeficiency virus, which challenge prevention and control measures of public health systems [7] . In the Americas, the recent outbreak of pandemic influenza A subtype H1N1 became a major target for control due to its rapid spread, and uncertainties in virulence and transmissibility, yet vaccine availability was limited when significant activity occurred in advance of the traditional influenza season [8] . However, in the last century outbreaks of several viral-related diseases have emerged or re-emerged involving arenaviruses and dengue viruses, and more recently, hantaviruses, and the expansion of the geographic range of West Nile virus. Among zoonotic diseases, small mammals are hosts of several pathogenic RNA viruses, especially Arenaviridae and Bunyaviridae: Hantavirus [9] [10] [11] . Hantavirus infections became a concern in the Americas after the description of an outbreak of acute respiratory distress occurred in the Four Corners area in 1993 [12] . The newly recognized disease, hantavirus cardiopulmonary syndrome, HCPS (or hantavirus pulmonary syndrome), was linked to infection by the newly-discovered Sin Nombre virus (SNV), and the rodent Peromyscus maniculatus (deer mouse) was identified as the reservoir [13] . However, hantavirus infections have a much longer history. A review of ancient Chinese writings, dating back to approximately 960 AD, revealed descriptions closely resembling hemorrhagic fever with renal syndrome (HFRS), the syndrome caused by Old World hantaviruses [14] . During the twentieth century, cases of acute febrile disease with renal compromise were described from several Eurasian countries and Japan, often in association with military engagements [15] . HFRS as a distinct syndrome, however, was first brought to the attention of western medicine in association with an outbreak that occurred among United Nations troops during the Korean conflict between 1951 and 1954, where more than 3,200 soldiers were afflicted [16] . It took more than two decades until the etiologic agent, Hantaan virus (HTNV), was isolated from the striped field mouse Apodemus agrarius, detected in part by the binding of antibodies from patient serum samples to the lung tissues of healthy, wild-caught field mice [17, 18] . The virus was later found to represent the type species of a new genus Hantavirus of the family Bunyaviridae, although it was later apparent that the first hantavirus to be isolated was the shrew-borne Thottapalayam virus [19] . The categorization of hantaviruses as belonging to the family Bunyaviridae is due in part to the consistent presence of three RNA genomes that are circularized in vivo as a result of the presence of terminal complementary nucleotides that help fold the genome into a -hairpin‖ morphology, first described for the Uukuniemi phlebovirus [19, 20] . Table 1 is a list of the predominant, serologically distinct pathogenic hantaviruses. Many other named genotypes are described, but such other pathogenic forms are generally closely related to Andes or, in some cases, Sin Nombre virus. During virus maturation, the precursor form GPC is processed using a membrane -bound protease into Gn and Gc, a cleavage that occurs, and appears to be signaled, after the conserved peptide signal WAASA at the C-terminal of Gn [24] . Although the two proteins can be expressed independently through transfection, they can be retained in the wrong cellular compartment (ER or aggresome); they thus must be co-expressed to allow them stability so that the two can be assembled correctly in the Golgi [25, [27] [28] [29] . A number of activities and properties have been identified for the hantavirus envelope glycoproteins, including some features that are suspected to be involved in the pathogenicity of the disease-causing serotypes, a possibility that has engendered experimental attention. The glycoproteins are the known or presumed ligands for at least two distinct cellular receptors, the 3 integrin chain and decay accelerating factor, or DAF [30, 31] ; with gC1qR/p32 also identified as another potential entry receptor [32] . Comparisons with the tick-borne encephalitis virus E protein, led Tischler et al. to consider the Gc glycoprotein as a potential class II fusion protein, perhaps imparting fusion activity to the virion, and this hypothesis has gained support in other studies [33, 34] . Additional activities have been identified with, or claimed to be related to, Gn. For many of these studies, an underlying premise has held that there are differences between the glycoproteins of -pathogenic‖ hantaviruses relative to viruses in the genus that are dubbed to be -non-pathogenic‖. While it is true that it has not yet been possible to link Prospect Hill virus (PHV) to human disease, the absence of evidence for its pathogenicity should perhaps not be equated with the evidence of its absence. One might only consider that the level of disease (e.g., lethargy, fever, proteinuria, and azotemia) associated with infection of nonhuman primates by PHV is not significantly different from that recorded for nonhuman primate models using the known-pathogen Puumala virus (PUUV) [35, 36] . For the purpose of this discussion we will presume that apathogenic hantaviruses are indeed apathogenic. While some studies have suggested that Gn glycoproteins are directed more rapidly into the ubiquitin-proteosome pathway than are apathogenic forms, others have interpreted differences in the handling of Gn glycoproteins across hantavirus species by the ubiquitin-proteosomal system as independent of pathogenicity [37] [38] [39] . Some investigators have directed their efforts toward identifying a differential capacity, either kinetic or in absolute magnitude, in the ability of pathogenic and apathogenic hantaviruses to elicit an interferon response in cells. One premise that emerges is that apathogenic forms would tend to induce an earlier innate response that would render it more likely that the virus would be quickly cleared or rendered less competent in its replication so as to blunt any pathological response in the host [40] [41] [42] . The anti-hantavirus innate response can in some cases be attributed to viral interaction as a ligand of TLR-3, but not in others, and in endothelial cells, it appears not to require more than the viral particle itself, even when introduced in replication-incompetent form [43, 44] . Proteins and mRNAs prominently induced by hantaviruses include MxA and IFIT-1 (ISG-56) and others including some with known or suspected anti-viral activity. Those hantaviruses, often highly pathogenic strains, that fail to induce a potent antiviral response, are suspected or presumed to have a (more) potent interferon-pathway antagonism mechanism relative to other viruses, a mechanism that acts positively to prevent an effective innate response from forming, at least early in infection [42, 45] . Yet some instances are reported wherein highly pathogenic hantaviruses, such as SNV, are also able to induce expression of interferon-stimulated gene mRNAs, even very early in infection, with ISG proteins, as expected, taking longer to appear in the cell [44] . Anti-interferon activities have also been attributed to the NSs protein that may be elaborated in cells infected by serotypes that encode this protein [46] . Other investigators have examined the activities of hantavirus glycoproteins and other proteins that might themselves directly affect some aspects of the pathogenic progression associated with hantavirus infection of humans, such as vascular permeability changes. While early attempts to directly cause increases in permeability of endothelial monolayers with viral particles or viral infection were largely disappointing, hantaviruses have been identified as adversely affecting endothelial migration over substrata and in potentiating VEG-F-induced endothelial permeability [47, 48] . The shorter (50-kD) nucleocapsid or N protein is a structural component of the viral nucleocapsid, along with the genomic viral RNA segments. As an RNA-binding protein that engages the hairpin termini of the genomic segments with high affinity [49, 50] , it limits the access of the RNA to host nucleases and helps to render viral replication a closed process within the cytoplasm. It also acts as a peripheral membrane protein, as does the L protein [51] , an activity that could play a role in its presumed, but not yet demonstrated function as matrix [52] . Until recently, it had not been appreciated that N has a wide variety of other activities, some of which can be linked, not only to fundamental requirements of replication, but also to the interference with an array of the intracellular processes of the normal cell. Thus, an interaction between the amino terminus of the hantavirus N protein and the cellular protein Daxx has been proposed, with the suggestion of potential pro-apoptotic consequences [51] . N is also reported to interact with actin microfilaments, and the SUMO-1 protein [53, 54] . Using reporter-gene based assays, Connie Schmaljohn and her colleagues have reported that Hantaan virus' nucleocapsid protein has an inhibitory role in inflammatory responses mediated by NF kappa B (NF-B). The effects on NF-B expression appeared to be confined to prevention of its nuclear translocation after its attempted activation with lipopolysaccharide, LPS [55] . In the cytoplasm of infected cells, N protein can be found in cellular P bodies where it sequesters and protects 5' caps. It may locate the caps through its interaction with DCP1, a key constituent of P bodies. During hantavirus infection, the viral RNAs become concentrated in P bodies, through their interaction with N and DCP1. The N protein demonstrates preferential protection of mRNAs engineered to prematurely terminate their encoded protein in comparison to native mRNAs [56] . N protein has been increasingly linked to viral replication and translation, sometimes in previously unanticipated ways. It is among a growing family of diverse viral proteins that can serve as a nonspecific -RNA chaperone‖, an activity that should facilitate the L polymerase's access to vRNA for transcription and replication, in that it can transiently dissociate misfolded RNA structures [57] . Some of N protein's effects on translation might not immediately be recognized to be adaptive in nature. It can replace the entire EIF4F translational initiation complex, simultaneously presenting the ribosome with a replacement for the cap-binding activity of eIF 4E, binding to the 43S pre-initiation complex as does eIF 4G, while replacing the helicase activity of eIF 4A, which is presumed to be needed to dissociate higher-order RNA structure [56, 58] . These three factors normally work together to achieve translational initiation. In P bodies, N protein's ability to bind at high affinity to capped native cellular oligoribonucleotides, along with its activity in protecting capped RNAs from degradation likely facilitates the access of capped oligonucleotides for use in transcriptional initiation by L polymerase (-cap snatching‖). Trafficking of N for viral assembly: Classically, N protein in infected cells appears to be clustered or particulate in nature, with a heavy concentration at a single perinuclear location, widely considered to be the Golgi [27] . The N proteins of hantaviruses are found in association with particulate fractions, and confocal microscopy and biochemical-inhibitor studies have shown that N tracks along microtubules but not with actin filaments [52] . The ultimate destination for N, for its assembly into viral particles is the Golgi, and it traffics there via the endoplasmic reticulum-Golgi intermediate complex (ERGIC), also known as vesicular-tubular cluster [52] . A dominant negative inhibitor, dynamitin, associated with dynein-mediated transport, reduced N's accumulation in the Golgi. Later studies suggested that the specific dependence on microtubular transport is specific to Old World hantaviruses such as HTNV, but that the New World hantavirus ANDV is instead associated with actin filaments [59] . However, recent data indicates that microtubular transport is indeed utilized for the New World hantavirus SNV [60] . Hantavirus diseases of man have long been suspected of having an immunopathogenic basis in part because of their relatively long incubation period of 2-3 weeks and the observed temporal association between immunologic derangements and the first appearance of signs and symptoms of hantavirus illness. HFRS and HCPS share many clinical features, leading many investigators to consider them to be, in essence, different manifestations of a similar pathogenic process, differing mainly in the primary target organs of disease expression ( Table 2 ). The pathogenesis of hantavirus infections is the topic of a continuously-updated review in the series UpToDate [61] . By the time symptoms appear in HCPS, both strong antiviral responses, and, for the more virulent viral genotypes, viral RNA can be detected in blood plasma or nucleated blood cells respectively [63, 64] . At least three studies have correlated plasma viral RNA with disease severity for HCPS and HFRS, suggesting that the replication of the virus plays an ongoing and real-time role in viral pathogenesis [65] [66] [67] . Several hallmark pathologic changes have been identified that occur in both HFRS and HCPS. A critical feature of both is a transient (~ 1-5 days) capillary leak involving the kidney and retroperitoneal space in HFRS and the lungs in HCPS. The resulting leakage is exudative in character, with chemical composition high in protein and resembling plasma. The continued experience indicating the strong tissue tropism for endothelial cells, specifically, is among the several factors that make β3 integrin an especially attractive candidate as an important in vivo receptor for hantaviruses. It is likely that hantaviruses arrive at their target tissues through uptake by regional lymph nodes, perhaps with or within an escorting lung histiocyte. The virus seeds local endothelium, where the first few infected cells give rise, ultimately, to a primary viremia, a process that appears to take a long time for hantavirus infections [62, 63] . By the time that secondary viremia emerges, the agents of the more severe forms of HFRS and HCPS have begun to achieve sufficient mass as to induce, through PAMP-PRR interactions and other means, the expression of proinflammatory cytokines [64] . For HCPS, that expression favors the pulmonary bed and lymphoid organs, yet, for unknown reasons, spares the retroperitoneum and, in general, the kidney. In HFRS the situation is reversed, and yet it is often not appreciated that the expected preferential tissue tropism of HFRS-associated viruses and their HCPS-associated counterparts for the renal and pulmonary beds, respectively, is not as one would predict through the manifestations of the two diseases. Local elaboration of inflammatory and chemotactic mediators is considered to be a requirement for the development of systemic disease symptoms, with those abnormalities sometimes culminating in shock and death. Yet it is not hypoxemia, due to the prominent pulmonary edema, that leads to death in most fatal cases of HCPS, but rather intoxication of the heart by as-yet-undefined mediators that leads to the low cardiac output state and the associated shock syndrome [64, 65] . It is tempting to speculate that mediators produced in the lung in connection with the inflammatory infiltrate can percolate through the coronary circulation with minimal dilution in HCPS, a disadvantageous consequence of the close anatomic juxtaposition of the two organs. Thus, at least three classes of potential mechanisms, some overlapping and all certainly nonexclusive of the others, could be presumed to underlie the pathogenesis of HCPS. These include: (1) Innate immune mechanisms. The nature of interactions between hantavirus pathogen-associated molecular patterns (PAMP) with the pattern recognition receptors (PRR) of susceptible endothelial cells are beginning to be clarified. The prototypical HTNV appears to be recognized by TLR-3 [43] . Such an infection has consequences such as increased expression of HLA-DR in dendritic cells [66] and differentiation of monocytes toward dendritic cells [67] . (2) Direct viral effects. The observed correlation between viral load and disease severity leaves the possibility open that hantavirus particles or RNA can themselves have toxic effects on cells or on signaling. Some investigators have favored direct viral toxicity, acting through the inhibition of endothelial cell barrier function, as an explanation for much of the capillary leak, although there is widespread agreement that multiple mechanisms that mediate pathogenesis likely operate simultaneously in the affected patient [68] . A potentially important clue toward the mechanism by which hantavirus infections deplete blood platelets and, in some cases cause hemorrhagic manifestations, was advanced by the recent discovery that pathogenic hantaviruses are able to recruit platelets to adhere to endothelial cell surfaces, with β3 integrin used as a critical binding element [69] . (3) Pathogenic effects caused by the activities of specific viral macromolecules. We have reviewed some of the activities associated with the Gn, Gc and N, virally-encoded polypeptides in previous sections. Testing models of pathogenesis can be done more effectively when there is an animal model that mimics key aspects of the disease. There is no such model that closely mimics HFRS, but animal models exist for both the asymptomatic carriage of PUUV and SNV by their native carrier rodents, the bank vole Myodes glareolus and the deer mouse P. maniculatus; as well as a Syrian hamster model using ANDV or the related Maporal virus from Venezuela, for which an HCPS-mimetic disease is observed [70] [71] [72] [73] . The ANDV-Syrian hamster model has a number of features in common with the human disease, as well as some differences. Unlike the neurologic diseases that have been possible to elicit with HTNV, the hamster model for HCPS appears to be caused by capillary leak that results in pulmonary edema and the production of a pleural effusion with exudative characteristics. Typically the hamsters die between 11 and 14-d post-inoculation, reflecting a slightly accelerated incubation period in comparison to human infections. As with human HCPS, the microscopic examination of the lung reveals abundant fibrin deposition, thickened alveolar septa, and viral antigen expressed abundantly in the microvascular endothelium. ANDV-infected hamsters fitted with physiologic monitoring devices exhibited diminished pulse pressures, tachycardia, and hypotension that appear to closely mimic the shock that is believed to be the proximate cause of demise in patients who succumb to HCPS [65, 74] . Compared to the human disease, ANDV-infected hamsters exhibit exceptionally high titers of live ANDV in their tissues, with much of the viral replication occurring in hepatocytes, which are spared in the human disease. Titers of live ANDV in some cases exceed 10 8 /g, whereas hantavirus isolates from human tissues have been notoriously difficult to obtain. Despite the universal occurrence of mildly-elevated hepatic enzymes in patients with HCPS, hepatic enzymes do not appear to be present at elevated levels in the blood of diseased hamsters even immediately before death [75] . The protracted incubation period associated with hantavirus disease gives the host considerable time to mount a mature immune response against the virus. Thus, in contradistinction to infections of comparable severity and related symptomatology associated with arenaviruses and filoviruses, hantavirus infections of humans are associated with antibody responses of significant titer by the time symptoms commence. Despite this observation, it appears to be possible that natural variation in individual neutralizing antibody responses among patients with SNV infections can be linked to disease severity, suggesting that administration of antiviral antibodies could prove effective therapeutically [76] . In the case of ANDV infection, new evidence has emerged indicating that the apparent clearance of the virus from the blood does not result in the complete removal of antigenic stimulus by the virus, suggesting that the virus may persist, perhaps in some as-yet undetermined immunologically privileged site [77] . A role for T cell-mediated pathological responses in HFRS and HCPS has been the source of speculation for a variety of reasons. The severity of SNV-associated HCPS may have made it more apparent that the onset of pulmonary edema, tachycardia and hypertension seemed to be all but universally temporally associated with the appearance of a spectrum of highly-activated cells of the lymphoid lineage in the peripheral blood. Cells with a close morphologic similarity to these -immunoblasts‖ were detected in the congested, heavy lungs of patients who came to autopsy, as well as in lymphoid organs and in the portal triads [63, [78] [79] [80] . These observations led to speculation that some component of hantavirus pathogenesis could be linked to the appearance of antiviral T cells that could stimulate or contribute to the appearance of a -storm‖ of mediators and the associated capillary leak phenotype. Subsequent studies have borne out the expectation that a significant fraction of the immunoblast population in patients with HCPS are T cells with specificity for specific class I HLA-presented epitopes of viral antigens, including Gn, Gc and N [77, [81] [82] [83] . Presumably, the antiviral activities of such cells, manifested in part through their elaboration of mediators in the affected interstitium, can contribute to the endothelial/capillary leak that lies at the heart of hantavirus pathogenesis. Because early cases of HCPS often came to autopsy, it became possible to examine necropsied tissues for expression of cytokines. The study by Mori et al. (1999) revealed high relative expression of proinflammatory cytokines including TNF, IL-1, IL-6, providing evidence in favor of a -cytokine storm‖ model for pathogenesis [64] . The authors believed, based on the morphology of cytokine-secreting cells, that both monocytes and lymphocytes were contributing to the production of cytokines. That proinflammatory mediators are found in elevated levels in the plasma as well as the renal interstitium of patients with acute hantaviral illness has been recognized for some time as well [84, 85] . While diagnosis of HCPS as well as HFRS is best accomplished with IgM serology, in the acute stage of SNV infection, RT-PCR can also be used if blood cells or blood clot are used instead of plasma or serum, where sensitivity even using nested PCR primers drops to about 70% [86] [87] [88] . In a facility at which many cases of HCPS are treated, the University of New Mexico medical center in Albuquerque, a diagnostic service has long been offered in which the patient's hematologic findings are analyzed to establish the probability that a patient has HCPS. The combination of thrombocytopenia, elevated abundance of -immunoblast‖ lymphocytes, left-shifted polymorphonuclear cell population without strong morphologic evidence for their activation, and elevated hemoglobin or hematocrit values is highly specific for HCPS and allows clinicians the ability to put presumptive-HCPS patients on extracorporeal membrane oxygenation (ECMO), which is believed to have saved many patients from a lethal outcome [89] . Human infection by hantaviruses is thought to follow contact with secretions or excretions produced by infected rodents. In the United States, 538 human infections by hantavirus were reported through late December 2009 [90] , with New Mexico, Arizona and Colorado exhibiting the highest case-loads. While the prototypical central American hantavirus in central America was Rio Segundo virus of Reithrodontomys mexicanus from Costa Rica, the first human disease appeared some years later in Panama, where Choclo virus (CHOV) arose as the etiologic agent and is believed to be responsible for all known cases of HCPS. The fulvous pygmy rice rat Oligoryzomys fulvescens has been identified as the rodent reservoir [91] . In Panama, the first cases of HCPS, albeit with little or no evident cardiac involvement, were reported in 1999, and since then, 106 human infections have occurred with a 26% mortality rate [92] . Serosurveys of mammals in Mexico and Costa Rica have found anti-hantavirus antibodies [93] [94] [95] [96] , and seroprevalences ranging between 0.6 to 1.6% in human populations were reported despite the absence of known HCPS cases [97] . In South America, HCPS cases have been indentified in Argentina, Bolivia, Brazil, Chile, Paraguay and Uruguay, and evidence for human exposure to hantaviruses have also been reported in Venezuela [98] and Perú [99] . In southern South America, ANDV is the main etiologic agent with cases in Chile and Argentina reported since 1995. In Chile, 671 cases of HCPS due to ANDV have occurred during the period 2001-2009 [100] . Since 1995, more than 1,000 HCPS cases have been reported in Argentina [101] ; in Brazil, approximately 1,100 HCPS cases have been identified between 1993 and 2008 [102] . Case-fatality ratios in those three countries have been similar, ranging from 30% (Argentina), 36% (Chile) and 39% (Brazil). Hantavirus infections occur more frequently in men than women, although the male/female ratio is highly variable. For example, Panamanian communities showed a ratio of 55 men to 45 women [103] , while in Chile the ratio is more biased to males (71%) [104] . In the Paraguayan Chaco the male-female ratio approaches 50% [105] . In North America, by December 2009 63% of case-patients were males [90] . All ethnic and racial groups seem to be susceptible to hantavirus infections, and the differences between certain groups (as indigenous and non-indigenous) are more likely correlated with the type habitat where the population resides (e.g., rural versus urban areas). In fact, rural communities account for the highest hantavirus incidences overall and are therefore at higher risk [92, [105] [106] [107] [108] [109] [110] [111] , although the importance of peridomestic settings as a major area of exposure has also been emphasized [112, 113] . The main mechanism by which humans acquire hantavirus infection is by exposure to aerosols of contaminated rodent feces, urine, and saliva [114, 115] . This can occur when humans reside in areas in close proximity to those that rodents inhabit, live in areas infested with rodents, or when rodents invade human settings, which are more frequent in rural habitats. There is a long history of human co-existence with rodents, raising questions about the apparent recent increases in hantavirus-related illnesses, especially HCPS. Other than an apparent association with El Niño southern oscillation (ENSO) events in some regions [116, 117] , the recent increases in incidence of HCPS do not seem to follow a readily-defined temporal or spatial pattern. However, some landscape features such as habitat fragmentation or human-disturbed areas may influence rodent population dynamics and impact viral incidence [118] [119] [120] [121] . Despite the stochasticity associated with contraction of hantavirus infection, certain scenarios have been recognized as posing higher risk. Human activities in poorly ventilated buildings that aerosolize particulates that are then inhaled (i.e., cleaning, shaking rugs, dusting) are frequently identified among patients admitted for HCPS [11, 122] . Outdoor activities are thought to convey lower risk due to lability of hantaviruses to UV radiation and the presumed tendency to be dispersed in wind, although certain environmental conditions seem to maintain the virus for longer periods outside its natural host allowing for indirect transmission [123] . An alternative but uncommon route of virus transmission is by rodent bites [124] [125] [126] . Field workers handling mammals are potentially at higher risk of exposure with hantavirus infections, although when quantified through serosurveys the absolute risk appears rather slight [127] . A new study in Colorado suggests the possibility that a rodent bite may have been the proximate vehicle for outdoor transmission of SNV [128] , which re-emphasizes the use of personal protective equipment during field work activities [129] . As a particular case within hantaviruses, person-to-person transmission has exclusively been documented for the South American Andes virus [130] [131] [132] [133] [134] [135] . The identification of this transmission route has been made using both molecular tools and epidemiological surveys, but the mechanism of interpersonal transmission is not well established. Recent findings show that family clusters and specifically sexual partners share the greater risk of interpersonal transmission, although sexual transmission per se can be neither inferred nor refuted presently [130, 135] . Interestingly, ANDV may also be shed by humans through other biological fluids such as urine [136] , illustrating the particular properties that differentiate this virus from other hantaviruses. Although interpersonal transmission seems to be unique for ANDV, viral RNA of PUUV has been detected in saliva of patients with HFRS, and some patients with SNV-HCPS have viral RNA in tracheal secretions [88, 137] . Hantaviruses in the Americas are naturally hosted by rodents (Muridae and Cricetidae) as well as shrews (Soricidae) and moles (Talpidae) (Figure 1) . Three shrew and one mole species have been reported to host hantaviruses and their pathogenicity for humans remains unknown [22, 138, 139] . At least 15 rodent species have been identified as carriers of different pathogenic hantaviruses, with some South American genotypes such as Castelo do Sonhos (CDSV) or Hu39694 only identified after human infections (Figure 1 ). Hantaviruses typically show high species-specificity and no intermediate host [140] . However, some hantavirus genotypes have been described in the same rodent species. Such is the case of Playa de Oro (OROV) and Catacamas (CATV) identified in Oryzomys couesi [141, 142] , or Maporal (MAPV) and Choclo (CHOV) hosted by O. fulvescens [91, 143] . In North America both Muleshoe and Black Creek Canal hantaviruses have been detected in geographically-distant Sigmodon hispidus [144, 145] . Also, one hantavirus genotype (e.g., Juquitiba-like virus) may be carried by more than one rodent species (O. nigripes, Oxymycterus judex, Akodon montesis). Another example is Laguna Negra virus (LANV) which after being identified in Calomys laucha [146] has also been reported in C. callosus [147] . The rapid increase in the discovery of new hantaviruses and the identification of their hosts does not seem likely to end soon as new small mammal species are screened [95] . This subject is complicated by continued controversy in the criteria for the classification of distinct hantaviruses [148, 149] , which is also tied to host taxonomic classification and taxonomic rearrangements. Cross-species transmission is a major process during spread, emergence, and evolution of RNA viruses [6, 150] . Particularly within hantaviruses, spillover to secondary hosts are increasingly identified as more extensive studies are performed [151] [152] [153] [154] [155] [156] . For example, ANDV is the predominant etiologic agent of HCPS in South America, and O. longicaudatus the main rodent reservoir. Spillover in at least four other rodent species that co-occur with the reservoir have been identified, with Abrothrix longipilis showing the second higher prevalence to ANDV-antibodies, and there is presently no question that the virus is extremely similar genetically between the two host rodents [157, 158] . In North America, spillover of Bayou virus (BAYV) may have occurred from the main reservoir O. palustris to S. hispidus, R. fulvescens, P. leucopus, and B. taylori [159] [160] [161] . Hantavirus spillover is more likely to occur with host populations inhabiting sympatric or syntopic regions [151, 162] , and cross-species transmission would presumably have greater chances of success if the host species are closely related [163] . An interesting exception is found between Oxbow virus (OXBV) and Asama virus (ASAV) in which a host-switch process seemed to have occurred between mammals belonging to two families (Talpidae and Soricidae), likely as a result of alternating and recurrent co-divergence of certain taxa through evolutionary time [138] . Hantaviruses are horizontally transmitted between rodents and are not transmitted by arthropods (unlike other viruses of the family Bunyaviridae). Spillover infection to nonhuman mammals usually results in no onward (or -dead-end‖) transmission, but if humans are infected may result in high morbidity and mortality [122, 164] . During the spring of 1993, an outbreak of patients with HCPS due to SNV occurred in the Four Corners states resulting in more than 60% case-fatality among the initial cases, many involving members of the Navajo tribe [12, 121] . In Panama, an outbreak was reported during 1999-2000 in Los Santos, and 12 cases where identified with three fatalities [165, 166] . This represented the first report of human hantavirus infections in Central America. In South America, the first largest identified outbreak occurred in the Chaco region in northwestern Paraguay during 1995-1996. Seventeen individuals were identified with SNV antibody (ELISA) or were antigen (IHC) positive out of 52 suspected cases [167] . Major outbreaks due to ANDV occurred in 1996 in southern Argentina [131, 134] ; in southern Chile clusters of patients presented with hantavirus illness in 1997 [158] . In Brazil, the first outbreak was identified in the Brazilian Amazon (Maranhão State) in 2000, and involved small villages that resulted in a 13.3% prevalence of those tested (398 total residents) [168] . The factors that trigger hantavirus outbreaks are still poorly understood, probably because they result from several interacting biotic and abiotic features whose key parameters are difficult to model. However, the use of new modeling approaches that involve geographical and environmental features seem to be promising in predicting potential hantavirus outbreaks and/or areas of higher risk [169] [170] [171] [172] . Because hantaviruses are known to be directly transmitted from infected to susceptible hosts, the first natural approach is to relate outbreaks to the ecology of the viral hosts. Hantavirus transmission and persistence in rodent populations depends on several factors that interact to affect ecological dynamics of the host, which in turn is strongly influenced by the behavioral characteristics of individual rodent species, to landscape structure, and environmental features [173, 174] . Viral transmission depends on contact rates among susceptible hosts, and despite the prevailing notion that a higher density increases encounters and hence secondary infected hosts, contrasting patterns relating rodent population size and virus prevalence can be found [175] . In addition, it has been shown that SNV transmission follows a contact heterogeneity pattern, where individuals in the population have different probability of transmitting the infection [176] . The understanding of viral transmission proves to be far more complex when species other than the main reservoir host are incorporated in the model. In fact, recent studies have shown that higher hosts species diversity is correlated with lower infection prevalence in North America for P. maniculatus [177] , in Central America for O. fulvescens (reservoir of Choclo virus) and Zygodontomys brevicauda (reservoir of Calabazo virus) [178] , and in South America for Akodon montensis (reservoir of Jabora virus) [162] . Contact rates vary according to the spatial distribution of populations and seem to be strongly influenced by landscape structure. For example, SNV prevalence in P. maniculatus was higher in landscapes with a higher level of fragmentation of the preferred habitat [179] . In addition, certain properties of the landscape such as elevation, slope, and land cover seem to be useful in detecting areas with persistent SNV infections, and therefore thought to be refugial areas where the virus can be maintained for years [169] . Changes in the natural environment of reservoir species, such as forest fragmentation and habitat loss, may alter population abundance and distribution and lead to hantavirus outbreaks, as observed in the Azurero Peninsula of Panama [118, 119] . Also, differences in the microhabitat, including overstory cover, may lead to differences in the ecological dynamics within populations and affect the rate of exposure to the virus [180] . Differences in hantavirus infections through contrasting landscapes in the latitudinal span have been found in rodent populations of O. longicaudatus in Chile, suggesting that humans are differentially exposed to the virus [107, 181] . Rodent population dynamics are affected by seasonal changes of weather and climate [182, 183] . In the case of the ENSO-associated outbreaks, a complex cascade of events triggered by highly unusual rains in the precedent year have been postulated to result in an increase of primary production and rodent densities, also increasing the likelihood of transmission of the virus to humans, but it has proved difficult to precisely demonstrate the suggested intermediate events such as increased rodent densities in the increased caseload [116, 121, 184] . In South America, effects of climate change and hantavirus outbreaks have not been well studied, despite the knowledge that several rodents species that are reservoirs of emerging diseases have dramatically been affected by events like El Niño [185] . Changes in host population dynamics are also affected by seasonality, which may lead to disease outbreaks when processes that equilibrate rodent populations from season to season are interrupted [186] . Viral emergence may continue to be promoted as human-introduced changes continue to increase in the environment at different geographical scales. Human incursions into previously uncultivated environments may lead to new contacts between rodent reservoirs and humans, increasing the likelihood of contracting infections [187] . These changes may also alter rodent's population structure and dynamics and interspecies interactions creating conditions that may lead to viral outbreaks, viral establishment in new hosts, and emergence of HCPS [102, 162] , even with seemingly slight ecological disturbance to the virus-host system [188] . Certain pathophysiologic characteristics, including thrombocytopenia and shock, of hantavirus diseases of humans, bear substantial similarity to the hemorrhagic fevers induced by other viruses such arenaviruses, filoviruses and flaviviruses, despite sharing essentially no sequence similarities therewith. Such observations raise questions about whether such commonalities in pathogenesis are chance similarities of phenotype, or instead report the presence of common molecular mechanisms among the viruses. In this review we discuss the general properties, discoveries and epidemiology/ecology of the New World forms of pathogenic hantaviruses, and also seek to identify some of the characteristics of the viral macromolecules and immunologic mechanisms that have been proposed as potential direct mediators of the pathogenic events that characterize the human disease HCPS. While it is unlikely that expression of any particular viral protein or RNAs in isolation can be relied upon to replicate key phenotypes of infection by the complete virus, some of the findings have been sufficiently consistent with what is known of the pathogenesis in vivo that they offer plausible first-pass leads in the search for therapeutic targets. We look forward to the mechanistic revelations that will follow the inevitably expanded usage of powerful methods such as deep sequencing, ever-more advanced imaging, and microscopic methods, and animal models that can at last be said to be close mimics of human hantavirus disease.

When does the cleavage appear to be signaled?

Genomic characterization of the 2019 novel human-pathogenic coronavirus isolated from a patient with atypical pneumonia after visiting Wuhan https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7067204/ SHA: c097a8a9a543d69c34f10e5c3fd78019e560026a Authors: Chan, Jasper Fuk-Woo; Kok, Kin-Hang; Zhu, Zheng; Chu, Hin; To, Kelvin Kai-Wang; Yuan, Shuofeng; Yuen, Kwok-Yung Date: 2020-01-28 DOI: 10.1080/22221751.2020.1719902 License: cc-by Abstract: A mysterious outbreak of atypical pneumonia in late 2019 was traced to a seafood wholesale market in Wuhan of China. Within a few weeks, a novel coronavirus tentatively named as 2019 novel coronavirus (2019-nCoV) was announced by the World Health Organization. We performed bioinformatics analysis on a virus genome from a patient with 2019-nCoV infection and compared it with other related coronavirus genomes. Overall, the genome of 2019-nCoV has 89% nucleotide identity with bat SARS-like-CoVZXC21 and 82% with that of human SARS-CoV. The phylogenetic trees of their orf1a/b, Spike, Envelope, Membrane and Nucleoprotein also clustered closely with those of the bat, civet and human SARS coronaviruses. However, the external subdomain of Spike’s receptor binding domain of 2019-nCoV shares only 40% amino acid identity with other SARS-related coronaviruses. Remarkably, its orf3b encodes a completely novel short protein. Furthermore, its new orf8 likely encodes a secreted protein with an alpha-helix, following with a beta-sheet(s) containing six strands. Learning from the roles of civet in SARS and camel in MERS, hunting for the animal source of 2019-nCoV and its more ancestral virus would be important for understanding the origin and evolution of this novel lineage B betacoronavirus. These findings provide the basis for starting further studies on the pathogenesis, and optimizing the design of diagnostic, antiviral and vaccination strategies for this emerging infection. Text: Coronaviruses (CoVs) are enveloped, positive-sense, single-stranded RNA viruses that belong to the subfamily Coronavirinae, family Coronavirdiae, order Nidovirales. There are four genera of CoVs, namely, Alphacoronavirus (αCoV), Betacoronavirus (βCoV), Deltacoronavirus (δCoV), and Gammacoronavirus (γCoV) [1] . Evolutionary analyses have shown that bats and rodents are the gene sources of most αCoVs and βCoVs, while avian species are the gene sources of most δCoVs and γCoVs. CoVs have repeatedly crossed species barriers and some have emerged as important human pathogens. The best-known examples include severe acute respiratory syndrome CoV (SARS-CoV) which emerged in China in 2002-2003 to cause a large-scale epidemic with about 8000 infections and 800 deaths, and Middle East respiratory syndrome CoV (MERS-CoV) which has caused a persistent epidemic in the Arabian Peninsula since 2012 [2, 3] . In both of these epidemics, these viruses have likely originated from bats and then jumped into another amplification mammalian host [the Himalayan palm civet (Paguma larvata) for SARS-CoV and the dromedary camel (Camelus dromedarius) for MERS-CoV] before crossing species barriers to infect humans. Prior to December 2019, 6 CoVs were known to infect human, including 2 αCoV (HCoV-229E and HKU-NL63) and 4 βCoV (HCoV-OC43 [ HCoV-OC43 and HCoV-HKU1 usually cause self-limiting upper respiratory infections in immunocompetent hosts and occasionally lower respiratory tract infections in immunocompromised hosts and elderly [4] . In contrast, SARS-CoV (lineage B βCoV) and MERS-CoV (lineage C βCoV) may cause severe lower respiratory tract infection with acute respiratory distress syndrome and extrapulmonary manifestations, such as diarrhea, lymphopenia, deranged liver and renal function tests, and multiorgan dysfunction syndrome, among both immunocompetent and immunocompromised hosts with mortality rates of ∼10% and ∼35%, respectively [5, 6] . On 31 December 2019, the World Health Organization (WHO) was informed of cases of pneumonia of unknown cause in Wuhan City, Hubei Province, China [7] . Subsequent virological testing showed that a novel CoV was detected in these patients. As of 16 January 2020, 43 patients have been diagnosed to have infection with this novel CoV, including two exported cases of mild pneumonia in Thailand and Japan [8, 9] . The earliest date of symptom onset was 1 December 2019 [10] . The symptomatology of these patients included fever, malaise, dry cough, and dyspnea. Among 41 patients admitted to a designated hospital in Wuhan, 13 (32%) required intensive care and 6 (15%) died. All 41 patients had pneumonia with abnormal findings on chest computerized tomography scans [10] . We recently reported a familial cluster of 2019-nCoV infection in a Shenzhen family with travel history to Wuhan [11] . In the present study, we analyzed a 2019-nCoV complete genome from a patient in this familial cluster and compared it with the genomes of related βCoVs to provide insights into the potential source and control strategies. The complete genome sequence of 2019-nCoV HKU-SZ-005b was available at GenBank (accession no. MN975262) ( Table 1 ). The representative complete genomes of other related βCoVs strains collected from human or mammals were included for comparative analysis. These included strains collected from human, bats, and Himalayan palm civet between 2003 and 2018, with one 229E coronavirus strain as the outgroup. Phylogenetic tree construction by the neighbour joining method was performed using MEGA X software, with bootstrap values being calculated from 1000 trees [12] . The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (1000 replicates) was shown next to the branches [13] . The tree was drawn to scale, with branch lengths in the same units as those of the evolutionary distances used to infer the phylogenetic tree. The evolutionary distances were computed using the Poisson correction method and were in the units of the number of amino acid substitutions per site [14] . All ambiguous positions were removed for each sequence pair (pairwise deletion option). Evolutionary analyses were conducted in MEGA X [15] . Multiple alignment was performed using CLUSTAL 2.1 and further visualized using BOX-SHADE 3.21. Structural analysis of orf8 was performed using PSI-blast-based secondary structure PREDiction (PSIPRED) [16] . For the prediction of protein secondary structure including beta sheet, alpha helix, and coil, initial amino acid sequences were input and analysed using neural networking and its own algorithm. Predicted structures were visualized and highlighted on the BOX-SHADE alignment. Prediction of transmembrane domains was performed using the TMHMM 2.0 server (http://www.cbs.dtu.dk/services/TMHMM/). Secondary structure prediction in the 5 ′ -untranslated region (UTR) and 3 ′ -UTR was performed using the RNAfold WebServer (http://rna.tbi.univie.ac.at/cgi-bin/ RNAWebSuite/RNAfold.cgi) with minimum free energy (MFE) and partition function in Fold algorithms and Table 2 . Putative functions and proteolytic cleavage sites of 16 nonstructural proteins in orf1a/b as predicted by bioinformatics. Putative function/domain Amino acid position Putative cleave site complex with nsp3 and 6: DMV formation complex with nsp3 and 4: DMV formation short peptide at the end of orf1a basic options. The human SARS-CoV 5 ′ -and 3 ′ -UTR were used as references to adjust the prediction results. The single-stranded RNA genome of the 2019-nCoV was 29891 nucleotides in size, encoding 9860 amino acids. The G + C content was 38%. Similar to other (Table 2 ). There are no remarkable differences between the orfs and nsps of 2019-nCoV with those of SARS-CoV (Table 3) . The major distinction between SARSr-CoV and SARS-CoV is in orf3b, Spike and orf8 but especially variable in Spike S1 and orf8 which were previously shown to be recombination hot spots. Spike glycoprotein comprised of S1 and S2 subunits. The S1 subunit contains a signal peptide, followed by an N-terminal domain (NTD) and receptor-binding domain (RBD), while the S2 subunit contains conserved fusion peptide (FP), heptad repeat (HR) 1 and 2, transmembrane domain (TM), and cytoplasmic domain (CP). We found that the S2 subunit of 2019-nCoV is highly conserved and shares 99% identity with those of the two bat SARS-like CoVs (SL-CoV ZXC21 and ZC45) and human SARS-CoV (Figure 2 ). Thus the broad spectrum antiviral peptides against S2 would be an important preventive and treatment modality for testing in animal models before clinical trials [18] . Though the S1 subunit of 2019-nCoV shares around 70% identity to that of the two bat SARS-like CoVs and human SARS-CoV (Figure 3(A) ), the core domain of RBD (excluding the external subdomain) are highly conserved (Figure 3(B) ). Most of the amino acid differences of RBD are located in the external subdomain, which is responsible for the direct interaction with the host receptor. Further investigation of this soluble variable external subdomain region will reveal its receptor usage, interspecies transmission and pathogenesis. Unlike 2019-nCoV and human SARS-CoV, most known bat SARSr-CoVs have two stretches of deletions in the spike receptor binding domain (RBD) when compared with that of human SARS-CoV. But some Yunnan strains such as the WIV1 had no such deletions and can use human ACE2 as a cellular entry receptor. It is interesting to note that the two bat SARS-related coronavirus ZXC21 and ZC45, being closest to 2019-nCoV, can infect suckling rats and cause inflammation in the brain tissue, and pathological changes in lung & intestine. However, these two viruses could not be isolated in Vero E6 cells and were not investigated further. The two retained deletion sites in the Spike genes of ZXC21 and ZC45 may lessen their likelihood of jumping species barriers imposed by receptor specificity. A novel short putative protein with 4 helices and no homology to existing SARS-CoV or SARS-r-CoV protein was found within Orf3b ( Figure 4 ). It is notable that SARS-CoV deletion mutants lacking orf3b replicate to levels similar to those of wildtype virus in several cell types [19] , suggesting that orf3b is dispensable for viral replication in vitro. But orf3b may have a role in viral pathogenicity as Vero E6 but not 293T cells transfected with a construct expressing Orf3b underwent necrosis as early as 6 h after transfection and underwent simultaneous necrosis and apoptosis at later time points [20] . Orf3b was also shown to inhibit expression of IFN-β at synthesis and signalling [21] . Subsequently, orf3b homologues identified from three bat SARSrelated-CoV strains were C-terminally truncated and lacked the C-terminal nucleus localization signal of SARS-CoV [22] . IFN antagonist activity analysis demonstrated that one SARS-related-CoV orf3b still possessed IFN antagonist and IRF3-modulating activities. These results indicated that different orf3b proteins display different IFN antagonist activities and this function is independent of the protein's nuclear localization, suggesting a potential link between bat SARS-related-CoV orf3b function and pathogenesis. The importance of this new protein in 2019-nCoV will require further validation and study. Orf8 orf8 is an accessory protein found in the Betacoronavirus lineage B coronaviruses. Human SARS-CoVs isolated from early-phase patients, all civet SARS-CoVs, and other bat SARS-related CoVs contain fulllength orf8 [23] . However, a 29-nucleotide deletion, Bat SL-CoV ZXC21 2018 Bat which causes the split of full length of orf8 into putative orf8a and orf8b, has been found in all SARS-CoV isolated from mid-and late-phase human patients [24] . In addition, we have previously identified two bat SARS-related-CoV (Bat-CoV YNLF_31C and YNLF_34C) and proposed that the original SARS-CoV full-length orf8 is acquired from these two bat SARS-related-CoV [25] . Since the SARS-CoV is the closest human pathogenic virus to the 2019-nCoV, we performed phylogenetic analysis and multiple alignments to investigate the orf8 amino acid sequences. The orf8 protein sequences used in the analysis derived from early phase SARS-CoV that includes full-length orf8 (human SARS-CoV GZ02), the mid-and late-phase SARS-CoV that includes the split orf8b (human SARS-CoV Tor2), civet SARS-CoV (paguma SARS-CoV), two bat SARS-related-CoV containing full-length orf8 (bat-CoV YNLF_31C and YNLF_34C), 2019-nCoV, the other two closest bat SARS-related-CoV to 2019-nCoV SL-CoV ZXC21 and ZC45), and bat SARS-related-CoV HKU3-1 ( Figure 5(A) ). As expected, orf8 derived from 2019-nCoV belongs to the group that includes the closest genome sequences of bat SARS-related-CoV ZXC21 and ZC45. Interestingly, the new 2019-nCoV orf8 is distant from the conserved orf8 or Figure 5(B) ) which was shown to trigger intracellular stress pathways and activates NLRP3 inflammasomes [26] , but this is absent in this novel orf8 of 2019-nCoV. Based on a secondary structure prediction, this novel orf8 has a high possibility to form a protein with an alpha-helix, following with a betasheet(s) containing six strands ( Figure 5(C) ). The genome of 2019-nCoV has overall 89% nucleotide identity with bat SARS-related-CoV SL-CoVZXC21 (MG772934.1), and 82% with human SARS-CoV BJ01 2003 (AY278488) and human SARS-CoV Tor2 (AY274119). The phylogenetic trees constructed using the amino acid sequences of orf1a/b and the 4 structural genes (S, E, M, and N) were shown (Figure 6(A-E) ). For all these 5 genes, the 2019-nCoV was clustered with lineage B βCoVs. It was most closely related to the bat SARS-related CoVs ZXC21 and ZC45 found in Chinese horseshoe As shown in Figure 7 (A-C), the SARS-CoV 5 ′ -UTR contains SL1, SL2, SL3, SL4, S5, SL5A, SL5B, SL5C, SL6, SL7, and SL8. The SL3 contains trans-cis motif [27] . The SL1, SL2, SL3, SL4, S5, SL5A, SL5B, and SL5C structures were similar among the 2019-nCoV, human SARS-CoV and the bat SARS-related ZC45. In the 2019-nCoV, part of the S5 found was inside Figure 7 Continued the orf1a/b (marked in red), which was similar to SARS-CoV. In bat SARS-related CoV ZC45, the S5 was not found inside orf1a/b. The 2019-nCoV had the same SL6, SL7, and SL8 as SARS-CoV, and an additional stem loop. Bat SARS-related CoV ZC45 did not have the SARS-COV SL6-like stem loop. Instead, it possessed two other stem loops in this region. All three strains had similar SL7 and SL8. The bat SARS-like CoV ZC45 also had an additional stem loop between SL7 and SL8. Overall, the 5 ′ -UTR of 2019-nCoV was more similar to that of SARS-CoV than the bat SARS-related CoV ZC 45. The biological relevance and effects of virulence of the 5 ′ -UTR structures should be investigated further. The 2019-nCoV had various 3 ′ -UTR structures, including BSL, S1, S2, S3, S4, L1, L2, L3, and HVR (Figure 7(D-F) ). The 3 ′ -UTR was conserved among 2019-nCoV, human SARS-CoV and SARS-related CoVs [27] . In summary, 2019-nCoV is a novel lineage B Betacoronavirus closely related to bat SARS-related coronaviruses. It also has unique genomic features which deserves further investigation to ascertain their roles in viral replication cycle and pathogenesis. More animal sampling to determine its natural animal reservoir and intermediate animal host in the market is important. This will shed light on the evolutionary history of this emerging coronavirus which has jumped into human after the other two zoonotic Betacoroanviruses, SARS-CoV and MERS-CoV.

What COVs were known to infect humans before December 2019?

Hantaviruses in the Americas and Their Role as Emerging Pathogens https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3185593/ SHA: efe13a8d42b60ef9f7387ea539a1b2eeb5f80101 Authors: Hjelle, Brian; Torres-Pérez, Fernando Date: 2010-11-25 DOI: 10.3390/v2122559 License: cc-by Abstract: The continued emergence and re-emergence of pathogens represent an ongoing, sometimes major, threat to populations. Hantaviruses (family Bunyaviridae) and their associated human diseases were considered to be confined to Eurasia, but the occurrence of an outbreak in 1993–94 in the southwestern United States led to a great increase in their study among virologists worldwide. Well over 40 hantaviral genotypes have been described, the large majority since 1993, and nearly half of them pathogenic for humans. Hantaviruses cause persistent infections in their reservoir hosts, and in the Americas, human disease is manifest as a cardiopulmonary compromise, hantavirus cardiopulmonary syndrome (HCPS), with case-fatality ratios, for the most common viral serotypes, between 30% and 40%. Habitat disturbance and larger-scale ecological disturbances, perhaps including climate change, are among the factors that may have increased the human caseload of HCPS between 1993 and the present. We consider here the features that influence the structure of host population dynamics that may lead to viral outbreaks, as well as the macromolecular determinants of hantaviruses that have been regarded as having potential contribution to pathogenicity. Text: Emerging pathogens cause new or previously unrecognized diseases, and among them, emerging zoonotic diseases are a major concern among scientists studying infectious diseases at different spatial and temporal scales [1, 2] . Changes in biotic and abiotic conditions may alter population disease dynamics and lead to the emergence of zoonotic infections [3] [4] [5] [6] . During the last decades, several outbreaks of emerging and re-emerging viral pathogens have occurred, affecting both purely-local and worldwide/pandemic involvement of human populations. Among the conspicuous examples are influenza A, Ebola virus, hepatitis C virus, severe adult respiratory distress (SARS), coronavirus, and human immunodeficiency virus, which challenge prevention and control measures of public health systems [7] . In the Americas, the recent outbreak of pandemic influenza A subtype H1N1 became a major target for control due to its rapid spread, and uncertainties in virulence and transmissibility, yet vaccine availability was limited when significant activity occurred in advance of the traditional influenza season [8] . However, in the last century outbreaks of several viral-related diseases have emerged or re-emerged involving arenaviruses and dengue viruses, and more recently, hantaviruses, and the expansion of the geographic range of West Nile virus. Among zoonotic diseases, small mammals are hosts of several pathogenic RNA viruses, especially Arenaviridae and Bunyaviridae: Hantavirus [9] [10] [11] . Hantavirus infections became a concern in the Americas after the description of an outbreak of acute respiratory distress occurred in the Four Corners area in 1993 [12] . The newly recognized disease, hantavirus cardiopulmonary syndrome, HCPS (or hantavirus pulmonary syndrome), was linked to infection by the newly-discovered Sin Nombre virus (SNV), and the rodent Peromyscus maniculatus (deer mouse) was identified as the reservoir [13] . However, hantavirus infections have a much longer history. A review of ancient Chinese writings, dating back to approximately 960 AD, revealed descriptions closely resembling hemorrhagic fever with renal syndrome (HFRS), the syndrome caused by Old World hantaviruses [14] . During the twentieth century, cases of acute febrile disease with renal compromise were described from several Eurasian countries and Japan, often in association with military engagements [15] . HFRS as a distinct syndrome, however, was first brought to the attention of western medicine in association with an outbreak that occurred among United Nations troops during the Korean conflict between 1951 and 1954, where more than 3,200 soldiers were afflicted [16] . It took more than two decades until the etiologic agent, Hantaan virus (HTNV), was isolated from the striped field mouse Apodemus agrarius, detected in part by the binding of antibodies from patient serum samples to the lung tissues of healthy, wild-caught field mice [17, 18] . The virus was later found to represent the type species of a new genus Hantavirus of the family Bunyaviridae, although it was later apparent that the first hantavirus to be isolated was the shrew-borne Thottapalayam virus [19] . The categorization of hantaviruses as belonging to the family Bunyaviridae is due in part to the consistent presence of three RNA genomes that are circularized in vivo as a result of the presence of terminal complementary nucleotides that help fold the genome into a -hairpin‖ morphology, first described for the Uukuniemi phlebovirus [19, 20] . Table 1 is a list of the predominant, serologically distinct pathogenic hantaviruses. Many other named genotypes are described, but such other pathogenic forms are generally closely related to Andes or, in some cases, Sin Nombre virus. During virus maturation, the precursor form GPC is processed using a membrane -bound protease into Gn and Gc, a cleavage that occurs, and appears to be signaled, after the conserved peptide signal WAASA at the C-terminal of Gn [24] . Although the two proteins can be expressed independently through transfection, they can be retained in the wrong cellular compartment (ER or aggresome); they thus must be co-expressed to allow them stability so that the two can be assembled correctly in the Golgi [25, [27] [28] [29] . A number of activities and properties have been identified for the hantavirus envelope glycoproteins, including some features that are suspected to be involved in the pathogenicity of the disease-causing serotypes, a possibility that has engendered experimental attention. The glycoproteins are the known or presumed ligands for at least two distinct cellular receptors, the 3 integrin chain and decay accelerating factor, or DAF [30, 31] ; with gC1qR/p32 also identified as another potential entry receptor [32] . Comparisons with the tick-borne encephalitis virus E protein, led Tischler et al. to consider the Gc glycoprotein as a potential class II fusion protein, perhaps imparting fusion activity to the virion, and this hypothesis has gained support in other studies [33, 34] . Additional activities have been identified with, or claimed to be related to, Gn. For many of these studies, an underlying premise has held that there are differences between the glycoproteins of -pathogenic‖ hantaviruses relative to viruses in the genus that are dubbed to be -non-pathogenic‖. While it is true that it has not yet been possible to link Prospect Hill virus (PHV) to human disease, the absence of evidence for its pathogenicity should perhaps not be equated with the evidence of its absence. One might only consider that the level of disease (e.g., lethargy, fever, proteinuria, and azotemia) associated with infection of nonhuman primates by PHV is not significantly different from that recorded for nonhuman primate models using the known-pathogen Puumala virus (PUUV) [35, 36] . For the purpose of this discussion we will presume that apathogenic hantaviruses are indeed apathogenic. While some studies have suggested that Gn glycoproteins are directed more rapidly into the ubiquitin-proteosome pathway than are apathogenic forms, others have interpreted differences in the handling of Gn glycoproteins across hantavirus species by the ubiquitin-proteosomal system as independent of pathogenicity [37] [38] [39] . Some investigators have directed their efforts toward identifying a differential capacity, either kinetic or in absolute magnitude, in the ability of pathogenic and apathogenic hantaviruses to elicit an interferon response in cells. One premise that emerges is that apathogenic forms would tend to induce an earlier innate response that would render it more likely that the virus would be quickly cleared or rendered less competent in its replication so as to blunt any pathological response in the host [40] [41] [42] . The anti-hantavirus innate response can in some cases be attributed to viral interaction as a ligand of TLR-3, but not in others, and in endothelial cells, it appears not to require more than the viral particle itself, even when introduced in replication-incompetent form [43, 44] . Proteins and mRNAs prominently induced by hantaviruses include MxA and IFIT-1 (ISG-56) and others including some with known or suspected anti-viral activity. Those hantaviruses, often highly pathogenic strains, that fail to induce a potent antiviral response, are suspected or presumed to have a (more) potent interferon-pathway antagonism mechanism relative to other viruses, a mechanism that acts positively to prevent an effective innate response from forming, at least early in infection [42, 45] . Yet some instances are reported wherein highly pathogenic hantaviruses, such as SNV, are also able to induce expression of interferon-stimulated gene mRNAs, even very early in infection, with ISG proteins, as expected, taking longer to appear in the cell [44] . Anti-interferon activities have also been attributed to the NSs protein that may be elaborated in cells infected by serotypes that encode this protein [46] . Other investigators have examined the activities of hantavirus glycoproteins and other proteins that might themselves directly affect some aspects of the pathogenic progression associated with hantavirus infection of humans, such as vascular permeability changes. While early attempts to directly cause increases in permeability of endothelial monolayers with viral particles or viral infection were largely disappointing, hantaviruses have been identified as adversely affecting endothelial migration over substrata and in potentiating VEG-F-induced endothelial permeability [47, 48] . The shorter (50-kD) nucleocapsid or N protein is a structural component of the viral nucleocapsid, along with the genomic viral RNA segments. As an RNA-binding protein that engages the hairpin termini of the genomic segments with high affinity [49, 50] , it limits the access of the RNA to host nucleases and helps to render viral replication a closed process within the cytoplasm. It also acts as a peripheral membrane protein, as does the L protein [51] , an activity that could play a role in its presumed, but not yet demonstrated function as matrix [52] . Until recently, it had not been appreciated that N has a wide variety of other activities, some of which can be linked, not only to fundamental requirements of replication, but also to the interference with an array of the intracellular processes of the normal cell. Thus, an interaction between the amino terminus of the hantavirus N protein and the cellular protein Daxx has been proposed, with the suggestion of potential pro-apoptotic consequences [51] . N is also reported to interact with actin microfilaments, and the SUMO-1 protein [53, 54] . Using reporter-gene based assays, Connie Schmaljohn and her colleagues have reported that Hantaan virus' nucleocapsid protein has an inhibitory role in inflammatory responses mediated by NF kappa B (NF-B). The effects on NF-B expression appeared to be confined to prevention of its nuclear translocation after its attempted activation with lipopolysaccharide, LPS [55] . In the cytoplasm of infected cells, N protein can be found in cellular P bodies where it sequesters and protects 5' caps. It may locate the caps through its interaction with DCP1, a key constituent of P bodies. During hantavirus infection, the viral RNAs become concentrated in P bodies, through their interaction with N and DCP1. The N protein demonstrates preferential protection of mRNAs engineered to prematurely terminate their encoded protein in comparison to native mRNAs [56] . N protein has been increasingly linked to viral replication and translation, sometimes in previously unanticipated ways. It is among a growing family of diverse viral proteins that can serve as a nonspecific -RNA chaperone‖, an activity that should facilitate the L polymerase's access to vRNA for transcription and replication, in that it can transiently dissociate misfolded RNA structures [57] . Some of N protein's effects on translation might not immediately be recognized to be adaptive in nature. It can replace the entire EIF4F translational initiation complex, simultaneously presenting the ribosome with a replacement for the cap-binding activity of eIF 4E, binding to the 43S pre-initiation complex as does eIF 4G, while replacing the helicase activity of eIF 4A, which is presumed to be needed to dissociate higher-order RNA structure [56, 58] . These three factors normally work together to achieve translational initiation. In P bodies, N protein's ability to bind at high affinity to capped native cellular oligoribonucleotides, along with its activity in protecting capped RNAs from degradation likely facilitates the access of capped oligonucleotides for use in transcriptional initiation by L polymerase (-cap snatching‖). Trafficking of N for viral assembly: Classically, N protein in infected cells appears to be clustered or particulate in nature, with a heavy concentration at a single perinuclear location, widely considered to be the Golgi [27] . The N proteins of hantaviruses are found in association with particulate fractions, and confocal microscopy and biochemical-inhibitor studies have shown that N tracks along microtubules but not with actin filaments [52] . The ultimate destination for N, for its assembly into viral particles is the Golgi, and it traffics there via the endoplasmic reticulum-Golgi intermediate complex (ERGIC), also known as vesicular-tubular cluster [52] . A dominant negative inhibitor, dynamitin, associated with dynein-mediated transport, reduced N's accumulation in the Golgi. Later studies suggested that the specific dependence on microtubular transport is specific to Old World hantaviruses such as HTNV, but that the New World hantavirus ANDV is instead associated with actin filaments [59] . However, recent data indicates that microtubular transport is indeed utilized for the New World hantavirus SNV [60] . Hantavirus diseases of man have long been suspected of having an immunopathogenic basis in part because of their relatively long incubation period of 2-3 weeks and the observed temporal association between immunologic derangements and the first appearance of signs and symptoms of hantavirus illness. HFRS and HCPS share many clinical features, leading many investigators to consider them to be, in essence, different manifestations of a similar pathogenic process, differing mainly in the primary target organs of disease expression ( Table 2 ). The pathogenesis of hantavirus infections is the topic of a continuously-updated review in the series UpToDate [61] . By the time symptoms appear in HCPS, both strong antiviral responses, and, for the more virulent viral genotypes, viral RNA can be detected in blood plasma or nucleated blood cells respectively [63, 64] . At least three studies have correlated plasma viral RNA with disease severity for HCPS and HFRS, suggesting that the replication of the virus plays an ongoing and real-time role in viral pathogenesis [65] [66] [67] . Several hallmark pathologic changes have been identified that occur in both HFRS and HCPS. A critical feature of both is a transient (~ 1-5 days) capillary leak involving the kidney and retroperitoneal space in HFRS and the lungs in HCPS. The resulting leakage is exudative in character, with chemical composition high in protein and resembling plasma. The continued experience indicating the strong tissue tropism for endothelial cells, specifically, is among the several factors that make β3 integrin an especially attractive candidate as an important in vivo receptor for hantaviruses. It is likely that hantaviruses arrive at their target tissues through uptake by regional lymph nodes, perhaps with or within an escorting lung histiocyte. The virus seeds local endothelium, where the first few infected cells give rise, ultimately, to a primary viremia, a process that appears to take a long time for hantavirus infections [62, 63] . By the time that secondary viremia emerges, the agents of the more severe forms of HFRS and HCPS have begun to achieve sufficient mass as to induce, through PAMP-PRR interactions and other means, the expression of proinflammatory cytokines [64] . For HCPS, that expression favors the pulmonary bed and lymphoid organs, yet, for unknown reasons, spares the retroperitoneum and, in general, the kidney. In HFRS the situation is reversed, and yet it is often not appreciated that the expected preferential tissue tropism of HFRS-associated viruses and their HCPS-associated counterparts for the renal and pulmonary beds, respectively, is not as one would predict through the manifestations of the two diseases. Local elaboration of inflammatory and chemotactic mediators is considered to be a requirement for the development of systemic disease symptoms, with those abnormalities sometimes culminating in shock and death. Yet it is not hypoxemia, due to the prominent pulmonary edema, that leads to death in most fatal cases of HCPS, but rather intoxication of the heart by as-yet-undefined mediators that leads to the low cardiac output state and the associated shock syndrome [64, 65] . It is tempting to speculate that mediators produced in the lung in connection with the inflammatory infiltrate can percolate through the coronary circulation with minimal dilution in HCPS, a disadvantageous consequence of the close anatomic juxtaposition of the two organs. Thus, at least three classes of potential mechanisms, some overlapping and all certainly nonexclusive of the others, could be presumed to underlie the pathogenesis of HCPS. These include: (1) Innate immune mechanisms. The nature of interactions between hantavirus pathogen-associated molecular patterns (PAMP) with the pattern recognition receptors (PRR) of susceptible endothelial cells are beginning to be clarified. The prototypical HTNV appears to be recognized by TLR-3 [43] . Such an infection has consequences such as increased expression of HLA-DR in dendritic cells [66] and differentiation of monocytes toward dendritic cells [67] . (2) Direct viral effects. The observed correlation between viral load and disease severity leaves the possibility open that hantavirus particles or RNA can themselves have toxic effects on cells or on signaling. Some investigators have favored direct viral toxicity, acting through the inhibition of endothelial cell barrier function, as an explanation for much of the capillary leak, although there is widespread agreement that multiple mechanisms that mediate pathogenesis likely operate simultaneously in the affected patient [68] . A potentially important clue toward the mechanism by which hantavirus infections deplete blood platelets and, in some cases cause hemorrhagic manifestations, was advanced by the recent discovery that pathogenic hantaviruses are able to recruit platelets to adhere to endothelial cell surfaces, with β3 integrin used as a critical binding element [69] . (3) Pathogenic effects caused by the activities of specific viral macromolecules. We have reviewed some of the activities associated with the Gn, Gc and N, virally-encoded polypeptides in previous sections. Testing models of pathogenesis can be done more effectively when there is an animal model that mimics key aspects of the disease. There is no such model that closely mimics HFRS, but animal models exist for both the asymptomatic carriage of PUUV and SNV by their native carrier rodents, the bank vole Myodes glareolus and the deer mouse P. maniculatus; as well as a Syrian hamster model using ANDV or the related Maporal virus from Venezuela, for which an HCPS-mimetic disease is observed [70] [71] [72] [73] . The ANDV-Syrian hamster model has a number of features in common with the human disease, as well as some differences. Unlike the neurologic diseases that have been possible to elicit with HTNV, the hamster model for HCPS appears to be caused by capillary leak that results in pulmonary edema and the production of a pleural effusion with exudative characteristics. Typically the hamsters die between 11 and 14-d post-inoculation, reflecting a slightly accelerated incubation period in comparison to human infections. As with human HCPS, the microscopic examination of the lung reveals abundant fibrin deposition, thickened alveolar septa, and viral antigen expressed abundantly in the microvascular endothelium. ANDV-infected hamsters fitted with physiologic monitoring devices exhibited diminished pulse pressures, tachycardia, and hypotension that appear to closely mimic the shock that is believed to be the proximate cause of demise in patients who succumb to HCPS [65, 74] . Compared to the human disease, ANDV-infected hamsters exhibit exceptionally high titers of live ANDV in their tissues, with much of the viral replication occurring in hepatocytes, which are spared in the human disease. Titers of live ANDV in some cases exceed 10 8 /g, whereas hantavirus isolates from human tissues have been notoriously difficult to obtain. Despite the universal occurrence of mildly-elevated hepatic enzymes in patients with HCPS, hepatic enzymes do not appear to be present at elevated levels in the blood of diseased hamsters even immediately before death [75] . The protracted incubation period associated with hantavirus disease gives the host considerable time to mount a mature immune response against the virus. Thus, in contradistinction to infections of comparable severity and related symptomatology associated with arenaviruses and filoviruses, hantavirus infections of humans are associated with antibody responses of significant titer by the time symptoms commence. Despite this observation, it appears to be possible that natural variation in individual neutralizing antibody responses among patients with SNV infections can be linked to disease severity, suggesting that administration of antiviral antibodies could prove effective therapeutically [76] . In the case of ANDV infection, new evidence has emerged indicating that the apparent clearance of the virus from the blood does not result in the complete removal of antigenic stimulus by the virus, suggesting that the virus may persist, perhaps in some as-yet undetermined immunologically privileged site [77] . A role for T cell-mediated pathological responses in HFRS and HCPS has been the source of speculation for a variety of reasons. The severity of SNV-associated HCPS may have made it more apparent that the onset of pulmonary edema, tachycardia and hypertension seemed to be all but universally temporally associated with the appearance of a spectrum of highly-activated cells of the lymphoid lineage in the peripheral blood. Cells with a close morphologic similarity to these -immunoblasts‖ were detected in the congested, heavy lungs of patients who came to autopsy, as well as in lymphoid organs and in the portal triads [63, [78] [79] [80] . These observations led to speculation that some component of hantavirus pathogenesis could be linked to the appearance of antiviral T cells that could stimulate or contribute to the appearance of a -storm‖ of mediators and the associated capillary leak phenotype. Subsequent studies have borne out the expectation that a significant fraction of the immunoblast population in patients with HCPS are T cells with specificity for specific class I HLA-presented epitopes of viral antigens, including Gn, Gc and N [77, [81] [82] [83] . Presumably, the antiviral activities of such cells, manifested in part through their elaboration of mediators in the affected interstitium, can contribute to the endothelial/capillary leak that lies at the heart of hantavirus pathogenesis. Because early cases of HCPS often came to autopsy, it became possible to examine necropsied tissues for expression of cytokines. The study by Mori et al. (1999) revealed high relative expression of proinflammatory cytokines including TNF, IL-1, IL-6, providing evidence in favor of a -cytokine storm‖ model for pathogenesis [64] . The authors believed, based on the morphology of cytokine-secreting cells, that both monocytes and lymphocytes were contributing to the production of cytokines. That proinflammatory mediators are found in elevated levels in the plasma as well as the renal interstitium of patients with acute hantaviral illness has been recognized for some time as well [84, 85] . While diagnosis of HCPS as well as HFRS is best accomplished with IgM serology, in the acute stage of SNV infection, RT-PCR can also be used if blood cells or blood clot are used instead of plasma or serum, where sensitivity even using nested PCR primers drops to about 70% [86] [87] [88] . In a facility at which many cases of HCPS are treated, the University of New Mexico medical center in Albuquerque, a diagnostic service has long been offered in which the patient's hematologic findings are analyzed to establish the probability that a patient has HCPS. The combination of thrombocytopenia, elevated abundance of -immunoblast‖ lymphocytes, left-shifted polymorphonuclear cell population without strong morphologic evidence for their activation, and elevated hemoglobin or hematocrit values is highly specific for HCPS and allows clinicians the ability to put presumptive-HCPS patients on extracorporeal membrane oxygenation (ECMO), which is believed to have saved many patients from a lethal outcome [89] . Human infection by hantaviruses is thought to follow contact with secretions or excretions produced by infected rodents. In the United States, 538 human infections by hantavirus were reported through late December 2009 [90] , with New Mexico, Arizona and Colorado exhibiting the highest case-loads. While the prototypical central American hantavirus in central America was Rio Segundo virus of Reithrodontomys mexicanus from Costa Rica, the first human disease appeared some years later in Panama, where Choclo virus (CHOV) arose as the etiologic agent and is believed to be responsible for all known cases of HCPS. The fulvous pygmy rice rat Oligoryzomys fulvescens has been identified as the rodent reservoir [91] . In Panama, the first cases of HCPS, albeit with little or no evident cardiac involvement, were reported in 1999, and since then, 106 human infections have occurred with a 26% mortality rate [92] . Serosurveys of mammals in Mexico and Costa Rica have found anti-hantavirus antibodies [93] [94] [95] [96] , and seroprevalences ranging between 0.6 to 1.6% in human populations were reported despite the absence of known HCPS cases [97] . In South America, HCPS cases have been indentified in Argentina, Bolivia, Brazil, Chile, Paraguay and Uruguay, and evidence for human exposure to hantaviruses have also been reported in Venezuela [98] and Perú [99] . In southern South America, ANDV is the main etiologic agent with cases in Chile and Argentina reported since 1995. In Chile, 671 cases of HCPS due to ANDV have occurred during the period 2001-2009 [100] . Since 1995, more than 1,000 HCPS cases have been reported in Argentina [101] ; in Brazil, approximately 1,100 HCPS cases have been identified between 1993 and 2008 [102] . Case-fatality ratios in those three countries have been similar, ranging from 30% (Argentina), 36% (Chile) and 39% (Brazil). Hantavirus infections occur more frequently in men than women, although the male/female ratio is highly variable. For example, Panamanian communities showed a ratio of 55 men to 45 women [103] , while in Chile the ratio is more biased to males (71%) [104] . In the Paraguayan Chaco the male-female ratio approaches 50% [105] . In North America, by December 2009 63% of case-patients were males [90] . All ethnic and racial groups seem to be susceptible to hantavirus infections, and the differences between certain groups (as indigenous and non-indigenous) are more likely correlated with the type habitat where the population resides (e.g., rural versus urban areas). In fact, rural communities account for the highest hantavirus incidences overall and are therefore at higher risk [92, [105] [106] [107] [108] [109] [110] [111] , although the importance of peridomestic settings as a major area of exposure has also been emphasized [112, 113] . The main mechanism by which humans acquire hantavirus infection is by exposure to aerosols of contaminated rodent feces, urine, and saliva [114, 115] . This can occur when humans reside in areas in close proximity to those that rodents inhabit, live in areas infested with rodents, or when rodents invade human settings, which are more frequent in rural habitats. There is a long history of human co-existence with rodents, raising questions about the apparent recent increases in hantavirus-related illnesses, especially HCPS. Other than an apparent association with El Niño southern oscillation (ENSO) events in some regions [116, 117] , the recent increases in incidence of HCPS do not seem to follow a readily-defined temporal or spatial pattern. However, some landscape features such as habitat fragmentation or human-disturbed areas may influence rodent population dynamics and impact viral incidence [118] [119] [120] [121] . Despite the stochasticity associated with contraction of hantavirus infection, certain scenarios have been recognized as posing higher risk. Human activities in poorly ventilated buildings that aerosolize particulates that are then inhaled (i.e., cleaning, shaking rugs, dusting) are frequently identified among patients admitted for HCPS [11, 122] . Outdoor activities are thought to convey lower risk due to lability of hantaviruses to UV radiation and the presumed tendency to be dispersed in wind, although certain environmental conditions seem to maintain the virus for longer periods outside its natural host allowing for indirect transmission [123] . An alternative but uncommon route of virus transmission is by rodent bites [124] [125] [126] . Field workers handling mammals are potentially at higher risk of exposure with hantavirus infections, although when quantified through serosurveys the absolute risk appears rather slight [127] . A new study in Colorado suggests the possibility that a rodent bite may have been the proximate vehicle for outdoor transmission of SNV [128] , which re-emphasizes the use of personal protective equipment during field work activities [129] . As a particular case within hantaviruses, person-to-person transmission has exclusively been documented for the South American Andes virus [130] [131] [132] [133] [134] [135] . The identification of this transmission route has been made using both molecular tools and epidemiological surveys, but the mechanism of interpersonal transmission is not well established. Recent findings show that family clusters and specifically sexual partners share the greater risk of interpersonal transmission, although sexual transmission per se can be neither inferred nor refuted presently [130, 135] . Interestingly, ANDV may also be shed by humans through other biological fluids such as urine [136] , illustrating the particular properties that differentiate this virus from other hantaviruses. Although interpersonal transmission seems to be unique for ANDV, viral RNA of PUUV has been detected in saliva of patients with HFRS, and some patients with SNV-HCPS have viral RNA in tracheal secretions [88, 137] . Hantaviruses in the Americas are naturally hosted by rodents (Muridae and Cricetidae) as well as shrews (Soricidae) and moles (Talpidae) (Figure 1) . Three shrew and one mole species have been reported to host hantaviruses and their pathogenicity for humans remains unknown [22, 138, 139] . At least 15 rodent species have been identified as carriers of different pathogenic hantaviruses, with some South American genotypes such as Castelo do Sonhos (CDSV) or Hu39694 only identified after human infections (Figure 1 ). Hantaviruses typically show high species-specificity and no intermediate host [140] . However, some hantavirus genotypes have been described in the same rodent species. Such is the case of Playa de Oro (OROV) and Catacamas (CATV) identified in Oryzomys couesi [141, 142] , or Maporal (MAPV) and Choclo (CHOV) hosted by O. fulvescens [91, 143] . In North America both Muleshoe and Black Creek Canal hantaviruses have been detected in geographically-distant Sigmodon hispidus [144, 145] . Also, one hantavirus genotype (e.g., Juquitiba-like virus) may be carried by more than one rodent species (O. nigripes, Oxymycterus judex, Akodon montesis). Another example is Laguna Negra virus (LANV) which after being identified in Calomys laucha [146] has also been reported in C. callosus [147] . The rapid increase in the discovery of new hantaviruses and the identification of their hosts does not seem likely to end soon as new small mammal species are screened [95] . This subject is complicated by continued controversy in the criteria for the classification of distinct hantaviruses [148, 149] , which is also tied to host taxonomic classification and taxonomic rearrangements. Cross-species transmission is a major process during spread, emergence, and evolution of RNA viruses [6, 150] . Particularly within hantaviruses, spillover to secondary hosts are increasingly identified as more extensive studies are performed [151] [152] [153] [154] [155] [156] . For example, ANDV is the predominant etiologic agent of HCPS in South America, and O. longicaudatus the main rodent reservoir. Spillover in at least four other rodent species that co-occur with the reservoir have been identified, with Abrothrix longipilis showing the second higher prevalence to ANDV-antibodies, and there is presently no question that the virus is extremely similar genetically between the two host rodents [157, 158] . In North America, spillover of Bayou virus (BAYV) may have occurred from the main reservoir O. palustris to S. hispidus, R. fulvescens, P. leucopus, and B. taylori [159] [160] [161] . Hantavirus spillover is more likely to occur with host populations inhabiting sympatric or syntopic regions [151, 162] , and cross-species transmission would presumably have greater chances of success if the host species are closely related [163] . An interesting exception is found between Oxbow virus (OXBV) and Asama virus (ASAV) in which a host-switch process seemed to have occurred between mammals belonging to two families (Talpidae and Soricidae), likely as a result of alternating and recurrent co-divergence of certain taxa through evolutionary time [138] . Hantaviruses are horizontally transmitted between rodents and are not transmitted by arthropods (unlike other viruses of the family Bunyaviridae). Spillover infection to nonhuman mammals usually results in no onward (or -dead-end‖) transmission, but if humans are infected may result in high morbidity and mortality [122, 164] . During the spring of 1993, an outbreak of patients with HCPS due to SNV occurred in the Four Corners states resulting in more than 60% case-fatality among the initial cases, many involving members of the Navajo tribe [12, 121] . In Panama, an outbreak was reported during 1999-2000 in Los Santos, and 12 cases where identified with three fatalities [165, 166] . This represented the first report of human hantavirus infections in Central America. In South America, the first largest identified outbreak occurred in the Chaco region in northwestern Paraguay during 1995-1996. Seventeen individuals were identified with SNV antibody (ELISA) or were antigen (IHC) positive out of 52 suspected cases [167] . Major outbreaks due to ANDV occurred in 1996 in southern Argentina [131, 134] ; in southern Chile clusters of patients presented with hantavirus illness in 1997 [158] . In Brazil, the first outbreak was identified in the Brazilian Amazon (Maranhão State) in 2000, and involved small villages that resulted in a 13.3% prevalence of those tested (398 total residents) [168] . The factors that trigger hantavirus outbreaks are still poorly understood, probably because they result from several interacting biotic and abiotic features whose key parameters are difficult to model. However, the use of new modeling approaches that involve geographical and environmental features seem to be promising in predicting potential hantavirus outbreaks and/or areas of higher risk [169] [170] [171] [172] . Because hantaviruses are known to be directly transmitted from infected to susceptible hosts, the first natural approach is to relate outbreaks to the ecology of the viral hosts. Hantavirus transmission and persistence in rodent populations depends on several factors that interact to affect ecological dynamics of the host, which in turn is strongly influenced by the behavioral characteristics of individual rodent species, to landscape structure, and environmental features [173, 174] . Viral transmission depends on contact rates among susceptible hosts, and despite the prevailing notion that a higher density increases encounters and hence secondary infected hosts, contrasting patterns relating rodent population size and virus prevalence can be found [175] . In addition, it has been shown that SNV transmission follows a contact heterogeneity pattern, where individuals in the population have different probability of transmitting the infection [176] . The understanding of viral transmission proves to be far more complex when species other than the main reservoir host are incorporated in the model. In fact, recent studies have shown that higher hosts species diversity is correlated with lower infection prevalence in North America for P. maniculatus [177] , in Central America for O. fulvescens (reservoir of Choclo virus) and Zygodontomys brevicauda (reservoir of Calabazo virus) [178] , and in South America for Akodon montensis (reservoir of Jabora virus) [162] . Contact rates vary according to the spatial distribution of populations and seem to be strongly influenced by landscape structure. For example, SNV prevalence in P. maniculatus was higher in landscapes with a higher level of fragmentation of the preferred habitat [179] . In addition, certain properties of the landscape such as elevation, slope, and land cover seem to be useful in detecting areas with persistent SNV infections, and therefore thought to be refugial areas where the virus can be maintained for years [169] . Changes in the natural environment of reservoir species, such as forest fragmentation and habitat loss, may alter population abundance and distribution and lead to hantavirus outbreaks, as observed in the Azurero Peninsula of Panama [118, 119] . Also, differences in the microhabitat, including overstory cover, may lead to differences in the ecological dynamics within populations and affect the rate of exposure to the virus [180] . Differences in hantavirus infections through contrasting landscapes in the latitudinal span have been found in rodent populations of O. longicaudatus in Chile, suggesting that humans are differentially exposed to the virus [107, 181] . Rodent population dynamics are affected by seasonal changes of weather and climate [182, 183] . In the case of the ENSO-associated outbreaks, a complex cascade of events triggered by highly unusual rains in the precedent year have been postulated to result in an increase of primary production and rodent densities, also increasing the likelihood of transmission of the virus to humans, but it has proved difficult to precisely demonstrate the suggested intermediate events such as increased rodent densities in the increased caseload [116, 121, 184] . In South America, effects of climate change and hantavirus outbreaks have not been well studied, despite the knowledge that several rodents species that are reservoirs of emerging diseases have dramatically been affected by events like El Niño [185] . Changes in host population dynamics are also affected by seasonality, which may lead to disease outbreaks when processes that equilibrate rodent populations from season to season are interrupted [186] . Viral emergence may continue to be promoted as human-introduced changes continue to increase in the environment at different geographical scales. Human incursions into previously uncultivated environments may lead to new contacts between rodent reservoirs and humans, increasing the likelihood of contracting infections [187] . These changes may also alter rodent's population structure and dynamics and interspecies interactions creating conditions that may lead to viral outbreaks, viral establishment in new hosts, and emergence of HCPS [102, 162] , even with seemingly slight ecological disturbance to the virus-host system [188] . Certain pathophysiologic characteristics, including thrombocytopenia and shock, of hantavirus diseases of humans, bear substantial similarity to the hemorrhagic fevers induced by other viruses such arenaviruses, filoviruses and flaviviruses, despite sharing essentially no sequence similarities therewith. Such observations raise questions about whether such commonalities in pathogenesis are chance similarities of phenotype, or instead report the presence of common molecular mechanisms among the viruses. In this review we discuss the general properties, discoveries and epidemiology/ecology of the New World forms of pathogenic hantaviruses, and also seek to identify some of the characteristics of the viral macromolecules and immunologic mechanisms that have been proposed as potential direct mediators of the pathogenic events that characterize the human disease HCPS. While it is unlikely that expression of any particular viral protein or RNAs in isolation can be relied upon to replicate key phenotypes of infection by the complete virus, some of the findings have been sufficiently consistent with what is known of the pathogenesis in vivo that they offer plausible first-pass leads in the search for therapeutic targets. We look forward to the mechanistic revelations that will follow the inevitably expanded usage of powerful methods such as deep sequencing, ever-more advanced imaging, and microscopic methods, and animal models that can at last be said to be close mimics of human hantavirus disease.

What leads to death in most fatal cases of HCPS?

Virus-Vectored Influenza Virus Vaccines https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4147686/ SHA: f6d2afb2ec44d8656972ea79f8a833143bbeb42b Authors: Tripp, Ralph A.; Tompkins, S. Mark Date: 2014-08-07 DOI: 10.3390/v6083055 License: cc-by Abstract: Despite the availability of an inactivated vaccine that has been licensed for >50 years, the influenza virus continues to cause morbidity and mortality worldwide. Constant evolution of circulating influenza virus strains and the emergence of new strains diminishes the effectiveness of annual vaccines that rely on a match with circulating influenza strains. Thus, there is a continued need for new, efficacious vaccines conferring cross-clade protection to avoid the need for biannual reformulation of seasonal influenza vaccines. Recombinant virus-vectored vaccines are an appealing alternative to classical inactivated vaccines because virus vectors enable native expression of influenza antigens, even from virulent influenza viruses, while expressed in the context of the vector that can improve immunogenicity. In addition, a vectored vaccine often enables delivery of the vaccine to sites of inductive immunity such as the respiratory tract enabling protection from influenza virus infection. Moreover, the ability to readily manipulate virus vectors to produce novel influenza vaccines may provide the quickest path toward a universal vaccine protecting against all influenza viruses. This review will discuss experimental virus-vectored vaccines for use in humans, comparing them to licensed vaccines and the hurdles faced for licensure of these next-generation influenza virus vaccines. Text: Seasonal influenza is a worldwide health problem causing high mobility and substantial mortality [1] [2] [3] [4] . Moreover, influenza infection often worsens preexisting medical conditions [5] [6] [7] . Vaccines against circulating influenza strains are available and updated annually, but many issues are still present, including low efficacy in the populations at greatest risk of complications from influenza virus infection, i.e., the young and elderly [8, 9] . Despite increasing vaccination rates, influenza-related hospitalizations are increasing [8, 10] , and substantial drug resistance has developed to two of the four currently approved anti-viral drugs [11, 12] . While adjuvants have the potential to improve efficacy and availability of current inactivated vaccines, live-attenuated and virus-vectored vaccines are still considered one of the best options for the induction of broad and efficacious immunity to the influenza virus [13] . The general types of influenza vaccines available in the United States are trivalent inactivated influenza vaccine (TIV), quadrivalent influenza vaccine (QIV), and live attenuated influenza vaccine (LAIV; in trivalent and quadrivalent forms). There are three types of inactivated vaccines that include whole virus inactivated, split virus inactivated, and subunit vaccines. In split virus vaccines, the virus is disrupted by a detergent. In subunit vaccines, HA and NA have been further purified by removal of other viral components. TIV is administered intramuscularly and contains three or four inactivated viruses, i.e., two type A strains (H1 and H3) and one or two type B strains. TIV efficacy is measured by induction of humoral responses to the hemagglutinin (HA) protein, the major surface and attachment glycoprotein on influenza. Serum antibody responses to HA are measured by the hemagglutination-inhibition (HI) assay, and the strain-specific HI titer is considered the gold-standard correlate of immunity to influenza where a four-fold increase in titer post-vaccination, or a HI titer of ≥1:40 is considered protective [4, 14] . Protection against clinical disease is mainly conferred by serum antibodies; however, mucosal IgA antibodies also may contribute to resistance against infection. Split virus inactivated vaccines can induce neuraminidase (NA)-specific antibody responses [15] [16] [17] , and anti-NA antibodies have been associated with protection from infection in humans [18] [19] [20] [21] [22] . Currently, NA-specific antibody responses are not considered a correlate of protection [14] . LAIV is administered as a nasal spray and contains the same three or four influenza virus strains as inactivated vaccines but on an attenuated vaccine backbone [4] . LAIV are temperature-sensitive and cold-adapted so they do not replicate effectively at core body temperature, but replicate in the mucosa of the nasopharynx [23] . LAIV immunization induces serum antibody responses, mucosal antibody responses (IgA), and T cell responses. While robust serum antibody and nasal wash (mucosal) antibody responses are associated with protection from infection, other immune responses, such as CD8 + cytotoxic lymphocyte (CTL) responses may contribute to protection and there is not a clear correlate of immunity for LAIV [4, 14, 24] . Currently licensed influenza virus vaccines suffer from a number of issues. The inactivated vaccines rely on specific antibody responses to the HA, and to a lesser extent NA proteins for protection. The immunodominant portions of the HA and NA molecules undergo a constant process of antigenic drift, a natural accumulation of mutations, enabling virus evasion from immunity [9, 25] . Thus, the circulating influenza A and B strains are reviewed annually for antigenic match with current vaccines, Replacement of vaccine strains may occur regularly, and annual vaccination is recommended to assure protection [4, 26, 27] . For the northern hemisphere, vaccine strain selection occurs in February and then manufacturers begin production, taking at least six months to produce the millions of vaccine doses required for the fall [27] . If the prediction is imperfect, or if manufacturers have issues with vaccine production, vaccine efficacy or availability can be compromised [28] . LAIV is not recommended for all populations; however, it is generally considered to be as effective as inactivated vaccines and may be more efficacious in children [4, 9, 24] . While LAIV relies on antigenic match and the HA and NA antigens are replaced on the same schedule as the TIV [4, 9] , there is some suggestion that LAIV may induce broader protection than TIV due to the diversity of the immune response consistent with inducing virus-neutralizing serum and mucosal antibodies, as well as broadly reactive T cell responses [9, 23, 29] . While overall both TIV and LAIV are considered safe and effective, there is a recognized need for improved seasonal influenza vaccines [26] . Moreover, improved understanding of immunity to conserved influenza virus antigens has raised the possibility of a universal vaccine, and these universal antigens will likely require novel vaccines for effective delivery [30] [31] [32] . Virus-vectored vaccines share many of the advantages of LAIV, as well as those unique to the vectors. Recombinant DNA systems exist that allow ready manipulation and modification of the vector genome. This in turn enables modification of the vectors to attenuate the virus or enhance immunogenicity, in addition to adding and manipulating the influenza virus antigens. Many of these vectors have been extensively studied or used as vaccines against wild type forms of the virus. Finally, each of these vaccine vectors is either replication-defective or causes a self-limiting infection, although like LAIV, safety in immunocompromised individuals still remains a concern [4, 13, [33] [34] [35] . Table 1 summarizes the benefits and concerns of each of the virus-vectored vaccines discussed here. There are 53 serotypes of adenovirus, many of which have been explored as vaccine vectors. A live adenovirus vaccine containing serotypes 4 and 7 has been in use by the military for decades, suggesting adenoviruses may be safe for widespread vaccine use [36] . However, safety concerns have led to the majority of adenovirus-based vaccine development to focus on replication-defective vectors. Adenovirus 5 (Ad5) is the most-studied serotype, having been tested for gene delivery and anti-cancer agents, as well as for infectious disease vaccines. Adenovirus vectors are attractive as vaccine vectors because their genome is very stable and there are a variety of recombinant systems available which can accommodate up to 10 kb of recombinant genetic material [37] . Adenovirus is a non-enveloped virus which is relatively stable and can be formulated for long-term storage at 4 °C, or even storage up to six months at room temperature [33] . Adenovirus vaccines can be grown to high titers, exceeding 10 1° plaque forming units (PFU) per mL when cultured on 293 or PER.C6 cells [38] , and the virus can be purified by simple methods [39] . Adenovirus vaccines can also be delivered via multiple routes, including intramuscular injection, subcutaneous injection, intradermal injection, oral delivery using a protective capsule, and by intranasal delivery. Importantly, the latter two delivery methods induce robust mucosal immune responses and may bypass preexisting vector immunity [33] . Even replication-defective adenovirus vectors are naturally immunostimulatory and effective adjuvants to the recombinant antigen being delivered. Adenovirus has been extensively studied as a vaccine vector for human disease. The first report using adenovirus as a vaccine vector for influenza demonstrated immunogenicity of recombinant adenovirus 5 (rAd5) expressing the HA of a swine influenza virus, A/Swine/Iowa/1999 (H3N2). Intramuscular immunization of mice with this construct induced robust neutralizing antibody responses and protected mice from challenge with a heterologous virus, A/Hong Kong/1/1968 (H3N2) [40] . Replication defective rAd5 vaccines expressing influenza HA have also been tested in humans. A rAd5-HA expressing the HA from A/Puerto Rico/8/1934 (H1N1; PR8) was delivered to humans epicutaneously or intranasally and assayed for safety and immunogenicity. The vaccine was well tolerated and induced seroconversion with the intranasal administration had a higher conversion rate and higher geometric meant HI titers [41] . While clinical trials with rAd vectors have overall been successful, demonstrating safety and some level of efficacy, rAd5 as a vector has been negatively overshadowed by two clinical trial failures. The first trial was a gene therapy examination where high-dose intravenous delivery of an Ad vector resulted in the death of an 18-year-old male [42, 43] . The second clinical failure was using an Ad5-vectored HIV vaccine being tested as a part of a Step Study, a phase 2B clinical trial. In this study, individuals were vaccinated with the Ad5 vaccine vector expressing HIV-1 gag, pol, and nef genes. The vaccine induced HIV-specific T cell responses; however, the study was stopped after interim analysis suggested the vaccine did not achieve efficacy and individuals with high preexisting Ad5 antibody titers might have an increased risk of acquiring HIV-1 [44] [45] [46] . Subsequently, the rAd5 vaccine-associated risk was confirmed [47] . While these two instances do not suggest Ad-vector vaccines are unsafe or inefficacious, the umbra cast by the clinical trials notes has affected interest for all adenovirus vaccines, but interest still remains. Immunization with adenovirus vectors induces potent cellular and humoral immune responses that are initiated through toll-like receptor-dependent and independent pathways which induce robust pro-inflammatory cytokine responses. Recombinant Ad vaccines expressing HA antigens from pandemic H1N1 (pH1N1), H5 and H7 highly pathogenic avian influenza (HPAI) virus (HPAIV), and H9 avian influenza viruses have been tested for efficacy in a number of animal models, including chickens, mice, and ferrets, and been shown to be efficacious and provide protection from challenge [48, 49] . Several rAd5 vectors have been explored for delivery of non-HA antigens, influenza nucleoprotein (NP) and matrix 2 (M2) protein [29, [50] [51] [52] . The efficacy of non-HA antigens has led to their inclusion with HA-based vaccines to improve immunogenicity and broaden breadth of both humoral and cellular immunity [53, 54] . However, as both CD8 + T cell and neutralizing antibody responses are generated by the vector and vaccine antigens, immunological memory to these components can reduce efficacy and limit repeated use [48] . One drawback of an Ad5 vector is the potential for preexisting immunity, so alternative adenovirus serotypes have been explored as vectors, particularly non-human and uncommon human serotypes. Non-human adenovirus vectors include those from non-human primates (NHP), dogs, sheep, pigs, cows, birds and others [48, 55] . These vectors can infect a variety of cell types, but are generally attenuated in humans avoiding concerns of preexisting immunity. Swine, NHP and bovine adenoviruses expressing H5 HA antigens have been shown to induce immunity comparable to human rAd5-H5 vaccines [33, 56] . Recombinant, replication-defective adenoviruses from low-prevalence serotypes have also been shown to be efficacious. Low prevalence serotypes such as adenovirus types 3, 7, 11, and 35 can evade anti-Ad5 immune responses while maintaining effective antigen delivery and immunogenicity [48, 57] . Prime-boost strategies, using DNA or protein immunization in conjunction with an adenovirus vaccine booster immunization have also been explored as a means to avoided preexisting immunity [52] . Adeno-associated viruses (AAV) were first explored as gene therapy vectors. Like rAd vectors, rAAV have broad tropism infecting a variety of hosts, tissues, and proliferating and non-proliferating cell types [58] . AAVs had been generally not considered as vaccine vectors because they were widely considered to be poorly immunogenic. A seminal study using AAV-2 to express a HSV-2 glycoprotein showed this virus vaccine vector effectively induced potent CD8 + T cell and serum antibody responses, thereby opening the door to other rAAV vaccine-associated studies [59, 60] . AAV vector systems have a number of engaging properties. The wild type viruses are non-pathogenic and replication incompetent in humans and the recombinant AAV vector systems are even further attenuated [61] . As members of the parvovirus family, AAVs are small non-enveloped viruses that are stable and amenable to long-term storage without a cold chain. While there is limited preexisting immunity, availability of non-human strains as vaccine candidates eliminates these concerns. Modifications to the vector have increased immunogenicity, as well [60] . There are limited studies using AAVs as vaccine vectors for influenza. An AAV expressing an HA antigen was first shown to induce protective in 2001 [62] . Later, a hybrid AAV derived from two non-human primate isolates (AAVrh32.33) was used to express influenza NP and protect against PR8 challenge in mice [63] . Most recently, following the 2009 H1N1 influenza virus pandemic, rAAV vectors were generated expressing the HA, NP and matrix 1 (M1) proteins of A/Mexico/4603/2009 (pH1N1), and in murine immunization and challenge studies, the rAAV-HA and rAAV-NP were shown to be protective; however, mice vaccinated with rAAV-HA + NP + M1 had the most robust protection. Also, mice vaccinated with rAAV-HA + rAAV-NP + rAAV-M1 were also partially protected against heterologous (PR8, H1N1) challenge [63] . Most recently, an AAV vector was used to deliver passive immunity to influenza [64, 65] . In these studies, AAV (AAV8 and AAV9) was used to deliver an antibody transgene encoding a broadly cross-protective anti-influenza monoclonal antibody for in vivo expression. Both intramuscular and intranasal delivery of the AAVs was shown to protect against a number of influenza virus challenges in mice and ferrets, including H1N1 and H5N1 viruses [64, 65] . These studies suggest that rAAV vectors are promising vaccine and immunoprophylaxis vectors. To this point, while approximately 80 phase I, I/II, II, or III rAAV clinical trials are open, completed, or being reviewed, these have focused upon gene transfer studies and so there is as yet limited safety data for use of rAAV as vaccines [66] . Alphaviruses are positive-sense, single-stranded RNA viruses of the Togaviridae family. A variety of alphaviruses have been developed as vaccine vectors, including Semliki Forest virus (SFV), Sindbis (SIN) virus, Venezuelan equine encephalitis (VEE) virus, as well as chimeric viruses incorporating portions of SIN and VEE viruses. The replication defective vaccines or replicons do not encode viral structural proteins, having these portions of the genome replaces with transgenic material. The structural proteins are provided in cell culture production systems. One important feature of the replicon systems is the self-replicating nature of the RNA. Despite the partial viral genome, the RNAs are self-replicating and can express transgenes at very high levels [67] . SIN, SFV, and VEE have all been tested for efficacy as vaccine vectors for influenza virus [68] [69] [70] [71] . A VEE-based replicon system encoding the HA from PR8 was demonstrated to induce potent HA-specific immune response and protected from challenge in a murine model, despite repeated immunization with the vector expressing a control antigen, suggesting preexisting immunity may not be an issue for the replicon vaccine [68] . A separate study developed a VEE replicon system expressing the HA from A/Hong Kong/156/1997 (H5N1) and demonstrated varying efficacy after in ovo vaccination or vaccination of 1-day-old chicks [70] . A recombinant SIN virus was use as a vaccine vector to deliver a CD8 + T cell epitope only. The well-characterized NP epitope was transgenically expressed in the SIN system and shown to be immunogenic in mice, priming a robust CD8 + T cell response and reducing influenza virus titer after challenge [69] . More recently, a VEE replicon system expressing the HA protein of PR8 was shown to protect young adult (8-week-old) and aged (12-month-old) mice from lethal homologous challenge [72] . The VEE replicon systems are particularly appealing as the VEE targets antigen-presenting cells in the lymphatic tissues, priming rapid and robust immune responses [73] . VEE replicon systems can induce robust mucosal immune responses through intranasal or subcutaneous immunization [72] [73] [74] , and subcutaneous immunization with virus-like replicon particles (VRP) expressing HA-induced antigen-specific systemic IgG and fecal IgA antibodies [74] . VRPs derived from VEE virus have been developed as candidate vaccines for cytomegalovirus (CMV). A phase I clinical trial with the CMV VRP showed the vaccine was immunogenic, inducing CMV-neutralizing antibody responses and potent T cell responses. Moreover, the vaccine was well tolerated and considered safe [75] . A separate clinical trial assessed efficacy of repeated immunization with a VRP expressing a tumor antigen. The vaccine was safe and despite high vector-specific immunity after initial immunization, continued to boost transgene-specific immune responses upon boost [76] . While additional clinical data is needed, these reports suggest alphavirus replicon systems or VRPs may be safe and efficacious, even in the face of preexisting immunity. Baculovirus has been extensively used to produce recombinant proteins. Recently, a baculovirus-derived recombinant HA vaccine was approved for human use and was first available for use in the United States for the 2013-2014 influenza season [4] . Baculoviruses have also been explored as vaccine vectors. Baculoviruses have a number of advantages as vaccine vectors. The viruses have been extensively studied for protein expression and for pesticide use and so are readily manipulated. The vectors can accommodate large gene insertions, show limited cytopathic effect in mammalian cells, and have been shown to infect and express genes of interest in a spectrum of mammalian cells [77] . While the insect promoters are not effective for mammalian gene expression, appropriate promoters can be cloned into the baculovirus vaccine vectors. Baculovirus vectors have been tested as influenza vaccines, with the first reported vaccine using Autographa californica nuclear polyhedrosis virus (AcNPV) expressing the HA of PR8 under control of the CAG promoter (AcCAG-HA) [77] . Intramuscular, intranasal, intradermal, and intraperitoneal immunization or mice with AcCAG-HA elicited HA-specific antibody responses, however only intranasal immunization provided protection from lethal challenge. Interestingly, intranasal immunization with the wild type AcNPV also resulted in protection from PR8 challenge. The robust innate immune response to the baculovirus provided non-specific protection from subsequent influenza virus infection [78] . While these studies did not demonstrate specific protection, there were antigen-specific immune responses and potential adjuvant effects by the innate response. Baculovirus pseudotype viruses have also been explored. The G protein of vesicular stomatitis virus controlled by the insect polyhedron promoter and the HA of A/Chicken/Hubei/327/2004 (H5N1) HPAIV controlled by a CMV promoter were used to generate the BV-G-HA. Intramuscular immunization of mice or chickens with BV-G-HA elicited strong HI and VN serum antibody responses, IFN-γ responses, and protected from H5N1 challenge [79] . A separate study demonstrated efficacy using a bivalent pseudotyped baculovirus vector [80] . Baculovirus has also been used to generate an inactivated particle vaccine. The HA of A/Indonesia/CDC669/2006(H5N1) was incorporated into a commercial baculovirus vector controlled by the e1 promoter from White Spot Syndrome Virus. The resulting recombinant virus was propagated in insect (Sf9) cells and inactivated as a particle vaccine [81, 82] . Intranasal delivery with cholera toxin B as an adjuvant elicited robust HI titers and protected from lethal challenge [81] . Oral delivery of this encapsulated vaccine induced robust serum HI titers and mucosal IgA titers in mice, and protected from H5N1 HPAIV challenge. More recently, co-formulations of inactivated baculovirus vectors have also been shown to be effective in mice [83] . While there is growing data on the potential use of baculovirus or pseudotyped baculovirus as a vaccine vector, efficacy data in mammalian animal models other than mice is lacking. There is also no data on the safety in humans, reducing enthusiasm for baculovirus as a vaccine vector for influenza at this time. Newcastle disease virus (NDV) is a single-stranded, negative-sense RNA virus that causes disease in poultry. NDV has a number of appealing qualities as a vaccine vector. As an avian virus, there is little or no preexisting immunity to NDV in humans and NDV propagates to high titers in both chicken eggs and cell culture. As a paramyxovirus, there is no DNA phase in the virus lifecycle reducing concerns of integration events, and the levels of gene expression are driven by the proximity to the leader sequence at the 3' end of the viral genome. This gradient of gene expression enables attenuation through rearrangement of the genome, or by insertion of transgenes within the genome. Finally, pathogenicity of NDV is largely determined by features of the fusion protein enabling ready attenuation of the vaccine vector [84] . Reverse genetics, a method that allows NDV to be rescued from plasmids expressing the viral RNA polymerase and nucleocapsid proteins, was first reported in 1999 [85, 86] . This process has enabled manipulation of the NDV genome as well as incorporation of transgenes and the development of NDV vectors. Influenza was the first infectious disease targeted with a recombinant NDV (rNDV) vector. The HA protein of A/WSN/1933 (H1N1) was inserted into the Hitchner B1 vaccine strain. The HA protein was expressed on infected cells and was incorporated into infectious virions. While the virus was attenuated compared to the parental vaccine strain, it induced a robust serum antibody response and protected against homologous influenza virus challenge in a murine model of infection [87] . Subsequently, rNDV was tested as a vaccine vector for HPAIV having varying efficacy against H5 and H7 influenza virus infections in poultry [88] [89] [90] [91] [92] [93] [94] . These vaccines have the added benefit of potentially providing protection against both the influenza virus and NDV infection. NDV has also been explored as a vaccine vector for humans. Two NHP studies assessed the immunogenicity and efficacy of an rNDV expressing the HA or NA of A/Vietnam/1203/2004 (H5N1; VN1203) [95, 96] . Intranasal and intratracheal delivery of the rNDV-HA or rNDV-NA vaccines induced both serum and mucosal antibody responses and protected from HPAIV challenge [95, 96] . NDV has limited clinical data; however, phase I and phase I/II clinical trials have shown that the NDV vector is well-tolerated, even at high doses delivered intravenously [44, 97] . While these results are promising, additional studies are needed to advance NDV as a human vaccine vector for influenza. Parainfluenza virus type 5 (PIV5) is a paramyxovirus vaccine vector being explored for delivery of influenza and other infectious disease vaccine antigens. PIV5 has only recently been described as a vaccine vector [98] . Similar to other RNA viruses, PIV5 has a number of features that make it an attractive vaccine vector. For example, PIV5 has a stable RNA genome and no DNA phase in virus replication cycle reducing concerns of host genome integration or modification. PIV5 can be grown to very high titers in mammalian vaccine cell culture substrates and is not cytopathic allowing for extended culture and harvest of vaccine virus [98, 99] . Like NDV, PIV5 has a 3'-to 5' gradient of gene expression and insertion of transgenes at different locations in the genome can variably attenuate the virus and alter transgene expression [100] . PIV5 has broad tropism, infecting many cell types, tissues, and species without causing clinical disease, although PIV5 has been associated with -kennel cough‖ in dogs [99] . A reverse genetics system for PIV5 was first used to insert the HA gene from A/Udorn/307/72 (H3N2) into the PIV5 genome between the hemagglutinin-neuraminidase (HN) gene and the large (L) polymerase gene. Similar to NDV, the HA was expressed at high levels in infected cells and replicated similarly to the wild type virus, and importantly, was not pathogenic in immunodeficient mice [98] . Additionally, a single intranasal immunization in a murine model of influenza infection was shown to induce neutralizing antibody responses and protect against a virus expressing homologous HA protein [98] . PIV5 has also been explored as a vaccine against HPAIV. Recombinant PIV5 vaccines expressing the HA or NP from VN1203 were tested for efficacy in a murine challenge model. Mice intranasally vaccinated with a single dose of PIV5-H5 vaccine had robust serum and mucosal antibody responses, and were protected from lethal challenge. Notably, although cellular immune responses appeared to contribute to protection, serum antibody was sufficient for protection from challenge [100, 101] . Intramuscular immunization with PIV5-H5 was also shown to be effective at inducing neutralizing antibody responses and protecting against lethal influenza virus challenge [101] . PIV5 expressing the NP protein of HPAIV was also efficacious in the murine immunization and challenge model, where a single intranasal immunization induced robust CD8 + T cell responses and protected against homologous (H5N1) and heterosubtypic (H1N1) virus challenge [102] . Currently there is no clinical safety data for use of PIV5 in humans. However, live PIV5 has been a component of veterinary vaccines for -kennel cough‖ for >30 years, and veterinarians and dog owners are exposed to live PIV5 without reported disease [99] . This combined with preclinical data from a variety of animal models suggests that PIV5 as a vector is likely to be safe in humans. As preexisting immunity is a concern for all virus-vectored vaccines, it should be noted that there is no data on the levels of preexisting immunity to PIV5 in humans. However, a study evaluating the efficacy of a PIV5-H3 vaccine in canines previously vaccinated against PIV5 (kennel cough) showed induction of robust anti-H3 serum antibody responses as well as high serum antibody levels to the PIV5 vaccine, suggesting preexisting immunity to the PIV5 vector may not affect immunogenicity of vaccines even with repeated use [99] . Poxvirus vaccines have a long history and the notable hallmark of being responsible for eradication of smallpox. The termination of the smallpox virus vaccination program has resulted in a large population of poxvirus-naï ve individuals that provides the opportunity for the use of poxviruses as vectors without preexisting immunity concerns [103] . Poxvirus-vectored vaccines were first proposed for use in 1982 with two reports of recombinant vaccinia viruses encoding and expressing functional thymidine kinase gene from herpes virus [104, 105] . Within a year, a vaccinia virus encoding the HA of an H2N2 virus was shown to express a functional HA protein (cleaved in the HA1 and HA2 subunits) and be immunogenic in rabbits and hamsters [106] . Subsequently, all ten of the primary influenza proteins have been expressed in vaccine virus [107] . Early work with intact vaccinia virus vectors raised safety concerns, as there was substantial reactogenicity that hindered recombinant vaccine development [108] . Two vaccinia vectors were developed to address these safety concerns. The modified vaccinia virus Ankara (MVA) strain was attenuated by passage 530 times in chick embryo fibroblasts cultures. The second, New York vaccinia virus (NYVAC) was a plaque-purified clone of the Copenhagen vaccine strain rationally attenuated by deletion of 18 open reading frames [109] [110] [111] . Modified vaccinia virus Ankara (MVA) was developed prior to smallpox eradication to reduce or prevent adverse effects of other smallpox vaccines [109] . Serial tissue culture passage of MVA resulted in loss of 15% of the genome, and established a growth restriction for avian cells. The defects affected late stages in virus assembly in non-avian cells, a feature enabling use of the vector as single-round expression vector in non-permissive hosts. Interestingly, over two decades ago, recombinant MVA expressing the HA and NP of influenza virus was shown to be effective against lethal influenza virus challenge in a murine model [112] . Subsequently, MVA expressing various antigens from seasonal, pandemic (A/California/04/2009, pH1N1), equine (A/Equine/Kentucky/1/81 H3N8), and HPAI (VN1203) viruses have been shown to be efficacious in murine, ferret, NHP, and equine challenge models [113] . MVA vaccines are very effective stimulators of both cellular and humoral immunity. For example, abortive infection provides native expression of the influenza antigens enabling robust antibody responses to native surface viral antigens. Concurrently, the intracellular influenza peptides expressed by the pox vector enter the class I MHC antigen processing and presentation pathway enabling induction of CD8 + T cell antiviral responses. MVA also induces CD4 + T cell responses further contributing to the magnitude of the antigen-specific effector functions [107, [112] [113] [114] [115] . MVA is also a potent activator of early innate immune responses further enhancing adaptive immune responses [116] . Between early smallpox vaccine development and more recent vaccine vector development, MVA has undergone extensive safety testing and shown to be attenuated in severely immunocompromised animals and safe for use in children, adults, elderly, and immunocompromised persons. With extensive pre-clinical data, recombinant MVA vaccines expressing influenza antigens have been tested in clinical trials and been shown to be safe and immunogenic in humans [117] [118] [119] . These results combined with data from other (non-influenza) clinical and pre-clinical studies support MVA as a leading viral-vectored candidate vaccine. The NYVAC vector is a highly attenuated vaccinia virus strain. NYVAC is replication-restricted; however, it grows in chick embryo fibroblasts and Vero cells enabling vaccine-scale production. In non-permissive cells, critical late structural proteins are not produced stopping replication at the immature virion stage [120] . NYVAC is very attenuated and considered safe for use in humans of all ages; however, it predominantly induces a CD4 + T cell response which is different compared to MVA [114] . Both MVA and NYVAC provoke robust humoral responses, and can be delivered mucosally to induce mucosal antibody responses [121] . There has been only limited exploration of NYVAC as a vaccine vector for influenza virus; however, a vaccine expressing the HA from A/chicken/Indonesia/7/2003 (H5N1) was shown to induce potent neutralizing antibody responses and protect against challenge in swine [122] . While there is strong safety and efficacy data for use of NYVAC or MVA-vectored influenza vaccines, preexisting immunity remains a concern. Although the smallpox vaccination campaign has resulted in a population of poxvirus-naï ve people, the initiation of an MVA or NYVAC vaccination program for HIV, influenza or other pathogens will rapidly reduce this susceptible population. While there is significant interest in development of pox-vectored influenza virus vaccines, current influenza vaccination strategies rely upon regular immunization with vaccines matched to circulating strains. This would likely limit the use and/or efficacy of poxvirus-vectored influenza virus vaccines for regular and seasonal use [13] . Intriguingly, NYVAC may have an advantage for use as an influenza vaccine vector, because immunization with this vector induces weaker vaccine-specific immune responses compared to other poxvirus vaccines, a feature that may address the concerns surrounding preexisting immunity [123] . While poxvirus-vectored vaccines have not yet been approved for use in humans, there is a growing list of licensed poxvirus for veterinary use that include fowlpox-and canarypox-vectored vaccines for avian and equine influenza viruses, respectively [124, 125] . The fowlpox-vectored vaccine expressing the avian influenza virus HA antigen has the added benefit of providing protection against fowlpox infection. Currently, at least ten poxvirus-vectored vaccines have been licensed for veterinary use [126] . These poxvirus vectors have the potential for use as vaccine vectors in humans, similar to the first use of cowpox for vaccination against smallpox [127] . The availability of these non-human poxvirus vectors with extensive animal safety and efficacy data may address the issues with preexisting immunity to the human vaccine strains, although the cross-reactivity originally described with cowpox could also limit use. Influenza vaccines utilizing vesicular stomatitis virus (VSV), a rhabdovirus, as a vaccine vector have a number of advantages shared with other RNA virus vaccine vectors. Both live and replication-defective VSV vaccine vectors have been shown to be immunogenic [128, 129] , and like Paramyxoviridae, the Rhabdoviridae genome has a 3'-to-5' gradient of gene expression enabling attention by selective vaccine gene insertion or genome rearrangement [130] . VSV has a number of other advantages including broad tissue tropism, and the potential for intramuscular or intranasal immunization. The latter delivery method enables induction of mucosal immunity and elimination of needles required for vaccination. Also, there is little evidence of VSV seropositivity in humans eliminating concerns of preexisting immunity, although repeated use may be a concern. Also, VSV vaccine can be produced using existing mammalian vaccine manufacturing cell lines. Influenza antigens were first expressed in a VSV vector in 1997. Both the HA and NA were shown to be expressed as functional proteins and incorporated into the recombinant VSV particles [131] . Subsequently, VSV-HA, expressing the HA protein from A/WSN/1933 (H1N1) was shown to be immunogenic and protect mice from lethal influenza virus challenge [129] . To reduce safety concerns, attenuated VSV vectors were developed. One candidate vaccine had a truncated VSV G protein, while a second candidate was deficient in G protein expression and relied on G protein expressed by a helper vaccine cell line to the provide the virus receptor. Both vectors were found to be attenuated in mice, but maintained immunogenicity [128] . More recently, single-cycle replicating VSV vaccines have been tested for efficacy against H5N1 HPAIV. VSV vectors expressing the HA from A/Hong Kong/156/97 (H5N1) were shown to be immunogenic and induce cross-reactive antibody responses and protect against challenge with heterologous H5N1 challenge in murine and NHP models [132] [133] [134] . VSV vectors are not without potential concerns. VSV can cause disease in a number of species, including humans [135] . The virus is also potentially neuroinvasive in some species [136] , although NHP studies suggest this is not a concern in humans [137] . Also, while the incorporation of the influenza antigen in to the virion may provide some benefit in immunogenicity, changes in tropism or attenuation could arise from incorporation of different influenza glycoproteins. There is no evidence for this, however [134] . Currently, there is no human safety data for VSV-vectored vaccines. While experimental data is promising, additional work is needed before consideration for human influenza vaccination. Current influenza vaccines rely on matching the HA antigen of the vaccine with circulating strains to provide strain-specific neutralizing antibody responses [4, 14, 24] . There is significant interest in developing universal influenza vaccines that would not require annual reformulation to provide protective robust and durable immunity. These vaccines rely on generating focused immune responses to highly conserved portions of the virus that are refractory to mutation [30] [31] [32] . Traditional vaccines may not be suitable for these vaccination strategies; however, vectored vaccines that have the ability to be readily modified and to express transgenes are compatible for these applications. The NP and M2 proteins have been explored as universal vaccine antigens for decades. Early work with recombinant viral vectors demonstrated that immunization with vaccines expressing influenza antigens induced potent CD8 + T cell responses [107, [138] [139] [140] [141] . These responses, even to the HA antigen, could be cross-protective [138] . A number of studies have shown that immunization with NP expressed by AAV, rAd5, alphavirus vectors, MVA, or other vector systems induces potent CD8 + T cell responses and protects against influenza virus challenge [52, 63, 69, 102, 139, 142] . As the NP protein is highly conserved across influenza A viruses, NP-specific T cells can protect against heterologous and even heterosubtypic virus challenges [30] . The M2 protein is also highly conserved and expressed on the surface of infected cells, although to a lesser extent on the surface of virus particles [30] . Much of the vaccine work in this area has focused on virus-like or subunit particles expressing the M2 ectodomain; however, studies utilizing a DNA-prime, rAd-boost strategies to vaccinate against the entire M2 protein have shown the antigen to be immunogenic and protective [50] . In these studies, antibodies to the M2 protein protected against homologous and heterosubtypic challenge, including a H5N1 HPAIV challenge. More recently, NP and M2 have been combined to induce broadly cross-reactive CD8 + T cell and antibody responses, and rAd5 vaccines expressing these antigens have been shown to protect against pH1N1 and H5N1 challenges [29, 51] . Historically, the HA has not been widely considered as a universal vaccine antigen. However, the recent identification of virus neutralizing monoclonal antibodies that cross-react with many subtypes of influenza virus [143] has presented the opportunity to design vaccine antigens to prime focused antibody responses to the highly conserved regions recognized by these monoclonal antibodies. The majority of these broadly cross-reactive antibodies recognize regions on the stalk of the HA protein [143] . The HA stalk is generally less immunogenic compared to the globular head of the HA protein so most approaches have utilized -headless‖ HA proteins as immunogens. HA stalk vaccines have been designed using DNA and virus-like particles [144] and MVA [142] ; however, these approaches are amenable to expression in any of the viruses vectors described here. The goal of any vaccine is to protect against infection and disease, while inducing population-based immunity to reduce or eliminate virus transmission within the population. It is clear that currently licensed influenza vaccines have not fully met these goals, nor those specific to inducing long-term, robust immunity. There are a number of vaccine-related issues that must be addressed before population-based influenza vaccination strategies are optimized. The concept of a -one size fits all‖ vaccine needs to be updated, given the recent ability to probe the virus-host interface through RNA interference approaches that facilitate the identification of host genes affecting virus replication, immunity, and disease. There is also a need for revision of the current influenza virus vaccine strategies for at-risk populations, particularly those at either end of the age spectrum. An example of an improved vaccine regime might include the use of a vectored influenza virus vaccine that expresses the HA, NA and M and/or NP proteins for the two currently circulating influenza A subtypes and both influenza B strains so that vaccine take and vaccine antigen levels are not an issue in inducing protective immunity. Recombinant live-attenuated or replication-deficient influenza viruses may offer an advantage for this and other approaches. Vectored vaccines can be constructed to express full-length influenza virus proteins, as well as generate conformationally restricted epitopes, features critical in generating appropriate humoral protection. Inclusion of internal influenza antigens in a vectored vaccine can also induce high levels of protective cellular immunity. To generate sustained immunity, it is an advantage to induce immunity at sites of inductive immunity to natural infection, in this case the respiratory tract. Several vectored vaccines target the respiratory tract. Typically, vectored vaccines generate antigen for weeks after immunization, in contrast to subunit vaccination. This increased presence and level of vaccine antigen contributes to and helps sustain a durable memory immune response, even augmenting the selection of higher affinity antibody secreting cells. The enhanced memory response is in part linked to the intrinsic augmentation of immunity induced by the vector. Thus, for weaker antigens typical of HA, vectored vaccines have the capacity to overcome real limitations in achieving robust and durable protection. Meeting the mandates of seasonal influenza vaccine development is difficult, and to respond to a pandemic strain is even more challenging. Issues with influenza vaccine strain selection based on recently circulating viruses often reflect recommendations by the World Health Organization (WHO)-a process that is cumbersome. The strains of influenza A viruses to be used in vaccine manufacture are not wild-type viruses but rather reassortants that are hybrid viruses containing at least the HA and NA gene segments from the target strains and other gene segments from the master strain, PR8, which has properties of high growth in fertilized hen's eggs. This additional process requires more time and quality control, and specifically for HPAI viruses, it is a process that may fail because of the nature of those viruses. In contrast, viral-vectored vaccines are relatively easy to manipulate and produce, and have well-established safety profiles. There are several viral-based vectors currently employed as antigen delivery systems, including poxviruses, adenoviruses baculovirus, paramyxovirus, rhabdovirus, and others; however, the majority of human clinical trials assessing viral-vectored influenza vaccines use poxvirus and adenovirus vectors. While each of these vector approaches has unique features and is in different stages of development, the combined successes of these approaches supports the virus-vectored vaccine approach as a whole. Issues such as preexisting immunity and cold chain requirements, and lingering safety concerns will have to be overcome; however, each approach is making progress in addressing these issues, and all of the approaches are still viable. Virus-vectored vaccines hold particular promise for vaccination with universal or focused antigens where traditional vaccination methods are not suited to efficacious delivery of these antigens. The most promising approaches currently in development are arguably those targeting conserved HA stalk region epitopes. Given the findings to date, virus-vectored vaccines hold great promise and may overcome the current limitations of influenza vaccines.

How did the VEE based replicon system incorporating HA from PR8perform?

Aetiology of Acute Respiratory Tract Infections in Hospitalised Children in Cyprus https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4720120/ SHA: efd27ff0ac04dd60838266386aaebb5df80f4fa9 Authors: Richter, Jan; Panayiotou, Christakis; Tryfonos, Christina; Koptides, Dana; Koliou, Maria; Kalogirou, Nikolas; Georgiou, Eleni; Christodoulou, Christina Date: 2016-01-13 DOI: 10.1371/journal.pone.0147041 License: cc-by Abstract: In order to improve clinical management and prevention of viral infections in hospitalised children improved etiological insight is needed. The aim of the present study was to assess the spectrum of respiratory viral pathogens in children admitted to hospital with acute respiratory tract infections in Cyprus. For this purpose nasopharyngeal swab samples from 424 children less than 12 years of age with acute respiratory tract infections were collected over three epidemic seasons and were analysed for the presence of the most common 15 respiratory viruses. A viral pathogen was identified in 86% of the samples, with multiple infections being observed in almost 20% of the samples. The most frequently detected viruses were RSV (30.4%) and Rhinovirus (27.4%). RSV exhibited a clear seasonality with marked peaks in January/February, while rhinovirus infections did not exhibit a pronounced seasonality being detected almost throughout the year. While RSV and PIV3 incidence decreased significantly with age, the opposite was observed for influenza A and B as well as adenovirus infections. The data presented expand our understanding of the epidemiology of viral respiratory tract infections in Cypriot children and will be helpful to the clinicians and researchers interested in the treatment and control of viral respiratory tract infections. Text: Viral Respiratory tract infections (RTI) represent a major public health problem because of their world-wide occurrence, ease of transmission and considerable morbidity and mortality effecting people of all ages. Children are on average infected two to three times more frequently than adults, with acute RTIs being the most common infection in childhood [1, 2] . Illnesses caused by respiratory viruses include, among others, common colds, pharyngitis, croup, bronchiolitis, viral pneumonia and otitis media. Rapid diagnosis is important not only for timely therapeutic intervention but also for the identification of a beginning influenza epidemic and the avoidance of unnecessary antibiotic treatment [3, 4] . RTIs are a major cause of morbidity and mortality worldwide. Acute RTI is most common in children under five years of age, and represents 30-50% of the paediatric medical admissions, as well as 20-40% of hospitalizations in children. Respiratory infections cluster during winter and early spring months. The leading viral agents include respiratory syncytial virus (RSV), influenza A and B (INF-A, INF-B) viruses, parainfluenza viruses (PIVs), and human adenoviruses (HAdVs). In addition, there is a continuously increasing list of new respiratory viruses that contribute significantly to the burden of acute respiratory infections, such as the recently identified human metapneumovirus (HMPV) and human Bocavirus (HBoV) [5] . Acute RTIs are classified as upper (UTRIs) and lower RTI (LRTIs), according to the involved anatomic localization. URTIs cause non-severe but widespread epidemics that are responsible for continuous circulation of pathogens in the community. LRTIs have been classified as frank pneumonia and bronchiolitis with clinical, radiological and etiological features that usually overlap [6, 7] . Viruses are again the foremost agents of LRTIs often misdiagnosed as bacterial in origin and hence treated with antibiotics unnecessarily [8] . The main aim of this study was to determine the aetiology of acute respiratory tract infections in Cypriot children and assess the epidemiology of the identified viral pathogens over three epidemic seasons. The study was approved by the Cyprus National Bioethics Committee. Accordingly, written informed consent was obtained from parents prior to sample taking. Between November 2010 and October 2013, 485 nasopharyngeal swab samples were collected from children up to 12 years of age, who had been hospitalized with acute respiratory tract infection at the Archbishop Makarios III hospital, Nicosia. Clinical and demographic information including symptoms, duration of hospitalisation, diagnosis and treatment were recorded. Nasal swab samples were collected using the BD Universal Viral Transport Collection Kit. Viral RNA/DNA was extracted from 400 μl sample using the iPrep PureLink Virus Kit on an iPrep purification instrument (Invitrogen). A set of four multiplex Real-Time RT-PCR assays was established and validated for the detection of the 15 most common respiratory viruses as follows: assay 1: influenzaviruses A and B, RSV, assay 2: parainfluenzaviruses 1-4, assay 3: HAdV, enteroviruses, HMPV and HBoV and assay 4: rhinoviruses and the human coronaviruses OC43, NL63 and 229E (Table 1) . Published primer and probe sets were used as a basis for designing the assays, however, all primer/probe sequences were checked against newly build sequence alignments of all viruses tested and were modified, if necessary, to account for possible sequence variations. For this purpose, all available complete genome sequences were obtained for each virus from GenBank, imported into the BioEdit Sequence Alignment Editor v7.1.7 and aligned using ClustalX. In case of mismatches between published primers/probe and target sequences, modifications were applied, as indicated in Table 1 . The alignments for the viruses, which necessitated changes to the primers/probe are available in Fasta-Format as supplement S1-S4 Files. Primer concentrations and reaction conditions for the four assays were subsequently optimised for multiplexing. In order to assess the sensitivity and specificity of the assays, the laboratory enrolled for two consecutive years in Quality Control for Molecular Diagnostics (QCMD) external quality assessment schemes for all viruses, except Bocavirus, which was unavailable. In summary, the established assays were able to correctly identify all viruses tested, proving their suitability for diagnostic application. A possible correlation of virus prevalence and age of infection was assessed using univariate analyses. The Fisher's exact test was used where cell counts below 5 were encountered; otherwise, the chi-squared test was performed. The same statistical tests were used to compare the frequency of subjects with single or multiple infections between age groups. In addition, Pearson correlation was used to examine co-infections of different viruses. All statistical analyses were performed using StataSE 12 (StatCorp. 2007. College Station, TX, USA). The present study was a prospective investigation of children hospitalized with acute respiratory tract infections between November 2010 and October 2013 in Cyprus. The median age of the children was 15 months (range: 0-140 months) with 243 being male and 181 female (male/ female ratio 1.34). The age distribution is shown in Fig 1. Out of the 424 samples analysed, 364 (85.8%) were positive for one or more viruses. Results are summarized in Table 2 .The most commonly detected viruses were RSV, which was found in 129 (30.4%) patients and rhinoviruses in 116 (27.4%) accounting together for almost 60% of all detections. With moderate frequency have been detected HAdV in 31(7.3%) patients, influenza A in 28 (6.6%), HBoV in 24 (5.7%), enteroviruses and PIV 3 in 23 (5.4%) of patients respectively, and Influenza B in 21 (5.0%). A low frequency was exhibited by HMPV with 16 (3.8%) positive samples, human coronavirus OC43 with 13 (3.1%), PIV 1 with 12 (2.8%), PIV 4 with 9 (2.1%), PIV 2 with 7 (1.7%) and HCoV NL63 with 6 (1.4%). Coronavirus 229E could be detected only in a single sample. Co-infections with two or more viruses were observed in 84 out of the 364 positive samples (see Table 2 ). Dual infections accounted for 17% of all positive samples and three viruses were detected in 2.7% of samples). A single patient sample displayed a quadruple infection being simultaneously positive for RSV, rhinovirus, HBoV and influenza B. Table 3 summarizes the frequency of each virus in single vs. multiple infections as well as the number of co-occurrences of viruses for each possible virus combination. In absolute terms the most common combination observed was RSV/rhinovirus. As a percentage, however, the virus appearing most often in co- infections was HBoV, which was found in more than 70% of cases together with another virus, followed by coronaviruses HCoV OC43 and HCoV NL63 with 61% and 67%, respectively. On the other hand, the viruses most rarely seen in co-infections were influenza viruses A and B as well as RSV. Pearson correlation coefficients were calculated to examine the likelihood of co-infections of different viruses. The results of the analysis are summarized in Table 1 in S1 Table. Significant correlation (P-value < 0.05) was seen mostly for co-infections with RSV, however correlations were very weak (r<0.3) and negative. This finding can probably be explained by the fact that RSV infections occurred predominantly in the very young, where co-infections were less frequently observed. On the other hand, a significant positive correlation was observed for enterovirus and rhinovirus co-infection hinting maybe at similarities in circulation patterns and/or transmission modes. Regarding seasonality, different patterns of circulations could be observed for RSV, rhinoviruses and influenzaviruses (A and B combined) (Fig 2) , with RSV and influenza exhibiting a clear seasonality with marked peaks in January/February, while rhinovirus infections did not exhibit a pronounced seasonality being detected almost throughout the year. However, as more than 100 different rhinovirus strains have been identified to be circulating worldwide in parallel and successively, a potential seasonality of individual rhinovirus serotypes may be masked by overlapping patterns [18, 19] . The data was further analysed with regard to the age distribution of virus infection (see Table 2 ). In infants up to 3 months old, RSV was by far the most common pathogen (58.1%), followed by rhinovirus (20.3%) and PIV3 with 8.1% each. The incidence of RSV, however, decreases significantly with increasing age (p-value < 0.0001) dropping to 13% in children older than 3 years old, while the reverse relationship is observed for Influenza A and B and HAdV. Rhinoviruses, HBoV and enteroviruses are most frequently observed in children from 4 months to 3 years of age. The age dependency of the virus incidence is visualized in Fig 3 for the seven most frequently observed viruses. The positivity rate also showed a trend according to the age group dropping from 90.5% in the under 3-month old to 78.3% in the 4-12 years old (p-value = 0.020). This may point to an increasing role of pathogens not included in the assays, such as bacterial infections in older children. Regarding multiple infections, children less than 3 month of age and those older than 4 years had a significantly smaller risk to present with multiple infections as compared to the other two age groups (p-value = 0.014). A reason for this could be that very young children have limited contact to others reducing thereby the chance for a co-infection, whereas children older than 3 years already established immunity to an increasing number of viruses encountered previously. This study for the first time examined the aetiology of acute respiratory tract infections in hospitalised children in Cyprus. Four multiplex Real-Time RT-PCR assays were developed in order to detect the most common respiratory viral pathogens in a fast and cost-effective way. The high rate of positive samples (85.8%) is evidence of the high sensitivity of the Multiplex-assays used and that the range of viruses included in the analysis is comprehensive. Many previous studies have shown detection rates ranging from below 50% to 75% [20] [21] [22] [23] [24] . The most common viruses detected were RSV and rhinovirus accounting for almost 60% of all cases. Both viruses were reported previously by others as the major aetiology for respiratory viral infections in young children with rhinoviruses being recognized increasingly for their role in lower respiratory tract infections [20, [25] [26] [27] [28] [29] [30] . Our data support the results of similar studies performed in the Middle East region. A recently published study found that RSV was the most commonly detected virus in nasopharyngeal swabs from children presenting symptoms of RTIs and in addition to that it also showed that RSV infections follow a similar circulation pattern peaking from December to March [31] . Another study has revealed that RSV and PIV3 incidence decreases significantly with age, whereas the opposite is observed for influenza and adenovirus infections, a trend that was also observed in our study [26] . Mixed infections were observed in approximately 20% of all samples, which is in the middle of previously reported rates ranging from 10 to almost 40%. HBoV, HCoV and EV were found most frequently in co-infections. All three subtypes of HCoV were co-detected with several other viruses, while HBoV was co-detected mainly with HRV and RSV. In the case of EV infections, EV were almost predominantly associated with HRV. The rare presence of InfA and InfB viruses in multiple infections witnessed in our study was also observed elsewhere [32, 33] . Even though this study did not allow for investigating a possible association between multiple infections and disease severity, a review of the literature shows that such a potential association is still subject to controversy, since there are reports showing no relationship of multiple virus infection with respiratoty illness severity on one hand or a significant association on the other. Studies have shown that viral co-infection was significantly associated with longer duration of illness symptoms, but with a decreased severity in hospitalized children regarding oxygen requirement and intensive care unit admission, whereas the findings of other studies have indicated that severe clinical phenotypes were more prevalent in co-infection patients, especially in RSV co-infections that may increase the severity of RSV associated disease in children [25, [34] [35] [36] [37] [38] [39] [40] . Viral respiratory infections continue to be a worldwide health concern. As the clinical symptoms of patients with acute respiratory tract infections do usually not allow a discrimination of viral or bacterial aetiology, rapid and reliable diagnostic tools are required for better antibiotic stewardship and the implementation of appropriate infection control measures [4, 41] . The data presented expand our understanding of the epidemiology of viral respiratory tract infections in Cypriot children and will be helpful to the clinicians and researchers interested in the treatment and control of viral respiratory tract infections.

What is the most common infection in childhood?

Estimating the number of infections and the impact of non- pharmaceutical interventions on COVID-19 in 11 European countries 30 March 2020 Imperial College COVID-19 Response Team Seth Flaxmani Swapnil Mishra*, Axel Gandy*, H JulietteT Unwin, Helen Coupland, Thomas A Mellan, Harrison Zhu, Tresnia Berah, Jeffrey W Eaton, Pablo N P Guzman, Nora Schmit, Lucia Cilloni, Kylie E C Ainslie, Marc Baguelin, Isobel Blake, Adhiratha Boonyasiri, Olivia Boyd, Lorenzo Cattarino, Constanze Ciavarella, Laura Cooper, Zulma Cucunuba’, Gina Cuomo—Dannenburg, Amy Dighe, Bimandra Djaafara, Ilaria Dorigatti, Sabine van Elsland, Rich FitzJohn, Han Fu, Katy Gaythorpe, Lily Geidelberg, Nicholas Grassly, Wi|| Green, Timothy Hallett, Arran Hamlet, Wes Hinsley, Ben Jeffrey, David Jorgensen, Edward Knock, Daniel Laydon, Gemma Nedjati—Gilani, Pierre Nouvellet, Kris Parag, Igor Siveroni, Hayley Thompson, Robert Verity, Erik Volz, Caroline Walters, Haowei Wang, Yuanrong Wang, Oliver Watson, Peter Winskill, Xiaoyue Xi, Charles Whittaker, Patrick GT Walker, Azra Ghani, Christl A. Donnelly, Steven Riley, Lucy C Okell, Michaela A C Vollmer, NeilM.Ferguson1and Samir Bhatt*1 Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analysis Abdul LatifJameeI Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford *Contributed equally 1Correspondence: nei|[email protected], [email protected] Summary Following the emergence of a novel coronavirus (SARS-CoV-Z) and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national Iockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number— a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of Iockdown (11th March), although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented a|| interventions considered in our analysis. This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March [95% credible interval 21,000-120,000]. Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-Z up to 28th March, representing between 1.88% and 11.43% ofthe population. The proportion of the population infected to date — the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-Z is slowing. SUGGESTED CITATION Seth Flaxman, Swapnil Mishra, Axel Gandy et 0/. Estimating the number of infections and the impact of non— pharmaceutical interventions on COVID—19 in 11 European countries. Imperial College London (2020), doi: https://doi.org/10.25561/77731 1 Introduction Following the emergence of a novel coronavirus (SARS-CoV-Z) in Wuhan, China in December 2019 and its global spread, large epidemics of the disease, caused by the virus designated COVID-19, have emerged in Europe. In response to the rising numbers of cases and deaths, and to maintain the capacity of health systems to treat as many severe cases as possible, European countries, like those in other continents, have implemented or are in the process of implementing measures to control their epidemics. These large-scale non-pharmaceutical interventions vary between countries but include social distancing (such as banning large gatherings and advising individuals not to socialize outside their households), border closures, school closures, measures to isolate symptomatic individuals and their contacts, and large-scale lockdowns of populations with all but essential internal travel banned. Understanding firstly, whether these interventions are having the desired impact of controlling the epidemic and secondly, which interventions are necessary to maintain control, is critical given their large economic and social costs. The key aim ofthese interventions is to reduce the effective reproduction number, Rt, ofthe infection, a fundamental epidemiological quantity representing the average number of infections, at time t, per infected case over the course of their infection. Ith is maintained at less than 1, the incidence of new infections decreases, ultimately resulting in control of the epidemic. If Rt is greater than 1, then infections will increase (dependent on how much greater than 1 the reproduction number is) until the epidemic peaks and eventually declines due to acquisition of herd immunity. In China, strict movement restrictions and other measures including case isolation and quarantine began to be introduced from 23rd January, which achieved a downward trend in the number of confirmed new cases during February, resulting in zero new confirmed indigenous cases in Wuhan by March 19th. Studies have estimated how Rt changed during this time in different areas ofChina from around 2-4 during the uncontrolled epidemic down to below 1, with an estimated 7-9 fold decrease in the number of daily contacts per person.1'2 Control measures such as social distancing, intensive testing, and contact tracing in other countries such as Singapore and South Korea have successfully reduced case incidence in recent weeks, although there is a riskthe virus will spread again once control measures are relaxed.3'4 The epidemic began slightly laterin Europe, from January or later in different regions.5 Countries have implemented different combinations of control measures and the level of adherence to government recommendations on social distancing is likely to vary between countries, in part due to different levels of enforcement. Estimating reproduction numbers for SARS-CoV-Z presents challenges due to the high proportion of infections not detected by health systems”7 and regular changes in testing policies, resulting in different proportions of infections being detected over time and between countries. Most countries so far only have the capacity to test a small proportion of suspected cases and tests are reserved for severely ill patients or for high-risk groups (e.g. contacts of cases). Looking at case data, therefore, gives a systematically biased view of trends. An alternative way to estimate the course of the epidemic is to back-calculate infections from observed deaths. Reported deaths are likely to be more reliable, although the early focus of most surveillance systems on cases with reported travel histories to China may mean that some early deaths will have been missed. Whilst the recent trends in deaths will therefore be informative, there is a time lag in observing the effect of interventions on deaths since there is a 2-3-week period between infection, onset of symptoms and outcome. In this report, we fit a novel Bayesian mechanistic model of the infection cycle to observed deaths in 11 European countries, inferring plausible upper and lower bounds (Bayesian credible intervals) of the total populations infected (attack rates), case detection probabilities, and the reproduction number over time (Rt). We fit the model jointly to COVID-19 data from all these countries to assess whether there is evidence that interventions have so far been successful at reducing Rt below 1, with the strong assumption that particular interventions are achieving a similar impact in different countries and that the efficacy of those interventions remains constant over time. The model is informed more strongly by countries with larger numbers of deaths and which implemented interventions earlier, therefore estimates of recent Rt in countries with more recent interventions are contingent on similar intervention impacts. Data in the coming weeks will enable estimation of country-specific Rt with greater precision. Model and data details are presented in the appendix, validation and sensitivity are also presented in the appendix, and general limitations presented below in the conclusions. 2 Results The timing of interventions should be taken in the context of when an individual country’s epidemic started to grow along with the speed with which control measures were implemented. Italy was the first to begin intervention measures, and other countries followed soon afterwards (Figure 1). Most interventions began around 12th-14th March. We analyzed data on deaths up to 28th March, giving a 2-3-week window over which to estimate the effect of interventions. Currently, most countries in our study have implemented all major non-pharmaceutical interventions. For each country, we model the number of infections, the number of deaths, and Rt, the effective reproduction number over time, with Rt changing only when an intervention is introduced (Figure 2- 12). Rt is the average number of secondary infections per infected individual, assuming that the interventions that are in place at time t stay in place throughout their entire infectious period. Every country has its own individual starting reproduction number Rt before interventions take place. Specific interventions are assumed to have the same relative impact on Rt in each country when they were introduced there and are informed by mortality data across all countries. Figure l: Intervention timings for the 11 European countries included in the analysis. For further details see Appendix 8.6. 2.1 Estimated true numbers of infections and current attack rates In all countries, we estimate there are orders of magnitude fewer infections detected (Figure 2) than true infections, mostly likely due to mild and asymptomatic infections as well as limited testing capacity. In Italy, our results suggest that, cumulatively, 5.9 [1.9-15.2] million people have been infected as of March 28th, giving an attack rate of 9.8% [3.2%-25%] of the population (Table 1). Spain has recently seen a large increase in the number of deaths, and given its smaller population, our model estimates that a higher proportion of the population, 15.0% (7.0 [18-19] million people) have been infected to date. Germany is estimated to have one of the lowest attack rates at 0.7% with 600,000 [240,000-1,500,000] people infected. Imperial College COVID-19 Response Team Table l: Posterior model estimates of percentage of total population infected as of 28th March 2020. Country % of total population infected (mean [95% credible intervall) Austria 1.1% [0.36%-3.1%] Belgium 3.7% [1.3%-9.7%] Denmark 1.1% [0.40%-3.1%] France 3.0% [1.1%-7.4%] Germany 0.72% [0.28%-1.8%] Italy 9.8% [3.2%-26%] Norway 0.41% [0.09%-1.2%] Spain 15% [3.7%-41%] Sweden 3.1% [0.85%-8.4%] Switzerland 3.2% [1.3%-7.6%] United Kingdom 2.7% [1.2%-5.4%] 2.2 Reproduction numbers and impact of interventions Averaged across all countries, we estimate initial reproduction numbers of around 3.87 [3.01-4.66], which is in line with other estimates.1'8 These estimates are informed by our choice of serial interval distribution and the initial growth rate of observed deaths. A shorter assumed serial interval results in lower starting reproduction numbers (Appendix 8.4.2, Appendix 8.4.6). The initial reproduction numbers are also uncertain due to (a) importation being the dominant source of new infections early in the epidemic, rather than local transmission (b) possible under-ascertainment in deaths particularly before testing became widespread. We estimate large changes in Rt in response to the combined non-pharmaceutical interventions. Our results, which are driven largely by countries with advanced epidemics and larger numbers of deaths (e.g. Italy, Spain), suggest that these interventions have together had a substantial impact on transmission, as measured by changes in the estimated reproduction number Rt. Across all countries we find current estimates of Rt to range from a posterior mean of 0.97 [0.14-2.14] for Norway to a posterior mean of2.64 [1.40-4.18] for Sweden, with an average of 1.43 across the 11 country posterior means, a 64% reduction compared to the pre-intervention values. We note that these estimates are contingent on intervention impact being the same in different countries and at different times. In all countries but Sweden, under the same assumptions, we estimate that the current reproduction number includes 1 in the uncertainty range. The estimated reproduction number for Sweden is higher, not because the mortality trends are significantly different from any other country, but as an artefact of our model, which assumes a smaller reduction in Rt because no full lockdown has been ordered so far. Overall, we cannot yet conclude whether current interventions are sufficient to drive Rt below 1 (posterior probability of being less than 1.0 is 44% on average across the countries). We are also unable to conclude whether interventions may be different between countries or over time. There remains a high level of uncertainty in these estimates. It is too early to detect substantial intervention impact in many countries at earlier stages of their epidemic (e.g. Germany, UK, Norway). Many interventions have occurred only recently, and their effects have not yet been fully observed due to the time lag between infection and death. This uncertainty will reduce as more data become available. For all countries, our model fits observed deaths data well (Bayesian goodness of fit tests). We also found that our model can reliably forecast daily deaths 3 days into the future, by withholding the latest 3 days of data and comparing model predictions to observed deaths (Appendix 8.3). The close spacing of interventions in time made it statistically impossible to determine which had the greatest effect (Figure 1, Figure 4). However, when doing a sensitivity analysis (Appendix 8.4.3) with uninformative prior distributions (where interventions can increase deaths) we find similar impact of Imperial College COVID-19 Response Team interventions, which shows that our choice of prior distribution is not driving the effects we see in the main analysis. Figure 2: Country-level estimates of infections, deaths and Rt. Left: daily number of infections, brown bars are reported infections, blue bands are predicted infections, dark blue 50% credible interval (CI), light blue 95% CI. The number of daily infections estimated by our model drops immediately after an intervention, as we assume that all infected people become immediately less infectious through the intervention. Afterwards, if the Rt is above 1, the number of infections will starts growing again. Middle: daily number of deaths, brown bars are reported deaths, blue bands are predicted deaths, CI as in left plot. Right: time-varying reproduction number Rt, dark green 50% CI, light green 95% CI. Icons are interventions shown at the time they occurred. Imperial College COVID-19 Response Team Table 2: Totalforecasted deaths since the beginning of the epidemic up to 31 March in our model and in a counterfactual model (assuming no intervention had taken place). Estimated averted deaths over this time period as a result of the interventions. Numbers in brackets are 95% credible intervals. 2.3 Estimated impact of interventions on deaths Table 2 shows total forecasted deaths since the beginning of the epidemic up to and including 31 March under ourfitted model and under the counterfactual model, which predicts what would have happened if no interventions were implemented (and R, = R0 i.e. the initial reproduction number estimated before interventions). Again, the assumption in these predictions is that intervention impact is the same across countries and time. The model without interventions was unable to capture recent trends in deaths in several countries, where the rate of increase had clearly slowed (Figure 3). Trends were confirmed statistically by Bayesian leave-one-out cross-validation and the widely applicable information criterion assessments —WA|C). By comparing the deaths predicted under the model with no interventions to the deaths predicted in our intervention model, we calculated the total deaths averted up to the end of March. We find that, across 11 countries, since the beginning of the epidemic, 59,000 [21,000-120,000] deaths have been averted due to interventions. In Italy and Spain, where the epidemic is advanced, 38,000 [13,000- 84,000] and 16,000 [5,400-35,000] deaths have been averted, respectively. Even in the UK, which is much earlier in its epidemic, we predict 370 [73-1,000] deaths have been averted. These numbers give only the deaths averted that would have occurred up to 31 March. lfwe were to include the deaths of currently infected individuals in both models, which might happen after 31 March, then the deaths averted would be substantially higher. Figure 3: Daily number of confirmed deaths, predictions (up to 28 March) and forecasts (after) for (a) Italy and (b) Spain from our model with interventions (blue) and from the no interventions counterfactual model (pink); credible intervals are shown one week into the future. Other countries are shown in Appendix 8.6. 03/0 25% 50% 753% 100% (no effect on transmissibility) (ends transmissibility Relative % reduction in R. Figure 4: Our model includes five covariates for governmental interventions, adjusting for whether the intervention was the first one undertaken by the government in response to COVID-19 (red) or was subsequent to other interventions (green). Mean relative percentage reduction in Rt is shown with 95% posterior credible intervals. If 100% reduction is achieved, Rt = 0 and there is no more transmission of COVID-19. No effects are significantly different from any others, probably due to the fact that many interventions occurred on the same day or within days of each other as shown in Figure l. 3 Discussion During this early phase of control measures against the novel coronavirus in Europe, we analyze trends in numbers of deaths to assess the extent to which transmission is being reduced. Representing the COVlD-19 infection process using a semi-mechanistic, joint, Bayesian hierarchical model, we can reproduce trends observed in the data on deaths and can forecast accurately over short time horizons. We estimate that there have been many more infections than are currently reported. The high level of under-ascertainment of infections that we estimate here is likely due to the focus on testing in hospital settings rather than in the community. Despite this, only a small minority of individuals in each country have been infected, with an attack rate on average of 4.9% [l.9%-ll%] with considerable variation between countries (Table 1). Our estimates imply that the populations in Europe are not close to herd immunity ("50-75% if R0 is 2-4). Further, with Rt values dropping substantially, the rate of acquisition of herd immunity will slow down rapidly. This implies that the virus will be able to spread rapidly should interventions be lifted. Such estimates of the attack rate to date urgently need to be validated by newly developed antibody tests in representative population surveys, once these become available. We estimate that major non-pharmaceutical interventions have had a substantial impact on the time- varying reproduction numbers in countries where there has been time to observe intervention effects on trends in deaths (Italy, Spain). lfadherence in those countries has changed since that initial period, then our forecast of future deaths will be affected accordingly: increasing adherence over time will have resulted in fewer deaths and decreasing adherence in more deaths. Similarly, our estimates of the impact ofinterventions in other countries should be viewed with caution if the same interventions have achieved different levels of adherence than was initially the case in Italy and Spain. Due to the implementation of interventions in rapid succession in many countries, there are not enough data to estimate the individual effect size of each intervention, and we discourage attributing associations to individual intervention. In some cases, such as Norway, where all interventions were implemented at once, these individual effects are by definition unidentifiable. Despite this, while individual impacts cannot be determined, their estimated joint impact is strongly empirically justified (see Appendix 8.4 for sensitivity analysis). While the growth in daily deaths has decreased, due to the lag between infections and deaths, continued rises in daily deaths are to be expected for some time. To understand the impact of interventions, we fit a counterfactual model without the interventions and compare this to the actual model. Consider Italy and the UK - two countries at very different stages in their epidemics. For the UK, where interventions are very recent, much of the intervention strength is borrowed from countries with older epidemics. The results suggest that interventions will have a large impact on infections and deaths despite counts of both rising. For Italy, where far more time has passed since the interventions have been implemented, it is clear that the model without interventions does not fit well to the data, and cannot explain the sub-linear (on the logarithmic scale) reduction in deaths (see Figure 10). The counterfactual model for Italy suggests that despite mounting pressure on health systems, interventions have averted a health care catastrophe where the number of new deaths would have been 3.7 times higher (38,000 deaths averted) than currently observed. Even in the UK, much earlier in its epidemic, the recent interventions are forecasted to avert 370 total deaths up to 31 of March. 4 Conclusion and Limitations Modern understanding of infectious disease with a global publicized response has meant that nationwide interventions could be implemented with widespread adherence and support. Given observed infection fatality ratios and the epidemiology of COVlD-19, major non-pharmaceutical interventions have had a substantial impact in reducing transmission in countries with more advanced epidemics. It is too early to be sure whether similar reductions will be seen in countries at earlier stages of their epidemic. While we cannot determine which set of interventions have been most successful, taken together, we can already see changes in the trends of new deaths. When forecasting 3 days and looking over the whole epidemic the number of deaths averted is substantial. We note that substantial innovation is taking place, and new more effective interventions or refinements of current interventions, alongside behavioral changes will further contribute to reductions in infections. We cannot say for certain that the current measures have controlled the epidemic in Europe; however, if current trends continue, there is reason for optimism. Our approach is semi-mechanistic. We propose a plausible structure for the infection process and then estimate parameters empirically. However, many parameters had to be given strong prior distributions or had to be fixed. For these assumptions, we have provided relevant citations to previous studies. As more data become available and better estimates arise, we will update these in weekly reports. Our choice of serial interval distribution strongly influences the prior distribution for starting R0. Our infection fatality ratio, and infection-to-onset-to-death distributions strongly influence the rate of death and hence the estimated number of true underlying cases. We also assume that the effect of interventions is the same in all countries, which may not be fully realistic. This assumption implies that countries with early interventions and more deaths since these interventions (e.g. Italy, Spain) strongly influence estimates of intervention impact in countries at earlier stages of their epidemic with fewer deaths (e.g. Germany, UK). We have tried to create consistent definitions of all interventions and document details of this in Appendix 8.6. However, invariably there will be differences from country to country in the strength of their intervention — for example, most countries have banned gatherings of more than 2 people when implementing a lockdown, whereas in Sweden the government only banned gatherings of more than 10 people. These differences can skew impacts in countries with very little data. We believe that our uncertainty to some degree can cover these differences, and as more data become available, coefficients should become more reliable. However, despite these strong assumptions, there is sufficient signal in the data to estimate changes in R, (see the sensitivity analysis reported in Appendix 8.4.3) and this signal will stand to increase with time. In our Bayesian hierarchical framework, we robustly quantify the uncertainty in our parameter estimates and posterior predictions. This can be seen in the very wide credible intervals in more recent days, where little or no death data are available to inform the estimates. Furthermore, we predict intervention impact at country-level, but different trends may be in place in different parts of each country. For example, the epidemic in northern Italy was subject to controls earlier than the rest of the country. 5 Data Our model utilizes daily real-time death data from the ECDC (European Centre of Disease Control), where we catalogue case data for 11 European countries currently experiencing the epidemic: Austria, Belgium, Denmark, France, Germany, Italy, Norway, Spain, Sweden, Switzerland and the United Kingdom. The ECDC provides information on confirmed cases and deaths attributable to COVID-19. However, the case data are highly unrepresentative of the incidence of infections due to underreporting as well as systematic and country-specific changes in testing. We, therefore, use only deaths attributable to COVID-19 in our model; we do not use the ECDC case estimates at all. While the observed deaths still have some degree of unreliability, again due to changes in reporting and testing, we believe the data are ofsufficient fidelity to model. For population counts, we use UNPOP age-stratified counts.10 We also catalogue data on the nature and type of major non-pharmaceutical interventions. We looked at the government webpages from each country as well as their official public health division/information webpages to identify the latest advice/laws being issued by the government and public health authorities. We collected the following: School closure ordered: This intervention refers to nationwide extraordinary school closures which in most cases refer to both primary and secondary schools closing (for most countries this also includes the closure of otherforms of higher education or the advice to teach remotely). In the case of Denmark and Sweden, we allowed partial school closures of only secondary schools. The date of the school closure is taken to be the effective date when the schools started to be closed (ifthis was on a Monday, the date used was the one of the previous Saturdays as pupils and students effectively stayed at home from that date onwards). Case-based measures: This intervention comprises strong recommendations or laws to the general public and primary care about self—isolation when showing COVID-19-like symptoms. These also include nationwide testing programs where individuals can be tested and subsequently self—isolated. Our definition is restricted to nationwide government advice to all individuals (e.g. UK) or to all primary care and excludes regional only advice. These do not include containment phase interventions such as isolation if travelling back from an epidemic country such as China. Public events banned: This refers to banning all public events of more than 100 participants such as sports events. Social distancing encouraged: As one of the first interventions against the spread of the COVID-19 pandemic, many governments have published advice on social distancing including the recommendation to work from home wherever possible, reducing use ofpublictransport and all other non-essential contact. The dates used are those when social distancing has officially been recommended by the government; the advice may include maintaining a recommended physical distance from others. Lockdown decreed: There are several different scenarios that the media refers to as lockdown. As an overall definition, we consider regulations/legislations regarding strict face-to-face social interaction: including the banning of any non-essential public gatherings, closure of educational and public/cultural institutions, ordering people to stay home apart from exercise and essential tasks. We include special cases where these are not explicitly mentioned on government websites but are enforced by the police (e.g. France). The dates used are the effective dates when these legislations have been implemented. We note that lockdown encompasses other interventions previously implemented. First intervention: As Figure 1 shows, European governments have escalated interventions rapidly, and in some examples (Norway/Denmark) have implemented these interventions all on a single day. Therefore, given the temporal autocorrelation inherent in government intervention, we include a binary covariate for the first intervention, which can be interpreted as a government decision to take major action to control COVID-19. A full list of the timing of these interventions and the sources we have used can be found in Appendix 8.6. 6 Methods Summary A Visual summary of our model is presented in Figure 5 (details in Appendix 8.1 and 8.2). Replication code is available at https://github.com/|mperia|CollegeLondon/covid19model/releases/tag/vl.0 We fit our model to observed deaths according to ECDC data from 11 European countries. The modelled deaths are informed by an infection-to-onset distribution (time from infection to the onset of symptoms), an onset-to-death distribution (time from the onset of symptoms to death), and the population-averaged infection fatality ratio (adjusted for the age structure and contact patterns of each country, see Appendix). Given these distributions and ratios, modelled deaths are a function of the number of infections. The modelled number of infections is informed by the serial interval distribution (the average time from infection of one person to the time at which they infect another) and the time-varying reproduction number. Finally, the time-varying reproduction number is a function of the initial reproduction number before interventions and the effect sizes from interventions. Figure 5: Summary of model components. Following the hierarchy from bottom to top gives us a full framework to see how interventions affect infections, which can result in deaths. We use Bayesian inference to ensure our modelled deaths can reproduce the observed deaths as closely as possible. From bottom to top in Figure 5, there is an implicit lag in time that means the effect of very recent interventions manifest weakly in current deaths (and get stronger as time progresses). To maximise the ability to observe intervention impact on deaths, we fit our model jointly for all 11 European countries, which results in a large data set. Our model jointly estimates the effect sizes of interventions. We have evaluated the effect ofour Bayesian prior distribution choices and evaluate our Bayesian posterior calibration to ensure our results are statistically robust (Appendix 8.4). 7 Acknowledgements Initial research on covariates in Appendix 8.6 was crowdsourced; we thank a number of people across the world for help with this. This work was supported by Centre funding from the UK Medical Research Council under a concordat with the UK Department for International Development, the NIHR Health Protection Research Unit in Modelling Methodology and CommunityJameel. 8 Appendix: Model Specifics, Validation and Sensitivity Analysis 8.1 Death model We observe daily deaths Dam for days t E 1, ...,n and countries m E 1, ...,p. These daily deaths are modelled using a positive real-Valued function dam = E(Dam) that represents the expected number of deaths attributed to COVID-19. Dam is assumed to follow a negative binomial distribution with The expected number of deaths (1 in a given country on a given day is a function of the number of infections C occurring in previous days. At the beginning of the epidemic, the observed deaths in a country can be dominated by deaths that result from infection that are not locally acquired. To avoid biasing our model by this, we only include observed deaths from the day after a country has cumulatively observed 10 deaths in our model. To mechanistically link ourfunction for deaths to infected cases, we use a previously estimated COVID- 19 infection-fatality-ratio ifr (probability of death given infection)9 together with a distribution oftimes from infection to death TE. The ifr is derived from estimates presented in Verity et al11 which assumed homogeneous attack rates across age-groups. To better match estimates of attack rates by age generated using more detailed information on country and age-specific mixing patterns, we scale these estimates (the unadjusted ifr, referred to here as ifr’) in the following way as in previous work.4 Let Ca be the number of infections generated in age-group a, Na the underlying size of the population in that age group and AR“ 2 Ca/Na the age-group-specific attack rate. The adjusted ifr is then given by: ifra = fififié, where AR50_59 is the predicted attack-rate in the 50-59 year age-group after incorporating country-specific patterns of contact and mixing. This age-group was chosen as the reference as it had the lowest predicted level of underreporting in previous analyses of data from the Chinese epidemic“. We obtained country-specific estimates of attack rate by age, AR“, for the 11 European countries in our analysis from a previous study which incorporates information on contact between individuals of different ages in countries across Europe.12 We then obtained overall ifr estimates for each country adjusting for both demography and age-specific attack rates. Using estimated epidemiological information from previous studies,“'11 we assume TE to be the sum of two independent random times: the incubation period (infection to onset of symptoms or infection- to-onset) distribution and the time between onset of symptoms and death (onset-to-death). The infection-to-onset distribution is Gamma distributed with mean 5.1 days and coefficient of variation 0.86. The onset-to-death distribution is also Gamma distributed with a mean of 18.8 days and a coefficient of va riation 0.45. ifrm is population averaged over the age structure of a given country. The infection-to-death distribution is therefore given by: um ~ ifrm ~ (Gamma(5.1,0.86) + Gamma(18.8,0.45)) Figure 6 shows the infection-to-death distribution and the resulting survival function that integrates to the infection fatality ratio. Figure 6: Left, infection-to-death distribution (mean 23.9 days). Right, survival probability of infected individuals per day given the infection fatality ratio (1%) and the infection-to-death distribution on the left. Using the probability of death distribution, the expected number of deaths dam, on a given day t, for country, m, is given by the following discrete sum: The number of deaths today is the sum of the past infections weighted by their probability of death, where the probability of death depends on the number of days since infection. 8.2 Infection model The true number of infected individuals, C, is modelled using a discrete renewal process. This approach has been used in numerous previous studies13'16 and has a strong theoretical basis in stochastic individual-based counting processes such as Hawkes process and the Bellman-Harris process.”18 The renewal model is related to the Susceptible-Infected-Recovered model, except the renewal is not expressed in differential form. To model the number ofinfections over time we need to specify a serial interval distribution g with density g(T), (the time between when a person gets infected and when they subsequently infect another other people), which we choose to be Gamma distributed: g ~ Gamma (6.50.62). The serial interval distribution is shown below in Figure 7 and is assumed to be the same for all countries. Figure 7: Serial interval distribution g with a mean of 6.5 days. Given the serial interval distribution, the number of infections Eamon a given day t, and country, m, is given by the following discrete convolution function: _ t—1 Cam — Ram ZT=0 Cr,mgt—‘r r where, similarto the probability ofdeath function, the daily serial interval is discretized by fs+0.5 1.5 gs = T=s—0.Sg(T)dT fors = 2,3, and 91 = fT=Og(T)dT. Infections today depend on the number of infections in the previous days, weighted by the discretized serial interval distribution. This weighting is then scaled by the country-specific time-Varying reproduction number, Ram, that models the average number of secondary infections at a given time. The functional form for the time-Varying reproduction number was chosen to be as simple as possible to minimize the impact of strong prior assumptions: we use a piecewise constant function that scales Ram from a baseline prior R0,m and is driven by known major non-pharmaceutical interventions occurring in different countries and times. We included 6 interventions, one of which is constructed from the other 5 interventions, which are timings of school and university closures (k=l), self—isolating if ill (k=2), banning of public events (k=3), any government intervention in place (k=4), implementing a partial or complete lockdown (k=5) and encouraging social distancing and isolation (k=6). We denote the indicator variable for intervention k E 1,2,3,4,5,6 by IkI’m, which is 1 if intervention k is in place in country m at time t and 0 otherwise. The covariate ”any government intervention” (k=4) indicates if any of the other 5 interventions are in effect,i.e.14’t’m equals 1 at time t if any of the interventions k E 1,2,3,4,5 are in effect in country m at time t and equals 0 otherwise. Covariate 4 has the interpretation of indicating the onset of major government intervention. The effect of each intervention is assumed to be multiplicative. Ram is therefore a function ofthe intervention indicators Ik’t’m in place at time t in country m: Ram : R0,m eXp(— 212:1 O(Rheum)- The exponential form was used to ensure positivity of the reproduction number, with R0,m constrained to be positive as it appears outside the exponential. The impact of each intervention on Ram is characterised by a set of parameters 0(1, ...,OL6, with independent prior distributions chosen to be ock ~ Gamma(. 5,1). The impacts ock are shared between all m countries and therefore they are informed by all available data. The prior distribution for R0 was chosen to be R0,m ~ Normal(2.4, IKI) with K ~ Normal(0,0.5), Once again, K is the same among all countries to share information. We assume that seeding of new infections begins 30 days before the day after a country has cumulatively observed 10 deaths. From this date, we seed our model with 6 sequential days of infections drawn from cl’m,...,66’m~EXponential(T), where T~Exponential(0.03). These seed infections are inferred in our Bayesian posterior distribution. We estimated parameters jointly for all 11 countries in a single hierarchical model. Fitting was done in the probabilistic programming language Stan,19 using an adaptive Hamiltonian Monte Carlo (HMC) sampler. We ran 8 chains for 4000 iterations with 2000 iterations of warmup and a thinning factor 4 to obtain 2000 posterior samples. Posterior convergence was assessed using the Rhat statistic and by diagnosing divergent transitions of the HMC sampler. Prior-posterior calibrations were also performed (see below). 8.3 Validation We validate accuracy of point estimates of our model using cross-Validation. In our cross-validation scheme, we leave out 3 days of known death data (non-cumulative) and fit our model. We forecast what the model predicts for these three days. We present the individual forecasts for each day, as well as the average forecast for those three days. The cross-validation results are shown in the Figure 8. Figure 8: Cross-Validation results for 3-day and 3-day aggregatedforecasts Figure 8 provides strong empirical justification for our model specification and mechanism. Our accurate forecast over a three-day time horizon suggests that our fitted estimates for Rt are appropriate and plausible. Along with from point estimates we all evaluate our posterior credible intervals using the Rhat statistic. The Rhat statistic measures whether our Markov Chain Monte Carlo (MCMC) chains have converged to the equilibrium distribution (the correct posterior distribution). Figure 9 shows the Rhat statistics for all of our parameters Figure 9: Rhat statistics - values close to 1 indicate MCMC convergence. Figure 9 indicates that our MCMC have converged. In fitting we also ensured that the MCMC sampler experienced no divergent transitions - suggesting non pathological posterior topologies. 8.4 SensitivityAnalysis 8.4.1 Forecasting on log-linear scale to assess signal in the data As we have highlighted throughout in this report, the lag between deaths and infections means that it ta kes time for information to propagate backwa rds from deaths to infections, and ultimately to Rt. A conclusion of this report is the prediction of a slowing of Rt in response to major interventions. To gain intuition that this is data driven and not simply a consequence of highly constrained model assumptions, we show death forecasts on a log-linear scale. On this scale a line which curves below a linear trend is indicative of slowing in the growth of the epidemic. Figure 10 to Figure 12 show these forecasts for Italy, Spain and the UK. They show this slowing down in the daily number of deaths. Our model suggests that Italy, a country that has the highest death toll of COVID-19, will see a slowing in the increase in daily deaths over the coming week compared to the early stages of the epidemic. We investigated the sensitivity of our estimates of starting and final Rt to our assumed serial interval distribution. For this we considered several scenarios, in which we changed the serial interval distribution mean, from a value of 6.5 days, to have values of 5, 6, 7 and 8 days. In Figure 13, we show our estimates of R0, the starting reproduction number before interventions, for each of these scenarios. The relative ordering of the Rt=0 in the countries is consistent in all settings. However, as expected, the scale of Rt=0 is considerably affected by this change — a longer serial interval results in a higher estimated Rt=0. This is because to reach the currently observed size of the epidemics, a longer assumed serial interval is compensated by a higher estimated R0. Additionally, in Figure 14, we show our estimates of Rt at the most recent model time point, again for each ofthese scenarios. The serial interval mean can influence Rt substantially, however, the posterior credible intervals of Rt are broadly overlapping. Figure 13: Initial reproduction number R0 for different serial interval (SI) distributions (means between 5 and 8 days). We use 6.5 days in our main analysis. Figure 14: Rt on 28 March 2020 estimated for all countries, with serial interval (SI) distribution means between 5 and 8 days. We use 6.5 days in our main analysis. 8.4.3 Uninformative prior sensitivity on or We ran our model using implausible uninformative prior distributions on the intervention effects, allowing the effect of an intervention to increase or decrease Rt. To avoid collinearity, we ran 6 separate models, with effects summarized below (compare with the main analysis in Figure 4). In this series of univariate analyses, we find (Figure 15) that all effects on their own serve to decrease Rt. This gives us confidence that our choice of prior distribution is not driving the effects we see in the main analysis. Lockdown has a very large effect, most likely due to the fact that it occurs after other interventions in our dataset. The relatively large effect sizes for the other interventions are most likely due to the coincidence of the interventions in time, such that one intervention is a proxy for a few others. Figure 15: Effects of different interventions when used as the only covariate in the model. 8.4.4 To assess prior assumptions on our piecewise constant functional form for Rt we test using a nonparametric function with a Gaussian process prior distribution. We fit a model with a Gaussian process prior distribution to data from Italy where there is the largest signal in death data. We find that the Gaussian process has a very similartrend to the piecewise constant model and reverts to the mean in regions of no data. The correspondence of a completely nonparametric function and our piecewise constant function suggests a suitable parametric specification of Rt. Nonparametric fitting of Rf using a Gaussian process: 8.4.5 Leave country out analysis Due to the different lengths of each European countries’ epidemic, some countries, such as Italy have much more data than others (such as the UK). To ensure that we are not leveraging too much information from any one country we perform a ”leave one country out” sensitivity analysis, where we rerun the model without a different country each time. Figure 16 and Figure 17 are examples for results for the UK, leaving out Italy and Spain. In general, for all countries, we observed no significant dependence on any one country. Figure 16: Model results for the UK, when not using data from Italy for fitting the model. See the Figure 17: Model results for the UK, when not using data from Spain for fitting the model. See caption of Figure 2 for an explanation of the plots. 8.4.6 Starting reproduction numbers vs theoretical predictions To validate our starting reproduction numbers, we compare our fitted values to those theoretically expected from a simpler model assuming exponential growth rate, and a serial interval distribution mean. We fit a linear model with a Poisson likelihood and log link function and extracting the daily growth rate r. For well-known theoretical results from the renewal equation, given a serial interval distribution g(r) with mean m and standard deviation 5, given a = mZ/S2 and b = m/SZ, and a subsequently R0 = (1 + %) .Figure 18 shows theoretically derived R0 along with our fitted estimates of Rt=0 from our Bayesian hierarchical model. As shown in Figure 18 there is large correspondence between our estimated starting reproduction number and the basic reproduction number implied by the growth rate r. R0 (red) vs R(FO) (black) Figure 18: Our estimated R0 (black) versus theoretically derived Ru(red) from a log-linear regression fit. 8.5 Counterfactual analysis — interventions vs no interventions Figure 19: Daily number of confirmed deaths, predictions (up to 28 March) and forecasts (after) for all countries except Italy and Spain from our model with interventions (blue) and from the no interventions counterfactual model (pink); credible intervals are shown one week into the future. DOI: https://doi.org/10.25561/77731 Page 28 of 35 30 March 2020 Imperial College COVID-19 Response Team 8.6 Data sources and Timeline of Interventions Figure 1 and Table 3 display the interventions by the 11 countries in our study and the dates these interventions became effective. Table 3: Timeline of Interventions. Country Type Event Date effective School closure ordered Nationwide school closures.20 14/3/2020 Public events banned Banning of gatherings of more than 5 people.21 10/3/2020 Banning all access to public spaces and gatherings Lockdown of more than 5 people. Advice to maintain 1m ordered distance.22 16/3/2020 Social distancing encouraged Recommendation to maintain a distance of 1m.22 16/3/2020 Case-based Austria measures Implemented at lockdown.22 16/3/2020 School closure ordered Nationwide school closures.23 14/3/2020 Public events All recreational activities cancelled regardless of banned size.23 12/3/2020 Citizens are required to stay at home except for Lockdown work and essential journeys. Going outdoors only ordered with household members or 1 friend.24 18/3/2020 Public transport recommended only for essential Social distancing journeys, work from home encouraged, all public encouraged places e.g. restaurants closed.23 14/3/2020 Case-based Everyone should stay at home if experiencing a Belgium measures cough or fever.25 10/3/2020 School closure Secondary schools shut and universities (primary ordered schools also shut on 16th).26 13/3/2020 Public events Bans of events >100 people, closed cultural banned institutions, leisure facilities etc.27 12/3/2020 Lockdown Bans of gatherings of >10 people in public and all ordered public places were shut.27 18/3/2020 Limited use of public transport. All cultural Social distancing institutions shut and recommend keeping encouraged appropriate distance.28 13/3/2020 Case-based Everyone should stay at home if experiencing a Denmark measures cough or fever.29 12/3/2020 School closure ordered Nationwide school closures.30 14/3/2020 Public events banned Bans of events >100 people.31 13/3/2020 Lockdown Everybody has to stay at home. Need a self- ordered authorisation form to leave home.32 17/3/2020 Social distancing encouraged Advice at the time of lockdown.32 16/3/2020 Case-based France measures Advice at the time of lockdown.32 16/03/2020 School closure ordered Nationwide school closures.33 14/3/2020 Public events No gatherings of >1000 people. Otherwise banned regional restrictions only until lockdown.34 22/3/2020 Lockdown Gatherings of > 2 people banned, 1.5 m ordered distance.35 22/3/2020 Social distancing Avoid social interaction wherever possible encouraged recommended by Merkel.36 12/3/2020 Advice for everyone experiencing symptoms to Case-based contact a health care agency to get tested and Germany measures then self—isolate.37 6/3/2020 School closure ordered Nationwide school closures.38 5/3/2020 Public events banned The government bans all public events.39 9/3/2020 Lockdown The government closes all public places. People ordered have to stay at home except for essential travel.40 11/3/2020 A distance of more than 1m has to be kept and Social distancing any other form of alternative aggregation is to be encouraged excluded.40 9/3/2020 Case-based Advice to self—isolate if experiencing symptoms Italy measures and quarantine if tested positive.41 9/3/2020 Norwegian Directorate of Health closes all School closure educational institutions. Including childcare ordered facilities and all schools.42 13/3/2020 Public events The Directorate of Health bans all non-necessary banned social contact.42 12/3/2020 Lockdown Only people living together are allowed outside ordered together. Everyone has to keep a 2m distance.43 24/3/2020 Social distancing The Directorate of Health advises against all encouraged travelling and non-necessary social contacts.42 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Norway measures cough or fever symptoms.44 15/3/2020 ordered Nationwide school closures.45 13/3/2020 Public events banned Banning of all public events by lockdown.46 14/3/2020 Lockdown ordered Nationwide lockdown.43 14/3/2020 Social distancing Advice on social distancing and working remotely encouraged from home.47 9/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a Spain measures cough or fever symptoms.47 17/3/2020 School closure ordered Colleges and upper secondary schools shut.48 18/3/2020 Public events banned The government bans events >500 people.49 12/3/2020 Lockdown ordered No lockdown occurred. NA People even with mild symptoms are told to limit Social distancing social contact, encouragement to work from encouraged home.50 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Sweden measures fever symptoms.51 10/3/2020 School closure ordered No in person teaching until 4th of April.52 14/3/2020 Public events banned The government bans events >100 people.52 13/3/2020 Lockdown ordered Gatherings of more than 5 people are banned.53 2020-03-20 Advice on keeping distance. All businesses where Social distancing this cannot be realised have been closed in all encouraged states (kantons).54 16/3/2020 Case-based Advice to self—isolate if experiencing a cough or Switzerland measures fever symptoms.55 2/3/2020 Nationwide school closure. Childminders, School closure nurseries and sixth forms are told to follow the ordered guidance.56 21/3/2020 Public events banned Implemented with lockdown.57 24/3/2020 Gatherings of more than 2 people not from the Lockdown same household are banned and police ordered enforceable.57 24/3/2020 Social distancing Advice to avoid pubs, clubs, theatres and other encouraged public institutions.58 16/3/2020 Case-based Advice to self—isolate for 7 days if experiencing a UK measures cough or fever symptoms.59 12/3/2020 9 References 1. Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV2). Science (2020) doi:10.1126/science.abb3221. 2. Zhang, J. et al. Patterns of human social contact and contact with animals in Shanghai, China. 5cLRep.9,1—11(2019) 3. Worldometers.info. Hong Kong: coronavirus cases. https://www.wo rldometers.info/co ronavirus/country/china-hong-kong-sar/. 4. Ferguson, N. et al. Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand (Report 9). https://www.imperial.ac.uk/mrc-global-infectious- disease-analysis/news--wuhan-coronavirus/. 5. Cereda, D. et al. The early phase of the COVID-19 outbreak in Lombardy, Italy. arXiv (2020). 6. Zhao, A. J. et al. Title: Antibody responses to SARS-CoV-2 in patients of novel coronavirus disease 2019 Brief Title : Antibody responses in COVID-19 patients. (2020). 7. Jombart, T. et al. Inferring the number of COVID-19 cases from recently reported deaths. medRXiV 2020.03.10.20033761(2020)doi:10.1101/2020.03.10.20033761. 8. Zhang, J. et al. Age profile of susceptibility, mixing, and social distancing shape the dynamics of the novel coronavirus disease 2019 outbreak in China. (2020) doi:10.1101/2020.03.19.20039107. 9. Lourenco, J. et al. Fundamental principles of epidemic spread highlight the immediate need for large-scale serological surveys to assess the stage of the SARS-CoV-2 epidemic. doi:10.1101/2020.03.24.20042291 10. United Nations, Department of Economic and Social Affairs, Population Division. World Population Prospects 2019: Data Booket. ST/ESA/SER.A/424. (2019). 11. Verity, R. et al. Estimates ofthe severity of COVID-19 disease. Lancet Infect Dis in press, (2020). 12. Walker, P. G. T. et al. Report 12: The Global Impact of COVID-19 and Strategies for Mitigation and Suppression. 13. Fraser, C. Estimating Individual and Household Reproduction Numbers in an Emerging Epidemic. PL05 ONE 2, e758 (2007). 14. Cori, A., Ferguson, N. M., Fraser, C. & Cauchemez, S. A New Framework and Software to Estimate Time-Varying Reproduction Numbers During Epidemics. Am. J. Epidemiol. 178, 1505—1512 (20131 15. Nouvellet, P. et al. A simple approach to measure transmissibility and forecast incidence. Epidemics 22, 29—35 (2018). 16. Cauchemez, 8., Valleron, A. J., Boelle, P. Y., Flahault, A. & Ferguson, N. M. Estimating the impact of school closure on influenza transmission from Sentinel data. Nature 452, 750—754 (2008). 17. Bellman, R. & Harris, T. On Age-Dependent Binary Branching Processes. Ann. Math. 55, 280— 295(19521 18. Bellman, R. & Harris, T. E. On the Theory of Age-Dependent Stochastic Branching Processes. Proc. Natl. Acad. Sci. 34, 601—604 (1948). 19. Stan Development Team. 2018. The Stan Core Library, Version 2.18.0. http://mc-stan.org. 20. Bundesministerium. Coronavirus (COVID-19): Status quo — Schulen, Hochschulen, Universitaten und Forschungsinstitutionen. https://www.bmbwf.gv.at/Ministerium/Informationspflicht/corona/corona_status.html. 21. Henley, J. Coronavirus: EU states enact tough measures to stem spread. The Guardian https://www.theguardian.com/world/2020/mar/10/coronavirus-several-eu-states-ban-mass-events- after-italian-lockdown (2020). 22. Bundesministerium. Coronavirus - Aktuelle MaBnahmen. https://www.sozialministerium.at/Informationen-zum-Coronavirus/Coronavirus—Aktuelle- MaBnahmen.html (2020). 23. Federal Public Service. Coronavirus : Phase 2 maintained, transition to the federal phase and additional measures. https://www.info-coronavirus.be/en/2020/03/12/phase-2-maintained- transition-to-the-federal-phase-and-additional-measures/ (2020). 24. Belgium.be. Coronavirus: reinforced measures | Belgium.be. https://www.belgium.be/en/news/2020/coronavirus_reinforced_measures (2020). 25. Federal Public Service. Protect yourself and protect the others. https://www.info- coronavirus.be/en/2020/03/10/protect-yourself—and-protect-the-others/ (2020). 26. Wikipedia. 2020 coronavirus pandemic in Denmark. Wikimedia Foundation https://en.wikipedia.org/wiki/2020_coronavirus_pandemic_in_Denmark. 27. Stephensen, Emma K|inker; Hansen, T. S. Danmark lukker ned: Her er regeringens nye tiltag. TV2 https://nyheder.tv2.dk/samfund/2020-03-11-danmark-lukker-ned-her-er-regeringens-nye-tiltag (20201 28. Politi. Nye tiltag mod covid-19. Politi https://politi.dk/coronavirus-i-danmark/seneste-nyt-fra- myndighederne/nye-tiltag-mod-covid-19 (2020). 29. Styrelsen for Patientsikkerhed. Indberetning om covid-19zlnformation om mulighed for p\aabud til enkeltpersoner (coronavirus/covid-19). https://stps.dk/da/ansvar-og- retningslinjer/vejledning/indberetning-om-covid-19/#. 30. Wikipedia. 2020 coronavirus pandemic in France. Wikimedia Foundation https://en.wikipedia.org/wiki/2020_coronavirus_pandemic_in_France. 31. The Local. France bans gatherings of more than 100 people as coronavirus death toll rises - The Local. The Local https://www.thelocal.fr/20200313/france-bans-gatherings-of—over-100-people- to-fight-coronavirus-pandemic (2020). 32. Henley, Jon; Willsher, Kim; Kassam, A. Coronavirus: France imposes lockdown as EU calls for 30-day travel ban. The Guardian https://www.theguardian.com/world/2020/mar/16/coronavirus- spain-takes-over-private-healthcare-amid-more-european-lockdowns (2020). 33. Wikipedia. 2020 coronavirus pandemic in Germany. Wikimedia Foundation https://en.wikipedia.org/wiki/2020_coronavirus_pandemic_in_Germany. 34. BMI. Coronavirus: Fragen und Antworten. Bundesministerium des Innern,fur Bau und Heimat https://web.archive.org/web/20200317073042/https://www.bmi.bund.de/SharedDocs/faqs/DE/the men/bevoelkerungsschutz/coronavirus/coronavirus-faqs.htmI#doc13738352bodyText7. 35. BBC News. Coronavirus: Germany tightens curbs and bans meetings of more than two. BBC News https://www.bbc.co.uk/news/world-europe-51999080 (2020). 36. Bundesregierung. Kanzlerin trifft Regierungschefs der Lander Sozialkontakte vermeiden, Ausbreitung verlangsamen. https://www.bundesregierung.de/breg-de/themen/coronavirus/mpk- 1730186(2020) 37. Robert Koch Institut. Antworten auf haufig gestellte Fragen zum Coronavirus SARS-CoV-2. Robert Koch Institut https://web.archive.org/web/20200312004624/https://www.rki.de/SharedDocs/FAQ/NCOV2019/F AQ_Liste.html (2020). 38. Ministero della Salute. Governo annuncia sospensione dell’attivita didattica dal 5 a|15 marzo. Ministero della Salute http://www.salute.gov.it/portale/nuovocoronavirus/dettaglioVideoNuovoCoronavirus.jsp?lingua=ita liano&menu=multimedia&p=video&id=2052 (2020). 39. CNN. Italy prohibits travel and cancels all public events in its northern region. CNN https://edition.cnn.com/2020/03/08/europe/italy-coronavirus-lockdown-europe-intl/index.html (2020). 40. Attualita. Coronavirus: stop a pub, cinema, teatro e discoteche anche a Roma. Ecco cosa prevede il nuovo decreto. Roma Today https://www.romatoday.it/attualita/coronavirus-pub-cinema- teatri-locali-chiusi-nuovo-decreto.html (2020). 41. Gazzetta Ufficiale. DECRETO DEL PRESIDENTE DEL CONSIGLIO DEl MINISTRI. Gazzetta Ufflclale https://www.gazzettaufficiale.it/eli/id/2020/03/08/20A01522/sg (2020). 42. Helsedirektoratet. The Norwegian Directorate of Health has issued a decision to close schools and other educational institutions. Helsedirektoratet https://www.helsedirektoratet.no/nyheter/the- norwegian-directorate-of—health-has-issued-a-decision-to-close-schools-and-other-educationa|- institutions (2020). 43. Krostensen, Mette; Hellem-Hansen, Viktoria L.; Tandstad, B. Folkehelseinstituttet mener 23.000 kan vaere smittet. NRK https://www.nrk.no/norge/folkehelseinstituttet-mener-23.000-kan- vaere-smittet-1.14958149 (2020). 44. Norweigen Government. The Government is establishing clear quarantine and isolation rules. regjeringen.no https://www.regjeringen.no/en/aktuelt/the-government-is-establishing-clear- quarantine-and-isolation-rules/id2693647/ (2020). 45. Wikipedia. 2020 coronavirus pandemic in Spain. Wikimedia Foundation https://en.wikipedia.org/wiki/2020_coronavirus_pandemic_in_Spain. 46. Gabinete de Prensa. El Gobierno anuncia nuevas medidas para evitar la extension del nuevo coronavirus COVID-19. Gobierno de Espana https://www.mscbs.gob.es/gabinete/notasPrensa.do?id=4807 (2020). 47. Gabinete de Prensa. El Consejo Interterritorial del SNS acuerda medidas concretas para zonas con transmision comunitaria significativa de coronavirus. Gobierno de Espana https://www.mscbs.gob.es/gabinete/notasPrensa.do?id=4806 (2020). 48. Folkhalsomyndigheten. Larosaten och gymnasieskolor uppmanas nu att bedriva distansundervisning. Folkhdlsomyndigheten https://www.folkhalsomyndigheten.se/nyheter-och- press/nyhetsarkiv/2020/mars/larosaten-och-gymnasieskolor-uppmanas-nu-att-bedriva- distansundervisning(2020). 49. The Local. Sweden bans large events to halt coronavirus spread. The Local https://www.theloca|.se/20200311/sweden-to-ban-large-public-gatherings-over-coronavirus (2020). 50. Radosevich. Stockholmers urged to work from home as COVID-19 community spread confirmed. Sveriges Radio https://sverigesradio.se/sida/artikel.aspx?programid=2054&artikel=7430511(2020). 51. Folkhalsomyndigheten. Flera tecken p\aa samhallsspridning av covid-19 i Sverige. Folkhdlsomyndigheten https://www.folkhalsomyndigheten.se/nyheter-och- press/nyhetsarkiv/2020/mars/flera-tecken-pa-samhallsspridning-av-covid-19-i-sverige/ (2020). 52. Bundesamt fur Gesendheit BAG. Bundesrat verscharft Massnahmen gegen das Coronavirus zum Schutz der Gesundheit und unterstUtzt betroffene Branchen. Schweizerische Eidgenossenschaft https://www.bag.admin.ch/bag/de/home/das-bag/aktuell/medienmitteilungen.msg-id-78437.html (20201 53. Bundesamt fur Gesundheit BAG. Coronavirus: Bundesrat verbietet Ansammlungen von mehr als fUnf Personen. Schweizerische Eidgenossenschaft https://www.bag.admin.ch/bag/de/home/das- bag/aktuell/medienmitteilungen.msg-id-78513.html (2020). 54. Bundesamt fur Gesundheit BAG. Coronavirus: Bundesrat erklart die «ausserordentliche Lage» und verscharft die Massnahmen. Schweizerische Eidgenossenschaft https://www.bag.admin.ch/bag/de/home/das-bag/aktuell/medienmitteilungen.msg-id-78454.html (20201 55. Bundesamt fur Gesundheit BAG. Neue Hygiene- und Verhaltensregeln zum Schutz gegen das neue Coronavirus. Schweizerische Eidgenossenschaft https://www.bag.admin.ch/bag/de/home/das- bag/a ktuell/medienmitteilungen.msg-id-78304.html (2020). 56. UK Government, D. for E. Schools, colleges and early years settings to close. UK Government https://www.gov.uk/government/news/schools-colleges-and-early-years-settings-to-close (2020). 57. UK Government. PM address to the nation on coronavirus: 23 March 2020. UK Government https://www.gov.uk/government/speeches/pm-address-to-the-nation-on-coronavirus-23-march- 2020(20201 58. Boycott-Owen, Mason; Bowman, Verity; Kelly-Linden, Jordan; Gartner, A. G. H. S. T. Coronavirus: Boris Johnson puts UK in lockdown as death tolls reaches 55. The Telegraph https://www.telegraph.co.uk/global-health/science-and-disease/coronavirus-news-uk-latest- update-covid-19-death-toll-cases/ (2020). 59. BBC News. Coronavirus: People with fever or ’continuous’ cough told to self—isolate. BBC News https://www.bbc.co.uk/news/uk-51857856 (2020).

What are examples of social distancing?

Epidemiological research priorities for public health control of the ongoing global novel coronavirus (2019-nCoV) outbreak https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7029449/ SHA: 90de2d957e1960b948b8c38c9877f9eca983f9eb Authors: Cowling, Benjamin J; Leung, Gabriel M Date: 2020-02-13 DOI: 10.2807/1560-7917.es.2020.25.6.2000110 License: cc-by Abstract: Infections with 2019-nCoV can spread from person to person, and in the earliest phase of the outbreak the basic reproductive number was estimated to be around 2.2, assuming a mean serial interval of 7.5 days [2]. The serial interval was not precisely estimated, and a potentially shorter mean serial interval would have corresponded to a slightly lower basic reproductive number. Control measures and changes in population behaviour later in January should have reduced the effective reproductive number. However, it is too early to estimate whether the effective reproductive number has been reduced to below the critical threshold of 1 because cases currently being detected and reported would have mostly been infected in mid- to late-January. Average delays between infection and illness onset have been estimated at around 5–6 days, with an upper limit of around 11-14 days [2,5], and delays from illness onset to laboratory confirmation added a further 10 days on average [2]. Text: It is now 6 weeks since Chinese health authorities announced the discovery of a novel coronavirus (2019-nCoV) [1] causing a cluster of pneumonia cases in Wuhan, the major transport hub of central China. The earliest human infections had occurred by early December 2019, and a large wet market in central Wuhan was linked to most, but not all, of the initial cases [2] . While evidence from the initial outbreak investigations seemed to suggest that 2019-nCoV could not easily spread between humans [3] , it is now very clear that infections have been spreading from person to person [2] . We recently estimated that more than 75,000 infections may have occurred in Wuhan as at 25 January 2020 [4] , and increasing numbers of infections continue to be detected in other cities in mainland China and around the world. A number of important characteristics of 2019-nCoV infection have already been identified, but in order to calibrate public health responses we need improved information on transmission dynamics, severity of the disease, immunity, and the impact of control and mitigation measures that have been applied to date. Infections with 2019-nCoV can spread from person to person, and in the earliest phase of the outbreak the basic reproductive number was estimated to be around 2.2, assuming a mean serial interval of 7.5 days [2] . The serial interval was not precisely estimated, and a potentially shorter mean serial interval would have corresponded to a slightly lower basic reproductive number. Control measures and changes in population behaviour later in January should have reduced the effective reproductive number. However, it is too early to estimate whether the effective reproductive number has been reduced to below the critical threshold of 1 because cases currently being detected and reported would have mostly been infected in mid-to late-January. Average delays between infection and illness onset have been estimated at around 5-6 days, with an upper limit of around 11-14 days [2, 5] , and delays from illness onset to laboratory confirmation added a further 10 days on average [2] . Chains of transmission have now been reported in a number of locations outside of mainland China. Within the coming days or weeks it will become clear whether sustained local transmission has been occurring in other cities outside of Hubei province in China, or in other countries. If sustained transmission does occur in other locations, it would be valuable to determine whether there is variation in transmissibility by location, for example because of different behaviours or control measures, or because of different environmental conditions. To address the latter, virus survival studies can be done in the laboratory to confirm whether there are preferred ranges of temperature or humidity for 2019-nCoV transmission to occur. In an analysis of the first 425 confirmed cases of infection, 73% of cases with illness onset between 12 and 22 January reported no exposure to either a wet market or another person with symptoms of a respiratory illness [2] . The lack of reported exposure to another ill person could be attributed to lack of awareness or recall bias, but China's health minister publicly warned that pre-symptomatic transmission could be occurring [6] . Determining the extent to which asymptomatic or pre-symptomatic transmission might be occurring is an urgent priority, because it has direct implications for public health and hospital infection control. Data on viral shedding dynamics could help in assessing duration of infectiousness. For severe acute respiratory syndrome-related coronavirus (SARS-CoV), infectivity peaked at around 10 days after illness onset [7] , consistent with the peak in viral load at around that time [8] . This allowed control of the SARS epidemic through prompt detection of cases and strict isolation. For influenza virus infections, virus shedding is highest on the day of illness onset and relatively higher from shortly before symptom onset until a few days after onset [9] . To date, transmission patterns of 2019-nCoV appear more similar to influenza, with contagiousness occurring around the time of symptom onset, rather than SARS. Transmission of respiratory viruses generally happens through large respiratory droplets, but some respiratory viruses can spread through fine particle aerosols [10] , and indirect transmission via fomites can also play a role. Coronaviruses can also infect the human gastrointestinal tract [11, 12] , and faecal-oral transmission might also play a role in this instance. The SARS-CoV superspreading event at Amoy Gardens where more than 300 cases were infected was attributed to faecal-oral, then airborne, spread through pressure differentials between contaminated effluent pipes, bathroom floor drains and flushing toilets [13] . The first large identifiable superspreading event during the present 2019-nCoV outbreak has apparently taken place on the Diamond Princess cruise liner quarantined off the coast of Yokohama, Japan, with at least 130 passengers tested positive for 2019-nCoV as at 10 February 2020 [14] . Identifying which modes are important for 2019-nCoV transmission would inform the importance of personal protective measures such as face masks (and specifically which types) and hand hygiene. The first human infections were identified through a surveillance system for pneumonia of unknown aetiology, and all of the earliest infections therefore had Modelling studies incorporating healthcare capacity and processes pneumonia. It is well established that some infections can be severe, particularly in older adults with underlying medical conditions [15, 16] , but based on the generally mild clinical presentation of 2019-nCoV cases detected outside China, it appears that there could be many more mild infections than severe infections. Determining the spectrum of clinical manifestations of 2019-nCoV infections is perhaps the most urgent research priority, because it determines the strength of public health response required. If the seriousness of infection is similar to the 1918/19 Spanish influenza, and therefore at the upper end of severity scales in influenza pandemic plans, the same responses would be warranted for 2019-nCoV as for the most severe influenza pandemics. If, however, the seriousness of infection is similar to seasonal influenza, especially during milder seasons, mitigation measures could be tuned accordingly. Beyond a robust assessment of overall severity, it is also important to determine high risk groups. Infections would likely be more severe in older adults, obese individuals or those with underlying medical conditions, but there have not yet been reports of severity of infections in pregnant women, and very few cases have been reported in children [2] . Those under 18 years are a critical group to study in order to tease out the relative roles of susceptibility vs severity as possible underlying causes for the very rare recorded instances of infection in this age group. Are children protected from infection or do they not fall ill after infection? If they are naturally immune, which is unlikely, we should understand why; otherwise, even if they do not show symptoms, it is important to know if they shed the virus. Obviously, the question about virus shedding of those being infected but asymptomatic leads to the crucial question of infectivity. Answers to these questions are especially pertinent as basis for decisions on school closure as a social distancing intervention, which can be hugely disruptive not only for students but also because of its knock-on effect for child care and parental duties. Very few children have been confirmed 2019-nCoV cases so far but that does not necessarily mean that they are less susceptible or that they could not be latent carriers. Serosurveys in affected locations could inform this, in addition to truly assessing the clinical severity spectrum. Another question on susceptibility is regarding whether 2019-nCoV infection confers neutralising immunity, usually but not always, indicated by the presence of neutralising antibodies in convalescent sera. Some experts already questioned whether the 2019-nCoV may behave similarly to MERS-CoV in cases exhibiting mild symptoms without eliciting neutralising antibodies [17] . A separate question pertains to the possibility of antibody-dependent enhancement of infection or of disease [18, 19] . If either of these were to be relevant, the transmission dynamics could become more complex. A wide range of control measures can be considered to contain or mitigate an emerging infection such as 2019-nCoV. Internationally, the past week has seen an increasing number of countries issue travel advisories or outright entry bans on persons from Hubei province or China as a whole, as well as substantial cuts in flights to and from affected areas out of commercial considerations. Evaluation of these mobility restrictions can confirm their potential effectiveness in delaying local epidemics [20] , and can also inform when as well as how to lift these restrictions. If and when local transmission begins in a particular location, a variety of community mitigation measures can be implemented by health authorities to reduce transmission and thus reduce the growth rate of an epidemic, reduce the height of the epidemic peak and the peak demand on healthcare services, as well as reduce the total number of infected persons [21] . A number of social distancing measures have already been implemented in Chinese cities in the past few weeks including school and workplace closures. It should now be an urgent priority to quantify the effects of these measures and specifically whether they can reduce the effective reproductive number below 1, because this will guide the response strategies in other locations. During the 1918/19 influenza pandemic, cities in the United States, which implemented the most aggressive and sustained community measures were the most successful ones in mitigating the impact of that pandemic [22] . Similarly to international travel interventions, local social distancing measures should be assessed for their impact and when they could be safely discontinued, albeit in a coordinated and deliberate manner across China such that recrudescence in the epidemic curve is minimised. Mobile telephony global positioning system (GPS) data and location services data from social media providers such as Baidu and Tencent in China could become the first occasion when these data inform outbreak control in real time. At the individual level, surgical face masks have often been a particularly visible image from affected cities in China. Face masks are essential components of personal protective equipment in healthcare settings, and should be recommended for ill persons in the community or for those who care for ill persons. However, there is now a shortage of supply of masks in China and elsewhere, and debates are ongoing about their protective value for uninfected persons in the general community. The Table summarises research gaps to guide the public health response identified. In conclusion, there are a number of urgent research priorities to inform the public health response to the global spread of 2019-nCoV infections. Establishing robust estimates of the clinical severity of infections is probably the most pressing, because flattening out the surge in hospital admissions would be essential if there is a danger of hospitals becoming overwhelmed with patients who require inpatient care, not only for those infected with 2019-nCoV but also for urgent acute care of patients with other conditions including those scheduled for procedures and operations. In addressing the research gaps identified here, there is a need for strong collaboration of a competent corps of epidemiological scientists and public health workers who have the flexibility to cope with the surge capacity required, as well as support from laboratories that can deliver on the ever rising demand for diagnostic tests for 2019-nCoV and related sequelae. The readiness survey by Reusken et al. in this issue of Eurosurveillance testifies to the rapid response and capabilities of laboratories across Europe should the outbreak originating in Wuhan reach this continent [23] . In the medium term, we look towards the identification of efficacious pharmaceutical agents to prevent and treat what may likely become an endemic infection globally. Beyond the first year, one interesting possibility in the longer term, perhaps borne of wishful hope, is that after the first few epidemic waves, the subsequent endemic re-infections could be of milder severity. Particularly if children are being infected and are developing immunity hereafter, 2019-nCoV could optimistically become the fifth human coronavirus causing the common cold. None declared.

What is assumed for the mean serial interval?

Estimating the Unreported Number of Novel Coronavirus (2019-nCoV) Cases in China in the First Half of January 2020: A Data-Driven Modelling Analysis of the Early Outbreak https://doi.org/10.3390/jcm9020388 SHA: bf20dda99538a594eafc258553634fd9195104cb Authors: Zhao, Shi; Musa, Salihu S.; Lin, Qianying; Ran, Jinjun; Yang, Guangpu; Wang, Weiming; Lou, Yijun; Yang, Lin; Gao, Daozhou; He, Daihai; Wang, Maggie H. Date: 2020 DOI: 10.3390/jcm9020388 License: cc-by Abstract: Background: In December 2019, an outbreak of respiratory illness caused by a novel coronavirus (2019-nCoV) emerged in Wuhan, China and has swiftly spread to other parts of China and a number of foreign countries. The 2019-nCoV cases might have been under-reported roughly from 1 to 15 January 2020, and thus we estimated the number of unreported cases and the basic reproduction number, R0, of 2019-nCoV. Methods: We modelled the epidemic curve of 2019-nCoV cases, in mainland China from 1 December 2019 to 24 January 2020 through the exponential growth. The number of unreported cases was determined by the maximum likelihood estimation. We used the serial intervals (SI) of infection caused by two other well-known coronaviruses (CoV), Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) CoVs, as approximations of the unknown SI for 2019-nCoV to estimate R0. Results: We confirmed that the initial growth phase followed an exponential growth pattern. The under-reporting was likely to have resulted in 469 (95% CI: 403&minus;540) unreported cases from 1 to 15 January 2020. The reporting rate after 17 January 2020 was likely to have increased 21-fold (95% CI: 18&minus;25) in comparison to the situation from 1 to 17 January 2020 on average. We estimated the R0 of 2019-nCoV at 2.56 (95% CI: 2.49&minus;2.63). Conclusion: The under-reporting was likely to have occurred during the first half of January 2020 and should be considered in future investigation. Text: A novel coronavirus (2019-nCoV) infected pneumonia infection, which is deadly [1] , was first identified in Wuhan, China in December 2019 [2] . The virus causes a range of symptoms including fever, cough, and shortness of breath [3] . The cumulative number of reported cases slowly increased to cumulative 41 cases by 1 January 2020, and rapidly increased after 16 January 2020. As of 26 January 2020, the still ongoing outbreak had resulted in 2066 (618 of them are in Wuhan) confirmed cases and 56 (45 of them were in Wuhan) deaths in mainland China [4] , and sporadic cases exported from Wuhan were reported in Thailand, Japan, Republic of Korea, Hong Kong, Taiwan, Australia, and the United States, please see the World Health Organization (WHO) news release via https://www.who.int/csr/don/en/ from 14 to 21 January 2020. Using the number of cases exported from Wuhan to other countries, a research group at Imperial College London estimated that there had been 4000 (95%CI: 1000-9700) cases in Wuhan with symptoms onset by 18 January 2020, and the basic reproduction number (R 0 ) was estimated at 2.6 (95%CI: 1.5-3.5) [5] . Leung et al. drew a similar conclusion and estimated the number of cases exported from Wuhan to other major cities in China [6] , and the potentials of travel related risks of disease spreading was also indicated by [7] . Due to an unknown reason, the cumulative number of cases remained at 41 from 1 to 15 January 2020 according to the official report, i.e., no new case was reported during these 15 days, which appears inconsistent with the following rapid growth of the epidemic curve since 16 January 2020. We suspect that the 2019-nCoV cases were under-reported roughly from 1 to 15 January 2020. In this study, we estimated the number of unreported cases and the basic reproduction number, R 0 , of 2019-nCoV in Wuhan from 1 to 15 January 2020 based on the limited data in the early outbreak. The time series data of 2019-nCoV cases in mainland China were initially released by the Wuhan Municipal Health Commission from 10 to 20 January 2020 [8] , and later by the National Health Commission of China after 21 January 2020 [9] . The case time series data in December 2019 were obtained from a published study [3] . All cases were laboratory confirmed following the case definition by the national health commission of China [10] . We chose the data up to 24 January 2020 instead of to the present study completion date. Given the lag between timings of case confirmation and news release of new cases, the data of the most recent few days were most likely to be tentative, and thus they were excluded from the analysis to be consistent. We suspected that there was a number of cases, denoted by ξ, under-reported from 1 to 15 January 2020. The cumulative total number of cases, denoted by C i , of the i-th day since 1 December 2019 is the summation of the cumulative reported, c i , and cumulative unreported cases, Ξ i . We have C i = c i + Ξ i , where c i is observed from the data, and Ξ i is 0 for i before 1 January and ξ for i after 15 January 2020. Following previous studies [11, 12] , we modelled the epidemic curve, i.e., the C i series, as an exponential growing Poisson process. Since the data from 1 to 15 January 2020 appeared constant due to unclear reason(s), we removed these data from the fitting of exponential growth. The ξ and the intrinsic growth rate (γ) of the exponential growth were to be estimated based on the log-likelihood, denoted by , from the Poisson priors. The 95% confidence interval (95% CI) of ξ was estimated by the profile likelihood estimation framework with cutoff threshold determined by a Chi-square quantile [13] , χ 2 pr = 0.95, df = 1 . With γ estimated, the basic reproduction number could be obtained by R 0 = 1/M(−γ) with 100% susceptibility for 2019-nCoV presumed at this early stage. Here, the function M(·) was the Laplace transform, i.e., the moment generating function, of the probability distribution for the serial interval (SI) of the disease [11, 14] , denoted by h(k) and k is the mean SI. Since the transmission chain of 2019-nCoV remained unclear, we adopted the SI information from Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS), which share the similar pathogen as 2019-nCoV [15] [16] [17] . We modelled h(k) as Gamma distributions with mean of 8.0 days and standard deviation (SD) of 3.6 days by averaging the SI mean and SD of SARS, mean of 7.6 days and SD of 3.4 days [18] , and MERS, mean of 8.4 days and SD of 3.8 days [19] . We were also interested in inferring the patterns of the daily number of cases, denoted by ε i for the i-th day, and thus it is obviously that C i = C i−1 + ε i . A simulation framework was developed for the iterative Poisson process such that E[ denoted the expectation. The simulation was implemented starting from 1 January 2020 with a cumulative number of cases seed of 40, the same as reported on 31 December 2019. We conducted 1000 samples and calculated the median and 95% CI. The number of 2019-nCoV unreported cases was estimated at 469 (95% CI: 403-540), see Figure 1a , which was significantly larger than 0. This finding implied the occurrence of under-reporting between 1 and 15 January 2020. After accounting for the effect of under-reporting, the R 0 was estimated at 2.56 (95% CI: 2.49-2.63), see Figure 1b , which is consistent with many existing online preprints with range from 2 to 4 [5, [20] [21] [22] . With the R 0 of 2.56 and ξ of 469, the exponential growing framework fitted the cumulative total number of cases (C i ) remarkably well, see Figure 1c iterative Poisson process such that denoted the expectation. The simulation was implemented starting from 1 January 2020 with a cumulative number of cases seed of 40, the same as reported on 31 December 2019. We conducted 1000 samples and calculated the median and 95% CI. The number of 2019-nCoV unreported cases was estimated at 469 (95% CI: 403−540), see Figure 1a , which was significantly larger than 0. This finding implied the occurrence of under-reporting between 1 and 15 January 2020. After accounting for the effect of under-reporting, the R0 was estimated at 2.56 (95% CI: 2.49−2.63), see Figure 1b , which is consistent with many existing online preprints with range from 2 to 4 [5, [20] [21] [22] . With the R0 of 2.56 and ξ of 469, the exponential growing framework fitted the cumulative total number of cases (Ci) remarkably well, see Figure 1c , referring to McFadden's pseudo-R-squared of 0.99. show the exponential growth fitting results of the cumulative number of cases (Ci) and the daily number of cases (εi) respectively. In panels (c) and (d), the gold squares are the reported cases, the blue bold curve represents the median of the fitting results, the dashed blue curves are the 95% CI of the fitting results, and the purple shading area represents the time window from 1 to 15 January 2020. In panel (c), the blue dots are the cumulative total, i.e., reported and unreported, number of cases. In panel (d), the grey curves are the 1000 simulation samples. Our estimation of R0 rely on the SI of 2019-nCoV, which remains unknown as of 26 January 2020. In this work, we employed the SIs of SARS and MERS as approximations to that of 2019-nCoV. The determination of SI requires the knowledge of the chain of disease transmission that needs a sufficient number of patient samples and periods of time for follow-up [23] , and thus this is unlikely to be achieved shortly. However, using SIs of SARS and MERS as approximation could provide an panels (a,b) , the green shading area represents the 95% CI (on the horizontal axis), and the vertical green line represents the maximum likelihood estimate (MLE) of the number of unreported cases. With the MLE of R 0 at 2.56, panels (c,d) show the exponential growth fitting results of the cumulative number of cases (C i ) and the daily number of cases (ε i ) respectively. In panels (c,d), the gold squares are the reported cases, the blue bold curve represents the median of the fitting results, the dashed blue curves are the 95% CI of the fitting results, and the purple shading area represents the time window from 1 to 15 January 2020. In panel (c), the blue dots are the cumulative total, i.e., reported and unreported, number of cases. In panel (d), the grey curves are the 1000 simulation samples. Our estimation of R 0 rely on the SI of 2019-nCoV, which remains unknown as of 26 January 2020. In this work, we employed the SIs of SARS and MERS as approximations to that of 2019-nCoV. The determination of SI requires the knowledge of the chain of disease transmission that needs a sufficient number of patient samples and periods of time for follow-up [23] , and thus this is unlikely to be achieved shortly. However, using SIs of SARS and MERS as approximation could provide an insight into the transmission potential of 2019-nCoV at the early outbreak. We note that slightly varying the mean and SD of SI would not affect our main conclusions. The R 0 of 2019-nCoV was estimated at 2.56 (95% CI: 2.49-2.63), and it is generally in line with those of SARS, i.e., 2-5 [19, 24, 25] , and MERS, i.e., 2.7-3.9 [26] . For the simulated daily number of cases (ε i ), see Figure 1d , we found that ε i matched the observed daily number after 17 January 2020, but was significantly larger than the observations from 1 to 17 January 2020. This finding implied that under-reporting was likely to have occurred in the first half of January 2020. We estimated that the reporting rate after 17 January 2020 increased 21-fold (95% CI: [18] [19] [20] [21] [22] [23] [24] [25] compared to the situation from 1 to 17 January 2020 on average. One of the possible reasons was that the official diagnostic protocol was released by WHO on 17 January 2020 [27] , and the diagnosis and reporting efforts of 2019-nCoV infections probably increased. Thereafter, the daily number of newly reported cases started increasing rapidly after 17 January 2020, see Figure 1d . We conducted additional sensitivity analysis by varying the starting date of the under-reporting time window, e.g., 1 January 2020 in the main results, from 2 December 2019 to 3 January 2020, and we report our estimates largely hold. The exact value of the reporting rate was difficult to determine due to lack of serological surveillance data. The reporting rate can be determined if serological surveillance data are available for a population; we would know who was infected (seropositive) and who was not (seronegative), with high confidence. The reporting rate is the ratio of reported cases over the number of seropositive individuals. It was statistically evident that increasing in reporting was likely, and thus it should be considered in the future investigation of this outbreak. Previous preprint suggested cumulative cases of 1723 (95% CI: 427-4471) as of 12 January 2020, and 4000 (95% CI: 1000-9700) as of 18 January 2020 based on the aggregated international export cases [5] . Our analysis yielded cumulative cases of 280 (95% CI: 128-613) as of 12 January 2020, and 609 (95% CI: 278-1333) as of 18 January 2020 based on the exponential growing mechanistic in the early outbreak. Although our estimate case number appeared to have a lower mean than those estimated by Imai et al. [5] , they are not statistically different. This study applied a different screening effort to detect the 2019-nCoV cases from that in Imai et al. [5] . Imai et al. assumed the average screening effort at overseas airports that covered travelers arriving from Wuhan. Whereas we assumed a constant screening effort applied in Wuhan at the same point of time, and then a number of cases (i.e., ξ) should have been reported yet failed to be reported in the first half of January 2020 due to all sorts of reasons. It is not surprising that different assumptions yielded different results, and this difference in screening effort also partly explained why the detected cases out of China mainly presented mild symptoms. Thus, it was reasonable that our estimates appeared lower than those estimated by Imai et al. [5] . It must be emphasized that such a gap in the knowledge would be resolved by serological survey study (for a large population to approximate the actual positive rate) or an explicit estimation of the actual reporting rate. Under-reporting was likely to have occurred and resulted in 469 (95% CI: 403-540) unreported cases from 1 to 15 January 2020. The reporting rate after 17 January 2020 was likely to have increased 21-fold (95% CI: 18-25) compared with the situation from 1 to 17 January 2020 on average, and it should be considered in future investigation. We estimated the R 0 at 2019-nCoV to be 2.56 (95% CI: 2.49-2.63). Author Contributions: All authors conceived the study, carried out the analysis, discussed the results, drafted the first manuscript. All authors have read and agreed to the published version of the manuscript.

What was the result of under-reporting?

Identifying Locations with Possible Undetected Imported Severe Acute Respiratory Syndrome Coronavirus 2 Cases by Using Importation Predictions, https://wwwnc.cdc.gov/eid/article/26/7/20-0250_article Volume 26, Number 7—July 2020 Research Pablo Martinez De Salazar1Comments to Author , René Niehus, Aimee Taylor1, Caroline O’Flaherty Buckee, and Marc LipsitchComments to Author Author affiliations: Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA Suggested citation for this article Abstract Cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection exported from mainland China could lead to self-sustained outbreaks in other countries. By February 2020, several countries were reporting imported SARS-CoV-2 cases. To contain the virus, early detection of imported SARS-CoV-2 cases is critical. We used air travel volume estimates from Wuhan, China, to international destinations and a generalized linear regression model to identify locations that could have undetected imported cases. Our model can be adjusted to account for exportation of cases from other locations as the virus spreads and more information on importations and transmission becomes available. Early detection and appropriate control measures can reduce the risk for transmission in all locations. A novel coronavirus, later named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified in December 2019 in the city of Wuhan, capital of Hubei Province, China, where cases were first confirmed (1). During December 2019–February 2020, the number of confirmed cases increased drastically. Model estimates suggested that >75,000 persons were infected by January 25, 2020, and the epidemic had a doubling time of ≈6 days (2). By the end of January 2020, travel restrictions were implemented for Wuhan and neighboring cities. Nonetheless, the virus spread from Wuhan to other cities in China and outside the country. By February 4, 2020, a total of 23 locations outside mainland China reported cases, 22 of which reported imported cases; Spain reported a case caused by secondary transmission (3). Most cases imported to other locations have been linked to recent travel history from China (3), suggesting that air travel plays a major role in exportation of cases to locations outside of China. To prevent other cities and countries from becoming epicenters of the SARS-CoV-2 epidemic, substantial targeted public health interventions are required to detect cases and control local spread of the virus. We collected estimates of air travel volume from Wuhan to 194 international destinations. We then identified 49 countries that had a score of >49.2/100 on category 2, Early Detection and Reporting of Epidemics of Potential International Concern, of the Global Health Security (GHS) Index (4). We assumed these locations would be proficient at detecting SARS-CoV-2 and reporting confirmed imported cases, which we refer to as imported-and-reported cases. We ran a generalized linear regression model on this subset; based on the results, we generated predictions for the remainder of the sample. Using these predictions, we identified locations that might not be detecting imported cases. Methods To identify locations reporting fewer than predicted imported SARS-CoV-2 infected cases, we fit a model to data from 49 locations outside mainland China with high surveillance capacity according to the GHS Index (4). Among these, 17 had high travel connectivity to Wuhan and 32 have low connectivity to Wuhan. We considered locations to be countries without any position on territorial claims. We performed a Poisson regression by using the cumulative number of imported-and-reported SARS-CoV-2 cases in these 49 countries and the estimated number of daily airline passengers from the Wuhan airport. We then compared predictions from this model with imported-and-reported cases across 194 locations from the GHS Index, excluding China as the epicenter of the outbreak. The model requires data on imported-and-reported cases of SARS-CoV-2 infection, daily air travel volume, and surveillance capacity. We obtained data on imported-and-reported cases aggregated by destination from the World Health Organization technical report issued February 4, 2020 (3). We assumed a case count of 0 for locations not listed. We used February 4 as the cutoff for cumulative imported-and-reported case counts because exported cases from Hubei Province dropped rapidly after this date (3), likely because of travel restrictions for the province implement on January 23. We defined imported-and-reported cases as those with known travel history from China; of those, 83% had a travel history from Hubei Province and 17% traveled from unknown locations in China (3). We excluded reported cases likely caused by transmission outside of China or cases in which the transmission source was still under investigation (3). In addition, we excluded Hong Kong, Macau, and Taiwan from our model because locally transmitted and imported cases were not disaggregated in these locations. We obtained data on daily air travel from a network-based modeling study (S. Lai et al., unpub. data, https://doi.org/10.1101/2020.02.04.20020479External Link) that reported monthly air travel volume estimates for the 27 locations outside mainland China that are most connected to Wuhan. These estimates were calculated from International Air Travel Association data from February 2018, which includes direct and indirect flight itineraries from Wuhan. For these 27 locations, estimated air travel volumes are >6 passengers/day. We assumed that travel volumes for locations not among the most connected are censored by a detection limit. We used a common method of dealing with censored data from environmental sampling (5), or metabolomics (6), to set the daily air travel volume to half the minimum previously reported. Therefore, we used 3 passengers/day for estimated travel volumes for the 167 locations from the GHS Index not listed by Lai et al. We tested the robustness of our results by using a set of alternative values of 0.1, 1, and 6 passengers/day for the censored data. We defined high surveillance locations as those with a GHS Index for category 2 above the 75th quantile. We assessed the number of high surveillance locations, those with 0 imported-and-reported cases, and low surveillance locations, those with case counts >1 (Table). For our model, we assumed that the cumulative imported-and-reported case counts across 49 high surveillance locations follow a Poisson distribution from the beginning of the epidemic until February 4, 2020. Then the expected case count is linearly proportional to the daily air travel volume in the following formula:where i denotes location, Ci denotes the imported-and-reported case count in a location, λi denotes the expected case count in a location, β denotes the regression coefficient, and xi denotes the daily air travel volume of a location. The Poisson model assumes cases are independent and that the variance is equal to the expected case count. Imported-and-reported cases likely meet the independence assumption because the value excludes cases with local transmission. We also checked the robustness of our results by using an over dispersed model with a negative binomial likelihood. We computed the p value of the overdispersion parameter as shown in Gelman and Hill (7). Thumbnail of Regression plot of locations with possible undetected imported cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by air travel volume from Wuhan, China. Air travel volume measured in number of persons/day. No. cases refers to possible undetected imported SARS-CoV-2 cases. Solid line indicates the expected imported-and-reported case counts for locations. Dashed lines represent 95% prediction interval bounds smoothed for all locations. Purple dots indicate location Figure 1. Regression plot of locations with possible undetected imported cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by air travel volume from Wuhan, China. Air travel volume measured in number of... We used R version 3.6.1 (https://www.r-project.orgExternal Link) to compute , the maximum likelihood estimate of β, and the expected imported-and-reported case count given high surveillance (Figure 1). We also computed the 95% prediction interval (PI) bounds under this model of high surveillance for all 194 values of daily air travel volume (Figure 1). First, we generated a bootstrapped dataset by sampling n locations with replacement among high surveillance locations. Then, we reestimated β by using the bootstrapped dataset. Finally, we simulated imported-and-reported case counts for all 194 locations under our model by using the estimate of β from the bootstrapped dataset. We repeated the 3 steps 50,000 times to generate 50,000 simulated imported-and-reported case counts for each of the locations computed to the lower and upper PI bounds (PI 2.5%–97.5%). We smoothed the 95% PI bounds by using ggplot2 in R (8). We fit the imported-and-reported case counts of the 49 high surveillance locations to the model and plotted these alongside 145 locations with low surveillance capacity (Figure 1). We noted some overlap between high and low surveillance locations (Figure 1). Thumbnail of Analyses of imported-and-reported cases and daily air travel volume using a model to predict locations with potentially undetected cases of severe acute respiratory virus 2 (SARS-CoV-2). Air travel volume measured in number of persons/day. No. cases refers to possible undetected imported SARS-CoV-2 cases. Solid line shows the expected imported-and-reported case counts based on our model fitted to high surveillance locations, indicated by purple dots. Dashed lines indicate the 95% pr Figure 2. Analyses of imported-and-reported cases and daily air travel volume using a model to predict locations with potentially undetected cases of severe acute respiratory virus 2 (SARS-CoV-2). Air travel volume measured in... To assess the robustness of our results we ran 8 additional regression analyses by implementing a series of changes to the analysis. The changes included the following: set the daily air travel volume to 0.1, 1, or 6 passengers/day for locations not listed by Lai et al. (unpub. data, https://doi.org/10.1101/2020.02.04.20020479External Link) (Figure 2, panels A–C); removed all locations not listed by Lai et al. before fitting (Figure 2, panel D); defined high surveillance locations by using a more lenient GHS Index criterion, 50th quantile (Figure 2, panel E), and a more stringent criterion, 95th quantile (Figure 2, panel F); excluded Thailand from the model because it is a high-leverage point (Figure 2, panel G); or used an overdispersed Poisson likelihood with a negative-binomial likelihood (Figure 2, panel H). We provide code for these analyses on GitHub (https://github.com/c2-d2/cov19flightimportExternal Link). Top Results We found that daily air travel volume positively correlates with imported-and-reported case counts of SARS-CoV-2 infection among high surveillance locations (Figure 1). We noted that increasing flight volume by 31 passengers/day is associated with 1 additional expected imported-and-reported case. In addition, Singapore and India lie above the 95% PI in our model; Singapore had 12 more imported-and-reported cases (95% PI 6–17 cases) than expected and India had 3 (95% PI 1–3 cases) more than expected. Thailand has a relatively high air travel volume compared with other locations, but it lies below the 95% PI, reporting 16 (95% PI 1–40 cases) fewer imported-and-reported cases than expected under the model. Indonesia lies below the PI and has no imported-and-reported cases, but the expected case count is 5 (95% PI 1–10 cases) in our model. Across all 8 robustness regression analyses, we consistently observed that Singapore lies above the 95% PI and Thailand and Indonesia lie below (Figure 2). India remains above the 95% PI in all robustness analyses except when we used the more stringent GHS Index, 95th quantile, for fitting; then India lies on the upper bound of the 95% PI (Figure 2, panel F). Top Discussion We aimed to identify locations with likely undetected or underdetected imported cases of SARS-CoV-2 by fitting a model to the case counts in locations with high surveillance capacity and Wuhan-to-location air travel volumes. Our model can be adjusted to account for exportation of cases from locations other than Wuhan as the outbreak develops and more information on importations and self-sustained transmission becomes available. One key advantage of this model is that it does not rely on estimates of incidence or prevalence in the epicenter of the outbreak. Also, we intentionally used a simple generalized linear model. The linearity of the expected case count means that we have only 1 regression coefficient in the model and no extra parameters. The Poisson likelihood then captures the many 0-counts observed for less highly connected locations but also describes the slope between case-count and flight data among more connected locations. We believe this model provides the most parsimonious phenomenologic description of the data. According to our model, locations above the 95% PI of imported-and-reported cases could have higher case-detection capacity. Locations below the 95% PI might have undetected cases because of expected imported-and-reported case counts under high surveillance. Underdetection of cases could increase the international spread of the outbreak because the transmission chain could be lost, reducing opportunities to deploy case-based control strategies. We recommend rapid strengthening of outbreak surveillance and control efforts in locations below the 95% PI lower bound, particularly Indonesia, to curb potential local transmission. Early detection of cases and implantation of appropriate control measures can reduce the risk for self-sustained transmission in all locations. Top Dr. De Salazar is a research fellow at Harvard T.H. Chan School of Public Health, working on multiscale statistical models of infectious diseases within host, population, and metapopulation models. His research interests include diagnostic laboratory methods and public health response. Top Acknowledgments We thank Pamela Martinez, Nicholas Jewel, and Stephen Kissler for valuable feedback. This work was supported by US National Institute of General Medical Sciences (award no. U54GM088558). P.M.D was supported by the Fellowship Foundation Ramon Areces. A.R.T. and C.O.B. were supported by a Maximizing Investigator’s Research Award (no. R35GM124715-02) from the US National Institute of General Medical Sciences. The authors are solely responsible for this content and it does not necessarily represent the official views of the National Institute of General Medical Sciences or the National Institutes of Health. Declaration of interests: Marc Lipsitch has received consulting fees from Merck. All other authors declare no competing interests. Top References Zhou P, Yang XL, Wang XG, Hu B, Zhang L, Zhang W, et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature. 2020;579:270–3. Wu JT, Leung K, Leung GM. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. Lancet. 2020;395:689–97. DOIExternal LinkPubMedExternal Link World Health Organization. Coronavirus disease 2019 (COVID-19) situation report—15, 4 Feb 2020 [cited 2020 Feb 14]. https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200204-sitrep-15-ncov.pdfExternal Link Nuclear Threat Initiative and Johns Hopkins Center for Health Security. Global health security index [cited 2020 Feb 14]. https://www.ghsindex.orgExternal Link US Environmental Protection Agency. Data quality assessment: statistical methods for practitioners EPA QA/G9-S [cited 2020 Feb 14]. Washington: The Agency; 2006. https://www.epa.gov/sites/production/files/2015-08/documents/g9s-final.pdfExternal Link Lamichhane S, Sen P, Dickens AM, Hyötyläinen T, Orešič M. An overview of metabolomics data analysis: current tools and future perspectives. In: Jaumot J, Bedia C, Tauler R, editors. Comprehensive analytical chemistry. Vol. 82. Amsterdam: Elsevier; 2018. p. 387–413. Gelman A, Hill J. Analytical methods for social research. In: Data analysis using regression and multilevel/hierarchical models. Cambridge: Cambridge University Press; 2006. p. 235–236. Wickham H. ggplot2: elegant graphics for data analysis. New York: Springer; 2016. Top Figures Figure 1. Regression plot of locations with possible undetected imported cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by air travel volume from Wuhan, China. Air travel volume measured in... Figure 2. Analyses of imported-and-reported cases and daily air travel volume using a model to predict locations with potentially undetected cases of severe acute respiratory virus 2 (SARS-CoV-2). Air travel volume... Table Table. Surveillance capacity of locations with and without imported-and-reported cases of severe acute respiratory syndrome coronavirus 2, 2020 Top Suggested citation for this article: De Salazar PM, Niehus R, Taylor A, O’Flaherty Buckee C, Lipsitch M. Identifying locations with possible undetected imported severe acute respiratory syndrome coronavirus 2 cases by using importation predictions. Emerg Infect Dis. 2020 Jul [date cited]. https://doi.org/10.3201/eid2607.200250 DOI: 10.3201/eid2607.200250 Original Publication Date: 3/24/2020 1These authors contributed equally to this article. Table of Contents – Volume 26, Number 7—July 2020

What factor positively correlates with imported-and-reported cases counts of SARS-CoV-2 infection?

Respiratory Viral Infections in Exacerbation of Chronic Airway Inflammatory Diseases: Novel Mechanisms and Insights From the Upper Airway Epithelium https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7052386/ SHA: 45a566c71056ba4faab425b4f7e9edee6320e4a4 Authors: Tan, Kai Sen; Lim, Rachel Liyu; Liu, Jing; Ong, Hsiao Hui; Tan, Vivian Jiayi; Lim, Hui Fang; Chung, Kian Fan; Adcock, Ian M.; Chow, Vincent T.; Wang, De Yun Date: 2020-02-25 DOI: 10.3389/fcell.2020.00099 License: cc-by Abstract: Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations (GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018) . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway (such as chronic rhinosinusitis, CRS) and lower airway (such as asthma and chronic obstructive pulmonary disease, COPD) which greatly affect the patients' quality of life (Calus et al., 2012; Bao et al., 2015) . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness (Xepapadaki and Papadopoulos, 2010) . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease (Hashimoto et al., 2008; Viniol and Vogelmeier, 2018) . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway (Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018) . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively (Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019) . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations (Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019) . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases (Rowan et al., 2015; Tan et al., 2017) . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection (Kutter et al., 2018) . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity (Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017) . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the "United Airway" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s (Pattemore et al., 1992) ; with bacterial infections previously considered as the likely culprit for acute exacerbation (Stevens, 1953; Message and Johnston, 2002) . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s (Message and Johnston, 2002) . Rhinovirus (RV) and respiratory syncytial virus (RSV) are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases (Jartti and Gern, 2017) . Other viruses such as parainfluenza virus (PIV), influenza virus (IFV) and adenovirus (AdV) have also been implicated in acute exacerbations but to a much lesser extent (Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019) . More recently, other viruses including bocavirus (BoV), human metapneumovirus (HMPV), certain coronavirus (CoV) strains, a specific enterovirus (EV) strain EV-D68, human cytomegalovirus (hCMV) and herpes simplex virus (HSV) have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway (Mallia and Johnston, 2006; Britto et al., 2017) . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche (Busse et al., 2010) . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection (Vareille et al., 2011; Braciale et al., 2012) . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms (Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019) . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole (Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019) . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I (IFNα/β) and type III (IFNλ) interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α (MIP-1α) and monocyte chemotactic protein 1 (MCP-1) (Wark and Gibson, 2006; Matsukura et al., 2013) . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells (APCs) that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon (IFNγ), IL-2, IL-4, IL-5, IL-9, and IL-12 (Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012) . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells (Wark and Gibson, 2006; Braciale et al., 2012) . The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp (CRSwNP), viral infections such as RV and RSV promote a Type 2-biased immune response (Becker, 2006; Jackson et al., 2014; Jurak et al., 2018) . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP (Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015) . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp (CRSsNP) are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 (Cukic et al., 2012; Brightling and Greening, 2019) . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases (MMPs) released from infiltrating neutrophils (Linden et al., 2019) . Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases (Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019) . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin (TSLP), and their interaction with group 2 innate lymphoid cells (ILC2) has also recently been identified (Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019) . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier (Gabryelska et al., 2019; Roan et al., 2019) . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated (Scanlon and McKenzie, 2012; Li and Hendriks, 2013) . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway (Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019) . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation (Camelo et al., 2017) . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation (Silver et al., 2016) . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals (Yan et al., 2016; Tan et al., 2018a) ; despite augmenting a type 2 exacerbation in chronically inflamed airways (Jurak et al., 2018) . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | www.frontiersin.org mechanisms have been implicated in acute exacerbations during and after viral infection (Murray et al., 2006) . Murray et al. (2006) has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance (Kim et al., 2008; Stolz et al., 2019) in particular the late onset of a bacterial infection (Singanayagam et al., 2018 (Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the "united airway" hypothesis (Kurai et al., 2013) . On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease (Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019) . Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance (Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b) . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells (Becker, 2006; McKendry et al., 2016) . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV (Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019) . Additionally, there are also evidence of reduced type I (IFNβ) and III (IFNλ) interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium (Contoli et al., 2006; Hwang et al., 2019; Wark, 2019) . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation (Wood et al., 2011; Essaidi-Laziosi et al., 2018) . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer (Imperiale and Jiang, 2015) , further contributing to chronic activation of inflammation when they infect the airway (Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017) . With that note, human papilloma virus (HPV), a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies (de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015) . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection (Chi et al., 2011; Ford et al., 2013; Papi et al., 2013) . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance (Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b) . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV (Yan et al., 2016; Guibas et al., 2018) and certain CoV (including the recently emerged COVID-19 virus) (Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020) , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection (Yan et al., 2016; Tan et al., 2019) . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium (Ampomah et al., 2018; Tan et al., 2019) . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway (Guibas et al., 2018) . Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections (Vasileiou et al., 2017; Zheng et al., 2018) ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M (OSM) which causes tight junction opening (Pothoven et al., 2015; Tian et al., 2018) . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases (Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018) . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 (ANGPTL4) and bactericidal/permeabilityincreasing fold-containing family member A1 (BPIFA1) are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018) . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment (Diver et al., 2019) . In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present (Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019) . In addition, viral infection may disrupt biofilm colonies in the upper airway (e.g., Streptococcus pneumoniae) microbiome to be release into the lower airway and worsening the inflammation (Marks et al., 2013; Chao et al., 2014) . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth (Siegel et al., 2014; Mallia et al., 2018) . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms (Singanayagam et al., 2018 (Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019) . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles (Teo et al., 2018) . These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases (Wark et al., 2013; Singanayagam et al., 2018) . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome (Bakken et al., 2011) . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism (with literature evidence) Oxidative stress ROS production (RV, RSV, IFV, HSV) As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation (see text for abbreviations). that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection (Griggs et al., 2017) . Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown (Peng et al., , 2019 Qiu et al., 2018) . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 (DNAH5) and multiciliate differentiation And DNA synthesis associated cell cycle protein (MCIDAS) (Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model (Zhu et al., 2020) . Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells (Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a) . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation (Zhu et al., 2009) . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs (miRNAs) are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases (Tan et al., 2014) . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation (Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018) . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases (Wardzynska et al., 2020) . Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids (Kim et al., 2017) . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections (Hsu et al., 2016 (Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations (Moheimani et al., 2018) . Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections (Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018) . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes (Cardenas et al., 2019; Gomez, 2019) . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations (McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019) . In addition, Spalluto et al. (2017) also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases (Spalluto et al., 2017) . Finally, viral infection can result in enhanced production of reactive oxygen species (ROS), oxidative stress and mitochondrial dysfunction in the airway epithelium (Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018) . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway (Barnes, 2017; van der Vliet et al., 2018) . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018) . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway (Tiwari et al., 2002) . A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing (RNA-seq) or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway (Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a) . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation (Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019) . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition (Ito et al., 2019; Li and Di Santo, 2019) . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment.

What happens in the case of COPD?

Accelerated viral dynamics in bat cell lines, with implications for zoonotic emergence https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7064339/ SHA: f2cc0d63ff2c4aaa127c4caae21d8f3a0067e3d5 Authors: Brook, Cara E; Boots, Mike; Chandran, Kartik; Dobson, Andrew P; Drosten, Christian; Graham, Andrea L; Grenfell, Bryan T; Müller, Marcel A; Ng, Melinda; Wang, Lin-Fa; van Leeuwen, Anieke Date: 2020-02-03 DOI: 10.7554/elife.48401 License: cc-by Abstract: Bats host virulent zoonotic viruses without experiencing disease. A mechanistic understanding of the impact of bats’ virus hosting capacities, including uniquely constitutive immune pathways, on cellular-scale viral dynamics is needed to elucidate zoonotic emergence. We carried out virus infectivity assays on bat cell lines expressing induced and constitutive immune phenotypes, then developed a theoretical model of our in vitro system, which we fit to empirical data. Best fit models recapitulated expected immune phenotypes for representative cell lines, supporting robust antiviral defenses in bat cells that correlated with higher estimates for within-host viral propagation rates. In general, heightened immune responses limit pathogen-induced cellular morbidity, which can facilitate the establishment of rapidly-propagating persistent infections within-host. Rapidly-transmitting viruses that have evolved with bat immune systems will likely cause enhanced virulence following emergence into secondary hosts with immune systems that diverge from those unique to bats. Text: Bats have received much attention in recent years for their role as reservoir hosts for emerging viral zoonoses, including rabies and related lyssaviruses, Hendra and Nipah henipaviruses, Ebola and Marburg filoviruses, and SARS coronavirus (Calisher et al., 2006; Wang and Anderson, 2019) . In most non-Chiropteran mammals, henipaviruses, filoviruses, and coronaviruses induce substantial morbidity and mortality, display short durations of infection, and elicit robust, long-term immunity in hosts surviving infection (Nicholls et al., 2003; Hooper et al., 2001; Mahanty and Bray, 2004) . Bats, by contrast, demonstrate no obvious disease symptoms upon infection with pathogens that are highly virulent in non-volant mammals (Schountz et al., 2017) but may, instead, support viruses as longterm persistent infections, rather than transient, immunizing pathologies (Plowright et al., 2016) . Recent research advances are beginning to shed light on the molecular mechanisms by which bats avoid pathology from these otherwise virulent pathogens (Brook and Dobson, 2015) . Bats leverage a suite of species-specific mechanisms to limit viral load, which include host receptor sequence incompatibilities for some bat-virus combinations (Ng et al., 2015; Takadate et al., 2020) and constitutive expression of the antiviral cytokine, IFN-a, for others (Zhou et al., 2016) . Typically, the presence of viral RNA or DNA in the cytoplasm of mammalian cells will induce secretion of type I interferon proteins (IFN-a and IFN-b), which promote expression and translation of interferon-stimulated genes (ISGs) in neighboring cells and render them effectively antiviral (Stetson and Medzhitov, 2006) . In some bat cells, the transcriptomic blueprints for this IFN response are expressed constitutively, even in the absence of stimulation by viral RNA or DNA (Zhou et al., 2016) . In non-flying mammals, constitutive IFN expression would likely elicit widespread inflammation and concomitant immunopathology upon viral infection, but bats support unique adaptations to combat inflammation (Zhang et al., 2013; Ahn et al., 2019; Xie et al., 2018; Pavlovich et al., 2018) that may have evolved to mitigate metabolic damage induced during flight (Kacprzyk et al., 2017) . The extent to which constitutive IFN-a expression signifies constitutive antiviral defense in the form of functional IFN-a protein remains unresolved. In bat cells constitutively expressing IFN-a, some protein-stimulated, downstream ISGs appear to be also constitutively expressed, but additional ISG induction is nonetheless possible following viral challenge and stimulation of IFN-b (Zhou et al., 2016; Xie et al., 2018) . Despite recent advances in molecular understanding of bat viral tolerance, the consequences of this unique bat immunity on within-host virus dynamics-and its implications for understanding zoonotic emergence-have yet to be elucidated. The field of 'virus dynamics' was first developed to describe the mechanistic underpinnings of long-term patterns of steady-state viral load exhibited by patients in chronic phase infections with HIV, who appeared to produce and clear virus at equivalent rates (Nowak and May, 2000; Ho et al., 1995) . Models of simple target cell depletion, in which viral load is dictated by a bottom-eLife digest Bats can carry viruses that are deadly to other mammals without themselves showing serious symptoms. In fact, bats are natural reservoirs for viruses that have some of the highest fatality rates of any viruses that people acquire from wild animals -including rabies, Ebola and the SARS coronavirus. Bats have a suite of antiviral defenses that keep the amount of virus in check. For example, some bats have an antiviral immune response called the interferon pathway perpetually switched on. In most other mammals, having such a hyper-vigilant immune response would cause harmful inflammation. Bats, however, have adapted anti-inflammatory traits that protect them from such harm, include the loss of certain genes that normally promote inflammation. However, no one has previously explored how these unique antiviral defenses of bats impact the viruses themselves. Now, Brook et al. have studied this exact question using bat cells grown in the laboratory. The experiments made use of cells from one bat species -the black flying fox -in which the interferon pathway is always on, and another -the Egyptian fruit bat -in which this pathway is only activated during an infection. The bat cells were infected with three different viruses, and then Brook et al. observed how the interferon pathway helped keep the infections in check, before creating a computer model of this response. The experiments and model helped reveal that the bats' defenses may have a potential downside for other animals, including humans. In both bat species, the strongest antiviral responses were countered by the virus spreading more quickly from cell to cell. This suggests that bat immune defenses may drive the evolution of faster transmitting viruses, and while bats are well protected from the harmful effects of their own prolific viruses, other creatures like humans are not. The findings may help to explain why bats are often the source for viruses that are deadly in humans. Learning more about bats' antiviral defenses and how they drive virus evolution may help scientists develop better ways to predict, prevent or limit the spread of viruses from bats to humans. More studies are needed in bats to help these efforts. In the meantime, the experiments highlight the importance of warning people to avoid direct contact with wild bats. up resource supply of infection-susceptible host cells, were first developed for HIV (Perelson, 2002) but have since been applied to other chronic infections, including hepatitis-C virus (Neumann et al., 1998) , hepatitis-B virus (Nowak et al., 1996) and cytomegalovirus (Emery et al., 1999) . Recent work has adopted similar techniques to model the within-host dynamics of acute infections, such as influenza A and measles, inspiring debate over the extent to which explicit modeling of top-down immune control can improve inference beyond the basic resource limitation assumptions of the target cell model (Baccam et al., 2006; Pawelek et al., 2012; Saenz et al., 2010; Morris et al., 2018) . To investigate the impact of unique bat immune processes on in vitro viral kinetics, we first undertook a series of virus infection experiments on bat cell lines expressing divergent interferon phenotypes, then developed a theoretical model elucidating the dynamics of within-host viral spread. We evaluated our theoretical model analytically independent of the data, then fit the model to data recovered from in vitro experimental trials in order to estimate rates of within-host virus transmission and cellular progression to antiviral status under diverse assumptions of absent, induced, and constitutive immunity. Finally, we confirmed our findings in spatially-explicit stochastic simulations of fitted time series from our mean field model. We hypothesized that top-down immune processes would overrule classical resource-limitation in bat cell lines described as constitutively antiviral in the literature, offering a testable prediction for models fit to empirical data. We further predicted that the most robust antiviral responses would be associated with the most rapid within-host virus propagation rates but also protect cells against virus-induced mortality to support the longest enduring infections in tissue culture. We first explored the influence of innate immune phenotype on within-host viral propagation in a series of infection experiments in cell culture. We conducted plaque assays on six-well plate monolayers of three immortalized mammalian kidney cell lines: [1] Vero (African green monkey) cells, which are IFN-defective and thus limited in antiviral capacity (Desmyter et al., 1968) ; [2] RoNi/7.1 (Rousettus aegyptiacus) cells which demonstrate idiosyncratic induced interferon responses upon viral challenge (Kuzmin et al., 2017; Arnold et al., 2018; Biesold et al., 2011; Pavlovich et al., 2018) ; and [3] PaKiT01 (Pteropus alecto) cells which constitutively express IFN-a (Zhou et al., 2016; Crameri et al., 2009) . To intensify cell line-specific differences in constitutive immunity, we carried out infectivity assays with GFP-tagged, replication-competent vesicular stomatitis Indiana viruses: rVSV-G, rVSV-EBOV, and rVSV-MARV, which have been previously described (Miller et al., 2012; Wong et al., 2010) . Two of these viruses, rVSV-EBOV and rVSV-MARV, are recombinants for which cell entry is mediated by the glycoprotein of the bat-evolved filoviruses, Ebola (EBOV) and Marburg (MARV), thus allowing us to modulate the extent of structural, as well as immunological, antiviral defense at play in each infection. Previous work in this lab has demonstrated incompatibilities in the NPC1 filovirus receptor which render PaKiT01 cells refractory to infection with rVSV-MARV (Ng and Chandrab, 2018, Unpublished results) , making them structurally antiviral, over and above their constitutive expression of IFN-a. All three cell lines were challenged with all three viruses at two multiplicities of infection (MOI): 0.001 and 0.0001. Between 18 and 39 trials were run at each cell-virus-MOI combination, excepting rVSV-MARV infections on PaKiT01 cells at MOI = 0.001, for which only eight trials were run (see Materials and methods; Figure 1 -figure supplements 1-3, Supplementary file 1). Because plaque assays restrict viral transmission neighbor-to-neighbor in two-dimensional cellular space (Howat et al., 2006) , we were able to track the spread of GFP-expressing virus-infected cells across tissue monolayers via inverted fluorescence microscopy. For each infection trial, we monitored and re-imaged plates for up to 200 hr of observations or until total monolayer destruction, processed resulting images, and generated a time series of the proportion of infectious-cell occupied plate space across the duration of each trial (see Materials and methods). We used generalized additive models to infer the time course of all cell culture replicates and construct the multi-trial dataset to which we eventually fit our mechanistic transmission model for each cell line-virus-specific combination ( Figure 1; Figure 1 -figure supplements 1-5). All three recombinant vesicular stomatitis viruses (rVSV-G, rVSV-EBOV, and rVSV-MARV) infected Vero, RoNi/7.1, and PaKiT01 tissue cultures at both focal MOIs. Post-invasion, virus spread rapidly across most cell monolayers, resulting in virus-induced epidemic extinction. Epidemics were less severe in bat cell cultures, especially when infected with the recombinant filoviruses, rVSV-EBOV and rVSV-MARV. Monolayer destruction was avoided in the case of rVSV-EBOV and rVSV-MARV infections on PaKiT01 cells: in the former, persistent viral infection was maintained throughout the 200 hr duration of each experiment, while, in the latter, infection was eliminated early in the time series, preserving a large proportion of live, uninfectious cells across the duration of the experiment. We assumed this pattern to be the result of immune-mediated epidemic extinction (Figure 1) . Patterns from MOI = 0.001 were largely recapitulated at MOI = 0.0001, though at somewhat reduced total proportions (Figure 1-figure supplement 5 ). A theoretical model fit to in vitro data recapitulates expected immune phenotypes for bat cells We next developed a within-host model to fit to these data to elucidate the effects of induced and constitutive immunity on the dynamics of viral spread in host tissue ( Figure 1 ). The compartmental within-host system mimicked our two-dimensional cell culture monolayer, with cells occupying five distinct infection states: susceptible (S), antiviral (A), exposed (E), infectious (I), and dead (D). We modeled exposed cells as infected but not yet infectious, capturing the 'eclipse phase' of viral integration into a host cell which precedes viral replication. Antiviral cells were immune to viral infection, in accordance with the 'antiviral state' induced from interferon stimulation of ISGs in tissues adjacent to infection (Stetson and Medzhitov, 2006) . Because we aimed to translate available data into modeled processes, we did not explicitly model interferon dynamics but instead scaled the rate of cell progression from susceptible to antiviral (r) by the proportion of exposed cells (globally) in the system. In systems permitting constitutive immunity, a second rate of cellular acquisition of antiviral status (") additionally scaled with the global proportion of susceptible cells in the model. Compared with virus, IFN particles are small and highly diffusive, justifying this global signaling assumption at the limited spatial extent of a six-well plate and maintaining consistency with previous modeling approximations of IFN signaling in plaque assay (Howat et al., 2006) . To best represent our empirical monolayer system, we expressed our state variables as proportions (P S , P A , P E , P I , and P D ), under assumptions of frequency-dependent transmission in a wellmixed population (Keeling and Rohani, 2008) , though note that the inclusion of P D (representing the proportion of dead space in the modeled tissue) had the functional effect of varying transmission with infectious cell density. This resulted in the following system of ordinary differential equations: We defined 'induced immunity' as complete, modeling all cells as susceptible to viral invasion at disease-free equilibrium, with defenses induced subsequent to viral exposure through the term r. By contrast, we allowed the extent of constitutive immunity to vary across the parameter range of " > 0, defining a 'constitutive' system as one containing any antiviral cells at disease-free equilibrium. In fitting this model to tissue culture data, we independently estimated both r and "; as well as the cell-to-cell transmission rate, b, for each cell-virus combination. Since the extent to which constitutively-expressed IFN-a is constitutively translated into functional protein is not yet known for bat hosts (Zhou et al., 2016) , this approach permitted our tissue culture data to drive modeling inference: even in PaKiT01 cell lines known to constitutively express IFN-a, the true constitutive extent of the system (i.e. the quantity of antiviral cells present at disease-free equilibrium) was allowed to vary through estimation of ": For the purposes of model-fitting, we fixed the value of c, the return rate of antiviral cells to susceptible status, at 0. The small spatial scale and short time course (max 200 hours) of our experiments likely prohibited any return of antiviral cells to susceptible status in our empirical system; nonetheless, we retained the term c in analytical evaluations of our model because regression from antiviral to susceptible status is possible over long time periods in vitro and at the scale of a complete organism (Radke et al., 1974; Rasmussen and Farley, 1975; Samuel and Knutson, 1982) . Before fitting to empirical time series, we undertook bifurcation analysis of our theoretical model and generated testable hypotheses on the basis of model outcomes. From our within-host model system (Equation 1-5), we derived the following expression for R 0 , the pathogen basic reproduction number (Supplementary file 2): Pathogens can invade a host tissue culture when R 0 >1. Rapid rates of constitutive antiviral acquisition (") will drive R 0 <1: tissue cultures with highly constitutive antiviral immunity will be therefore resistant to virus invasion from the outset. Since, by definition, induced immunity is stimulated following initial virus invasion, the rate of induced antiviral acquisition (r) is not incorporated into the equation for R 0 ; while induced immune processes can control virus after initial invasion, they cannot prevent it from occurring to begin with. In cases of fully induced or absent immunity (" ¼ 0), the R 0 equation thus reduces to a form typical of the classic SEIR model: At equilibrium, the theoretical, mean field model demonstrates one of three infection states: endemic equilibrium, stable limit cycles, or no infection ( Figure 2) . Respectively, these states approximate the persistent infection, virus-induced epidemic extinction, and immune-mediated epidemic extinction phenotypes previously witnessed in tissue culture experiments ( Figure 1 ). Theoretically, endemic equilibrium is maintained when new infections are generated at the same rate at which infections are lost, while limit cycles represent parameter space under which infectious and susceptible populations are locked in predictable oscillations. Endemic equilibria resulting from cellular regeneration (i.e. births) have been described in vivo for HIV (Coffin, 1995) and in vitro for herpesvirus plaque assays (Howat et al., 2006) , but, because they so closely approach zero, true limit cycles likely only occur theoretically, instead yielding stochastic extinctions in empirical time series. Bifurcation analysis of our mean field model revealed that regions of no infection (pathogen extinction) were bounded at lower threshold (Branch point) values for b, below which the pathogen was unable to invade. We found no upper threshold to invasion for b under any circumstances (i.e. b high enough to drive pathogen-induced extinction), but high b values resulted in Hopf bifurcations, which delineate regions of parameter space characterized by limit cycles. Since limit cycles so closely approach zero, high bs recovered in this range would likely produce virus-induced epidemic extinctions under experimental conditions. Under more robust representations of immunity, with higher values for either or both induced (r) and constitutive (") rates of antiviral acquisition, Hopf bifurcations occurred at increasingly higher values for b, meaning that persistent infections could establish at higher viral transmission rates ( Figure 2 ). Consistent with our derivation for R 0 , we found that the Branch point threshold for viral invasion was independent of changes to the induced immune parameter (r) but saturated at high values of " that characterize highly constitutive immunity ( Figure 3) . We next fit our theoretical model by least squares to each cell line-virus combination, under absent, induced, and constitutive assumptions of immunity. In general, best fit models recapitulated expected outcomes based on the immune phenotype of the cell line in question, as described in the general literature (Table 1 Ironically, the induced immune model offered a slightly better fit than the constitutive to rVSV-MARV infections on the PaKiT01 cell line (the one cell line-virus combination for which we know a constitutively antiviral cell-receptor incompatibility to be at play). Because constitutive immune assumptions can prohibit pathogen invasion (R 0 <1), model fits to this time series under constitutive assumptions were handicapped by overestimations of ", which prohibited pathogen invasion. Only by incorporating an exceedingly rapid rate of induced antiviral acquisition could the model guarantee that initial infection would be permitted and then rapidly controlled. In all panel (A) plots, the rate of induced immune antiviral acquisition (r) was fixed at 0.01. Panel (B) depicts dynamics under variably induced immunity, ranging from absent (left: r=0) to high (right: r=1). In all panel (B) plots, the rate of constitutive antiviral acquisition (") was fixed at 0.0001 Branch point curves are represented as solid lines and Hopf curves as dashed lines. White space indicates endemic equilibrium (persistence), gray space indicates limit cycles, and black space indicates no infection (extinction). Other parameter values for equilibrium analysis were fixed at: b = .025, m = .001, s = 1/6, c = 0. Special points from bifurcations analyses are listed in Supplementary file 3. In fitting our theoretical model to in vitro data, we estimated the within-host virus transmission rate (b) and the rate(s) of cellular acquisition to antiviral status (r or r + ") ( Table 1 ; Supplementary file 4). Under absent immune assumptions, r and " were fixed at 0 while b was estimated; under induced immune assumptions, " was fixed at 0 while r and b were estimated; and under constitutive immune assumptions, all three parameters (r, ", and b) were simultaneously estimated for each cell-virus combination. Best fit parameter estimates for MOI=0.001 data are visualized in conjunction with br and b -" bifurcations in (r) and (B) the constitutive immunity rate of antiviral acquisition ("). Panels show variation in the extent of immunity, from absent (left) to high (right). Branch point curves are represented as solid lines and Hopf curves as dashed lines. White space indicates endemic equilibrium (persistence), gray space indicates limit cycling, and black space indicates no infection (extinction). Other parameter values for equilibrium analysis were fixed at: b = .025, m = .001, s = 1/6, a = 1/6, c = 0. Special points from bifurcations analyses are listed in Supplementary file 3. space corresponding to theoretical limit cycles, consistent with observed virus-induced epidemic extinctions in stochastic tissue cultures. In contrast to Vero cells, the induced immunity model offered the best fit to all RoNi/7.1 data, consistent with reported patterns in the literature and our own validation by qPCR ( Table 1; Arnold et al., 2018; Kuzmin et al., 2017; Biesold et al., 2011; Pavlovich et al., 2018) . As in Vero cell trials, we estimated highest b values for rVSV-G infections on RoNi/7.1 cell lines but here recovered higher b estimates for rVSV-MARV than for rVSV-EBOV. This reversal was balanced by a higher estimated rate of acquisition to antiviral status (r) for rVSV-EBOV versus rVSV-MARV. In general, we observed that more rapid rates of antiviral acquisition (either induced, r, constitutive, ", or both) correlated with higher transmission rates (b). When offset by r, b values estimated for RoNi/7.1 infections maintained the same amplitude as those estimated for immune-absent Vero cell lines but caused gentler epidemics and reduced cellular mortality (Figure 1) . RoNi/7.1 parameter estimates localized in the region corresponding to endemic equilibrium for the deterministic, theoretical model (Figure 4) , yielding less acute epidemics which nonetheless went extinct in stochastic experiments. Finally, rVSV-G and rVSV-EBOV trials on PaKiT01 cells were best fit by models assuming constitutive immunity, while rVSV-MARV infections on PaKiT01 were matched equivalently by models assuming either induced or constitutive immunity-with induced models favored over constitutive in AIC comparisons because one fewer parameter was estimated (Figure 1-figure supplements 4-5; Supplementary file 4). For all virus infections, PaKiT01 cell lines yielded b estimates a full order of magnitude higher than Vero or RoNi/7.1 cells, with each b balanced by an immune response (either r, or r combined with ") also an order of magnitude higher than that recovered for the other cell lines ( Figure 4 ; Table 1 ). As in RoNi/7.1 cells, PaKiT01 parameter fits localized in the region corresponding to endemic equilibrium for the deterministic theoretical model. Because constitutive immune processes can actually prohibit initial pathogen invasion, constitutive immune fits to rVSV-MARV infections on PaKiT01 cell lines consistently localized at or below the Branch point threshold for virus invasion (R 0 ¼ 1). During model fitting for optimization of ", any parameter tests of " values producing R 0 <1 resulted in no infection and, consequently, produced an exceedingly poor fit to infectious time series data. In all model fits assuming constitutive immunity, across all cell lines, antiviral contributions from " prohibited virus from invading at all. The induced immune model thus produced a more parsimonious recapitulation of these data because virus invasion was always permitted, then rapidly controlled. In order to compare the relative contributions of each cell line's disparate immune processes to epidemic dynamics, we next used our mean field parameter estimates to calculate the initial 'antiviral rate'-the initial accumulation rate of antiviral cells upon virus invasion for each cell-virus-MOI combination-based on the following equation: where P E was calculated from the initial infectious dose (MOI) of each infection experiment and P S was estimated at disease-free equilibrium: Because and " both contribute to this initial antiviral rate, induced and constitutive immune assumptions are capable of yielding equally rapid rates, depending on parameter fits. Indeed, under fully induced immune assumptions, the induced antiviral acquisition rate (r) estimated for rVSV-MARV infection on PaKiT01 cells was so high that the initial antiviral rate exceeded even that estimated under constitutive assumptions for this cell-virus combination (Supplementary file 4) . In reality, we know that NPC1 receptor incompatibilities make PaKiT01 cell lines constitutively refractory to rVSV-MARV infection (Ng and Chandrab, 2018, Unpublished results) and that PaKiT01 cells also constitutively express the antiviral cytokine, IFN-a. Model fitting results suggest that this constitutive expression of IFN-a may act more as a rapidly inducible immune response following virus invasion than as a constitutive secretion of functional IFN-a protein. Nonetheless, as hypothesized, PaKiT01 cell lines were by far the most antiviral of any in our study-with initial antiviral rates estimated several orders of magnitude higher than any others in our study, under either induced or constitutive assumptions ( Table 1 ; Supplementary file 4). RoNi/7.1 cells displayed the second-most-pronounced signature of immunity, followed by Vero cells, for which the initial antiviral rate was essentially zero even under forced assumptions of induced or constitutive immunity ( Table 1 ; Supplementary file 4). Using fitted parameters for b and ", we additionally calculated R 0 , the basic reproduction number for the virus, for each cell line-virus-MOI combination ( Table 1 ; Supplementary file 4). We found that R 0 was essentially unchanged across differing immune assumptions for RoNi/7.1 and Vero cells, for which the initial antiviral rate was low. In the case of PaKiT01 cells, a high initial antiviral rate under either induced or constitutive immunity resulted in a correspondingly high estimation of b (and, consequently, R 0 ) which still produced the same epidemic curve that resulted from the much lower estimates for b and R 0 paired with absent immunity. These findings suggest that antiviral immune responses protect host tissues against virus-induced cell mortality and may facilitate the establishment of more rapid within-host transmission rates. Total monolayer destruction occurred in all cell-virus combinations excepting rVSV-EBOV infections on RoNi/7.1 cells and rVSV-EBOV and rVSV-MARV infections on PaKiT01 cells. Monolayer destruction corresponded to susceptible cell depletion and epidemic turnover where R-effective (the product of R 0 and the proportion susceptible) was reduced below one ( Figure 5) . For rVSV-EBOV infections on RoNi/7.1, induced antiviral cells safeguarded remnant live cells, which birthed new susceptible cells late in the time series. In rVSV-EBOV and rVSV-MARV infections on PaKiT01 cells, this antiviral protection halted the epidemic ( Figure 5 ; R-effective <1) before susceptibles fully declined. In the case of rVSV-EBOV on PaKiT01, the birth of new susceptibles from remnant live cells protected by antiviral status maintained late-stage transmission to facilitate long-term epidemic persistence. Importantly, under fixed parameter values for the infection incubation rate (s) and infectioninduced mortality rate (a), models were unable to reproduce the longer-term infectious time series captured in data from rVSV-EBOV infections on PaKiT01 cell lines without incorporation of cell births, an assumption adopted in previous modeling representations of IFN-mediated viral dynamics in tissue culture (Howat et al., 2006) . In our experiments, we observed that cellular reproduction took place as plaque assays achieved confluency. Finally, because the protective effect of antiviral cells is more clearly observable spatially, we confirmed our results by simulating fitted time series in a spatially-explicit, stochastic reconstruction of our mean field model. In spatial simulations, rates of antiviral acquisition were fixed at fitted values for r and " derived from mean field estimates, while transmission rates (b) were fixed at values ten times greater than those estimated under mean field conditions, accounting for the intensification of parameter thresholds permitting pathogen invasion in local spatial interactions (see Materials and methods; Videos 1-3; Figure 5-figure supplement 3; Supplementary file 5; Webb et al., 2007) . In immune capable time series, spatial antiviral cells acted as 'refugia' which protected live cells from infection as each initial epidemic wave 'washed' across a cell monolayer. Eventual birth of new susceptibles from these living refugia allowed for sustained epidemic transmission in cases where some infectious cells persisted at later timepoints in simulation (Videos 1-3; Figure 5-figure supplement 3 ). Bats are reservoirs for several important emerging zoonoses but appear not to experience disease from otherwise virulent viral pathogens. Though the molecular biological literature has made great progress in elucidating the mechanisms by which bats tolerate viral infections (Zhou et al., 2016; Ahn et al., 2019; Xie et al., 2018; Pavlovich et al., 2018; Zhang et al., 2013) , the impact of unique bat immunity on virus dynamics within-host has not been well-elucidated. We used an innovative combination of in vitro experimentation and within-host modeling to explore the impact of unique bat immunity on virus dynamics. Critically, we found that bat cell lines demonstrated a signature of enhanced interferon-mediated immune response, of either constitutive or induced form, which allowed for establishment of rapid within-host, cell-to-cell virus transmission rates (b). These results were supported by both data-independent bifurcation analysis of our mean field theoretical model, as well as fitting of this model to viral infection time series established in bat cell culture. Additionally, we demonstrated that the antiviral state induced by the interferon pathway protects live cells from mortality in tissue culture, resulting in in vitro epidemics of extended duration that enhance the probability of establishing a long-term persistent infection. Our findings suggest that viruses evolved in bat reservoirs possessing enhanced IFN capabilities could achieve more rapid within-host transmission rates without causing pathology to their hosts. Such rapidly-reproducing viruses would likely generate extreme virulence upon spillover to hosts lacking similar immune capacities to bats. To achieve these results, we first developed a novel, within-host, theoretical model elucidating the effects of unique bat immunity, then undertook bifurcation analysis of the model's equilibrium properties under immune absent, induced, and constitutive assumptions. We considered a cell line to be constitutively immune if possessing any number of antiviral cells at disease-free equilibrium but allowed the extent of constitutive immunity to vary across the parameter range for ", the constitutive rate of antiviral acquisition. In deriving the equation for R 0 , the basic reproduction number, which defines threshold conditions for virus invasion of a tissue (R 0 >1), we demonstrated how the invasion threshold is elevated at high values of constitutive antiviral acquisition, ". Constitutive immune processes can thus prohibit pathogen invasion, while induced responses, by definition, can only control infections post-hoc. Once thresholds for pathogen invasion have been met, assumptions of constitutive immunity will limit the cellular mortality (virulence) incurred at high transmission rates. Regardless of mechanism (induced or constitutive), interferon-stimulated antiviral cells appear to play a key role in maintaining longer term or persistent infections by safeguarding susceptible cells from rapid infection and concomitant cell death. Fitting of our model to in vitro data supported expected immune phenotypes for different bat cell lines as described in the literature. Simple target cell models that ignore the effects of immunity best recapitulated infectious time series derived from IFN-deficient Vero cells, while models assuming induced immune processes most accurately reproduced trials derived from RoNi/7.1 (Rousettus aegyptiacus) cells, which possess a standard virusinduced IFN-response. In most cases, models assuming constitutive immune processes best recreated virus epidemics produced on PaKiT01 (Pteropus alecto) cells, which are known to constitutively express the antiviral cytokine, IFN-a (Zhou et al., 2016) . Model support for induced immune assumptions in fits to rVSV-MARV infections on PaKiT01 cells suggests that the constitutive IFN-a expression characteristic of P. alecto cells may represent more of a constitutive immune priming process than a perpetual, functional, antiviral defense. Results from mean field model fitting were additionally confirmed in spatially explicit stochastic simulations of each time series. As previously demonstrated in within-host models for HIV (Coffin, 1995; Perelson et al., 1996; Nowak et al., 1995; Bonhoeffer et al., 1997; Ho et al., 1995) , assumptions of simple target-cell depletion can often provide satisfactory approximations of viral dynamics, especially those reproduced in simple in vitro systems. Critically, our model fitting emphasizes the need for incorporation of top-down effects of immune control in order to accurately reproduce infectious time series derived from bat cell tissue cultures, especially those resulting from the robustly antiviral PaKiT01 P. alecto cell line. These findings indicate that enhanced IFN-mediated immune pathways in bat reservoirs may promote elevated within-host virus replication rates prior to cross-species emergence. We nonetheless acknowledge the limitations imposed by in vitro experiments in tissue culture, especially involving recombinant viruses and immortalized cell lines. Future work should extend these cell culture studies to include measurements of multiple state variables (i.e. antiviral cells) to enhance epidemiological inference. The continued recurrence of Ebola epidemics across central Africa highlights the importance of understanding bats' roles as reservoirs for virulent zoonotic disease. The past decade has born witness to emerging consensus regarding the unique pathways by which bats resist and tolerate highly virulent infections (Brook and Dobson, 2015; Xie et al., 2018; Zhang et al., 2013; Ahn et al., 2019; Zhou et al., 2016; Ng et al., 2015; Pavlovich et al., 2018) . Nonetheless, an understanding of the mechanisms by which bats support endemic pathogens at the population level, or promote the evolution of virulent pathogens at the individual level, remains elusive. Endemic maintenance of infection is a defining characteristic of a pathogen reservoir (Haydon et al., 2002) , and bats appear to merit such a title, supporting long-term persistence of highly transmissible viral infections in isolated island populations well below expected critical community sizes (Peel et al., 2012) . Researchers debate the relative influence of population-level and within-host mechanisms which might explain these trends (Plowright et al., 2016) , but increasingly, field data are difficult to reconcile without acknowledgement of a role for persistent infections (Peel et al., 2018; Brook et al., 2019) . We present general methods to study cross-scale viral dynamics, which suggest that within-host persistence is supported by robust antiviral responses characteristic of bat immune processes. Viruses which evolve rapid replication rates under these robust antiviral defenses may pose the greatest hazard for cross-species pathogen emergence into spillover hosts with immune systems that differ from those unique to bats. All experiments were carried out on three immortalized mammalian kidney cell lines: Vero (African green monkey), RoNi/7.1 (Rousettus aegyptiacus) (Kühl et al., 2011; Biesold et al., 2011) and PaKiT01 (Pteropus alecto) (Crameri et al., 2009) . The species identifications of all bat cell lines was confirmed morphologically and genetically in the publications in which they were originally described (Kühl et al., 2011; Biesold et al., 2011; Crameri et al., 2009) . Vero cells were obtained from ATCC. Monolayers of each cell line were grown to 90% confluency (~9Â10 5 cells) in 6-well plates. Cells were maintained in a humidified 37˚C, 5% CO 2 incubator and cultured in Dulbecco's modified Eagle medium (DMEM) (Life Technologies, Grand Island, NY), supplemented with 2% fetal bovine serum (FBS) (Gemini Bio Products, West Sacramento, CA), and 1% penicillin-streptomycin (Life Technologies). Cells were tested monthly for mycoplasma contamination while experiments were taking place; all cells assayed negative for contamination at every testing. Previous work has demonstrated that all cell lines used are capable of mounting a type I IFN response upon viral challenge, with the exception of Vero cells, which possess an IFN-b deficiency (Desmyter et al., 1968; Rhim et al., 1969; Emeny and Morgan, 1979) . RoNi/7.1 cells have been shown to mount idiosyncratic induced IFN defenses upon viral infection (Pavlovich et al., 2018; Kuzmin et al., 2017; Arnold et al., 2018; Kühl et al., 2011; Biesold et al., 2011) , while PaKiT01 cells are known to constitutively express the antiviral cytokine, IFN-a (Zhou et al., 2016) . This work is the first documentation of IFN signaling induced upon challenge with the particular recombinant VSVs outlined below. We verified known antiviral immune phenotypes via qPCR. Results were consistent with the literature, indicating a less pronounced role for interferon defense against viral infection in RoNi/7.1 versus PaKiT01 cells. Replication-capable recombinant vesicular stomatitis Indiana viruses, expressing filovirus glycoproteins in place of wild type G (rVSV-G, rVSV-EBOV, and rVSV-MARV) have been previously described (Wong et al., 2010; Miller et al., 2012) . Viruses were selected to represent a broad range of anticipated antiviral responses from host cells, based on a range of past evolutionary histories between the virus glycoprotein mediating cell entry and the host cell's entry receptor. These interactions ranged from the total absence of evolutionary history in the case of rVSV-G infections on all cell lines to a known receptor-level cell entry incompatibility in the case of rVSV-MARV infections on PaKiT01 cell lines. To measure infectivities of rVSVs on each of the cell lines outlined above, so as to calculate the correct viral dose for each MOI, NH 4 Cl (20 mM) was added to infected cell cultures at 1-2 hr postinfection to block viral spread, and individual eGFP-positive cells were manually counted at 12-14 hr post-infection. Previously published work indicates that immortalized kidney cell lines of Rousettus aegyptiacus (RoNi/7.1) and Pteropus alecto (PaKiT01) exhibit different innate antiviral immune phenotypes through, respectively, induced (Biesold et al., 2011; Pavlovich et al., 2018; Kühl et al., 2011; Arnold et al., 2018) and constitutive (Zhou et al., 2016 ) expression of type I interferon genes. We verified these published phenotypes on our own cell lines infected with rVSV-G, rVSV-EBOV, and rVSV-MARV via qPCR of IFN-a and IFN-b genes across a longitudinal time series of infection. Specifically, we carried out multiple time series of infection of each cell line with each of the viruses described above, under mock infection conditions and at MOIs of 0.0001 and 0.001-with the exception of rVSV-MARV on PaKiT01 cell lines, for which infection was only performed at MOI = 0.0001 due to limited viral stocks and the extremely low infectivity of this virus on this cell line (thus requiring high viral loads for initial infection). All experiments were run in duplicate on 6well plates, such that a typical plate for any of the three viruses had two control (mock) wells, two MOI = 0.0001 wells and two MOI = 0.001 wells, excepting PaKiT01 plates, which had two control and four MOI = 0.0001 wells at a given time. We justify this PaKiT01 exemption through the expectation that IFN-a expression is constitutive for these cells, and by the assumption that any expression exhibited at the lower MOI should also be present at the higher MOI. For these gene expression time series, four 6-well plates for each cell line-virus combination were incubated with virus for one hour at 37˚C. Following incubation, virus was aspirated off, and cell monolayers were washed in PBS, then covered with an agar plaque assay overlay to mimic conditions under which infection trials were run. Plates were then harvested sequentially at timepoints of roughly 5, 10, 15, and 20 hr post-infection (exact timing varied as multiple trials were running simultaneously). Upon harvest of each plate, agar overlay was removed, and virus was lysed and RNA extracted from cells using the Zymo Quick RNA Mini Prep kit, according to the manufacturer's instructions and including the step for cellular DNA digestion. Post-extraction, RNA quality was verified via nanodrop, and RNA was converted to cDNA using the Invitrogen Superscript III cDNA synthesis kit, according to the manufacturer's instructions. cDNA was then stored at 4˚C and as a frozen stock at À20˚C to await qPCR. We undertook qPCR of cDNA to assess expression of the type I interferon genes, IFN-a and IFNb, and the housekeeping gene, b-Actin, using primers previously reported in the literature (Supplementary file 6) . For qPCR, 2 ml of each cDNA sample was incubated with 7 ml of deionized water, 1 ml of 5 UM forward/reverse primer mix and 10 ml of iTaq Universal SYBR Green, then cycled on a QuantStudio3 Real-Time PCR machine under the following conditions: initial denaturation at 94 C for 2 min followed by 40 cycles of: denaturation at 95˚C (5 s), annealing at 58˚C (15 s), and extension at 72˚C (10 s). We report simple d-Ct values for each run, with raw Ct of the target gene of interest (IFN-a or IFN-b) subtracted from raw Ct of the b-Actin housekeeping gene in Figure 1 -figure supplement 6. Calculation of fold change upon viral infection in comparison to mock using the d-d-Ct method (Livak and Schmittgen, 2001) was inappropriate in this case, as we wished to demonstrate constitutive expression of IFN-a in PaKiT01 cells, whereby data from mock cells was identical to that produced from infected cells. After being grown to~90% confluency, cells were incubated with pelleted rVSVs expressing eGFP (rVSV-G, rVSV-EBOV, rVSV-MARV). Cell lines were challenged with both a low (0.0001) and high (0.001) multiplicity of infection (MOI) for each virus. In a cell monolayer infected at a given MOI (m), the proportion of cells (P), infected by k viral particles can be described by the Poisson distribution: P k ð Þ ¼ e Àm m k k! , such that the number of initially infected cells in an experiment equals: 1 À e Àm . We assumed that a~90% confluent culture at each trial's origin was comprised of~9x10 5 cells and conducted all experiments at MOIs of 0.0001 and 0.001, meaning that we began each trial by introducing virus to, respectively,~81 or 810 cells, representing the state variable 'E' in our theoretical model. Low MOIs were selected to best approximate the dynamics of mean field infection and limit artifacts of spatial structuring, such as premature epidemic extinction when growing plaques collide with plate walls in cell culture. Six-well plates were prepared with each infection in duplicate or triplicate, such that a control well (no virus) and 2-3 wells each at MOI 0.001 and 0.0001 were incubated simultaneously on the same plate. In total, we ran between 18 and 39 trials at each cell-virus-MOI combination, excepting r-VSV-MARV infections on PaKiT01 cells at MOI = 0.001, for which we ran only eight trials due to the low infectivity of this virus on this cell line, which required high viral loads for initial infection. Cells were incubated with virus for one hour at 37˚C. Following incubation, virus was aspirated off, and cell monolayers were washed in PBS, then covered with a molten viscous overlay (50% 2X MEM/Lglutamine; 5% FBS; 3% HEPES; 42% agarose), cooled for 20 min, and re-incubated in their original humidified 37˚C, 5% CO 2 environment. After application of the overlay, plates were monitored periodically using an inverted fluorescence microscope until the first signs of GFP expression were witnessed (~6-9.5 hr post-infection, depending on the cell line and virus under investigation). From that time forward, a square subset of the center of each well (comprised of either 64-or 36-subframes and corresponding to roughly 60% and 40% of the entire well space) was imaged periodically, using a CellInsight CX5 High Content Screening (HCS) Platform with a 4X air objective (ThermoFisher, Inc, Waltham, MA). Microscope settings were held standard across all trials, with exposure time fixed at 0.0006 s for each image. One color channel was imaged, such that images produced show GFP-expressing cells in white and non-GFP-expressing cells in black (Figure 1-figure supplement 1) . Wells were photographed in rotation, as frequently as possible, from the onset of GFP expression until the time that the majority of cells in the well were surmised to be dead, GFP expression could no longer be detected, or early termination was desired to permit Hoechst staining. In the case of PaKiT01 cells infected with rVSV-EBOV, where an apparently persistent infection established, the assay was terminated after 200+ hours (8+ days) of continuous observation. Upon termination of all trials, cells were fixed in formaldehyde (4% for 15 min), incubated with Hoechst stain (0.0005% for 15 min) (ThermoFisher, Inc, Waltham, MA), then imaged at 4X on the CellInsight CX5 High Content Screening (HCS) Platform. The machine was allowed to find optimal focus for each Hoechst stain image. One color channel was permitted such that images produced showed live nuclei in white and dead cells in black. Hoechst stain colors cellular DNA, and viral infection is thought to interfere with the clarity of the stain (Dembowski and DeLuca, 2015) . As such, infection termination, cell fixation, and Hoechst staining enables generation of a rough time series of uninfectious live cells (i.e. susceptible + antiviral cells) to complement the images which produced time series of proportions infectious. Due to uncertainty over the exact epidemic state of Hoechst-stained cells (i.e. exposed but not yet infectious cells may still stain), we elected to fit our models only to the infectious time series derived from GFPexpressing images and used Hoechst stain images as a post hoc visual check on our fit only ( Figure 5 ; Figure 5 -figure supplements 1-2). Images recovered from the time series above were processed into binary ('infectious' vs. 'non-infectious' or, for Hoechst-stained images, 'live' vs. 'dead') form using the EBImage package (Pau et al., 2010) in R version 3.6 for MacIntosh, after methods further detailed in Supplementary file 7. Binary images were then further processed into time series of infectious or, for Hoechst-stained images, live cells using a series of cell counting scripts. Because of logistical constraints (i.e. many plates of simultaneously running infection trials and only one available imaging microscope), the time course of imaging across the duration of each trial was quite variable. As such, we fitted a series of statistical models to our processed image data to reconstruct reliable values of the infectious proportion of each well per hour for each distinct trial in all cell line-virus-MOI combinations (Figure 1 To derive the expression for R 0 , the basic pathogen reproductive number in vitro, we used Next Generation Matrix (NGM) techniques (Diekmann et al., 1990; Heffernan et al., 2005) , employing Wolfram Mathematica (version 11.2) as an analytical tool. R 0 describes the number of new infections generated by an existing infection in a completely susceptible host population; a pathogen will invade a population when R 0 >1 (Supplementary file 2). We then analyzed stability properties of the system, exploring dynamics across a range of parameter spaces, using MatCont (version 2.2) (Dhooge et al., 2008) for Matlab (version R2018a) (Supplementary file 3). The birth rate, b, and natural mortality rate, m, balance to yield a population-level growth rate, such that it is impossible to estimate both b and m simultaneously from total population size data alone. As such, we fixed b at. 025 and estimated m by fitting an infection-absent version of our mean field model to the susceptible time series derived via Hoechst staining of control wells for each of the three cell lines (Figure 1-figure supplement 7) . This yielded a natural mortality rate, m, corresponding to a lifespan of approximately 121, 191, and 84 hours, respectively, for Vero, RoNi/7.1, and PaKiT01 cell lines (Figure 1-figure supplement 7) . We then fixed the virus incubation rate, s, as the inverse of the shortest observed duration of time from initial infection to the observation of the first infectious cells via fluorescent microscope for all nine cell line -virus combinations (ranging 6 to 9.5 hours). We fixed a, the infection-induced mortality rate, at 1/6, an accepted standard for general viral kinetics (Howat et al., 2006) , and held c, the rate of antiviral cell regression to susceptible status, at 0 for the timespan (<200 hours) of the experimental cell line infection trials. We estimated cell line-virus-MOI-specific values for b, r, and " by fitting the deterministic output of infectious proportions in our mean field model to the full suite of statistical outputs of all trials for each infected cell culture time series (Figure 1-figure supplements 2-3) . Fitting was performed by minimizing the sum of squared differences between the deterministic model output and cell linevirus-MOI-specific infectious proportion of the data at each timestep. We optimized parameters for MOI = 0.001 and 0.0001 simultaneously to leverage statistical power across the two datasets, estimating a different transmission rate, b, for trials run at each infectious dose but, where applicable, estimating the same rates of r and " across the two time series. We used the differential equation solver lsoda() in the R package deSolve (Soetaert et al., 2010) to obtain numerical solutions for the mean field model and carried out minimization using the 'Nelder-Mead' algorithm of the optim() function in base R. All model fits were conducted using consistent starting guesses for the parameters, b (b = 3), and where applicable, r (r = 0.001) and " (" = 0.001). In the case of failed fits or indefinite hessians, we generated a series of random guesses around the starting conditions and continued estimation until successful fits were achieved. All eighteen cell line-virus-MOI combinations of data were fit by an immune absent (" = r = 0) version of the theoretical model and, subsequently, an induced immunity (" = 0; r >0) and constitutive immunity (" >0; r >0) version of the model. Finally, we compared fits across each cell line-virus-MOI combination via AIC. In calculating AIC, the number of fitted parameters in each model (k) varied across the immune phenotypes, with one parameter (b) estimated for absent immune assumptions, two (b and r) for induced immune assumptions, and three (b, r, and ") for constitutive immune assumptions. The sample size (n) corresponded to the number of discrete time steps across all empirical infectious trials to which the model was fitted for each cell-line virus combination. All fitting and model comparison scripts are freely available for download at the following FigShare repository: DOI: 10.6084/m9.figshare.8312807. Finally, we verified all mean field fits in a spatial context, in order to more thoroughly elucidate the role of antiviral cells in each time series. We constructed our spatial model in C++ implemented in R using the packages Rcpp and RcppArmadillo (Eddelbuettel and Francois, 2011; Eddelbuettel and Sanderson, 2017) . Following Nagai and Honda (2001) and Howat et al. (2006) , we modeled this system on a two-dimensional hexagonal lattice, using a ten-minute epidemic timestep for cell state transitions. At the initialization of each simulation, we randomly assigned a duration of natural lifespan, incubation period, infectivity period, and time from antiviral to susceptible status to all cells in a theoretical monolayer. Parameter durations were drawn from a normal distribution centered at the inverse of the respective fixed rates of m, s, a, and c, as reported with our mean field model. Transitions involving the induced (r) and constitutive (") rates of antiviral acquisition were governed probabilistically and adjusted dynamically at each timestep based on the global environment. As such, we fixed these parameters at the same values estimated in the mean field model, and multiplied both r and " by the global proportion of, respectively, exposed and susceptible cells at a given timestep. In contrast to antiviral acquisition rates, transitions involving the birth rate (b) and the transmission rate (b) occurred probabilistically based on each cell's local environment. The birth rate, b, was multiplied by the proportion of susceptible cells within a six-neighbor circumference of a focal dead cell, while b was multiplied by the proportion of infectious cells within a thirty-six neighbor vicinity of a focal susceptible cell, thus allowing viral transmission to extend beyond the immediate nearestneighbor boundaries of an infectious cell. To compensate for higher thresholds to cellular persistence and virus invasion which occur under local spatial conditions (Webb et al., 2007) , we increased the birth rate, b, and the cell-to-cell transmission rate, b, respectively, to six and ten times the values used in the mean field model (Supplementary file 4) . We derived these increases based on the assumption that births took place exclusively based on pairwise nearest-neighbor interactions (the six immediately adjacent cells to a focal dead cell), while viral transmission was locally concentrated but included a small (7.5%) global contribution, representing the thirty-six cell surrounding vicinity of a focal susceptible. We justify these increases and derive their origins further in Supplementary file 5. We simulated ten stochastic spatial time series for all cell-virus combinations under all three immune assumptions at a population size of 10,000 cells and compared model output with data in . Transparent reporting form Data availability All data generated or analysed during this study are included in the manuscript and supporting files. All images and code used in this study have been made available for download at the following Figshare

What was the finding in this study?

Host resilience to emerging coronaviruses https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7079962/ SHA: f7cfc37ea164f16393d7f4f3f2b32214dea1ded4 Authors: Jamieson, Amanda M Date: 2016-07-01 DOI: 10.2217/fvl-2016-0060 License: cc-by Abstract: Recently, two coronaviruses, severe acute respiratory syndrome coronavirus and Middle East respiratory syndrome coronavirus, have emerged to cause unusually severe respiratory disease in humans. Currently, there is a lack of effective antiviral treatment options or vaccine available. Given the severity of these outbreaks, and the possibility of additional zoonotic coronaviruses emerging in the near future, the exploration of different treatment strategies is necessary. Disease resilience is the ability of a given host to tolerate an infection, and to return to a state of health. This review focuses on exploring various host resilience mechanisms that could be exploited for treatment of severe acute respiratory syndrome coronavirus, Middle East respiratory syndrome coronavirus and other respiratory viruses that cause acute lung injury and acute respiratory distress syndrome. Text: The 21st century was heralded with the emergence of two novel coronaviruses (CoV) that have unusually high pathogenicity and mortality [1] [2] [3] [4] [5] . Severe acute respiratory syndrome coronavirus (SARS-Cov) was first identified in 2003 [6] [7] [8] [9] . While there was initially great concern about SARS-CoV, once no new cases emerged, funding and research decreased. However, a decade later Middle East respiratory syndrome coronavirus (MERS-CoV), also known as HCoV-EMC, emerged initially in Saudi Arabia [3, 10] . SARS-CoV infected about 8000 people, and resulted in the deaths of approximately 10% of those infected [11] . While MERS-CoV is not as widespread as SARS-CoV, it appears to have an even higher mortality rate, with 35-50% of diagnosed infections resulting in death [3, [12] [13] . These deadly betacoronavirus viruses existed in animal reservoirs [4] [5] 9, [14] [15] . Recently, other CoVs have been detected in animal populations raising the possibility that we will see a repeat of these types of outbreaks in the near future [11, [16] [17] [18] [19] [20] . Both these zoonotic viruses cause a much more severe disease than what is typically seen for CoVs, making them a global health concern. Both SARS-CoV and MERS-CoV result in severe lung pathology. Many infected patients have acute lung injury (ALI), a condition that is diagnosed based on the presence of pulmonary edema and respiratory failure without a cardiac cause. In some patients there is a progression to the more severe form of ALI, acute respiratory distress syndrome (ARDS) [21] [22] [23] . In order to survive a given infection, a successful host must not only be able to clear the pathogen, but tolerate damage caused by the pathogen itself and also by the host's immune response [24] [25] [26] . We refer to resilience as the ability of a host to tolerate the effects of pathogens and the immune response to pathogens. A resilient host is able to return to a state of health after responding to an infection [24, [27] [28] . Most currently available treatment options for infectious diseases are antimicrobials, For reprint orders, please contact: [email protected] REviEW Jamieson future science group and thus target the pathogen itself. Given the damage that pathogens can cause this focus on rapid pathogen clearance is understandable. However, an equally important medical intervention is to increase the ability of the host to tolerate the direct and indirect effects of the pathogen, and this is an area that is just beginning to be explored [29] . Damage to the lung epithelium by respiratory pathogens is a common cause of decreased resilience [30] [31] [32] . This review explores some of the probable host resilience pathways to viral infections, with a particular focus on the emerging coronaviruses. We will also examine factors that make some patients disease tolerant and other patients less tolerant to the viral infection. These factors can serve as a guide to new potential therapies for improved patient care. Both SARS-CoV and MERS-CoV are typified by a rapid progression to ARDS, however, there are some distinct differences in the infectivity and pathogenicity. The two viruses have different receptors leading to different cellular tropism, and SARS-CoV is more ubiquitous in the cell type and species it can infect. SARS-CoV uses the ACE2 receptor to gain entry to cells, while MERS-CoV uses the ectopeptidase DPP4 [33] [34] [35] [36] . Unlike SARS-CoV infection, which causes primarily a severe respiratory syndrome, MERS-CoV infection can also lead to kidney failure [37, 38] . SARS-CoV also spreads more rapidly between hosts, while MERS-CoV has been more easily contained, but it is unclear if this is due to the affected patient populations and regions [3] [4] 39 ]. Since MERS-CoV is a very recently discovered virus, [40, 41] more research has been done on SARS-CoV. However, given the similarities it is hoped that some of these findings can also be applied to MERS-CoV, and other potential emerging zoonotic coronaviruses. Both viral infections elicit a very strong inflammatory response, and are also able to circumvent the immune response. There appears to be several ways that these viruses evade and otherwise redirect the immune response [1, [42] [43] [44] [45] . The pathways that lead to the induction of the antiviral type I interferon (IFN) response are common targets of many viruses, and coronaviruses are no exception. SARS-CoV and MERS-CoV are contained in double membrane vesicles (DMVs), that prevents sensing of its genome [1, 46] . As with most coronaviruses several viral proteins suppress the type I IFN response, and other aspects of innate antiviral immunity [47] . These alterations of the type I IFN response appear to play a role in immunopathology in more than one way. In patients with high initial viral titers there is a poor prognosis [39, 48] . This indicates that reduction of the antiviral response may lead to direct viral-induced pathology. There is also evidence that the delayed type I IFN response can lead to misregulation of the immune response that can cause immunopathology. In a mouse model of SARS-CoV infection, the type I IFN response is delayed [49] . The delay of this potent antiviral response leads to decreased viral clearance, at the same time there is an increase in inflammatory cells of the immune system that cause excessive immunopathology [49] . In this case, the delayed antiviral response not only causes immunopathology, it also fails to properly control the viral replication. While more research is needed, it appears that MERS has a similar effect on the innate immune response [5, 50] . The current treatment and prevention options for SARS-CoV and MERS-CoV are limited. So far there are no licensed vaccines for SAR-CoV or MERS-CoV, although several strategies have been tried in animal models [51, 52] . There are also no antiviral strategies that are clearly effective in controlled trials. During outbreaks several antiviral strategies were empirically tried, but these uncontrolled studies gave mixed results [5, 39] . The main antivirals used were ribavirin, lopinavir and ritonavir [38, 53] . These were often used in combination with IFN therapy [54] . However, retrospective analysis of these data has not led to clear conclusions of the efficacy of these treatment options. Research in this area is still ongoing and it is hoped that we will soon have effective strategies to treat novel CoV [3,36,38,40, [55] [56] [57] [58] [59] [60] [61] [62] [63] [64] . The lack of effective antivirals makes it necessary to examine other potential treatments for SARS-CoV and MERS-CoV. Even if there were effective strategies to decrease viral burden, for these viruses, the potential for new emerging zoonotic CoVs presents additional complications. Vaccines cannot be produced in time to stop the spread of an emerging virus. In addition, as was demonstrated during SARS-CoV and MERS-CoV outbreaks, there is always a challenge during a crisis situation to know which Host resilience to emerging coronaviruses REviEW future science group www.futuremedicine.com antiviral will work on a given virus. One method of addressing this is to develop broad-spectrum antivirals that target conserved features of a given class of virus [65] . However, given the fast mutation rates of viruses there are several challenges to this strategy. Another method is to increase the ability of a given patient to tolerate the disease, i.e., target host resilience mechanisms. So far this has largely been in the form of supportive care, which relies on mechanical ventilation and oxygenation [29, 39, 66] . Since SARS-CoV and MERS-CoV were discovered relatively recently there is a lack of both patient and experimental data. However, many other viruses cause ALI and ARDS, including influenza A virus (IAV). By looking at data from other high pathology viruses we can extrapolate various pathways that could be targeted during infection with these emerging CoVs. This can add to our understanding of disease resilience mechanisms that we have learned from direct studies of SARS-CoV and MERS-CoV. Increased understanding of host resilience mechanisms can lead to future host-based therapies that could increase patient survival [29] . One common theme that emerges in many respiratory viruses including SARS-CoV and MERS-CoV is that much of the pathology is due to an excessive inflammatory response. A study from Josset et al. examines the cell host response to both MERS-CoV and SARS-CoV, and discovered that MERS-CoV dysregulates the host transcriptome to a much greater extent than SARS-CoV [67] . It demonstrates that glucocorticoids may be a potential way of altering the changes in the host transcriptome at late time points after infection. If host gene responses are maintained this may increase disease resilience. Given the severe disease that manifested during the SARS-CoV outbreak, many different treatment options were empirically tried on human patients. One immunomodulatory treatment that was tried during the SARS-CoV outbreak was systemic corticosteroids. This was tried with and without the use of type I IFNs and other therapies that could directly target the virus [68] . Retrospective analysis revealed that, when given at the correct time and to the appropriate patients, corticosteroid use could decrease mortality and also length of hospital stays [68] . In addition, there is some evidence that simultaneous treatment with IFNs could increase the potential benefits [69] . Although these treatments are not without complications, and there has been a lack of a randomized controlled trial [5, 39] . Corticosteroids are broadly immunosuppressive and have many physiological effects [5, 39] . Several recent studies have suggested that other compounds could be useful in increasing host resilience to viral lung infections. A recent paper demonstrates that topoisomerase I can protect against inflammation-induced death from a variety of viral infections including IAV [70] . Blockade of C5a complement signaling has also been suggested as a possible option in decreasing inflammation during IAV infection [71] . Other immunomodulators include celecoxib, mesalazine and eritoran [72, 73] . Another class of drugs that have been suggested are statins. They act to stabilize the activation of aspects of the innate immune response and prevent excessive inflammation [74] . However, decreasing immunopathology by immunomodulation is problematic because it can lead to increased pathogen burden, and thus increase virus-induced pathology [75, 76] . Another potential treatment option is increasing tissue repair pathways to increase host resilience to disease. This has been shown by bioinformatics [77] , as well as in several animal models [30-31,78-79]. These therapies have been shown in cell culture model systems or animal models to be effective, but have not been demonstrated in human patients. The correct timing of the treatments is essential. Early intervention has been shown to be the most effective in some cases, but other therapies work better when given slightly later during the course of the infection. As the onset of symptoms varies slightly from patient to patient the need for precise timing will be a challenge. Examination of potential treatment options for SARS-CoV and MERS-CoV should include consideration of host resilience [29] . In addition to the viral effects, and the pathology caused by the immune response, there are various comorbidities associated with SARS-CoV and MERS-CoV that lead to adverse outcomes. Interestingly, these additional risk factors that lead to a more severe disease are different between the two viruses. It is unclear if these differences are due to distinct populations affected by the viruses, because of properties of the virus themselves, or both. Understanding these factors could be a key to increasing host resilience to the infections. MERS-CoV patients had increased morbidity and mortality if they were obese, immunocompromised, diabetic or had cardiac disease [4, 12] . REviEW Jamieson future science group Risk factors for SARS-CoV patients included an older age and male [39] . Immune factors that increased mortality for SARS-CoV were a higher neutrophil count and low T-cell counts [5, 39, 77] . One factor that increased disease for patients infected with SARS-CoV and MERS-CoV was infection with other viruses or bacteria [5, 39] . This is similar to what is seen with many other respiratory infections. A recent study looking at malaria infections in animal models and human patients demonstrated that resilient hosts can be predicted [28] . Clinical studies have started to correlate specific biomarkers with disease outcomes in ARDS patients [80] . By understanding risk factors for disease severity we can perhaps predict if a host may be nonresilient and tailor the treatment options appropriately. A clear advantage of targeting host resilience pathways is that these therapies can be used to treat a variety of different infections. In addition, there is no need to develop a vaccine or understand the antiviral susceptibility of a new virus. Toward this end, understanding why some patients or patient populations have increased susceptibility is of paramount importance. In addition, a need for good model systems to study responses to these new emerging coronaviruses is essential. Research into both these subjects will lead us toward improved treatment of emerging viruses that cause ALI, such as SARS-CoV and MERS-CoV. The author has no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties. No writing assistance was utilized in the production of this manuscript. • Severe acute respiratory syndrome coronavirus and Middle East respiratory syndrome coronavirus are zoonotic coronaviruses that cause acute lung injury and acute respiratory distress syndrome. • Antivirals have limited effects on the course of the infection with these coronaviruses. • There is currently no vaccine for either severe acute respiratory syndrome coronavirus or Middle East respiratory syndrome coronavirus. • Host resilience is the ability of a host to tolerate the effects of an infection and return to a state of health. • Several pathways, including control of inflammation, metabolism and tissue repair may be targeted to increase host resilience. • The future challenge is to target host resilience pathways in such a way that there are limited effects on pathogen clearance pathways. Future studies should determine the safety of these types of treatments for human patients. Papers of special note have been highlighted as:

What is the role of statins in increasing host resilience to viral lung infections?

Viruses and Evolution – Viruses First? A Personal Perspective https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6433886/ SHA: f3b9fc0f8e0a431366196d3e835e1ec368b379d1 Authors: Moelling, Karin; Broecker, Felix Date: 2019-03-19 DOI: 10.3389/fmicb.2019.00523 License: cc-by Abstract: The discovery of exoplanets within putative habitable zones revolutionized astrobiology in recent years. It stimulated interest in the question about the origin of life and its evolution. Here, we discuss what the roles of viruses might have been at the beginning of life and during evolution. Viruses are the most abundant biological entities on Earth. They are present everywhere, in our surrounding, the oceans, the soil and in every living being. Retroviruses contributed to about half of our genomic sequences and to the evolution of the mammalian placenta. Contemporary viruses reflect evolution ranging from the RNA world to the DNA-protein world. How far back can we trace their contribution? Earliest replicating and evolving entities are the ribozymes or viroids fulfilling several criteria of life. RNA can perform many aspects of life and influences our gene expression until today. The simplest structures with non-protein-coding information may represent models of life built on structural, not genetic information. Viruses today are obligatory parasites depending on host cells. Examples of how an independent lifestyle might have been lost include mitochondria, chloroplasts, Rickettsia and others, which used to be autonomous bacteria and became intracellular parasites or endosymbionts, thereby losing most of their genes. Even in vitro the loss of genes can be recapitulated all the way from coding to non-coding RNA. Furthermore, the giant viruses may indicate that there is no sharp border between living and non-living entities but an evolutionary continuum. Here, it is discussed how viruses can lose and gain genes, and that they are essential drivers of evolution. This discussion may stimulate the thinking about viruses as early possible forms of life. Apart from our view “viruses first”, there are others such as “proteins first” and “metabolism first.” Text: Mycoplasma mycoides by systematic deletion of individual genes resulted in a synthetic minimal genome of 473 genes (Hutchison et al., 2016) . Can one consider simpler living entities? There are elements with zero genes that fulfill many criteria for early life: ribozymes, catalytic RNAs closely related to viroids. They were recovered in vitro from 10 15 molecules (aptamers), 220 nucleotides in length, by 10 rounds of selection. Among the many RNA species present in this collection of quasispecies RNAs were catalytically active members, enzymatically active ribozymes. The sequence space for 220-mer RNAs is about 3 × 10 132 (Eigen, 1971; Wilson and Szostak, 1999; Brackett and Dieckmann, 2006) . The selected ribozymes were able to replicate, cleave, join, and form peptide bonds. They can polymerize progeny chemically, allow for mutations to occur and can evolve. One molecule serves as catalyst, the other one as substrate. Replication of ribozymes was demonstrated in the test tube (Lincoln and Joyce, 2009) . Ribozymes can form peptide bonds between amino acids (Zhang and Cech, 1997) . Thus, small peptides were available by ribozyme activity. Consequently, an RNA modification has been proposed as peptide nucleic acid (PNA), with more stable peptide bonds instead of phosphodiester bonds (Zhang and Cech, 1997; Joyce, 2002) . Replication of RNA molecules can be performed chemically from RNA without polymerase enzymes. In addition, deoxyribozymes can form from ribonucleotides (Wilson and Szostak, 1999) . Thus, DNA can arise from RNA chemically, without the key protein enzyme, the reverse transcriptase. An entire living world is possible from non-coding RNA (ncRNA) before evolution of the genetic code and protein enzymes. Ribozymes naturally consist of circular single-stranded RNAs (Orgel, 2004) . They lack the genetic triplet code and do not encode proteins. Instead, they exhibit structural information by hairpin-loops that form hydrogen bonds between incomplete double strands, and loops free to interact with other molecules. They represent a quasispecies in which many species of RNA may form, such as ribozymes, tRNA-like molecules, and other ncRNAs. RNAs within such a pool can bind amino acids. Ninety different amino acids have been identified on the Murchison meteorite found in Australia, while on Earth only about 20 of them are used for protein synthesis (Meierhenrich, 2008) . Where formation of ribozymes occurred on the early Earth is a matter of speculation. The hydrothermal vents such as black smokers in the deep ocean are possibilities where life may have started (Martin et al., 2008) . There, temperature gradients and clay containing minerals such as magnesium or manganese are available. Pores or niches offer possibilities for concentration of building blocks, which is required for chemical reactions to occur. Interestingly, also ice is a candidate for ribozyme formation and chemical reactions. Ice crystals displace the biomolecules into the liquid phase, which leads to concentration, creating a quasicellular compartmentalization where de novo synthesis of nucleotide precursors is promoted. There, RNA and ribozymes can emerge, which are capable of self-replication (Attwater et al., 2010) . tRNA-amino acid complexes can find RNAs as "mRNAs." Such interactions could have contributed to the evolution of the genetic code. This sequence of events can lead to primitive ribosome precursors. Ribozymes are the essential catalytic elements in ribosomes: "The ribosome is a ribozyme" (Cech, 2000) , supplemented with about a hundred scaffold proteins later during evolution. The proteins have structural functions and contribute indirectly to enzymatic activity. Are these ribosomebound ribozymes fossils from the early Earth? Small peptides can be formed by ribozymes before ribosomes evolved, whereby single or dimeric amino acids may originate from the universe (Meierhenrich, 2008) . Small peptides with basic amino acids can increase the catalytic activity of ribozymes as shown in vitro (Müller et al., 1994) . Such proteins are known as RNA-binding proteins from RNA viruses that protect the RNA genome, with motifs such as RAPRKKG of the nucleocapsid NCp7 of HIV (Schmalzbauer et al., 1996) . Peptides can enhance the catalytic activity of ribozymes up to a 100-fold (Müller et al., 1994) . Such peptides of RNA viruses serve as chaperones that remove higher ordered RNA structures, allowing for more efficient interaction of RNA molecules and increasing transcription rates of RNA polymerases (Müller et al., 1994) . Ribonucleoproteins may have also been functionally important during the evolution of ribosomes (Harish and Caetano-Anolles, 2012) . These pre-ribosomal structures are also similar to precursorlike structures of retroviruses. Reverse transcription can be performed by ribozymes chemically. This action does not necessarily require a protein polymerase such as the reverse transcriptase. Similarly, deoxyribonucleotides can arise by removal of an oxygen without the need of a protein enzyme (a reductase) as today, and allow for DNA polymerization (Wilson and Szostak, 1999; Joyce, 2002) . The same elements of the precursors for ribosomes are also building blocks of retroviruses, which may have a similar evolutionary origin (Moelling, 2012 (Moelling, , 2013 . tRNAs serve as primers for the reverse transcriptase, and the sequence of promoters of transposable elements are derived from tRNAs (Lander et al., 2001) . The ribozymes developed into more complex self-cleaving group II introns with insertion of genes encoding a reverse transcriptase and additional proteins (Moelling and Broecker, 2015; Moelling et al., 2017) (Figure 1) . It came as a surprise that the genomes of almost all species are rich in ncDNA, transcribed into ncRNAs but not encoding proteins, as evidenced, for instance, by the "Encyclopedia of DNA Elements" (ENCODE) project. ncDNA amounts to more than 98% of the human DNA genome (Deveson et al., 2017) . Higher organisms tend to have more non-coding information, which allows for more complex modes of gene regulation. The ncRNAs are regulators of the protein-coding sequences. Highly complex organisms such as humans typically have a high number of ncRNA and regulatory mechanisms. ncRNA can range from close to zero in the smallest bacteria such as Pelagibacter ubique to about 98% in the human genome. RNA viruses such as the retrovirus HIV harbor ncRNAs for gene regulation such as the trans-activating response element (TAR), the binding site for the Tat protein for early viral gene expression. Tat has a highly basic domain comprising mostly Lys and Arg residues, resembling other RNA binding proteins. ncRNA also serves on viral RNA genomes as ribosomal entry sites, primer binding sites or packaging signals. DNA synthesis depends on RNA synthesis as initial event, with RNA primers as starters for DNA replication, inside of cells as FIGURE 1 | A compartment is shown with essential components of life as discussed in the text. Non-coding RNA (ncRNA), ribozymes or viroids, can perform many steps for life without protein-coding genes but only by structural information. Individual amino acids are indicated as black dots and may be available on Earth from the universe. DNA may have existed before retroviruses. The compartment can be interpreted as pre-virus or pre-cell. Viroid, green; RNA, red; DNA, black. well as during retroviral replication, proving a requirement of RNA (Flint, 2015) . The number of mammalian protein-coding genes is about 20,000. Surprisingly, this is only a fifth of the number of genes of bread wheat (Appels et al., 2018) . Tulips, maize and other plants also have larger genomes, indicating that the number of genes does not necessarily reflect the complexity of an organism. What makes these plant genomes so large, is still an open question. Could the giant genomes possibly be the result to breeding of plants by farmers or gardeners? According to Szostak there are molecules which appear like relics from the RNA world such as acetyl-CoA or vitamin B12, both of which are bound to a ribonucleotide for no obvious reason -was it "forgotten" to be removed? (Roberts and Szostak, 1997; Szostak et al., 2001; Szostak, 2011) . Perhaps the connected RNA serves as structural stabilizer. Lipid vesicles could have formed the first compartments and enclosed ribozymes, tRNAs with selected amino acids, and RNA which became mRNA. Is this a pre-cell or pre-virus (Figure 1) ? Patel et al. (2015) demonstrated that the building blocks of life, ribonucleotides, lipids and amino acids, can be formed from C, H, O, P, N, S in a "one pot" synthesis. This study can be regarded as a follow-up study of the classical Urey-Miller in vitro synthesis of biomolecules (Miller, 1953; Miller and Urey, 1959) . Transition from the RNA to the DNA world was promoted by the formation of the reverse transcriptase. The enzyme was first described in retroviruses but it is almost ubiquitous and found in numerous cellular species, many of which with unknown functions (Simon and Zimmerly, 2008; Lescot et al., 2016) . It is an important link between the RNA and the DNA worlds. The name reverse transcriptase is historical and irritating because it is the "real" transcriptase during the transition from the RNA to the DNA world. Similarly, the ribonuclease H (RNase H) is an essential enzyme of retroviruses (Mölling et al., 1971) . The RNase H turned out to be one of the five most frequent and ancient proteins (Ma et al., 2008 ) that belongs to a superfamily of more than sixty different unique representatives and 152 families with numerous functions (Majorek et al., 2014) . Some of the many tRNAs can become loaded with amino acids. There are viruses containing tRNA-like structures (TLS), resembling these early RNAs (Dreher, 2009) . The TLS of these viruses typically bind to a single amino acid. TLS-viruses include plant viruses, such as Turnip yellow mosaic virus, in Peanut clump virus, Tobacco mosaic virus (TMV), and Brome mosaic virus. Only half a tRNA is found in Narnaviruses of fungi. The amino acids known to be components of tRNA-like viruses are valine, histidine and tyrosine. The structures were also designated as "mimicry, " enhancing translation (Dreher, 2009 (Dreher, , 2010 . They look like "frozen" precursor-like elements for protein synthesis. This combination of a partial tRNA linked to one amino acid can be interpreted as an evolutionary early step toward protein synthesis, trapped in a viral element. Ribozymes are related to the protein-free viroids. Viroids are virus-like elements that belong to the virosphere, the world of viruses (Chela-Flores, 1994) . Viroids lack protein coats and therefore were initially not designated as viruses but virus-like viroids when they were discovered in 1971 by Theodor Diener. He described viroids as "living fossils" (Diener, 2016) (Figure 2) . From infected potatoes, Diener isolated the Potato spindle tuber viroid (PSTVd) whose genome was about a 100-fold smaller than those of viruses known at that time. The viroids known today are ranging from 246 to 467 nucleotides. They contain circular single-stranded RNA, are protein-free and self-replicating with no genetic information, but only structural FIGURE 2 | Viroids are hairpin-loop structures and are shown schematically and as electron micrograph. Viroids are, like ribozymes, without genetic information and play major biological roles today in plant diseases, in carnation flowers, in liver cancer, as catalyst of protein synthesis in ribosomes and as circular regulatory RNAs, as "sponges" for other regulatory RNAs. information in the form of hairpin-loops (Riesner et al., 1979) . They can generate copies of themselves in the appropriate environment. They were designated as the "frontiers of life" (Flores et al., 2014) . The knowledge of virus composition was based on TMV and its crystallization by Wendell Stanley in 1935 (Pennazio and Roggero, 2000) . The genome of TMV is protein-coding singlestranded RNA of about 6,400 nucleotides that is enclosed by a rod-like protein coat. Viroids, in contrast, do not encode proteins and lack coats but they are closely related to viruses. Viroids can lose their autonomy and rely on host RNA polymerases to replicate, are capable of infecting plants and many are economically important pathogens. There are two families, the nucleus-replicating Pospiviroidae such as PSTVd and the chloroplast-replicating Avsunviroidae like the Avocado sunblotch viroid (ASBVd). Their replication requires host enzymes. Thus, autonomy is replaced by dependence on host enzymes and an intracellular lifestyle. Most viroids are often enzymatically active ribozymes -yet they are examples that this trait can get lost as a result of changing environmental conditions. Loss of ribozyme activity is a functional, not a genetic loss. Only the nuclear variants, the Pospiviroidae, can lose their ribozyme activity and use the cellular RNase III enzyme for their replication. In contrast, the Avsunviroidae are still active hammerhead ribozymes. Thus, inside the nucleus of a host cell, the enzymatic RNA function can become unnecessary. Not genes, but a function, the catalytic activity, gets lost. Viroids did apparently not gain genes but cooperated for a more complex lifestyle. For example, Carnation small viroid-like RNA (CarSV RNA) cooperates with a retrovirus and is accompanied by a homologous DNA generated by a reverse transcriptase. This enzyme presumably originates from a pararetrovirus of plants. Pararetroviruses package virus particles at a different stage during replication than retroviruses, the DNA, not the RNA. This unique combination between two viral elements has so far only been detected with CarSV in carnation flowers (Flores et al., 2005 (Flores et al., , 2014 . Why did such a cooperation evolve -perhaps by breeding gardeners? RNA is sensitive to degradation; therefore, genetic increase and growth of the genome may not be favorable energetically -at least not in plants. Gain of function is, in this case, cooperation. The circular RNA (circRNA) is related to ribozymes/viroids as a chief regulator of other regulatory RNAs, a "sponge" absorbing small RNAs. Micro RNAs (miRNAs) are post-transcriptional regulators that are affected by the presence of circRNAs. circRNAs were detected in human and mouse brains and testes as well as in plants. They can bind 70 conserved miRNAs in a cell and amount up to 25,000 molecules (Hansen et al., 2013) . Their structure is reminiscent of catalytically active ribozymes. There is an exceptional viroid that gained coding information and entered the human liver (Taylor, 2009) . The viroid is known as hepatitis delta virus (HDV). It has the smallest genome of any known animal virus of about 1,680 nucleotides. It has properties typical of viroids, since it contains circRNA, forms similar hairpin-loops and replicates in the nucleus using host enzymes. Two polymerases have to redirect their specificity from DNA to RNA to generate the HDV genome and antigenome. Both of them have ribozyme activity. In contrast to other ribozymes, HDV encodes a protein, the hepatitis delta antigen (HDVAg) that occurs in two forms, the small-HDVAg (24 kDa) supporting replication and the large-HDVAg (27 kDa) that helps virion assembly. The gene was presumably picked up from the host cell by recombination of HDV's mRNA intermediate with a host mRNA. Transmission depends on a helper virus, the Hepatitis B virus (HBV), which delivers the coat (Taylor, 2009 ) Does packaging by a helper virus protect the genome and thereby allow for a larger viroid to exist? In plants, viroids may not be able to become bigger possibly due to their sensitivity to degradation -but they cannot become much smaller either. Only a single viroid is known that is completely composed of protein-coding RNA with triplets (AbouHaidar et al., 2014). Viroids and related replicating RNAs are error-prone replicating units and the error frequency imposes a certain minimal size onto them, as they would otherwise become extinct. This mechanism has been described as "error catastrophe, " which prevents survival (Eigen, 1971 (Eigen, , 2013 . The viroids and related RNAs are the smallest known replicons. Smaller ones would become extinct in the absence of repair systems. In summary, RNA can catalyze many reactions. Protein enzymes which may have evolved later have higher catalytic activities. Ribozymes are carriers of information, but do not require coding genes. Information is stored in their sequence and structure. Thus, replication of an initial RNA is followed by flow of information, from DNA to RNA to protein, as described the Central Dogma (Crick, 1968) . Even an information flow from protein to DNA has been described for some archaeal proteins (Béguin et al., 2015) . The DNA-protein world contains numerous ncRNAs with key functions. ncRNA may serve as a model compound for the origin of life on other planets. Hereby not the chemical composition of this molecule is of prime relevance, but its simplicity and multifunctionality. Furthermore, RNA is software and hardware in a single molecule, which makes it unique in our world. There are other scenarios besides the here discussed "virus-first, " such as "protein-first", "metabolism-fist" or the "lipid world" (Segré et al., 2001; Andras and Andras, 2005; Vasas et al., 2010; Moelling, 2012) . Some of these alternative concepts were built on phylogenomics, the reconstruction of the tree of life by genome sequencing (Delsuc et al., 2005) . Surprisingly, it was Sir Francis Crick, one of the discoverers of the DNA double-helix, who stated that he would not be surprised about a world completely built of RNA. A similar prediction was made by Walter Gilbert (Crick, 1968; Gilbert, 1986) . What a vision! Our world was almost 50 years later defined as "RNAprotein" world (Altman, 2013) . One can speculate our world was built of ribozymes or viroids, which means "viruses first." ncRNAs appear as relics from the past RNA world, before DNA, the genetic code and proteins evolved. However, ncRNA is essential in our biological DNA world today. It is possible to produce such ncRNA today in the test tube by loss of genic information from protein-coding RNA. This reduction to ncRNA was demonstrated in vitro with phage RNA. Phage Qβ genomic RNA, 4,217 nucleotides in length, was incubated in the presence of Qβ replicase, free nucleotides and salts, a rich milieu in the test tube. The RNA was allowed to replicate by means of the Qβ replicase. Serial transfer of aliquots to fresh medium led to ever faster replication rates and reduction of genomic size, down to 218 nucleotides of ncRNA in 74 generations. This study demonstrated that, depending on environmental conditions, an extreme gene reduction can take place. This experiment performed in 1965 was designated as "Spiegelman's Monster." Coding RNA became replicating ncRNA (Spiegelman et al., 1965; Kacian et al., 1972) ! Manfred Eigen extended this experiment and demonstrated further that a mixture containing no RNA to start with but only ribonucleotides and the Qβ replicase can under the right conditions in a test tube spontaneously generate self-replicating ncRNA. This evolved into a form similar to Spiegelman's Monster. The presence of the replicase enzyme was still necessary in these studies. Furthermore, a change in enzyme concentration and addition of short RNAs or an RNA intercalator influenced the arising RNA population (Sumper and Luce, 1975; Eigen, 2013) . Thus, the complexity of genomes depends on the environment: poor conditions lead to increased complexity and rich environments to reduced complexity. The process demonstrated in this experiment with viral components indicates that reversion to simplicity, reduction in size, loss of genetic information and speed in replication can be major forces of life, even though this appears to be like a reversion of evolution. The experiment can perhaps be generalized from the test tube to a principle, that the most successful survivors on our planet are the viruses and microorganisms, which became the most abundant entities. Perhaps life can start from there again. These studies raise the question of how RNA molecules can become longer, if the small polymers become smaller and smaller, replicate faster and outcompete longer ones. This may be overcome by heat flow across an open pore in submerged rocks, which concentrates replicating oligonucleotides from a constant feeding flow and selection for longer strands. This has been described for an increase from 100 to 1,000 nucleotides in vitro. RNA molecules shorter than 75 nucleotides will die out (Kreysing et al., 2015) . Could a poor environment lead to an increase of complexity? This could be tested. Ribozymes were shown to grow in size by uptake of genes, as demonstrated for HDV (Taylor, 2009 ). An interesting recent unexpected example supporting the notion that environmental conditions influence genetic complexity, is the human gut microbiome. Its complexity increases with diverse food, while uniform rich food reduces its diversity and may lead to diseases such as obesity. Colonization of the human intestinal tract starts at birth. A few dozen bacterial and viral/phage species are conserved between individuals (core sequences) as a stable composition (Broecker et al., 2016c . Dysbiosis has been observed in several chronic diseases and in obesity, a loss of bacterial richness and diversity. Nutrition under affluent conditions with sugar-rich food contributes to obesity, which results in a significant reduction of the complexity of the microbiome. This reduction is difficult to revert (Cotillard et al., 2013; Le Chatelier et al., 2013) . The gut microbiome in human patients with obesity is reminiscent of the gene reduction described in the Spiegelman's Monster experiment: reduction of genes in a rich environment. The reduction of the complexity of the microbiome is in part attributed to the action of phages, which under such conditions, defined as stress, lyse the bacteria. Fecal microbiota transplantation can even be replaced by soluble fractions containing phages or metabolites from the donor without bacteria (Ott et al., 2017) . Analogously, the most highly complex microbiomes are found in indigenous human tribes in Africa, which live on a broad variety of different nutrients. It is a slow process, though, to increase gut microbiota complexity by diverse nutrition. The obesity-associated microbiota that survive are fitter and more difficult to counteract. Urbanization and westernization of the diet is associated with a loss of microbial biodiversity, loss of microbial organisms and genes (Segata, 2015) . To understand the mechanism and driving force for genome reduction, deletion rates were tested by insertion of an indicator gene into the Salmonella enterica genome. The loss of the indicator gene was monitored by serial passage in rich medium. After 1,000 generations about 25% of the deletions caused increased bacterial fitness. Deletions resulted in smaller genomes with reduced or absence of DNA repair genes (Koskiniemi et al., 2012) . Gene loss conferred a higher fitness to the bacteria under these experimental conditions. The recently discovered mimiviruses and other giant viruses are worth considering for understanding the evolution of life with respect to the contribution of viruses. Their hosts are, for example, Acanthamoeba, Chlorella, and Coccolithus algae (Emiliania huxleyi), but also corals or sponges as discussed more recently. Mimiviruses were first discovered in cooling water towers in Bradford, United Kingdom in 2003 with about 1,000 genes, most of which unrelated to previously known genes. Mimiviruses have received attention because they contain elements that were considered hallmarks of living cells, not of viruses, such as elements required for protein synthesis, tRNAs and amino acid transferases. The mimiviruses harbor these building blocks as incomplete sets not sufficient for independent protein synthesis as bacteria or archaea can perform, preventing them from leading an autonomous life (La Scola et al., 2003 Scola et al., , 2008 . They are larger than some bacteria. Giant viruses can be looked at as being on an evolutionary path toward a cellular organism. Alternatively, they may have evolved from a cellular organism by loss of genetic information (Nasir and Caetano-Anolles, 2015) . Giant viruses have frequently taken up genes from their hosts by horizontal gene transfer (HGT) (La Scola et al., 2008; Nasir and Caetano-Anolles, 2015; Colson et al., 2018) . A graph on genome sizes shows that mimiviruses and bacteria overlap in size, indicating a continuous transition between viruses and bacteria and between living and non-living worlds (based on Holmes, 2011) (Figure 3) . Other giant viruses, such as megaviruses, were discovered in the ocean of Chile with 1,120 genes. Most recently the Klosneuvirus was identified in the sewage of the monastery Klosterneuburg in Austria in 2017 with 1.57 million (mio) basepairs (Mitch, 2017) . Pithovirus sibericum is the largest among giant viruses discovered to date with a diameter of 1.5 microns, a genome of 470,000 bp with 467 putative genes, 1.6 microns in length, and it is presumably 30,000 years old as it was recovered from permafrost in Siberia (Legendre et al., 2014) . The smaller Pandoraviruses with 1 micron in length have five times larger genomes, 2,500,000 bp (Philippe et al., 2013) (Figure 3) . The giant viruses can even be hosts to smaller viruses, the virophages, reminiscent of bacteriophages, the viruses of bacteria. These virophages such as Sputnik are only 50 nm in size with 18,343 bp of circular dsDNA and 21 predicted proteincoding genes. They replicate in viral factories and consume the resources of the mimivirus, thereby destroying it. Some, virophages can even integrate into the genome of the cellular host and can be reactivated when the host is infected by giant viruses. Thus, giant viruses suggest that viruses are close to living entities or may have been alive (La Scola et al., 2008; Fischer and Hackl, 2016) . In biology it is common to distinguish between living and dead matter by the ability to synthesize proteins and replicate autonomously. The giant viruses may be considered as missing link between the two, because they harbor "almost" the protein synthesis apparatus. The transition from living to the non-living world is continuous, not separated by a sharp borderline (Figure 3) . Viruses are not considered alive by most of the scientific community and as written in textbooks, because they cannot replicate autonomously. Yet some of the giant viruses are equipped with almost all components of the protein synthesis machinery close to bacteria suggesting that they belong to the living matter (Schulz et al., 2017) . The ribozymes may have been the earliest replicating entity. Perhaps also other viruses were initially more independent of the early Earth than they are today. As described in Figure 1 there may have been initially no major difference between an early virus or an early cell. Only later viruses may have given up their autonomous replication and became parasites -as has been described for some bacteria (see below). Efforts have been made to identify the smallest living cell that is still autonomously replicating. Among the presumably smallest naturally occurring bacteria is Pelagibacter ubique of the SAR11 clade of bacteria (Giovannoni, 2017) , which was discovered in 1990. It is an alpha-proteobacterium with 1,389 genes present ubiquitously in all oceans. It can reach up to 10 28 free living cells in total and represents about 25% of microbial plankton cells. Very little of its DNA is non-coding. It harbors podophage-type phages, designated as "pelagiphage" (Zhao et al., 2013) . This small bacterium was designated as the most common organism on the planet. Why is it so successful? This autonomous bacterium is smaller than some parasitic giant viruses. Craig Venter, who first succeeded in sequencing the human genome, tried to minimize the putative smallest genome of a living species, from Mycoplasma mycoides, a parasitic bacterium that lives in ruminants (Gibson et al., 2008 (Gibson et al., , 2010 . His group synthesized a genome of 531,000 bp with 473 genes, 149 of them (32%) with unknown functions (Hutchison et al., 2016) . Among the smallest parasitic living organisms is Nanoarchaeum equitans. It is a thermophile archaeon which lives at 80 • C and at pH 6 with 2% salt (Huber et al., 2003) . Its genome has a size of 490,000 bp and encodes 540 genes. N. equitans is an obligate symbiont of a bigger archaeon, Ignicoccus riding on it as on a horse, hence the name (Huber et al., 2003) . The world of viruses covers a range of three logs in size of their genomes: from zero genes to about 2,500 genes amounting to about 2,500,000 bp of DNA. The zero-gene viroids are about 300 bases in length (Figure 3) . The virosphere is the most successful reservoir of biological entities on our planet in terms of numbers of particles, speed of replication, growth rates, and sequence space. There are about 10 33 viruses on our planet and they are present in every single existing species (Suttle, 2005) . There is no living species without viruses! Viruses also occur freely in the oceans, in the soil, in clouds up to the stratosphere and higher, to at least 300 km in altitude. They populate the human intestine, birth canal, and the outside of the body as protective layer against microbial populations. Microbes contain phages that are activated during stress conditions such as lack of nutrients, change in temperatures, lack of space and other changes of environmental conditions. One of the most earth-shaking papers of this century was the publication of the human genome sequence (Lander et al., 2001) . About half, possibly even two-thirds of the sequence are composed of more or less complete endogenous retroviruses (ERVs) and related retroelements (REs) (de Koning et al., 2011) . REs amplify via copy-and-paste mechanisms involving a reverse transcriptase step from an RNA intermediate into DNA. In addition, DNA transposable elements (TEs) move by a cutand-paste mechanism. The origin of REs is being discussed as remnants of ancient retroviral germline infections that became evolutionarily fixed in the genome. About 450,000 human ERV (HERV) elements constitute about 8% of the human genome consisting of hallmark retroviral elements like the gag, pol, env genes and flanking long terminal repeats (LTR) that act as promoters (Lander et al., 2001) . Howard Temin, one of the discoverers of the reverse transcriptase, in 1985 already described endogenous retrovirus-like elements, which he estimated to about 10% of the human and mouse genome sequence (Temin, 1985) . The actual number is about 45% as estimated today (Lander et al., 2001) . In some genes such as the Protein Kinase Inhibitor B (PKIB) gene we determined about 70% retrovirusrelated sequences (Moelling and Broecker, 2015) . Is there a limit? Could it have been 100%? Retroviruses are estimated to have entered the lineage of the mammalian genome 550 million years ago (MYA) (Hayward, 2017) . Older ERV sequences may exist but are unrecognizable today due to the accumulation of mutations. ERVs undergo mutations, deletions or homologous recombination events with large deletions and can become as short as solo LTR elements, which are a few hundred bp in length -the left-overs from full-length retroviral genomes of about 10,000 bp. The LTR promoters can deregulate neighboring genes. Homologous recombination events may be considered as gene loss or gene reduction events. It is the assumption that the ERVs, which were no longer needed for host cell defense, were no longer selected for by evolution and consequently deleted as unnecessary consumers of energy. Eugene Koonin points out that infection and integration are unique events occurring at a fast pace, while loss and gene reduction may take much longer time frames (Wolf and Koonin, 2013) . A frequent gene reduction of eukaryotic genomes is the loss of the viral envelope protein encoded by the env gene. Without a coat, retroviruses can no longer leave the cell and infect other cells. They lose mobility and become obligatory intracellular elements. Helper viruses can supply envelope proteins in trans and mobilize the viruses. TEs or REs can be regarded as examples of coat-free intracellular virus relics -or could it have been the other way round, perhaps precursors of full-length retroviruses? These elements can be amplified intracellularly and modify the host genomes by integration with the potential danger of gene disruption and genetic changes. REs can lead to gene duplications and pseudogene development, with one copy for stable conservation of acquired functions and the other one for innovations (Cotton and Page, 2005) . Such duplications constitute large amounts of mammalian genomes (Zhang, 2003) . Retroviruses have an RNase H moiety duplication, one of which serves as a catalytically inactive linker between the RT polymerase and the enzymatically active RNase H (Xiong and Eickbush, 1990; Malik and Eickbush, 2001; Moelling and Broecker, 2015; Moelling et al., 2017) . This gene duplication dates back to 500 mio years (Cotton and Page, 2005) . Gene duplications are a common cause of cancer, which often occurs only in the genome of the cancer cell itself, less affecting offsprings. Myc, Myb, ErbB2, Ras, and Raf are oncogenes amplified in diverse types of human cancers (Vogelstein and Kinzler, 2002) . The ability of retroviruses to integrate makes them distinct from endosymbionts which stay separate. Yet the net result is very similar, acquisition of new genetic information, which is transmitted to the next generation, if the germline is infected and endogenization of the virus occurred. Viral integration is not limited to eukaryotic cells but also a mechanism in prokaryotes for maintenance of the lysogenic state of phages inside bacteria. Also, for other eukaryotic viruses such as HBV, the envelope surface antigen BHsAg can be deleted, which leads to an obligatory intracellular life style for the virus, which especially in the presence of HCV promotes cancer (Yang et al., 2016) . HIV has been shown to rapidly lose one of its auxiliary genes, nef, originally for negative factor. The gene was lost within a rather low number of passages of the virus grown under tissue culture conditions by selection for high virus titer producing cells. Deletion of nef resulted in a significant increase of the virus titer in culture -hence the name. The nef gene product was of no need inside tissue culture cells, rather it was inhibitory for replication. However, it is essential for pathogenicity in animals, and subsequently nef was reinterpreted as "necessary factor" (Flint, 2015) . Also, the human hosts of HIV can lose a significant terminal portion of a seven transmembrane receptor in lymphocytes, the primary target cell for HIV entry and for virus uptake. This molecule, the CCR5 cytokine receptor is truncated by 32 carboxy-terminal amino acids (CCR5-32), disabling the receptor functionally. The allele frequency of the mutant CCR5-32 mutant is about 10% in the European population, making these people resistant to HIV infections (Solloch et al., 2017) . This gene loss in Europeans has been shown to make the individuals resistant not only against HIV infection but also against malaria. This may have been the selective pressure in the past before HIV/AIDS arose. No side effect for humans lacking this gene has been described (Galvani and Slatkin, 2003) . Viruses have been proven to be drivers of evolution (Villarreal and Witzany, 2010) , including the human genome, which by at least 45% is composed of sequences related to retroviruses. In addition, endogenized retroviruses supplied the syncytin genes that are essential for the development of the mammalian placenta, and allowed the growth of embryos without its rejection by the maternal immune system (Dupressoir et al., 2012) . Thus, the same property which causes immunodeficiency in HIV-infected patients and leads to AIDS causes syncytia formation, cell fusion after infection by a retrovirus. Viruses have also been proposed to be at the origin of the evolution of adaptive immunity (Villarreal, 2009 ). Thus, viruses shaped genomes by supplying essential genes and mechanisms. Endogenization of retroviruses has occurred in the mammalian genomes for at least 550 mio years (Hayward, 2017) . If the integrated ERVs did not provide any selective advantage, they deteriorated and accumulated mutations with loss of function. This was directly proven by reconstruction of an infectious retrovirus from the consensus sequence of 9 defective endogenous virus sequences, designated as Phoenix. The virus was expressed from a constructed synthetic DNA clone in cell culture and formed virus particles identified by high resolution microscopic analysis (Dewannieux and Heidmann, 2013) . The koalas in Australia are currently undergoing endogenization of a retrovirus (koala retrovirus, KoRV) in "real time" and demonstrate possible consequences for immunity. In the early 1900s, some individuals were transferred to islands, including Kangaroo Island, close to the Australian mainland for repopulation purposes, as koalas were threatened to become extinct. Today, the majority of the koala population is infected by KoRV, which is closely related to the Gibbon ape leukemia virus (GALV). Yet, koalas isolated on Kangaroo Island are KoRV negative, which allows dating the introduction of KoRV into the koala population to about one hundred years ago. Many of the infected koalas fell ill and died, yet some populations became resistant within about 100 years, corresponding to about 10 generations. The koalas likely developed resistance due to the integrated DNA proviruses. The retrovirus is transmitted as exogenous as well as endogenous virus, similar to the Jaagsiekte sheep retrovirus (JSRV), whereby the endogenized viruses protect with a viral gene product, such as Env, against de novo infections by "superinfection exclusion" (Tarlinton, 2012) . The contribution of retroviruses to the antiviral defense is striking, since all retroviral genes have analogous genes in the siRNA/RNAi defense mechanism of eukaryotic cells (Moelling et al., 2006) . Retroviruses can protect against infection by other related viruses, for example, by expressing Env proteins that block cellsurface receptors (Villarreal, 2011) . A comparable mechanism protects bacterial cells against DNA phages, by integrated phage DNA fragments that are transcribed into mRNA and hybridize to incoming new DNA phages and thereby lead to their destruction by hybrid-specific nucleases, CRISPR/Cas immunity (Charpentier and Doudna, 2013) . It is often not realized that immunity acquisition in bacteria and mammalian cells follow analogous mechanisms (Figure 4) . Integration of retroviruses normally occurs in somatic cells after infection as an obligatory step during the viral life cycle. Infection of germline cells can lead to transmission to the next generation and ultimately result in inherited resistance. Endogenized retroviruses likely caused resistance FIGURE 4 | Viruses protect against viruses: retroviruses protect a cell against a new infection by a similar virus designated as "superinfection exclusion" or viral interference. This is mediated by viral gene products such as proteins or nucleic acids. Similarly, phages protect against phages: superinfection of bacteria is prevented by CRISPR/Cas RNA originating from previous infections. The mechanisms of defense against viruses and phages are analogous. Protection by viruses or phages against superinfections represents cellular defense and acquired immunity. The four examples are discussed in the text. to the exogenous counterparts. Similarly, resistance to Simian Immune Deficiency virus (SIV) in some monkey species may be explained by endogenization (Li et al., 2017 (Li et al., , 2018 . In the case of phages and their prokaryotic hosts the mechanism is described as CRISPR/Cas, which follow analogous principles of "endogenization" of incoming genetic material for subsequent exclusion. One may speculate that HIV may also eventually become endogenized into the human genome. There is some evidence that HIV can infect human germline cells and can be transmitted to the embryonic genome (Wang et al., 2011) . How long this may take is not known -10 generations? The loss of function of ERVs can occur by mutations, deletions of the env or other genes and ultimately all coding genes by homologous recombination, leaving behind only one LTR. The number of retrovirus-like elements add up to about 450,000, corresponding to 8% of the human genome (Lander et al., 2001; Cordaux and Batzer, 2009 ). The promoter regions were analyzed for their contribution to cancer by activating neighboring genes -as a consequence of a former retrovirus infection. Indeed, activated cellular genes by "downstream promotion" were identified in animal studies with activation of the myc gene as one of many examples, leading to chronic, not acute development of cancer (Ott et al., 2013) . As a general mechanism for human cancer today the LTRs are, however, not identified as a major culprit. Most of the ERVs we find today have been integrated during evolution in introns or other regions where their presence is relatively harmless. Did the other ones result in death of the carriers which disappeared? The effects of LTRs on the expression levels of neighboring host genes was studied with the endogenous human virus, HERV-K, as a possible cause of cancer, but this appears not to be a general phenomenon (Broecker et al., 2016b) . As shown for the koalas, ERVs can confer immunity to viral infections (Feschotte and Gilbert, 2012) . A related ERV, HERV-H, was shown to produce an RNA that keeps early embryonic cells pluripotent and even revert adult cells to regain pluripotency (Grow et al., 2015) . Thus, the role of ERVs may be more complex than we presently know. Transposable elements and REs that lost the ability of cellular transmission by deletion of the coat protein majorly contribute to genetic complexity of host cells. They are "locked" inside the cells and are major drivers of the increase of genetic complexity (Cordaux and Batzer, 2009 ). One could speculate that these intracellular elements are replicationincompetent retroviruses lacking coats (Lander et al., 2001) . Bats transmit viruses such as Ebola and SARS coronavirus without suffering from disease (Beltz, 2018) . Even RNA viruses such as Bornaviruses have been shown to integrate by illegitimate reverse transcription, possibly also supplying immunity against superinfection (Katzourakis and Gifford, 2010) . There are two prominent events that significantly contributed to the success of life and the formation of cells. Both of them are associated with gene reduction. This phenomenon may play a role for the evolution of viruses from autonomous to parasitic lifestyles. In the 1960s Lynn Margulis proposed an extracellular origin for mitochondria (Margulis, 1970 (Margulis, , 1993 ). An ancestral cell, perhaps an archaeon, was infected by an anaerobic bacterium, which gave rise to mitochondria. Similarly, cyanobacteria formed the chloroplasts in modern plant cells. Mitochondria arose around 1.45 billion years ago (BYA) (Embley and Martin, 2006) . Mitochondria and chloroplasts are the most striking examples for a change in lifestyle from autonomous bacteria to endosymbionts. This transition is often considered as extremely rare and a hallmark of evolution of life on our planet. However, there are many other obligate intracellular parasites such as Rickettsia, Chlamydia trachomatis, Coxiella burnetii (the causative agent of Q fever), Mycobacterium leprae, M. tuberculosis, and M. mycoides (Beare et al., 2006) . The change of lifestyle of the endosymbionts in the two cases of mitochondria and chloroplasts is striking. Both of them drastically reduced their genetic make-up. Mitochondria contain less than 37 genes, left from the original about 3,000 genes. Is endogenization of retroviruses, the ERVs, which are integrated into germline cells, related to endosymbiosis? Are these endosymbionts models for the transition from autonomous lifestyle to a parasitic life-which may have taken place with viruses? A more recent typical example for a reductive evolution are Rickettsia. These bacteria were assumed for some time to be viruses because of their obligatory intracellular parasitic existence. Rickettsia have evolved from autonomously replicating bacteria. Reductive evolution of endosymbionts can yield bacteria with tiny genomes on the expense of autonomous extracellular life. Their genomes are 1.11 mio bp in length with about 834 protein-coding genes, and loss of 24% by reductive evolution (Ogata et al., 2001) . Rickettsia may have some relationship with cyanobacteria, which are considered as the major symbionts. Can one speculate that viruses may have been autonomous entities initially? Viroids may have undergone transition from autonomy to parasites, just as shown for mitochondria, chloroplasts or Rickettsia? To which extent have viruses been autonomous and independent of cellular metabolisms originally -and contributed to the origin of cells? Could they only later have lost their autonomy and become parasitic? Viruses are minimalistic in their composition and must have undergone stringent gene reductions (Flint, 2015) . How small can their genomes become? Most coding RNA viruses still contain regulatory elements, ncRNA at the 3 and 5 terminal regions for ribosomal entry, protein synthesis, transcriptional regulation, and others. A subgroup of retroviruses is an interesting example in respect to simultaneous loss and gain of genetic information. The oncogenic retroviruses or tumorviruses can recombine with cellular genes which under the promoters of retroviruses can become oncogenes and drivers of cancer. About a hundred oncogenes have been selected for in the laboratories and studied over decades for understanding the molecular mechanisms of cancer. Selection for growth advantages of the host cells led to the discovery of the fastest growth-promoting oncogenes we know today, such as Ras, Raf, ErbB or Myc, which are in part successful targets for anticancer drugs (Moelling et al., 1984) . These oncogenes were in most cases taken up by the retroviruses at the expense of structural (gag), replicating (pol) or envelope (env) genes, and are often expressed as fusion proteins with Gag. Thus, oncogenic retroviruses are obligatory intracellular defective viruses and were selected for in the laboratory by researchers for the oncogenes with the most potent growth promoting ability. They need the supply of replicatory genes in trans from co-infecting helper viruses to infect other cells (Flint, 2015) . Retroviruses are able to pick up cellular genes, transfer and integrate them into neighboring cells. Some strains of Rous sarcoma virus maintain replication competent when carrying the cell-derived src (for sarcoma) oncogene encoding a protein of 536 amino acids that apparently can fit into the retroviral particle along with the full-size viral genome (Broecker et al., 2016a) . Spatial reasons may have influenced the formation of oncogenic retroviruses and limited their size and thereby led to their defective phenotypes. There are indications that the uncontrolled activity of (retro)transposons in germline cells can result in diseases such as male infertility -presumably by "error catastrophe, " caused by too many transposition events. In mammals, piRNAs tame transposon activity by means of the RNase H activity of PIWI proteins during spermatogenesis (Girard et al., 2006) . Only a minority of viruses are pathogens; most of them do not cause diseases. On the contrary, they are most important as drivers of evolution, as transmitters of genetic material, as innovative agents. In particular, the RNA viruses are the most innovative ones. Some of them are pathogenic and dangerous, such as HIV or influenza virus, or viroids in plants. RNA viruses are able to change so rapidly that the host immune system is unable to counteract the infection. Pathogenicity arises when environmental conditions change, for instance, when a virus enters a new organism or species. Increase of cellular complexity by viruses is an important feature of evolution. Such major evolutionary changes are recently taken as arguments against the evolutionary theory by Charles Darwin who considered gradual changes, small increments by mutations as the main basis for selection and evolution. New criticism is addressing this thinking, considering larger changes as evolutionary drivers. Such changes arise by many complex phenomena such as endosymbiosis, infection by prokaryotes, viruses and fungi, recombination of genes, HGT, infections, sex. Dramatic changes such as endosymbiosis or pathogen infections extend Darwin's concept of evolution. There are numerous examples for the contribution of viruses to the evolution of life since at least as long as 550 MYA (Hayward, 2017) . But genetic noise through random mutations does not allow us to go back to the origin of life. It may not be impossible that the earliest compartment was indistinguishable, either a pre-cell or a pre-virus. By analogy one may speculate that at some point autonomous viruses gave up independence for an obligatory intracellular life -as has been described for mitochondria and chloroplasts but also intracellular bacteria such as Rickettsia. This speculation is based on the concept that early life must have started simple and with high genetic variability and then became more complex. But complexity can be given up for a less energy consuming lifestyle with small genomes and high speed of replication (Moelling, 2012 (Moelling, , 2013 . Therefore, the question may be repeated: "Are viruses our oldest ancestors?" Some fossil life can be partially reproduced in vitro by Spiegelman's Monster and Eigen's follow-up experiments, explaining the great surviving potential of simple ncRNA. Viruses can be pathogens, but their recognition as primarily causing diseases is wrong. This notion is based on the history of viruses in medicine, as explained in a book entitled "Viruses: More Friends Than Foes" (Moelling, 2017) . The scenario described here focuses on viruses as drivers of evolution. The early RNA world gained interest 20-30 years ago as evidenced by the references provided above. Surprisingly, there are scientists who still believe in the "pansperm hypothesis" and think that retroviruses are of extraterrestric origin (Steele et al., 2018) . The recent interest in the origin of life arose from the newly discovered exoplanets whose number increases daily -and which may be as numerous as 10 25 . Thus, pure statistics make some people believe that there is extraterrestrial life. The extraterrestric life is mimicked in laboratories on Earth with many assumptions -perhaps this overview stimulates some thinking. The discussion presented here should be taken as concept about simple replicating and evolving entities possibly arising from different building blocks in other environments, with structure being more relevant than sequence.

Which are the most abundant biological entities on Earth?

MERS coronavirus: diagnostics, epidemiology and transmission https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4687373/ SHA: f6fcf1a99cbd073c5821d1c4ffa3f2c6daf8ae29 Authors: Mackay, Ian M.; Arden, Katherine E. Date: 2015-12-22 DOI: 10.1186/s12985-015-0439-5 License: cc-by Abstract: The first known cases of Middle East respiratory syndrome (MERS), associated with infection by a novel coronavirus (CoV), occurred in 2012 in Jordan but were reported retrospectively. The case first to be publicly reported was from Jeddah, in the Kingdom of Saudi Arabia (KSA). Since then, MERS-CoV sequences have been found in a bat and in many dromedary camels (DC). MERS-CoV is enzootic in DC across the Arabian Peninsula and in parts of Africa, causing mild upper respiratory tract illness in its camel reservoir and sporadic, but relatively rare human infections. Precisely how virus transmits to humans remains unknown but close and lengthy exposure appears to be a requirement. The KSA is the focal point of MERS, with the majority of human cases. In humans, MERS is mostly known as a lower respiratory tract (LRT) disease involving fever, cough, breathing difficulties and pneumonia that may progress to acute respiratory distress syndrome, multiorgan failure and death in 20 % to 40 % of those infected. However, MERS-CoV has also been detected in mild and influenza-like illnesses and in those with no signs or symptoms. Older males most obviously suffer severe disease and MERS patients often have comorbidities. Compared to severe acute respiratory syndrome (SARS), another sometimes- fatal zoonotic coronavirus disease that has since disappeared, MERS progresses more rapidly to respiratory failure and acute kidney injury (it also has an affinity for growth in kidney cells under laboratory conditions), is more frequently reported in patients with underlying disease and is more often fatal. Most human cases of MERS have been linked to lapses in infection prevention and control (IPC) in healthcare settings, with approximately 20 % of all virus detections reported among healthcare workers (HCWs) and higher exposures in those with occupations that bring them into close contact with camels. Sero-surveys have found widespread evidence of past infection in adult camels and limited past exposure among humans. Sensitive, validated reverse transcriptase real-time polymerase chain reaction (RT-rtPCR)-based diagnostics have been available almost from the start of the emergence of MERS. While the basic virology of MERS-CoV has advanced over the past three years, understanding of the interplay between camel, environment, and human remains limited. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12985-015-0439-5) contains supplementary material, which is available to authorized users. Text: An email from Dr Ali Mohamed Zaki, an Egyptian virologist working at the Dr Soliman Fakeeh Hospital in Jeddah in the Kingdom of Saudi Arabia (KSA) announced the first culture of a new coronavirus to the world. The email was published on the website of the professional emerging diseases (ProMED) network on 20 th September 2012 [1] (Fig. 1) and described the first reported case, a 60 year old man from Bisha in the KSA. This information led to the rapid discovery of a second case of the virus, this time in an ill patient in the United Kingdom, who had been transferred from Qatar for care [2] . The new virus was initially called novel coronavirus (nCoV) and subsequentlty entitled the Middle East respiratoy syndrome coronavirus (MERS-CoV). As of 2 nd of September 2015, there have been 1,493 detections of viral RNA or virus-specific antibodies across 26 countries (Additional file 1: Figure S1 ) confirmed by the World Health Organization (WHO), with over a third of the positive people dying (at least 527, 35 %) [3] . Since that first report, a slow discovery process over the following two to three years revealed a virus that had infected over 90 % of adult dromedary camels (DC; Camelus dromedarius) in the KSA [4] , also DCs across the Arabian Peninsula and parts of Africa that are a source of DC imports for the KSA [5] . To date, MERS-CoV has not been detected in DCs tested in zoos or herds from other parts of the world [6] [7] [8] [9] . Occasionally, virus is transmitted from infected DCs to exposed humans. Subsequent transmission to other humans requires relatively close and prolonged exposure [10] . The first viral isolate was patented and concerns were raised that this would restrict access to both the virus and to viral diagnostics [11, 12] . However, sensitive, validated reverse transcriptase real-time polymerase chain reaction (RT-rtPCR)-based diagnostics were quickly described and virus was made freely available subject to routine biosafety considerations [13] . Subsequent epidemiology and research has identified the cell receptor as exopeptidase dipeptidyl peptidase 4 (DPP4; also called CD26); that MERS-CoV has a broad tropism, replicating better in some cells lines and eliciting a more proinflammatory response than SARS-CoV; is widespread in DCs; has the potential to infect other animals and that MERS kills its human host more often than SARS did (20-40 % versus 9 % for SARS [14] ) [15] [16] [17] [18] [19] . In humans, overt disease was given the name Middle East respiratory syndrome, with the acronym MERS. From intermittent animal-to-human spill-over events, the MERS-CoV spreads sporadically among people, causing more severe disease among older adults, especially males, with pre-existing diseases. The spread of MERS-CoV among humans has often been associated with outbreaks in hospitals, with around 20 % of all cases to date involving healthcare workers (HCWs). Although DCs appear to suffer the equivalent of a 'common cold' from MERS-CoV infection, in humans, the virus can be a more serious and opportunistic pathogen associated with the death of up to 40 % of reported cases. It has yet to be established whether infections thought to have been acquired from an animal source produce a more severe outcome than those spread between humans [20] . Studies have established that the mean incubation period for MERS is five to six days, ranging from two to 16 days, with 13 to 14 days between when illness begins in one person and subsequently spreads to another [21] [22] [23] [24] . Among those with progressive illness, the median time to death is 11 to 13 days, ranging from five to 27 days [23, 24] . Fever and gastrointestinal symptoms may form a prodrome, after which symptoms decline, only to be followed by a more severe systemic and respiratory syndrome [25, 26] . The first WHO case definition [27] defined probable cases of MERS based on the presence of febrile illness, cough and requirement for hospitalization with suspicion of lower respiratory tract (LRT) involvement. It also included roles for contact with a probable or confirmed case or for travel or residence within the Arabian Peninsula. If strictly adhered to, only the severe syndrome would be subject to laboratory testing, which was the paradigm early on [21] . From July 2013, the revised WHO case definition included the importance of seeking out and understanding the role of asymptomatic cases and from June 2014, the WHO definition more clearly stated that a confirmed case included any person whose sample was RT-PCR positive for MERS-CoV, or who produced a seroconversion, irrespective of clinical signs and symptoms. [28] [29] [30] Apart from the WHO and the KSA Ministry of Health reports, asymptomatic or subclinical cases of MERS-CoV infection were documented in the scientific literature although not always as often as occurred early on [31, 32] . The KSA definition of a case became more strict on 13 th May 2014, relying on the presence of both clinical features and laboratory confirmation [33] . Testing of asymptomatic people was recommended against from December 2014 [34] , reinforced by a case definition released by the KSA Ministry of Health in June 2015 [35] . The KSA has been the source of 79 % of human cases. Severe MERS is notable for its impact among older men with comorbid diseases including diabetes mellitus, cirrhosis and various lung, renal and cardiac conditions [36] [37] [38] . Interestingly in June 2015, an outbreak in South Korea followed a similar distribution [39, 40] . Among laboratory confirmed cases, fever, cough and upper respiratory tract (URT) signs and symptoms usually occur first, followed within a week by progressive LRT distress and lymphopaenia [37] . Patients often present to a hospital with pneumonia, or worse, and secondary bacterial infections have been reported [37, 41] . Disease can progress to acute respiratory distress syndrome and multiorgan system failure [37] . MERS has reportedly killed approximately 35 % of all reported cases, 42 % of cases in the KSA, yet only 19 % of cases in South Korea, where mortality ranged from 7 % among younger age groups to 40 % among those aged 60 years and above [42] ; all may be inflated values with asymptomatic or mild infections sometimes not sought or not reported [34] . General supportive care is key to managing severe cases [43] . Children under the age of 14 years are rarely reported to be positive for MERS-CoV, comprising only 1.1 % (n = 16) of total reported cases. Between 1 st September 2012 and 2 nd December 2013, a study described the then tally of paediatric cases in the KSA, which stood at 11 (two to 16 years of age; median 13 years); nine were asymptomatic (72 %) and one infant died [44] . In Amman, Jordan, 1,005 samples from hospitalized children under the age of two years with fever and/or respiratory signs and symptoms were tested but none were positive for MERS-CoV RNA, despite being collected at a similar time to the first known outbreak of MERS-CoV in the neighbouring town of Al-Zarqa [45] . A second trimester stillbirth occurred in a pregnant woman during an acute respiratory illness and while not RT-rtPCR positive, the mother did subsequently develop antibodies to MERS-CoV, suggestive of recent infection [46] . Her exposure history to a MERS-CoV RT-rtPCR positive relative and an antibody-reactive husband, her incubation period and her symptom history met the WHO criteria for being a probable MERS-CoV case [46] . Diagnostic methods were published within days of the ProMED email announcing the first MERS case [47] , including several now gold standard in-house RT-rtPCR assays (Fig. 2 ) as well as virus culture in Vero and LLC-MK2 cells [18, 47, 48] . A colorectal adenocarcinoma (Caco-2) epithelial cell line has since been recommended for isolation of infections MERS-CoV [49] . We previously [18] .). Open reading frames are indicated as yellow rectangles bracketed by terminal untranslated regions (UTR; grey rectangles). FS-frame-shift. Predicted regions encompassing recombination break-points are indicated by orange pills. Created using Geneious v8.1 [211] and annotated using Adobe Illustrator. Beneath this is a schematic depicting the location of RT-PCR primers (blue arrows indicate direction) and oligoprobes (green rectangles) used in the earliest RT-rtPCR screening assays and conventional, semi-nested (three primers) RT-PCR confirmatory sequencing assays [47, 48] . Publication order is noted by first [27 th September 2012; red] and second [6 th December 2012; orange] coloured rectangles; both from Corman et al. [47, 48] Those assays recommended by the WHO are highlighted underneath by yellow dots [53] . The NSeq reverse primer has consistently contained one sequence mismatch with some MERS-CoV variants. An altered version of that from Mackay IM, Arden KE. Middle East respiratory syndrome: An emerging coronavirus infection tracked by the crowd. Virus Res 2015 Vol 202:60-88 with permission from Elsevier [5] reviewed the broad tropism of MERS-CoV [5] . However, as is well described, cell culture is a slow, specialised and insensitive method [50] while PCR-based techniques are the preferred method for MERS-CoV detection. The first open reading frames (ORF 1a and 1b; Fig. 2 ) have become a key diagnostic and taxonomic target for CoV species identification. With less than 80 % identity between the amino acid sequence of MERS ORF 1ab and betacoronavirus relatives, Tylonycteris bat HKU4 and Pipistrellus bat HKU5, it can be concluded that it is a novel and distinct virus. MERS-CoV is predicted to encode ten open reading frames with 5' and 3' untranslated regions [51] . The structural proteins include the spike (S), envelope (E), membrane (M) and nucleocapsid (N) [52] . The products of ORF1a and ORF1b are predicted to encode nonstructural proteins. The majority of specimen testing to date has employed validated RT-rtPCR assays shown to be sensitive and specific [47, 48, 53] . The RealStar® kit uses these WHOrecommended assays [54] . The target sequences of these screening assays have not changed among genomes examined until at least mid-2015 (IMM observation). Other RT-rtPCR assays have been developed and validated for use as laboratory-based diagnostic tools [55] [56] [57] . Additionally, loop-mediated [58, 59] or recombinase polymerase [60] isothermal assays have been designed for field deployment. The detection of MERS-CoV antigen has not been common to date but the combination of short turnaround time from test to result, high throughput and identification of viral proteins makes this an attractive option. Detection of viral proteins rather than viral RNA indicates the likely presence of infectious virus. The first rapid immunochromatographic tool described could detect recombinant MERS-CoV nucleocapsid protein from DC nasal swabs with 94 % sensitivity and 100 % specificity compared to RT-rtPCR [61] . A different approach used a monoclonal antibody-based capture ELISA targeting the MERS-CoV nucleocapsid protein with a sensitivity of 10 3 TCID 50 and 100 % specificity [62] . Demonstration of a seroconversion to a MERS-CoV infection meets the current WHO definition of a case so optimized and thoroughly validated sero-assays employed alongside good clinical histories are useful to both identify prior MERS-CoV infection and help support transmission studies. Because serology testing is, by its nature, retrospective, it is usual to detect a viral footprint, in the form of antibodies, in the absence of any signs or symptoms of disease and often in the absence of any viral RNA [63] . Strategic, widespread sero-surveys of humans using samples collected after 2012 are infrequent. Much of the Arabian Peninsula and all of the Horn of Africa lack baseline data describing the proportion of the community who may have been infected by a MERS-CoV. However, sero-surveys have had widespread use in elucidating the role of DCs as a transmission source for MERS-CoV. Because of the identity shared between DC and human MERS-CoV (see Molecular epidemiology: using genomes to understand outbreaks), serological assays for DC sero-surveys should be transferrable to human screening with minimal re-configuration. Also, no diagnostically relevant variation in neutralization activity have been found from among a range of circulating tested MERS-CoV isolates and sera, so whole virus or specific protein-based sero-assays should perform equivalently in detecting serological responses to the single MERS-CoV serotype [49] . The development of robust serological assays requires reliable panels of wellcharacterized animal or human sera, including those positive for antibodies specific to MERS-CoV, as well as to likely sources of cross-reaction [64] . Obtaining these materials was problematic and slowed the development and commercialization of antibody detection assays for human testing [64] . A number of commercial ELISA kits, immunofluorescent assays (IFA) kits, recombinant proteins and monoclonal antibodies have been released [31, [65] [66] [67] [68] . Initially, conventional IFAs were used for human sero-surveys. These relied on MERS-CoV-infected cell culture as an antigen source, detecting the presence of human anti-MERS-CoV IgG, IgM or neutralizing antibodies in human samples [18, 48, 69] . No sign of MERS-CoV antibodies was found among 2,400 sera from patients visiting Hospital in Jeddah, from 2010 through 2012, prior to the description of MERS-CoV [18] . Nor did IFA methods detect any sign of prior MERS-CoV infection among a small sample of 130 healthy blood donors from another Hospital in Jeddah (collected between Jan and Dec 2012) [70] . Of 226 slaughterhouse workers, only eight (3.5 %) were positive by IFA, and those sera could not be confirmed by virus neutralization (NT) test. The study indicated that HCoV-HKU1 was a likely source of crossreactive antigen in the whole virus IFA [70] . Whole virus MERS-CoV IFA also suffered from some cross-reactivity with convalescent SARS patient sera and this could not be resolved by an NT test which was also cross-reactive [71] . IFA using recombinant proteins instead of whole-virus IFA, has been shown to be a more specific tool [31] . Since asymptomatic zoonoses have been posited [72] , an absence of antibodies to MERS-CoV among some humans who have regular and close contact with camels may reflect the rarity of actively infected animals at butcheries, a limited transmission risk associated with slaughtering DCs [70] , a pre-existing cross-protective immune status or some other factor(s) resulting in a low risk of disease and concurrent seroconversion developing after exposure in this group. IFA using recombinant proteins instead. Some sero-assays have bypassed the risks of working with infectious virus by creating transfected cells expressing recombinant portions of the MERS-CoV nucleocapsid and spike proteins [48, 73] , or using a recombinant lentivirus expressing MERS-CoV spike protein and luciferase [74, 75] . A pseudo particle neutralization (ppNT) assay has seen widespread used in animal studies and was at least as sensitive as the traditional microneutralization (MNT) test. [10, 74, [76] [77] [78] ] Studies using small sample numbers and ppNT found no evidence of MERS-CoV neutralizing antibody in sera from 158 children with LRT infections between May 2010 and May 2011, 110 sera from 19 to 52 year old male blood donors and 300 selfidentified animal workers from the Jazan Region of the KSA during 2012 [79, 80] . Similarly, a study of four herdsmen in contact with an infected DC herd in Al-Ahsa, eight people who had intermittent contact with the herd, 30 veterinary surgeons and support staff who were not exposed to the herd, three unprotected abattoir workers in Al-Ahsa and 146 controls who were not exposed to DCs in any professional role, found none with serological evidence of past MERS-CoV infection using the ppNT assay [10] . A delay in the neutralizing antibody response to MERS-CoV infection was associated with increased disease severity in South Korea cases with most responses detectable by week three of illness while others, even though disease was severe, did not respond for four or more weeks [81] . The implications for our ability to detect any response in mild or asymptomatic cases was not explored but may be a signifcant factor in understanding exposure in the wider community. A Jordanian outbreak of acute LRT disease in a hospital in 2012 was retrospectively found to be associated with MERS-CoV infection, initially using RT-rtPCR, but subsequently, and on a larger scale, through positivity by ELISA and IFA or MNT test. [46, 82, 83] This outbreak predated the first case of MERS in the KSA. The ELISA used a recombinant nucleocapsid protein from the group 2 betacoronavirus bat-CoV HKU5 to identify antibodies against the equivalent crossreactive MERS-CoV protein [71] . It was validated using 545 sera collected from people with prior HCoV-OC43, HCoV-229E, SARS-CoV, HCoV-NL63, HRV, HMPV or influenza A(H1N1) infections but was reportedly less specific than the recombinant IFA discussed above. It was still considered an applicable tool for screening large sample numbers [82] . A protein microarray expressing the S1 protein subunit has also been validated and widely used for DC testing [5, 84] . Detection of MERS-CoV infection using ELISA or S1 subunit protein microarray [84] is usually followed by confirmatory IFA and/ or a plaque-reduction neutralization (PRNT) [69, 70, 85] or MNT test. [74, 85, 86] This confirmatory process aims toensure the antibodies detected are able to specifically neutralize the intended virus and are not more broadly reactive to other coronaviruses found in DCs (bovine CoV, BCoV) or humans (HCoV-OC43, HCoV-229E, HCoV-NL63, HCoV-HKU1, SARS-CoV). In the largest study of human sera, a tiered diagnostic process assigned both recombinant IFA and recombinant ELISA positive sera to 'stage 1' seropositivity. A stage 2 seropositive result additionally required a suitably titred PRNT result [87] . The study found 15 sera collected in 2012 to 2013 from 10,009 (0.2 %) people in 13 KSA provinces contained MERS-CoV antibodies, but significantly higher proportions in occurred in camel shepherds (two of 87; 2.3 %) and slaughterhouse workers (five of 140; 3.6 %) [87] . Contemporary surveys are needed. MERS-CoV does not appear to be easily transmitted from DCs to humans, or perhaps it is [72] , but generally does not trigger a detectable immune response if only mild disease or asymptomatic infection results. Serology assays are in need of further validation in this area so care is required when moving newly developed diagnostic serology algorithms from a research setting to one that informs public health decisions. This was reinforced when a false positive US case, purported to have been infected after a handshake and two face-to-face meetings, did not withstand further confirmatory analysis using a more specific, NT assay and was subsequently retracted [88, 89] . The WHO recommends sampling from the LRT for MERS-CoV RT-rtPCR testing, especially when sample collection is delayed by a week or more after onset of symptoms. [53] LRT samples are also best for attempting isolation of infectious virus, although the success of culture is reduced when disease persists [49] . Recommended sample types include bronchoalveolar lavage (BAL), tracheal/tracheobronchial aspirate, pleural fluid and sputum [53, 90] . Fresh samples yield better diagnostic results than refrigerated material [69] and if delays in testing of ≥72 h are likely, samples (except for blood) should be frozen at −70°C [90] . If available, lung biopsy or autopsy tissues can also be tested [53] . The URT is a less invasive and more convenient sampling site however, and an oropharyngeal and throat swab or a nasopharyngeal aspirate/wash are recommended when URT sampling is to be conducted [90] . Paired sera, collected two to three weeks apart are preferable for serological testing while a single sample is suggested to be sufficient if collected two weeks after onset of disease or a single serum collected during the first 10-12 days if conducting RT-rtPCR [53, 90] . Human urine and stool have been found to contain MERS-CoV RNA 12 to 26 days after symptom onset [25, 69, 91] and are listed as samples that should be considered [53, 90] . In two cases that arrived in the Netherlands, urine was RT-rtPCR negative but faeces was weakly positive and sera were RT-rtPCR positive for five days or more [25] . The finding of MERS-CoV viral RNA in serum provides an avenue for retrospective PCR-based studies if respiratory samples are unavailable [83] . RNAaemia may also correlate with disease severity; signs of virus were cleared from the serum of a recovered patient, yet lingered until the death of another [92] . Clinically suspected MERS cases may return negative results by RT-rtPCR. Data have shown one or more negative URT samples may be contradicted by further URT sampling or the use of LRT samples, which is preferred [2, 43, 93] . Higher viral loads occur in the LRT compared to the URT. [22, 69, 88, 94] This fits with the observation that the majority of disease symptoms are reported to manifest as systemic and LRT disease [21] . However, on occasion, even LRT specimens from MERS cases may initially be negative, only to later become positive by RT-PCR [95] . This may be due to poor sampling when a cough is absent or non-productive or because the viral load is low [95] . Despite this both the largest human MERS-CoV studies [32, [96] [97] [98] and smaller ones [22, 25, 99] , use samples from the URT. It is then noteworthy that one study reported an association between higher loads in the URT and worse clinical outcome including intensive care and death [94] . At writing, no human data exist to define whether the virus replicates solely or preferentially in the LRT or URT, or replicates in other human tissues in vivo although MERS-CoV RNA has been detected from both the URT and LRT in a macaque monkey model [100] .The distribution of DPP4 in the human upper airways is also not well described. Individual human case studies report long periods of viral shedding, sometimes intermittently and not necessarily linked to the presence of disease symptoms. [25, 69, 99, 101] In one instance, a HCW shed viral RNA for 42 days in the absence of disease [99] . It is an area of high priority to better understand whether such cases are able to infect others. Over three quarters of MERS cases shed viral RNA in their LRT specimens (tracheal aspirates and sputum) for at least 30 days, while only 30 % of contacts were still shedding RNA in their URT specimens [91, 102] . In the only study to examine the effect of sample type on molecular analysis, 64 nasopharyngeal aspirates (NPA; an URT sample), 30 tracheal aspirates, 13 sputa and three BAL were examined. The tracheal aspirates and BAL returned the highest viral load values followed by NPA and sputum. Unsurprisingly, higher viral loads generally paralleled whole genome sequencing and culture success and, in NPA testing, were significantly correlated with severe disease and death [49, 94, 103] . This study demonstrated the importance of LRT sampling for whole genome sequencing. When tested, samples positive for MERS-CoV are often negative for other pathogens [2, 25, 93, 104] . However, many studies make no mention of additional testing for endemic human respiratory viruses [21, 23, 73, 105] . When viruses are sought, they have included human herpesvirus (HHV), rhinoviruses (HRV), enteroviruses (EV), respiratory syncytial virus (RSV), parainfluenzavirus types 1, 2 and 3 (PIVs),influenzaviruses (IFVs), endemic HCoVs, adenoviruses (AdVs) metapneumovirus (MPV) and influenza A\H1N1 virus; co-detections with MERS-CoV have been found on occasion [2, 22, 37, 69, 97] . Bacterial testing is sometimes included (for example, for Legionella and Pneumococcus) but the impact of bacterial co-presence is also unclear [22, [104] [105] [106] . Further testing of the LRT sample from the first MERS case used IFA to screen for some viruses (negative for IFV, PIVs, RSV and AdVs) and RT-PCR for others (negative for AdV, EVs, MPV and HHVs) [18] . RT-PCR also detected MERS-CoV. The WHO strongly recommends testing for other respiratory pathogens [53] but with this recommendation often discounted, there are limited data to address the occurrence and impact of co-infections or alternative viral diagnoses among both MERS cases and their contacts. Little is known of other causes of MERS-like pneumonia in the KSA or of the general burden of disease due to the known classical respiratory viruses. Testing of adult pilgrims performing the Hajj in 2012 to 2014 has not detected any MERS-CoV. In 2012, nasal swabs from 154 pilgrims collected prior to leaving for or departing from the KSA were tested [47] . In 2013, testing was significantly scaled up with 5,235 nasopharyngeal swabs from 3,210 incoming pilgrims and 2,025 swabs from outgoing pilgrims tested [98] . It should be noted that most pilgrims arrived from MERS-free countries. A further 114 swabs were taken from pilgrims with influenza-like illness [96, 107] . In earlier Hajj gatherings, it was found that influenza viruses circulated widely, whilst other viruses, often rhinoviruses, circulated more selectively, interpreted as indicating their importation along with foreign pilgrims. [107] [108] [109] Over time, increased influenza vaccination has been credited for a fall in the prevalence of influenza like illnesses among Hajj pilgrims. [110] A LRT sample is often not collected for these studies [98, 107, 109] , so false negative findings are a possibility although little is known about the initial site of MERS-CoV infection and replication; it may have been assumed it was the LRT because disease was first noticed there but the URT may be the site of the earliest replication. In Jeddah between March and July 2014 (hereafter called the Jeddah-2014 outbreak; Fig. 3 ), there was a rapid increase in MERS cases, accompanied by intense screening; approximately 5,000 samples from in and around the region were tested in a month yielding around 140 MERS-CoV detections (~3 % prevalence) [111] . Among 5,065 individuals sampled and tested across the KSA between October 2012 and September 2013,108 (2.1 %) detections were made in a hospital-centric population which included hospitalized cases (n = 2,908; 57.4 %), their families (n = 462; 9.1 %) and associated HCWs (n = 1,695; 33.5 %) [32] . Among the detections, 19 (17.8 %) were HCWs and 10 (9.3 %) were family contacts [32] . The 2-3 % prevalence of active MERS-CoV infections is not dissimilar to the hospital-based prevalence of other human CoVs. [112] However, the proportion of deaths among those infected with MERS-CoV is much higher than that known for the HCoVs NL63, HKU1, 229E or OC43 in other countries, and even above that for SARS-CoV; it is not a virus that could reasonably be described as a "storm in a teacup". It is the low transmission rate that has prevented worldwide spread, despite many "opportunities". Very early in the MERS outbreak, some animals were highly regarded as either the reservoir or intermediate host(s) of MERS-CoV with three of the first five cases having contact with DCs [73, 113, 114] . Today, animal MERS-CoV infections must be reported to the world organization for animal health as an emerging disease [115] . A summary of the first MERS cases reported by the WHO defined animal contact with humans as being direct and within 10 days prior to symptom onset [20] . This definition made no specific allowance for acquisition from DCs through a droplet-based route, which is very likely route for acquisition of a virus that initially and predominantly causes respiratory disease [23] . Camels are known to produce high levels of MERS-CoV RNA in their URT and lungs [116] . Providing support for a droplet transmission route and perhaps indicating the presence of RNA in smaller, drier droplet nuclei, MERS-CoV RNA was identified in a high volume air sample collected from a barn housing an infected DC [117] . The precise source from which humans acquire MERS-CoV remains poorly studied but it seems likely that animal and human behavioural factors may play roles (Fig. 3) [118] . These factors may prove important for human cases who do not describe any DC contact [119] nor any contact with a confirmed case. Whether the WHO definition of animal contact is sufficient to identify exposure to this respiratory virus remains unclear. Wording focuses on consumption of DC products but does not specifically ascribe risk to a droplet route for acquisition of MERS-CoV from DC [120] . Some MERS patients are listed in WHO disease notices as being in proximity to DCs or farms, but the individuals have not described coming into contact with the animals. No alternative path for acquiring infection is reported in many of these instances. What constitutes a definition of "contact" during these interviews has been defined for one study [72] . Despite this lack of clarity, the WHO consider that evidence linking MERS-CoV transmission between DCs to humans is irrefutable (Fig. 4) [120] . The possibility that bats were an animal host of MERS-CoV was initially widely discussed because of the existing diversity of coronaviruses known to reside among them [121] [122] [123] [124] . Conclusive evidence supporting bats as a source for human infections by MERS-CoV has yet to be found, but bats do appear to host ancestral representatives [53, 125] . However, these are not variants of the same virus nor always within the same phylogenetic lineage as MERS-CoV; they are each a genetically distinct virus. Bat-to-human infection by MERS-CoV is a purely speculative event. The only piece of MERS-CoV-specific evidence pointing to bats originates from amplification of a 190 nt fragment of the RNAdependent RNA polymerase gene of the MERS-CoV genome, identified in a faecal pellet from an insectivorous Emballonuridae bat, Taphozous perforatus found in Bisha, the KSA [121] . While very short, the sequence of the fragment defined it as a diagnostic discovery. Subsequently a link to DCs was reported [85] and that link has matured into a verified association [38, 126] (Fig. 4) . (See figure on previous page.) Fig. 3 Monthly detections of MERS-CoV (blue bars) and of cases who died (red bars) with some dates of interest marked for 2012 to 4 th September 2015. An approximation of when DC calving season [128] and when recently born DCs are weaned is indicated. Spring (green) and summer (orange) in the Arabian Peninsula are also shaded. Note the left-hand y-axis scale for 2014 and 2015 which is greater than for 2012/13. Sources of these public data include the WHO, Ministries of Health and FluTrackers [207] [208] [209] . Earlier and subsequent versions of this chart are maintained on a personal blog [210] . Modified and reprinted from Mackay IM, Arden KE. Middle East respiratory syndrome: An emerging coronavirus infection tracked by the crowd. Virus Res 2015 Vol 202:60-88 with permission from Elsevier [5] DCs, which make up 95 % of all camels, have a central presence in the Arabian Peninsula where human-DC contact ranges from little to close [119] . Contact may be commonplace and could occur in variety of ways (Fig. 4a) . There are several large well-attended festivals, races, sales and parades which feature DCs and DCs are also kept and bred close to populated areas in the KSA [127, 128] . DC milk and meat are widely consumed and the older DC is an animal of ritual significance after the Hajj pilgrimage [129] . However, MERS-CoV infection frequency is reportedly much lower than is the widespread and frequent habit of eating, drinking and preparing DC products. Daily ingestion of fresh unpasteurized DC milk is common among the desert Bedouin and many others in the KSA. DC urine is also consumed or used for supposed health benefits. Despite camel butchery being a local occupation, neither butchers nor other at-risk groups are identifiable among MERS cases; this may simply be a reporting issue rather than an unexplainable absence of MERS. A small case-control study published in 2015 identified direct DC contact, and not ingestion of products, to be associated with onset of MERS [38] . The first sero-survey of livestock living in the Middle East region was conducted during 2012-2013 [85] . DCs were sampled from a mostly Canary Island-born herd and from Omani DCs (originally imported from the Horn of Africa) [85] . A neutralising antibody assay found only 10 % of strongly seropositive Canary Island [5] . b Camel-to-human infections appear to be infrequent, while human-to-human spread of infection is regularly facilitated by poor IPC in healthcare settings where transmission is amplified, accounting for the bulk of cases. There are human MERS cases that do not fall into either category of source and it is unclear if these acquired infection through some entirely separate route, or from cases that escaped diagnosis. c Hypothetical ways in which subclinical (when infection may not meet a previously defined clinical threshold of signs and/or symptoms) or asymptomatic (no obvious signs or measured, noticed or recalled symptoms of illness) MERS-CoV infection may be implicated in transmission DC sera could neutralise MERS-CoV while all Omani DC sera had high levels of specific MERS-CoV neutralizing antibody [85] . This indicated that DCs had in the past been infected by MERS-CoV, or a very similar virus. Since this study, a host of peer-reviewed reports have looked at both DCs and other animals, and the possibility that they may host MERS-CoV infection. Seropositive DCs have been found throughout the Arabian Peninsula including Oman, the KSA, Qatar, Jordan, the United Arab Emirates (UAE), Kuwait as well as Sudan, Somalia, Egypt, Tunisia, Nigeria, Kenya and Ethiopia in Africa and the Canary Islands [85, [130] [131] [132] [133] [134] . Other animals tested include sheep, cows, pigs, horses, donkeys, mules, birds, water buffalo, goats, Bactrian camels, llamas and guanaco (south American camelids) but none had detectable neutralising antibody against MERS-CoV [4, 74, 78, 85, 86, 135, 136] . No virology or serology studies of human samples from areas in Africa where there are camels with a history of MERS-CoV have been reported to date. However,an absence of unexplained pneumonia that may be attributable to MERS-CoV infection may not signal the absence of virus among humans in each country but simply reflect a lack of expensive epidemiology studies conducted by resource-poor countries. It is thus unclear whether MERS-CoV, or an antigenically related CoV, is an unrecognized pathogen in these regions, perhaps circulating for even longer than it has been known in the Arabian Peninsula [133] . MERS-CoV RNA has also been detected in DC samples, and recovery of infectious virus has also been achieved from DC samples [4, 77, 117, 132, [137] [138] [139] [140] [141] . From some of these, full or majority length genomes of MERS-CoV have been sequenced [77, 137, 138] . DC versions of MERS-CoV were found to be as similar to each other, as were variants detected from different humans over time and across distance. Antibody screening assays have also detected crossreactive antibodies in sera. These were identified as such by screening sera against similar viruses, for example BCoV or HCoV-OC43 (as an antigenic facsimile for BCoV). It is possible that other MERS-CoV-like viruses also reside within DCs, but this does not detract from the definitive finding of MERS-CoV genetic sequences in both DCs and humans [117, 142, 143] . Screening studies have shown that juvenile DCs are more often positive for virus or viral RNA while older DCs are more likely to be seropositive and RNA or virus negative [76, 77, 144] . In adult DCs, MERS-CoV RNA has been detected among animals with pre-existing antibody, suggesting re-infection is possible [77, 144] . Viral loads among positive DCs can be very high [4, 76, 77, 139, 144] and DCs have been found positive both when ill with URT respiratory signs [77, 117, 142, 145] or when apparently healthy [137] . These findings indicate DCs host natural MERS-CoV infections. Furthermore, stored DC sera have revealed signs of MERS-CoV in DCs which date back over three decades (the earliest collected in 1983) [4, 133, 135] . Older sera have not been tested and so precisely how long DCs have been afflicted by MERS-CoV, whether the virus is enzootic among them, introduced to them decades or centuries ago from bats in Africa or the Arabian Peninsula, or they are the subject of regular but short-lived viral incursions from an as yet unknown host, cannot be answered. Researchers sought to determine a direction for infection; were DCs transmitting virus to humans or were humans infecting DCs? At a Qatari site, a farm owner and his employee became ill in mid-October 2013 and tested positive for MERS-CoV RNA in a sputum and throat swab sample, respectively. RT-rtPCRs found MERS-CoV RNA in 11 of 14 positive DC nasal swabs at the farm; six (43 %) positive by two or more assays [138] . The results indicated a recent outbreak had occurred in this herd; the first indication of MERS-CoV RNA found within DCs with a temporal association to human infections. Three positive DC samples were confirmed by sequencing a 358 nt portion of the spike gene; these sequences were identical to each other, again with close homology to other human and DC MERS-CoV sequences [138] . The DCs and human contacts yielded ORF1a and ORF4b sequences differing by only a single nucleotide each, clustering closely with the Hafr-Al-Batin_1_2013 variant [138] . Subsequent case studies found evidence of a concurrent human and DC infection and the direction of that infection was inferred to be from the ill DCs and to their human owners [117, 142, 146] . Partial genome sequences indicated that a human and a MERS-CoV RT-rtPCR positive DC had been infected by a variant of the same virus, harbouring the same distinct pattern of nucleotide polymorphisms. [142] All nine DC in the owner's herd, serially sampled, reacted in a recombinant S1 antigen ELISA, with the two animals that had been RT-rtPCR positive showing a small, verifiable rise in antibody titre [142] . A rise in titre theoretically begins 10 to 21 days after DC infection [142] . The authors suggested that the rise in titre in DC sera which occurred alongside a declining RNA load, while the patient was actively ill and hospitalized, indicated that the DCs were infected first followed by the owner [117, 142] . BCoV antibodies were also present, and rising in one of the two RT-rtPCR positive animals but no animal's antibodies could neutralise BCoV infection [142] . Camel calving season occurs in the winter months (between late October and late February; Fig. 3 ) and this may be a time when there is increased risk to humans of spill-over due to new infections among naïve DC populations [128] . What role maternal camel antibody might play in delaying infection of calves remains unknown [128, 142] . Juvenile DCs appear to host active infection more often than adult DCs and thus the sacrificial slaughter of DCs, which must be five years of age or older (termed a thane), may not be accompanied by significant risk of exposure to infection. In contrast to earlier results, slaughterhouse workers who kill both younger and older DCs, may be an occupational group with significantly higher incidence of seropositivity to MERS-CoV when animals have active MERS-CoV infections [129, 139, [147] [148] [149] . Expanded virological investigations of African DCs may lead to more seropositive animals and geographic areas in which humans may be at risk. It is possible that there are areas where humans already harbour MERS-CoV infections that have not been identified because of an absence of laboratory surveillance. Virological investigations of bats may lead to findings of ancestral viruses and viral 'missing links' and identifying any other animal sources of zoonotic spread is important to inform options for reducing human exposures [56, 76] . Infectious MERS-CoV added to DC, goat or cow milk and stored at 4°C could be recovered at least 72 h later and, if stored at 22°C, recovery was possible for up to 48 h [150] . MERS-CoV titre decreased somewhat when recovered from milk at 22°C but pasteurization completely ablated MERS-CoV infectivity [150] . In a subsequent study, MERS-CoV RNA was identified in the milk, nasal secretion and faeces of DCs from Qatar [151] . A single study has examined the ability of MERS-CoV to survive in the environment [150] . Plastic or steel surfaces were inoculated with 10 6 TCID 50 of MERS-CoV at different temperature and relative humidity (RH) and virus recovery was attempted in cell culture. At high ambient temperature (30°C) and low RH (30 %) MERS-CoV remained viable for 24 h [150] . By comparison, a well known and efficently transmitted respiratory virus, influenza A virus, could not be recovered in culture beyond four hours under any conditions [150] . Aerosol experiments found MERS-CoV viability only decreased 7 % at low RH at 20°C. In comparison, influenza A virus decreased by 95 % [150] . MERS-CoV survival is inferior to that previously demonstrated for SARS-CoV [152] . For context, pathogenic bacteria can remain viable and airborne for 45 min in a coughed aerosol and can spread 4 m. MERS-CoV's ability to remain viable over long time periods gives it the capacity to thoroughly contaminate a room's surfaces when occupied by an infected and symptomatic patient [153] . Whether MERS-CoV can remain adrift and infectious for extended periods (truly airborne) remains unknown. Such findings expand our understanding of the possibilities for droplets to transmit respiratory viruses in many settings, including hospital waiting rooms, emergency departments, treatment rooms, open intensive care facilities and private patient rooms. The nature and quality of air exchange, circulation and filtration are important variables in risk measurement and reduction as is the use of negative pressure rooms to contain known cases. Droplet spread between humans is considered the mechanism of human-to-human transmission and the need for droplet precautions was emphasized after the Al-Ahsa hospital, the KSA and the South Korean outbreaks [21, 23, 154, 155] . By extrapolation, aerosol-generating events involving DCs (urination, defecation, and preparation and consumption of DC products) should be factored into risk measurement and reduction efforts and messaged using appropriate context. The provision of evidence supporting the best formulation of personal protective equipment to be worn by HCWs who receive, manage or conduct procedures on infectious cases remains a priority. MERS-CoV was found and characterized because of its apparent association with severe, and therefore more obvious, illness in humans; we were the canaries in the coal mine. Sero-assays and prospective cohort studies have yet to determine the extent to which milder or asymptomatic cases contribute to MERS-CoV transmission chains. However, transmission of MERS-CoV is defined as sporadic (not sustained), intra-familial, often healthcare associated, inefficient and requiring close and prolonged contact [22, 31, 63, 93, 97, 102, 156] In a household study, 14 of 280 (5 %) contacts of 26 MERS-CoV positive index patients were RNA or antibody positive; the rate of general transmission, even in outbreaks is around 3 % [31] . It seems that the majority of human cases of MERS-CoV, even when numbers appear to increase suddenly, do not readily transmit to more than one other human so to date, the localized epidemic of MERS-CoV has not been self-sustaining [157] [158] [159] [160] [161] . That is to say, the basic reproduction number (R 0 ) -the average number of infections caused by one infected individual in a fully susceptible populationhas been close to one throughout various clusters and outbreaks. If R 0 was greater than 1, a sustained increase in case numbers would be expected. Some R o calculations may be affected by incomplete case contact tracing, limited community testing and how a case is defined. That MERS has had a constant presence in the Arabian Peninsula since 2012 is due to ongoing, sporadic spill-over events from DCs amplified by poorly controlled hospital outbreaks. The first known MERS human-to-human transmission event was one characterized by acute LRT disease in a healthcare setting in Jordan. In stark contrast, a sero-survey of HCW who were sometimes in close and prolonged contact with the first, fatal MERS-CoV case in 2012 [162] , found none of the HCW had seroconverted four months later, despite an absence of eye protection and variable compliance with required PPE standards [162] . Early on in the MERS story, samples for testing were mostly collected from patients with severe illness and not those with milder acute respiratory tract infections. Contacts of confirmed MERS cases were often observed for clinical illness, but not tested. These omissions may have confounded our understanding of MERS-CoV transmission and biased early data towards higher numbers of seriously ill and hospitalized patients, inflating the apparent proportion of fatal cases. Case-control studies were not a focus. As testing paradigms changed and contacts were increasingly tested, more asymptomatic and mild infections were recognized [163] . A rise in the cases termed asymptomatic (which enlarge the denominator for calculations of the proportion of fatal cases, defined in [164] ) resulted in a drop in the proportion of fatal cases during the Jeddah-2014 outbreak. Historically, such rises are consistent with changing definitions and laboratory responses and clinical management of a newly discovered virus infection that was first noted only among the severely ill. Upon follow-up, over three-quarters of such MERS-CoV RNA positive people did recall having one or more symptoms at the time, despite being reported as asymptomatic [165] raising some question over the reliability of other reported data. The proportion of fatal MERS cases within the KSA compared to outside the KSA, as well as the age, and sex distribution change in different ways when comparing MERS outbreaks. Approximately 43 % of MERS cases (549 of 1277) in the KSA were fatal betwen 2012 and December 2015 while 21 % (72 of 330) died among those occurring outside of the KSA. The total number of male cases always outnumber females and the proportion of male deaths is always greater than the proportion of females who die. However the proportion of male deaths from total males with MERS is a similar figure to that for females. In the KSA, there is a greater proportion of younger males among cases and deaths than were observed from the 2015 South Korean or the Jeddah-2014 outbreaks (Additional file 2: Figure S2 ). Why these aspects have differed may be due to differences in the time to presentation and diagnosis, the nature and quality of supportive care, the way a person became infected (habits, exposure to a human or zoonotic source, viral load, route of infection) or the extent to which different populations are burdened by underlying diseases [40] . As a group, HCWs comprised 16 % of MERS cases in the KSA and South Korea. It is apparent that the weekly proportion of infected HCWs increases alongside each steep rise in overall detections (Fig. 5) . In May 2013, the WHO published guidelines for IPC during care of probable or confirmed cases of MERS-CoV infection in a healthcare setting [166] . This is explainable because to date, each case rise has been intimately associated with healthcare-facility related outbreaks [118] . These rises in MERS-CoV detections can decrease the average age during each event because HCWs are usually younger than inpatients with MERS. Healthcare facilities have been a regular target for suggested improvements aimed at improving infection prevention and control (IPC) procedures [115, 118] . Most of the analysis of MERS-CoV genetics has been performed using high throughput or "deep" sequencing methods for complete genome deduction [167] [168] [169] . MERS-CoV was the first subject of such widespread use of deep sequencing to study an emerging viral outbreak with global reach. The technique can produce genomic [207] [208] [209] . Earlier and subsequent versions of this chart are maintained on a personal blog [210] length coverage in a single experiment with highly repetitious measurement of each nucleotide position [52, 140] . Despite assays having been published early on, subgenomic sequencing, once the mainstay of viral outbreak studies, has less often been published during MERS-CoV characterization [48] . As more genomes from both humans and DCs have been characterized, two clades have become apparent; A and B (Fig. 6) . Clade A contains only human-derived MERS-CoV genomes from Jordan, while Clade B comprises the majority of human and camel genomes deduced thus far [168] . Two studies during 2015, one looking at Jeddah-2014 MERS-CoV variants and another looking at a variant exported from South Korea to China, have now identified signs of genetic recombination among MERS-CoV variants. While human and camel whole genome sequences have retained >99 % identity with each other, members of genetically distinct lineages can and do swap genetic material when suitable conditions and coinfections co-occur [170] [171] [172] . Shared identity implies that the major source for human acquisition is the DC, rather than another animal, although more testing of other animal species is needed to confirm that conclusion. Over a month, a DC virus sequenced on different occasions did not change at all indicating a degree of genomic stability in its host, supporting that DCs are the natural, rather than intermediate, host for the MERS-CoV we know today [77] . To date, recombination has been localised to breakpoints near the boundary between ORF1a and ORF1b regions, within the spike gene [170] and in the ORF1b region (Fig. 2) [172] . It is not unexpected that recombination should occur since it is well known among other CoVs [124] and because the majority of MERS-CoV whole genomes collected from samples spanning three years (2012-2015) and from humans, camels and different countries have shown close genetic identity to each other, with just enough subtle variation to support outbreak investigations so long as whole genome sequencing is applied [52, 77, 135, 138, 168, [173] [174] [175] . Changes in genome sequence may herald alterations to virus transmissibility, replication, persistence, lethality or response to future drugs. If we have prior knowledge of the impact of genetic changes because of thorough characterization studies, we can closely Fig. 6 The genetic relationship between MERS-CoV nucleotide sequences (downloaded from GenBank using the listed accession numbers and from virological.org [212] ). This neighbour joining tree was created in MEGA v6 using an alignment of human and DCderived MERS-CoV sequences (Geneious v8.1 [211] ). Clades are indicated next to dark (Clade A) or pale (Clade B) blue vertical bars. Camel icons denote genomes from DCs. Healthcare or community outbreaks are boxed and labelled using previously described schemes [212, 213] monitor the genomic regions and better understand any changes in transmission or disease patterns as they occur. Genetic mutations noted during the largest of human outbreaks, Jeddah-2014, did not impart any major replicative or immunomodulatory changes when compared to earlier viral variants in vitro [156, 176] . However, we understand very little of the phenotypic outcomes that result from subtle genetic change in MERS-CoV genomes. To date no clinical relevance or obvious in vivo changes to viral replication, shedding or transmission has been reported or attributed to mutations or to new recombinant viruses [156] . But vigilance and larger, more contemporary and in vivo studies are needed. Genome sequence located to a distinct clade were identified from an Egyptian DC that was probably imported from Sudan. This does not fit into either of the current clades [125, 168, 177] . A virus sequenced from a Neoromicia capensis bat was more closely related to MERS-CoV than other large bat-derived sequences had been to that point, but the genome of a variant of a MERS-CoV has yet to be discovered and deduced from any bat [125] . Analyses of MERS-CoV genomes have shown that most single nucleotide differences among variants were located in the last third of the genome (Fig. 2) , which encodes the spike protein and accessory proteins [168] . At least nine MERS-CoV genomes contained amino acid substitutions in the receptor binding domain (RBD) of the spike protein and codons 158 (N-terminal region), 460 (RBD), 1020 (in heptad repeat 1), 1202 and 1208 bear investigation as markers of adaptive change [140, 169] . The spike protein had not changed in the recombinant MERS-CoV genome identified in China in 2015 but was reported to have varied at a higher rate than that for complete MERS-CoV genomes, among South Korean variants [172, 178] . This highlights that subgenomic regions may not always contain enough genetic diversity to prove useful for differentiating viral variants. Despite this, one assay amplifying a 615 nucleotide fragment of the spike S2 domain gene for Sanger sequencing agreed with the results generated by the sequencing of a some full genomes and was useful to define additional sequence groupings [177] . Genomic sequence can also be used to define the geographic boundaries of a cluster or outbreak and monitor its progress, based on the similarity of the variants found among infected humans and animals when occurring together, or between different sites and times (Fig. 6 ) [169] . This approach was employed when defining the geographically constrained MERS hospital outbreak in Al-Ahsa, which occurred between 1 st April and 23 rd May 2013, as well as clusters in Buraidah and a community outbreak in Hafr Al-Batin, the KSA. Genomic sequencing identified that approximately 12 MERS-CoV detections from a community outbreak in Hafr Al-Batin between June and August 2013 may have been triggered by an index case becoming infected through DC contact [175] . Sequencing MERS-CoV genomes from the 2013 Al-Ahsa hospital outbreak indicated that multiple viral variants contributed to the cases but that most were similar enough to each other to be consistent with human-tohuman transmission. Molecular epidemiology has revealed otherwise hidden links in transmission chains encompassing a period of up to five months [179] . However, most outbreaks have not continued for longer than two to three months and so opportunities for the virus to adapt further to humans through co-infection and sustained serial passage have been rare [169] . In Riyadh-2014, genetic evidence supported the likelihood of multiple external introductions of virus, implicating a range of healthcare facilities in an event that otherwise looked contiguous [23, 168, 179] . Riyadh is a nexus for camel and human travel and has had more MERS cases than any other region of the KSA to date but also harbours a wide range of MERS-CoV variants [128, 167, 179] . However the South Korean outbreak originated from a single infected person, resulting in three to four generations of cases [180, 181] . Studies of this apparently recombinant viral variant did not find an increased evolutionary rate and no sign of virus adaptation thus the outbreak seems to have been driven by circumstance rather than circumstance together with mutation [181] . For many MERS cases detected outside the Arabian Peninsula, extensive contact tracing has been performed and the results described in detail. Contact tracing is essential to contain the emergence and transmission of a new virus and today it is supported by molecular epidemiology. Although it is an expensive and time consuming process, contact tracing can identify potential new infections and through active or passive monitoring, react more rapidly if disease does develop. Results of contact tracing to date have found that onward transmission among humans is an infrequent event. For example, there were 83 contacts, both symptomatic and asymptomatic, of a case treated in Germany who travelled from the UAE but no sign of virus or antibody were found in any of them [73] . The very first MERS case had made contact with 56 HCWs and 48 others, but none developed any indication of infection [162] . In a study of 123 contacts of a case treated in France, only seven matched the definition for a possible case and were tested; one who had shared a 20 m 2 hospital room while in a bed 1.5 m away from the index case for a prolonged period was positive [26] . None of the contacts of the first two MERS cases imported into the USA in 2014 contained any MERS-CoV footprint [182] and none of the 131 contacts of two travellers returning to the Netherlands developed MERS-CoV antibodies or tested RNA positive [25, 183] . Analyses of public data reveal many likely instances of nosocomial acquisition of infection in the Arabian Peninsula and these data may be accompanied by some details noting contact with a known case or facility. One example identified the likely role of a patient with a subclinical infection, present in a hospital during their admission for other reasons, as the likeliest index case triggering a family cluster [93] . Contact tracing was a significant factor in the termination of a 2015 outbreak involving multiple South Korean hospitals [184] . Such studies demonstrate the necessity of finding and understanding a role for mild and asymptomatic cases, together with restricting close contact or prolonged exposure of infected people to others, especially older family members and friends with underlying disease (Fig. 4c) . The hospital-associated outbreak in Jeddah in 2014 was the largest and most rapid accumulation of MERS-CoV detections to date. The greatest number of MERS-CoV detections of any month on record occurred in Jeddah in April. The outbreak was mostly (>60 % of cases) associated with human-to-human spread within hospital environments and resulted from a lack of, or breakdown in, infection prevention and control [37, 185, 186] . A rise in fatalities followed the rapid increase in case numbers. In 2015 two large outbreaks occurred. South Korea was the site of the first large scale outbreak outside the Arabian Peninsula and produced the first cases in both South Korea and China, occurring between May and July 2015. This was closely followed by a distinct outbreak in Ar Riyad province in the KSA which appeared to come under control in early November. After staying in Bahrain for two weeks, a 68 year old male (68 M) travelled home to South Korea via Qatar, arriving free of symptoms on the 4 th May 2015 [187] . He developed fever, myalgia and a cough nearly a week later (11 th ). He visited a clinic as an outpatient between the 12 th and 15 th of May and was admitted to Hospital A on the 15 th [188] . He was discharged from Hospital A on the 17 th then visited and was admitted to the emergency department of Hospital B on the 18 th . During this second stay, a sputum sample was taken and tested positive for MERS-CoV on the 20 th [187, 188] , triggering transfer to the designated isolation treatment facility. Over a period of 10 days, the index case was seen at three different hospitals, demonstrating a key feature of "hospital shopping" that shaped the South Korean outbreak. Approximately 34 people were infected during this time [187] . In total 186 cases were generated in this outbreak, all linked through a single transmission chain to 68 M; 37 cases died [189] . In South Korea, the national health insurance system provides for relatively low cost medical care, defraying some costs by making family members responsible for a portion of the ministration of the sick, resulting in them sometimes staying for long periods in the rooms that often have more than four beds in them [24] . Other factors thought to have enabled this outbreak included unfamiliarity of local clinicians with MERS, ease with which the public can visit and be treated by tertiary hospitals, the custom of visiting sick friends and relatives in hospitals, the hierarchical nature of Korean society, crowded emergency rooms, poor IPC measures, a lack of negative pressure isolation rooms and poor inter-hospital communication of patient disease histories [24, [190] [191] [192] . All of the reported transmission occurred across three or four generations and apart from one unknown source, were all hospital-acquired [24, 120, 181, [193] [194] [195] . Few clinical details about these cases have been reported to date and detail on transmission and contact tracing is minimal. The hospitals involved were initially not identified, governmental guidance and actions produced confusing messages and there was very limited communication at all early on which resulted in unnecessary concern, distrust and a distinct economic impact [191, [196] [197] [198] . Early in the outbreak, a infected traveller, the son of an identified case in South Korea, passed through Hong Kong on his way to China where he was located, isolated and cared for in China [91, 199, 200] . No contacts became ill. The outbreak was brought under control in late July/ early August [201] after improved IPC measures were employed, strong contact tracing monitoring and quarantine, expanded laboratory testing, hospitals were better secured, specialized personnel were dispatched to manage cases and international cooperation increased [202, 203] . A review of public data showed that, as for MERS in the KSA, older age and the presence of underlying disease were significantly associated with a fatal outcome in South Korea. [40] Even though R 0 is <1, super-spreading events facilitated by circumstances created in healthcare settings and characterized by cluster sizes over 150, such as this one, are not unexpected from MERS-CoV infection [204] . The dynamic of an outbreak depends on the R 0 and an individual's viral shedding patterns, contact type and frequency, hospital procedures and population structure and density [204] . In the region of Ar Riyad, including the capital city of Riyadh, a hospital based cluster began, within a single hospital, from late June 2015 [205] . By mid-September there had been approximately170 cases reported but the outbreak appeared to been brought under control in November. It became apparent early on that MERS-CoV spread relatively ineffectively from human-to-human. Despite ongoing and possibly seasonal introduction of virus to the human population via infected DCs and perhaps other animals yet to be identified, the vast majority of MERS-CoV transmission has occurred from infected to uninfected humans in close and prolonged contact through circumstances created by poor infection control in health care settings. This opportunistic virus has had its greatest impact on those with underlying diseases and such vulnerable people, sometimes suffering multiple comorbidities, have been most often associated with hospitals, creating a perfect storm of exposure, transmission and mortality. It remains unclear if this group are uniquely affected by MERS-CoV or if other respiratory virus infections, including those from HCoVs, produce a similarly serious impact. In South Korea, a single imported case created an outbreak of 185 cases and 36 deaths that had a disproportionate impact on economic performance, community behaviour and trust in government and the health care system. Household human-to human transmission occurs but is also limited. Educational programs will be essential tools for combatting the spread of MERS-CoV both within urban and regional communities and for the health care setting. Vigilance remains important for containment since MERS-CoV is a virus with a genetic makeup that has been observed for only three years and is not stable. Among all humans reported to be infected, nearly 40 % have died. Continued laboratory testing, sequencing, analysis, timely data sharing and clear communication are essential for such vigilance to be effective. Global alignment of case definitions would further aid accurate calculation of a case fatality ratio by including subclinical case numbers. Whole genome sequencing has been used extensively to study MERS-CoV travel and variation and although it remains a tool for experts, it appears to be the best tool for the job. MERS and SARS have some clinical similarities but they also diverge significantly [206] . Defining characteristics include the higher PFC among MERS cases (above 50 % in 2013 and currently at 30-40 %; well above the 9 % of SARS) and the higher association between fatal MERS and older males with underlying comorbidities. For the viruses, MERS-CoV has a broader tropism, grows more rapidly in vitro, more rapidly induces cytopathogenic change, triggers distinct transcriptional responses, makes use of a different receptor, induces a more proinflammatory state and has a delayed innate antiviral response compared to SARS-CoV. There appears to be a 2-3 % prevalence of MERS-CoV in the KSA with a 5 % chance of secondary transmission within the household. There is an increased risk of infection through certain occupations at certain times and a much greater chance for spread to other humans during circumstances created by humans, which drives more effective transmission than any R 0 would predict on face value. Nonetheless, despite multiple mass gatherings that have afforded the virus many millions of opportunities to spread, there have remarkably been no reported outbreaks of MERS or MERS-CoV during or immediately after these events. There is no evidence that MERS-CoV is a virus of pandemic concern. Nonetheless, hospital settings continue to describe MERS cases and outbreaks in the Arabian Peninsula. As long as we facilitate the spread of MERS-CoV among our most vulnerable populations, the world must remain on alert for cases which may be exported more frequently when a host country with infected camel reservoirs is experiencing human clusters or outbreaks. The MERS-CoV appears to be an enzootic virus infecting the DC URT with evidence of recent genetic recombination. It may once have had its origins among bats, but evidence is lacking and the relevance of that to today's ongoing epidemic is academic. Thanks to quick action, the sensitive and rapid molecular diagnostic tools required to achieve rapid and sensitive detection goal have been in place and made widely available since the virus was reported in 2012. RT-PCR testing of LRT samples remains the gold standard for MERS-CoV confirmation. Serological tools continue to emerge but they are in need of further validation using samples from mild and asymptomatic infections and a densely sampled cohort study to follow contacts of new cases may address this need. Similarly, the important question of whether those who do shed MERS-CoV RNA for extended periods are infectious while appearing well, continues to go unanswered. It is even unclear just how many 'asymptomatic' infections have been described and reported correctly which in turn raises questions about the reliability of other clinical data collection to date. While the basic virology of MERS-CoV has advanced over the course of the past three years, understanding what is happening in, and the interplay between, camel, environment and human is still in its infancy. Additional file 1: Figure S1 . The

When did the first known cases of Middle East respiratory syndrome (MERS) occur?

1918 Influenza: the Mother of All Pandemics Jeffery K. Taubenberger" and David M. Morens1- The “Spanish" influenza pandemic of 1918—1919, which caused :50 million deaths worldwide, remains an ominous warning to public health. Many questions about its origins, its unusual epidemiologic features, and the basis of its pathogenicity remain unanswered. The public health implications of the pandemic therefore remain in doubt even as we now grapple with the feared emergence of a pandemic caused by H5N1 or other virus. However, new information about the 1918 virus is emerging, for example, sequencing of the entire genome from archival autopsy tis- sues. But, the viral genome alone is unlikely to provide answers to some critical questions. Understanding the 1918 pandemic and its implications for future pandemics requires careful experimentation and in-depth historical analysis. ”Curiouser and curiouser/ ” criedAlice Lewis Carroll, Alice’s Adventures in Wonderland, 1865 An estimated one third of the world’s population (or z500 million persons) were infected and had clinical- ly apparent illnesses (1,2) during the 191871919 influenza pandemic. The disease was exceptionally severe. Case- fatality rates were >2.5%, compared to <0.1% in other influenza pandemics (3,4). Total deaths were estimated at z50 million (577) and were arguably as high as 100 mil- lion (7). The impact of this pandemic was not limited to 191871919. All influenza A pandemics since that time, and indeed almost all cases of influenza A worldwide (except- ing human infections from avian Viruses such as H5N1 and H7N7), have been caused by descendants of the 1918 Virus, including “drifted” H1N1 Viruses and reassorted H2N2 and H3N2 Viruses. The latter are composed of key genes from the 1918 Virus, updated by subsequently-incor— porated avian influenza genes that code for novel surface *Armed Forces Institute of Pathology, Rockville, Maryland, USA; and TNational Institutes of Health, Bethesda, Maryland, USA proteins, making the 1918 Virus indeed the “mother” of all pandemics. In 1918, the cause of human influenza and its links to avian and swine influenza were unknown. Despite clinical and epidemiologic similarities to influenza pandemics of 1889, 1847, and even earlier, many questioned whether such an explosively fatal disease could be influenza at all. That question did not begin to be resolved until the 1930s, when closely related influenza Viruses (now known to be H1N1 Viruses) were isolated, first from pigs and shortly thereafter from humans. Seroepidemiologic studies soon linked both of these viruses to the 1918 pandemic (8). Subsequent research indicates that descendants of the 1918 Virus still persists enzootically in pigs. They probably also circulated continuously in humans, undergoing gradual antigenic drift and causing annual epidemics, until the 1950s. With the appearance of a new H2N2 pandemic strain in 1957 (“Asian flu”), the direct H1N1 Viral descen- dants 0f the 1918 pandemic strain disappeared from human circulation entirely, although the related lineage persisted enzootically in pigs. But in 1977, human H1N1 Viruses suddenly “reemerged” from a laboratory freezer (9). They continue to circulate endemically and epidemically. Thus in 2006, 2 major descendant lineages of the 1918 H1N1 Virus, as well as 2 additional reassortant lineages, persist naturally: a human epidemic/endemic H1N1 line- age, a porcine enzootic H1N1 lineage (so-called classic swine flu), and the reassorted human H3N2 Virus lineage, which like the human H1N1 Virus, has led to a porcine H3N2 lineage. None of these Viral descendants, however, approaches the pathogenicity of the 1918 parent Virus. Apparently, the porcine H1N1 and H3N2 lineages uncom- monly infect humans, and the human H1N1 and H3N2 lin- eages have both been associated with substantially lower rates ofillness and death than the virus of 1918. In fact, cur- rent H1N1 death rates are even lower than those for H3N2 lineage strains (prevalent from 1968 until the present). H1N1 Viruses descended from the 1918 strain, as well as H3N2 Viruses, have now been cocirculating worldwide for 29 years and show little evidence of imminent extinction. Trying To Understand What Happened By the early 1990s, 75 years of research had failed to answer a most basic question about the 1918 pandemic: why was it so fatal? No Virus from 1918 had been isolated, but all of its apparent descendants caused substantially milder human disease. Moreover, examination of mortality data from the 1920s suggests that within a few years after 1918, influenza epidemics had settled into a pattern of annual epidemicity associated with strain drifting and sub- stantially lowered death rates. Did some critical Viral genet- ic event produce a 1918 Virus of remarkable pathogenicity and then another critical genetic event occur soon after the 1918 pandemic to produce an attenuated H1N1 Virus? In 1995, a scientific team identified archival influenza autopsy materials collected in the autumn of 1918 and began the slow process of sequencing small Viral RNA fragments to determine the genomic structure of the causative influenza Virus (10). These efforts have now determined the complete genomic sequence of 1 Virus and partial sequences from 4 others. The primary data from the above studies (11717) and a number of reviews covering different aspects of the 1918 pandemic have recently been published ([8720) and confirm that the 1918 Virus is the likely ancestor of all 4 of the human and swine H1N1 and H3N2 lineages, as well as the “extinct” H2N2 lineage. No known mutations correlated with high pathogenicity in other human or animal influenza Viruses have been found in the 1918 genome, but ongoing studies to map Virulence factors are yielding interesting results. The 1918 sequence data, however, leave unanswered questions about the ori- gin of the Virus (19) and about the epidemiology of the pandemic. When and Where Did the 1918 Influenza Pandemic Arise? Before and after 1918, most influenza pandemics developed in Asia and spread from there to the rest of the world. Confounding definite assignment of a geographic point of origin, the 1918 pandemic spread more or less simultaneously in 3 distinct waves during an z12-month period in 191871919, in Europe, Asia, and North America (the first wave was best described in the United States in March 1918). Historical and epidemiologic data are inade- quate to identify the geographic origin of the Virus (21), and recent phylogenetic analysis of the 1918 Viral genome does not place the Virus in any geographic context ([9). Although in 1918 influenza was not a nationally reportable disease and diagnostic criteria for influenza and pneumonia were vague, death rates from influenza and pneumonia in the United States had risen sharply in 1915 and 1916 because of a major respiratory disease epidemic beginning in December 1915 (22). Death rates then dipped slightly in 1917. The first pandemic influenza wave appeared in the spring of 1918, followed in rapid succes- sion by much more fatal second and third waves in the fall and winter of 191871919, respectively (Figure 1). Is it pos- sible that a poorly-adapted H1N1 Virus was already begin- ning to spread in 1915, causing some serious illnesses but not yet sufficiently fit to initiate a pandemic? Data consis- tent with this possibility were reported at the time from European military camps (23), but a counter argument is that if a strain with a new hemagglutinin (HA) was caus- ing enough illness to affect the US national death rates from pneumonia and influenza, it should have caused a pandemic sooner, and when it eventually did, in 1918, many people should have been immune or at least partial- ly immunoprotected. “Herald” events in 1915, 1916, and possibly even in early 1918, if they occurred, would be dif- ficult to identify. The 1918 influenza pandemic had another unique fea- ture, the simultaneous (or nearly simultaneous) infection of humans and swine. The Virus of the 1918 pandemic like- ly expressed an antigenically novel subtype to which most humans and swine were immunologically naive in 1918 (12,20). Recently published sequence and phylogenetic analyses suggest that the genes encoding the HA and neu- raminidase (NA) surface proteins of the 1918 Virus were derived from an avianlike influenza Virus shortly before the start of the pandemic and that the precursor Virus had not circulated widely in humans or swine in the few decades before (12,15, 24). More recent analyses of the other gene segments of the Virus also support this conclu- sion. Regression analyses of human and swine influenza sequences obtained from 1930 to the present place the ini- tial circulation of the 1918 precursor Virus in humans at approximately 191571918 (20). Thus, the precursor was probably not circulating widely in humans until shortly before 1918, nor did it appear to have jumped directly from any species of bird studied to date (19). In summary, its origin remains puzzling. Were the 3 Waves in 1918—1 919 Caused by the Same Virus? If So, How and Why? Historical records since the 16th century suggest that new influenza pandemics may appear at any time of year, not necessarily in the familiar annual winter patterns of interpandemic years, presumably because newly shifted influenza Viruses behave differently when they find a uni- versal or highly susceptible human population. Thereafter, confronted by the selection pressures of population immu- nity, these pandemic Viruses begin to drift genetically and eventually settle into a pattern of annual epidemic recur- rences caused by the drifted Virus variants. Figure 1. Three pandemic waves: weekly combined influenza and pneumonia mortality, United Kingdom, 1918—1919 (21). In the 1918-1919 pandemic, a first or spring wave began in March 1918 and spread unevenly through the United States, Europe, and possibly Asia over the next 6 months (Figure 1). Illness rates were high, but death rates in most locales were not appreciably above normal. A sec- ond or fall wave spread globally from September to November 1918 and was highly fatal. In many nations, a third wave occurred in early 1919 (21). Clinical similari- ties led contemporary observers to conclude initially that they were observing the same disease in the successive waves. The milder forms of illness in all 3 waves were identical and typical of influenza seen in the 1889 pandem- ic and in prior interpandemic years. In retrospect, even the rapid progressions from uncomplicated influenza infec- tions to fatal pneumonia, a hallmark of the 191871919 fall and winter waves, had been noted in the relatively few severe spring wave cases. The differences between the waves thus seemed to be primarily in the much higher fre- quency of complicated, severe, and fatal cases in the last 2 waves. But 3 extensive pandemic waves of influenza within 1 year, occurring in rapid succession, with only the briefest of quiescent intervals between them, was unprecedented. The occurrence, and to some extent the severity, of recur- rent annual outbreaks, are driven by Viral antigenic drift, with an antigenic variant Virus emerging to become domi- nant approximately every 2 to 3 years. Without such drift, circulating human influenza Viruses would presumably disappear once herd immunity had reached a critical threshold at which further Virus spread was sufficiently limited. The timing and spacing of influenza epidemics in interpandemic years have been subjects of speculation for decades. Factors believed to be responsible include partial herd immunity limiting Virus spread in all but the most favorable circumstances, which include lower environ- mental temperatures and human nasal temperatures (bene- ficial to thermolabile Viruses such as influenza), optimal humidity, increased crowding indoors, and imperfect ven- tilation due to closed windows and suboptimal airflow. However, such factors cannot explain the 3 pandemic waves of 1918-1919, which occurred in the spring-sum- mer, summer—fall, and winter (of the Northern Hemisphere), respectively. The first 2 waves occurred at a time of year normally unfavorable to influenza Virus spread. The second wave caused simultaneous outbreaks in the Northern and Southern Hemispheres from September to November. Furthermore, the interwave peri- ods were so brief as to be almost undetectable in some locales. Reconciling epidemiologically the steep drop in cases in the first and second waves with the sharp rises in cases of the second and third waves is difficult. Assuming even transient postinfection immunity, how could suscep- tible persons be too few to sustain transmission at 1 point, and yet enough to start a new explosive pandemic wave a few weeks later? Could the Virus have mutated profoundly and almost simultaneously around the world, in the short periods between the successive waves? Acquiring Viral drift sufficient to produce new influenza strains capable of escaping population immunity is believed to take years of global circulation, not weeks of local circulation. And hav- ing occurred, such mutated Viruses normally take months to spread around the world. At the beginning of other “off season” influenza pan- demics, successive distinct waves within a year have not been reported. The 1889 pandemic, for example, began in the late spring of 1889 and took several months to spread throughout the world, peaking in northern Europe and the United States late in 1889 or early in 1890. The second recurrence peaked in late spring 1891 (more than a year after the first pandemic appearance) and the third in early 1892 (21 ). As was true for the 1918 pandemic, the second 1891 recurrence produced of the most deaths. The 3 recur- rences in 1889-1892, however, were spread over >3 years, in contrast to 191871919, when the sequential waves seen in individual countries were typically compressed into z879 months. What gave the 1918 Virus the unprecedented ability to generate rapidly successive pandemic waves is unclear. Because the only 1918 pandemic Virus samples we have yet identified are from second-wave patients ([6), nothing can yet be said about whether the first (spring) wave, or for that matter, the third wave, represented circulation of the same Virus or variants of it. Data from 1918 suggest that persons infected in the second wave may have been pro- tected from influenza in the third wave. But the few data bearing on protection during the second and third waves after infection in the first wave are inconclusive and do lit- tle to resolve the question of whether the first wave was caused by the same Virus or whether major genetic evolu- tionary events were occurring even as the pandemic exploded and progressed. Only influenza RNAipositive human samples from before 1918, and from all 3 waves, can answer this question. What Was the Animal Host Origin of the Pandemic Virus? Viral sequence data now suggest that the entire 1918 Virus was novel to humans in, or shortly before, 1918, and that it thus was not a reassortant Virus produced from old existing strains that acquired 1 or more new genes, such as those causing the 1957 and 1968 pandemics. On the con- trary, the 1918 Virus appears to be an avianlike influenza Virus derived in toto from an unknown source (17,19), as its 8 genome segments are substantially different from contemporary avian influenza genes. Influenza Virus gene sequences from a number offixed specimens ofwild birds collected circa 1918 show little difference from avian Viruses isolated today, indicating that avian Viruses likely undergo little antigenic change in their natural hosts even over long periods (24,25). For example, the 1918 nucleoprotein (NP) gene sequence is similar to that ofviruses found in wild birds at the amino acid level but very divergent at the nucleotide level, which suggests considerable evolutionary distance between the sources of the 1918 NP and of currently sequenced NP genes in wild bird strains (13,19). One way of looking at the evolutionary distance of genes is to com- pare ratios of synonymous to nonsynonymous nucleotide substitutions. A synonymous substitution represents a silent change, a nucleotide change in a codon that does not result in an amino acid replacement. A nonsynonymous substitution is a nucleotide change in a codon that results in an amino acid replacement. Generally, a Viral gene sub- jected to immunologic drift pressure or adapting to a new host exhibits a greater percentage of nonsynonymous mutations, while a Virus under little selective pressure accumulates mainly synonymous changes. Since little or no selection pressure is exerted on synonymous changes, they are thought to reflect evolutionary distance. Because the 1918 gene segments have more synony- mous changes from known sequences of wild bird strains than expected, they are unlikely to have emerged directly from an avian influenza Virus similar to those that have been sequenced so far. This is especially apparent when one examines the differences at 4-fold degenerate codons, the subset of synonymous changes in which, at the third codon position, any of the 4 possible nucleotides can be substituted without changing the resulting amino acid. At the same time, the 1918 sequences have too few amino acid difierences from those of wild-bird strains to have spent many years adapting only in a human or swine intermedi- ate host. One possible explanation is that these unusual gene segments were acquired from a reservoir of influenza Virus that has not yet been identified or sampled. All of these findings beg the question: where did the 1918 Virus come from? In contrast to the genetic makeup of the 1918 pandem- ic Virus, the novel gene segments of the reassorted 1957 and 1968 pandemic Viruses all originated in Eurasian avian Viruses (26); both human Viruses arose by the same mech- anismireassortment of a Eurasian wild waterfowl strain with the previously circulating human H1N1 strain. Proving the hypothesis that the Virus responsible for the 1918 pandemic had a markedly different origin requires samples of human influenza strains circulating before 1918 and samples of influenza strains in the wild that more closely resemble the 1918 sequences. What Was the Biological Basis for 1918 Pandemic Virus Pathogenicity? Sequence analysis alone does not ofier clues to the pathogenicity of the 1918 Virus. A series of experiments are under way to model Virulence in Vitro and in animal models by using Viral constructs containing 1918 genes produced by reverse genetics. Influenza Virus infection requires binding of the HA protein to sialic acid receptors on host cell surface. The HA receptor-binding site configuration is different for those influenza Viruses adapted to infect birds and those adapted to infect humans. Influenza Virus strains adapted to birds preferentially bind sialic acid receptors with 01 (273) linked sugars (27729). Human-adapted influenza Viruses are thought to preferentially bind receptors with 01 (2%) link- ages. The switch from this avian receptor configuration requires of the Virus only 1 amino acid change (30), and the HAs of all 5 sequenced 1918 Viruses have this change, which suggests that it could be a critical step in human host adaptation. A second change that greatly augments Virus binding to the human receptor may also occur, but only 3 of5 1918 HA sequences have it (16). This means that at least 2 H1N1 receptor-binding vari- ants cocirculated in 1918: 1 with high—affinity binding to the human receptor and 1 with mixed-affinity binding to both avian and human receptors. No geographic or chrono- logic indication eXists to suggest that one of these variants was the precursor of the other, nor are there consistent dif- ferences between the case histories or histopathologic fea- tures of the 5 patients infected with them. Whether the Viruses were equally transmissible in 1918, whether they had identical patterns of replication in the respiratory tree, and whether one or both also circulated in the first and third pandemic waves, are unknown. In a series of in Vivo experiments, recombinant influen- za Viruses containing between 1 and 5 gene segments of the 1918 Virus have been produced. Those constructs bearing the 1918 HA and NA are all highly pathogenic in mice (31). Furthermore, expression microarray analysis performed on whole lung tissue of mice infected with the 1918 HA/NA recombinant showed increased upregulation of genes involved in apoptosis, tissue injury, and oxidative damage (32). These findings are unexpected because the Viruses with the 1918 genes had not been adapted to mice; control experiments in which mice were infected with modern human Viruses showed little disease and limited Viral replication. The lungs of animals infected with the 1918 HA/NA construct showed bronchial and alveolar epithelial necrosis and a marked inflammatory infiltrate, which suggests that the 1918 HA (and possibly the NA) contain Virulence factors for mice. The Viral genotypic basis of this pathogenicity is not yet mapped. Whether pathogenicity in mice effectively models pathogenicity in humans is unclear. The potential role of the other 1918 pro- teins, singularly and in combination, is also unknown. Experiments to map further the genetic basis of Virulence of the 1918 Virus in various animal models are planned. These experiments may help define the Viral component to the unusual pathogenicity of the 1918 Virus but cannot address whether specific host factors in 1918 accounted for unique influenza mortality patterns. Why Did the 1918 Virus Kill So Many Healthy Young Ad ults? The curve of influenza deaths by age at death has histor- ically, for at least 150 years, been U-shaped (Figure 2), exhibiting mortality peaks in the very young and the very old, with a comparatively low frequency of deaths at all ages in between. In contrast, age-specific death rates in the 1918 pandemic exhibited a distinct pattern that has not been documented before or since: a “W—shaped” curve, similar to the familiar U-shaped curve but with the addition of a third (middle) distinct peak of deaths in young adults z20410 years of age. Influenza and pneumonia death rates for those 1534 years of age in 191871919, for example, were 20 times higher than in previous years (35). Overall, near- ly half of the influenza—related deaths in the 1918 pandem- ic were in young adults 20410 years of age, a phenomenon unique to that pandemic year. The 1918 pandemic is also unique among influenza pandemics in that absolute risk of influenza death was higher in those <65 years of age than in those >65; persons <65 years of age accounted for >99% of all excess influenza—related deaths in 191871919. In com- parison, the <65-year age group accounted for 36% of all excess influenza—related deaths in the 1957 H2N2 pandem- ic and 48% in the 1968 H3N2 pandemic (33). A sharper perspective emerges when 1918 age-specific influenza morbidity rates (21) are used to adj ust the W- shaped mortality curve (Figure 3, panels, A, B, and C [35,37]). Persons 65 years of age in 1918 had a dispro- portionately high influenza incidence (Figure 3, panel A). But even after adjusting age-specific deaths by age-specif— ic clinical attack rates (Figure 3, panel B), a W—shaped curve with a case-fatality peak in young adults remains and is significantly different from U-shaped age-specific case- fatality curves typically seen in other influenza years, e.g., 192871929 (Figure 3, panel C). Also, in 1918 those 5 to 14 years of age accounted for a disproportionate number of influenza cases, but had a much lower death rate from influenza and pneumonia than other age groups. To explain this pattern, we must look beyond properties of the Virus to host and environmental factors, possibly including immunopathology (e.g., antibody-dependent infection enhancement associated with prior Virus exposures [38]) and exposure to risk cofactors such as coinfecting agents, medications, and environmental agents. One theory that may partially explain these findings is that the 1918 Virus had an intrinsically high Virulence, tem- pered only in those patients who had been born before 1889, e.g., because of exposure to a then-circulating Virus capable of providing partial immunoprotection against the 1918 Virus strain only in persons old enough (>35 years) to have been infected during that prior era (35). But this the- ory would present an additional paradox: an obscure pre- cursor Virus that left no detectable trace today would have had to have appeared and disappeared before 1889 and then reappeared more than 3 decades later. Epidemiologic data on rates of clinical influenza by age, collected between 1900 and 1918, provide good evi- dence for the emergence of an antigenically novel influen- za Virus in 1918 (21). Jordan showed that from 1900 to 1917, the 5- to 15-year age group accounted for 11% of total influenza cases, while the >65-year age group accounted for 6 % of influenza cases. But in 1918, cases in Figure 2. “U-” and “W—” shaped combined influenza and pneumo- nia mortality, by age at death, per 100,000 persons in each age group, United States, 1911—1918. Influenza- and pneumonia- specific death rates are plotted for the interpandemic years 1911—1917 (dashed line) and for the pandemic year 1918 (solid line) (33,34). Incidence male per 1 .nao persunslage group Mortality per 1.000 persunslige group + Case—fataiity rale 1918—1919 Case fatalily par 100 persons ill wilh P&I pel age group Figure 3. Influenza plus pneumonia (P&l) (combined) age-specific incidence rates per 1,000 persons per age group (panel A), death rates per 1,000 persons, ill and well combined (panel B), and case-fatality rates (panel C, solid line), US Public Health Service house-to-house surveys, 8 states, 1918 (36). A more typical curve of age-specific influenza case-fatality (panel C, dotted line) is taken from US Public Health Service surveys during 1928—1929 (37). the 5 to 15-year-old group jumped to 25% of influenza cases (compatible with exposure to an antigenically novel Virus strain), while the >65-year age group only accounted for 0.6% of the influenza cases, findings consistent with previously acquired protective immunity caused by an identical or closely related Viral protein to which older per- sons had once been exposed. Mortality data are in accord. In 1918, persons >75 years had lower influenza and pneumonia case-fatality rates than they had during the prepandemic period of 191171917. At the other end of the age spectrum (Figure 2), a high proportion of deaths in infancy and early childhood in 1918 mimics the age pat- tern, if not the mortality rate, of other influenza pandemics. Could a 1918-like Pandemic Appear Again? If So, What Could We Do About It? In its disease course and pathologic features, the 1918 pandemic was different in degree, but not in kind, from previous and subsequent pandemics. Despite the extraordi- nary number of global deaths, most influenza cases in 1918 (>95% in most locales in industrialized nations) were mild and essentially indistinguishable from influenza cases today. Furthermore, laboratory experiments with recombi- nant influenza Viruses containing genes from the 1918 Virus suggest that the 1918 and 1918-like Viruses would be as sensitive as other typical Virus strains to the Food and Drug Administrationiapproved antiinfluenza drugs riman- tadine and oseltamivir. However, some characteristics of the 1918 pandemic appear unique: most notably, death rates were 5 7 20 times higher than expected. Clinically and pathologically, these high death rates appear to be the result of several factors, including a higher proportion of severe and complicated infections of the respiratory tract, rather than involvement of organ systems outside the normal range of the influenza Virus. Also, the deaths were concentrated in an unusually young age group. Finally, in 1918, 3 separate recurrences of influenza followed each other with unusual rapidity, resulting in 3 explosive pandemic waves within a year’s time (Figure 1). Each of these unique characteristics may reflect genetic features of the 1918 Virus, but understand- ing them will also require examination of host and envi- ronmental factors. Until we can ascertain which of these factors gave rise to the mortality patterns observed and learn more about the formation of the pandemic, predictions are only educated guesses. We can only conclude that since it happened once, analogous conditions could lead to an equally devastating pandemic. Like the 1918 Virus, H5N1 is an avian Virus (39), though a distantly related one. The evolutionary path that led to pandemic emergence in 1918 is entirely unknown, but it appears to be different in many respects from the cur- rent situation with H5N1. There are no historical data, either in 1918 or in any other pandemic, for establishing that a pandemic “precursor” Virus caused a highly patho- genic outbreak in domestic poultry, and no highly patho- genic avian influenza (HPAI) Virus, including H5N1 and a number of others, has ever been known to cause a major human epidemic, let alone a pandemic. While data bearing on influenza Virus human cell adaptation (e.g., receptor binding) are beginning to be understood at the molecular level, the basis for Viral adaptation to efficient human-to- human spread, the chief prerequisite for pandemic emer- gence, is unknown for any influenza Virus. The 1918 Virus acquired this trait, but we do not know how, and we cur- rently have no way of knowing whether H5N1 Viruses are now in a parallel process of acquiring human-to-human transmissibility. Despite an explosion of data on the 1918 Virus during the past decade, we are not much closer to understanding pandemic emergence in 2006 than we were in understanding the risk of H1N1 “swine flu” emergence in 1976. Even with modern antiviral and antibacterial drugs, vaccines, and prevention knowledge, the return of a pan- demic Virus equivalent in pathogenicity to the Virus of 1918 would likely kill >100 million people worldwide. A pandemic Virus with the (alleged) pathogenic potential of some recent H5N1 outbreaks could cause substantially more deaths. Whether because of Viral, host or environmental fac- tors, the 1918 Virus causing the first or ‘spring’ wave was not associated with the exceptional pathogenicity of the second (fall) and third (winter) waves. Identification of an influenza RNA-positive case from the first wave could point to a genetic basis for Virulence by allowing differ- ences in Viral sequences to be highlighted. Identification of pre-1918 human influenza RNA samples would help us understand the timing of emergence of the 1918 Virus. Surveillance and genomic sequencing of large numbers of animal influenza Viruses will help us understand the genet- ic basis of host adaptation and the extent of the natural reservoir of influenza Viruses. Understanding influenza pandemics in general requires understanding the 1918 pan- demic in all its historical, epidemiologic, and biologic aspects. Dr Taubenberger is chair of the Department of Molecular Pathology at the Armed Forces Institute of Pathology, Rockville, Maryland. His research interests include the molecular patho- physiology and evolution of influenza Viruses. 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Grove RD, Hetzel AM. Vital statistics rates in the United States: 1940–1960. Washington: US Government Printing Office, 1968. 34. Linder FE, Grove RD. Vital statistics rates in the United States: 1900–1940. Washington: US Government Printing Office, 1943. 35. Simonsen L, Clarke MJ, Schonberger LB, Arden NH, Cox NJ, Fukuda K. Pandemic versus epidemic influenza mortality: a pattern of changing age distribution. J Infect Dis 1998;178:53–60. 36. Frost WH. The epidemiology of influenza. Public Health Rep. 1919;34:1823–61. 37. Collins SD. Age and sex incidence of influenza and pneumonia morbidity and mortality in the epidemic of 1928-1929 with comparative data for the epidemic of 1918–1919. Public Health Rep. 1931;46:1909–37. 38. Majde JA. Influenza: Learn from the past. ASM News. 1996;62:514. 39. Peiris JS, Yu WC, Leung CW, Cheung CY, Ng WF, Nicholls JM, et al. Re-emergence of fatal human influenza A subtype H5N1 disease. Lancet. 2004;363:617–9. Address for correspondence: Jeffery K. Taubenberger, Department of Molecular Pathology, Armed Forces Institute of Pathology, 1413 Research Blvd, Bldg 101, Rm 1057, Rockville, MD 20850-3125, USA; fax. 301-295-9507; email: [email protected] The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the Centers for Disease Control and Prevention or the institutions with which the authors are affiliated.

Do seasonal temperatures and humidity explain the appearance of the three waves of the 1918 swine flu?

Accelerated viral dynamics in bat cell lines, with implications for zoonotic emergence https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7064339/ SHA: f2cc0d63ff2c4aaa127c4caae21d8f3a0067e3d5 Authors: Brook, Cara E; Boots, Mike; Chandran, Kartik; Dobson, Andrew P; Drosten, Christian; Graham, Andrea L; Grenfell, Bryan T; Müller, Marcel A; Ng, Melinda; Wang, Lin-Fa; van Leeuwen, Anieke Date: 2020-02-03 DOI: 10.7554/elife.48401 License: cc-by Abstract: Bats host virulent zoonotic viruses without experiencing disease. A mechanistic understanding of the impact of bats’ virus hosting capacities, including uniquely constitutive immune pathways, on cellular-scale viral dynamics is needed to elucidate zoonotic emergence. We carried out virus infectivity assays on bat cell lines expressing induced and constitutive immune phenotypes, then developed a theoretical model of our in vitro system, which we fit to empirical data. Best fit models recapitulated expected immune phenotypes for representative cell lines, supporting robust antiviral defenses in bat cells that correlated with higher estimates for within-host viral propagation rates. In general, heightened immune responses limit pathogen-induced cellular morbidity, which can facilitate the establishment of rapidly-propagating persistent infections within-host. Rapidly-transmitting viruses that have evolved with bat immune systems will likely cause enhanced virulence following emergence into secondary hosts with immune systems that diverge from those unique to bats. Text: Bats have received much attention in recent years for their role as reservoir hosts for emerging viral zoonoses, including rabies and related lyssaviruses, Hendra and Nipah henipaviruses, Ebola and Marburg filoviruses, and SARS coronavirus (Calisher et al., 2006; Wang and Anderson, 2019) . In most non-Chiropteran mammals, henipaviruses, filoviruses, and coronaviruses induce substantial morbidity and mortality, display short durations of infection, and elicit robust, long-term immunity in hosts surviving infection (Nicholls et al., 2003; Hooper et al., 2001; Mahanty and Bray, 2004) . Bats, by contrast, demonstrate no obvious disease symptoms upon infection with pathogens that are highly virulent in non-volant mammals (Schountz et al., 2017) but may, instead, support viruses as longterm persistent infections, rather than transient, immunizing pathologies (Plowright et al., 2016) . Recent research advances are beginning to shed light on the molecular mechanisms by which bats avoid pathology from these otherwise virulent pathogens (Brook and Dobson, 2015) . Bats leverage a suite of species-specific mechanisms to limit viral load, which include host receptor sequence incompatibilities for some bat-virus combinations (Ng et al., 2015; Takadate et al., 2020) and constitutive expression of the antiviral cytokine, IFN-a, for others (Zhou et al., 2016) . Typically, the presence of viral RNA or DNA in the cytoplasm of mammalian cells will induce secretion of type I interferon proteins (IFN-a and IFN-b), which promote expression and translation of interferon-stimulated genes (ISGs) in neighboring cells and render them effectively antiviral (Stetson and Medzhitov, 2006) . In some bat cells, the transcriptomic blueprints for this IFN response are expressed constitutively, even in the absence of stimulation by viral RNA or DNA (Zhou et al., 2016) . In non-flying mammals, constitutive IFN expression would likely elicit widespread inflammation and concomitant immunopathology upon viral infection, but bats support unique adaptations to combat inflammation (Zhang et al., 2013; Ahn et al., 2019; Xie et al., 2018; Pavlovich et al., 2018) that may have evolved to mitigate metabolic damage induced during flight (Kacprzyk et al., 2017) . The extent to which constitutive IFN-a expression signifies constitutive antiviral defense in the form of functional IFN-a protein remains unresolved. In bat cells constitutively expressing IFN-a, some protein-stimulated, downstream ISGs appear to be also constitutively expressed, but additional ISG induction is nonetheless possible following viral challenge and stimulation of IFN-b (Zhou et al., 2016; Xie et al., 2018) . Despite recent advances in molecular understanding of bat viral tolerance, the consequences of this unique bat immunity on within-host virus dynamics-and its implications for understanding zoonotic emergence-have yet to be elucidated. The field of 'virus dynamics' was first developed to describe the mechanistic underpinnings of long-term patterns of steady-state viral load exhibited by patients in chronic phase infections with HIV, who appeared to produce and clear virus at equivalent rates (Nowak and May, 2000; Ho et al., 1995) . Models of simple target cell depletion, in which viral load is dictated by a bottom-eLife digest Bats can carry viruses that are deadly to other mammals without themselves showing serious symptoms. In fact, bats are natural reservoirs for viruses that have some of the highest fatality rates of any viruses that people acquire from wild animals -including rabies, Ebola and the SARS coronavirus. Bats have a suite of antiviral defenses that keep the amount of virus in check. For example, some bats have an antiviral immune response called the interferon pathway perpetually switched on. In most other mammals, having such a hyper-vigilant immune response would cause harmful inflammation. Bats, however, have adapted anti-inflammatory traits that protect them from such harm, include the loss of certain genes that normally promote inflammation. However, no one has previously explored how these unique antiviral defenses of bats impact the viruses themselves. Now, Brook et al. have studied this exact question using bat cells grown in the laboratory. The experiments made use of cells from one bat species -the black flying fox -in which the interferon pathway is always on, and another -the Egyptian fruit bat -in which this pathway is only activated during an infection. The bat cells were infected with three different viruses, and then Brook et al. observed how the interferon pathway helped keep the infections in check, before creating a computer model of this response. The experiments and model helped reveal that the bats' defenses may have a potential downside for other animals, including humans. In both bat species, the strongest antiviral responses were countered by the virus spreading more quickly from cell to cell. This suggests that bat immune defenses may drive the evolution of faster transmitting viruses, and while bats are well protected from the harmful effects of their own prolific viruses, other creatures like humans are not. The findings may help to explain why bats are often the source for viruses that are deadly in humans. Learning more about bats' antiviral defenses and how they drive virus evolution may help scientists develop better ways to predict, prevent or limit the spread of viruses from bats to humans. More studies are needed in bats to help these efforts. In the meantime, the experiments highlight the importance of warning people to avoid direct contact with wild bats. up resource supply of infection-susceptible host cells, were first developed for HIV (Perelson, 2002) but have since been applied to other chronic infections, including hepatitis-C virus (Neumann et al., 1998) , hepatitis-B virus (Nowak et al., 1996) and cytomegalovirus (Emery et al., 1999) . Recent work has adopted similar techniques to model the within-host dynamics of acute infections, such as influenza A and measles, inspiring debate over the extent to which explicit modeling of top-down immune control can improve inference beyond the basic resource limitation assumptions of the target cell model (Baccam et al., 2006; Pawelek et al., 2012; Saenz et al., 2010; Morris et al., 2018) . To investigate the impact of unique bat immune processes on in vitro viral kinetics, we first undertook a series of virus infection experiments on bat cell lines expressing divergent interferon phenotypes, then developed a theoretical model elucidating the dynamics of within-host viral spread. We evaluated our theoretical model analytically independent of the data, then fit the model to data recovered from in vitro experimental trials in order to estimate rates of within-host virus transmission and cellular progression to antiviral status under diverse assumptions of absent, induced, and constitutive immunity. Finally, we confirmed our findings in spatially-explicit stochastic simulations of fitted time series from our mean field model. We hypothesized that top-down immune processes would overrule classical resource-limitation in bat cell lines described as constitutively antiviral in the literature, offering a testable prediction for models fit to empirical data. We further predicted that the most robust antiviral responses would be associated with the most rapid within-host virus propagation rates but also protect cells against virus-induced mortality to support the longest enduring infections in tissue culture. We first explored the influence of innate immune phenotype on within-host viral propagation in a series of infection experiments in cell culture. We conducted plaque assays on six-well plate monolayers of three immortalized mammalian kidney cell lines: [1] Vero (African green monkey) cells, which are IFN-defective and thus limited in antiviral capacity (Desmyter et al., 1968) ; [2] RoNi/7.1 (Rousettus aegyptiacus) cells which demonstrate idiosyncratic induced interferon responses upon viral challenge (Kuzmin et al., 2017; Arnold et al., 2018; Biesold et al., 2011; Pavlovich et al., 2018) ; and [3] PaKiT01 (Pteropus alecto) cells which constitutively express IFN-a (Zhou et al., 2016; Crameri et al., 2009) . To intensify cell line-specific differences in constitutive immunity, we carried out infectivity assays with GFP-tagged, replication-competent vesicular stomatitis Indiana viruses: rVSV-G, rVSV-EBOV, and rVSV-MARV, which have been previously described (Miller et al., 2012; Wong et al., 2010) . Two of these viruses, rVSV-EBOV and rVSV-MARV, are recombinants for which cell entry is mediated by the glycoprotein of the bat-evolved filoviruses, Ebola (EBOV) and Marburg (MARV), thus allowing us to modulate the extent of structural, as well as immunological, antiviral defense at play in each infection. Previous work in this lab has demonstrated incompatibilities in the NPC1 filovirus receptor which render PaKiT01 cells refractory to infection with rVSV-MARV (Ng and Chandrab, 2018, Unpublished results) , making them structurally antiviral, over and above their constitutive expression of IFN-a. All three cell lines were challenged with all three viruses at two multiplicities of infection (MOI): 0.001 and 0.0001. Between 18 and 39 trials were run at each cell-virus-MOI combination, excepting rVSV-MARV infections on PaKiT01 cells at MOI = 0.001, for which only eight trials were run (see Materials and methods; Figure 1 -figure supplements 1-3, Supplementary file 1). Because plaque assays restrict viral transmission neighbor-to-neighbor in two-dimensional cellular space (Howat et al., 2006) , we were able to track the spread of GFP-expressing virus-infected cells across tissue monolayers via inverted fluorescence microscopy. For each infection trial, we monitored and re-imaged plates for up to 200 hr of observations or until total monolayer destruction, processed resulting images, and generated a time series of the proportion of infectious-cell occupied plate space across the duration of each trial (see Materials and methods). We used generalized additive models to infer the time course of all cell culture replicates and construct the multi-trial dataset to which we eventually fit our mechanistic transmission model for each cell line-virus-specific combination ( Figure 1; Figure 1 -figure supplements 1-5). All three recombinant vesicular stomatitis viruses (rVSV-G, rVSV-EBOV, and rVSV-MARV) infected Vero, RoNi/7.1, and PaKiT01 tissue cultures at both focal MOIs. Post-invasion, virus spread rapidly across most cell monolayers, resulting in virus-induced epidemic extinction. Epidemics were less severe in bat cell cultures, especially when infected with the recombinant filoviruses, rVSV-EBOV and rVSV-MARV. Monolayer destruction was avoided in the case of rVSV-EBOV and rVSV-MARV infections on PaKiT01 cells: in the former, persistent viral infection was maintained throughout the 200 hr duration of each experiment, while, in the latter, infection was eliminated early in the time series, preserving a large proportion of live, uninfectious cells across the duration of the experiment. We assumed this pattern to be the result of immune-mediated epidemic extinction (Figure 1) . Patterns from MOI = 0.001 were largely recapitulated at MOI = 0.0001, though at somewhat reduced total proportions (Figure 1-figure supplement 5 ). A theoretical model fit to in vitro data recapitulates expected immune phenotypes for bat cells We next developed a within-host model to fit to these data to elucidate the effects of induced and constitutive immunity on the dynamics of viral spread in host tissue ( Figure 1 ). The compartmental within-host system mimicked our two-dimensional cell culture monolayer, with cells occupying five distinct infection states: susceptible (S), antiviral (A), exposed (E), infectious (I), and dead (D). We modeled exposed cells as infected but not yet infectious, capturing the 'eclipse phase' of viral integration into a host cell which precedes viral replication. Antiviral cells were immune to viral infection, in accordance with the 'antiviral state' induced from interferon stimulation of ISGs in tissues adjacent to infection (Stetson and Medzhitov, 2006) . Because we aimed to translate available data into modeled processes, we did not explicitly model interferon dynamics but instead scaled the rate of cell progression from susceptible to antiviral (r) by the proportion of exposed cells (globally) in the system. In systems permitting constitutive immunity, a second rate of cellular acquisition of antiviral status (") additionally scaled with the global proportion of susceptible cells in the model. Compared with virus, IFN particles are small and highly diffusive, justifying this global signaling assumption at the limited spatial extent of a six-well plate and maintaining consistency with previous modeling approximations of IFN signaling in plaque assay (Howat et al., 2006) . To best represent our empirical monolayer system, we expressed our state variables as proportions (P S , P A , P E , P I , and P D ), under assumptions of frequency-dependent transmission in a wellmixed population (Keeling and Rohani, 2008) , though note that the inclusion of P D (representing the proportion of dead space in the modeled tissue) had the functional effect of varying transmission with infectious cell density. This resulted in the following system of ordinary differential equations: We defined 'induced immunity' as complete, modeling all cells as susceptible to viral invasion at disease-free equilibrium, with defenses induced subsequent to viral exposure through the term r. By contrast, we allowed the extent of constitutive immunity to vary across the parameter range of " > 0, defining a 'constitutive' system as one containing any antiviral cells at disease-free equilibrium. In fitting this model to tissue culture data, we independently estimated both r and "; as well as the cell-to-cell transmission rate, b, for each cell-virus combination. Since the extent to which constitutively-expressed IFN-a is constitutively translated into functional protein is not yet known for bat hosts (Zhou et al., 2016) , this approach permitted our tissue culture data to drive modeling inference: even in PaKiT01 cell lines known to constitutively express IFN-a, the true constitutive extent of the system (i.e. the quantity of antiviral cells present at disease-free equilibrium) was allowed to vary through estimation of ": For the purposes of model-fitting, we fixed the value of c, the return rate of antiviral cells to susceptible status, at 0. The small spatial scale and short time course (max 200 hours) of our experiments likely prohibited any return of antiviral cells to susceptible status in our empirical system; nonetheless, we retained the term c in analytical evaluations of our model because regression from antiviral to susceptible status is possible over long time periods in vitro and at the scale of a complete organism (Radke et al., 1974; Rasmussen and Farley, 1975; Samuel and Knutson, 1982) . Before fitting to empirical time series, we undertook bifurcation analysis of our theoretical model and generated testable hypotheses on the basis of model outcomes. From our within-host model system (Equation 1-5), we derived the following expression for R 0 , the pathogen basic reproduction number (Supplementary file 2): Pathogens can invade a host tissue culture when R 0 >1. Rapid rates of constitutive antiviral acquisition (") will drive R 0 <1: tissue cultures with highly constitutive antiviral immunity will be therefore resistant to virus invasion from the outset. Since, by definition, induced immunity is stimulated following initial virus invasion, the rate of induced antiviral acquisition (r) is not incorporated into the equation for R 0 ; while induced immune processes can control virus after initial invasion, they cannot prevent it from occurring to begin with. In cases of fully induced or absent immunity (" ¼ 0), the R 0 equation thus reduces to a form typical of the classic SEIR model: At equilibrium, the theoretical, mean field model demonstrates one of three infection states: endemic equilibrium, stable limit cycles, or no infection ( Figure 2) . Respectively, these states approximate the persistent infection, virus-induced epidemic extinction, and immune-mediated epidemic extinction phenotypes previously witnessed in tissue culture experiments ( Figure 1 ). Theoretically, endemic equilibrium is maintained when new infections are generated at the same rate at which infections are lost, while limit cycles represent parameter space under which infectious and susceptible populations are locked in predictable oscillations. Endemic equilibria resulting from cellular regeneration (i.e. births) have been described in vivo for HIV (Coffin, 1995) and in vitro for herpesvirus plaque assays (Howat et al., 2006) , but, because they so closely approach zero, true limit cycles likely only occur theoretically, instead yielding stochastic extinctions in empirical time series. Bifurcation analysis of our mean field model revealed that regions of no infection (pathogen extinction) were bounded at lower threshold (Branch point) values for b, below which the pathogen was unable to invade. We found no upper threshold to invasion for b under any circumstances (i.e. b high enough to drive pathogen-induced extinction), but high b values resulted in Hopf bifurcations, which delineate regions of parameter space characterized by limit cycles. Since limit cycles so closely approach zero, high bs recovered in this range would likely produce virus-induced epidemic extinctions under experimental conditions. Under more robust representations of immunity, with higher values for either or both induced (r) and constitutive (") rates of antiviral acquisition, Hopf bifurcations occurred at increasingly higher values for b, meaning that persistent infections could establish at higher viral transmission rates ( Figure 2 ). Consistent with our derivation for R 0 , we found that the Branch point threshold for viral invasion was independent of changes to the induced immune parameter (r) but saturated at high values of " that characterize highly constitutive immunity ( Figure 3) . We next fit our theoretical model by least squares to each cell line-virus combination, under absent, induced, and constitutive assumptions of immunity. In general, best fit models recapitulated expected outcomes based on the immune phenotype of the cell line in question, as described in the general literature (Table 1 Ironically, the induced immune model offered a slightly better fit than the constitutive to rVSV-MARV infections on the PaKiT01 cell line (the one cell line-virus combination for which we know a constitutively antiviral cell-receptor incompatibility to be at play). Because constitutive immune assumptions can prohibit pathogen invasion (R 0 <1), model fits to this time series under constitutive assumptions were handicapped by overestimations of ", which prohibited pathogen invasion. Only by incorporating an exceedingly rapid rate of induced antiviral acquisition could the model guarantee that initial infection would be permitted and then rapidly controlled. In all panel (A) plots, the rate of induced immune antiviral acquisition (r) was fixed at 0.01. Panel (B) depicts dynamics under variably induced immunity, ranging from absent (left: r=0) to high (right: r=1). In all panel (B) plots, the rate of constitutive antiviral acquisition (") was fixed at 0.0001 Branch point curves are represented as solid lines and Hopf curves as dashed lines. White space indicates endemic equilibrium (persistence), gray space indicates limit cycles, and black space indicates no infection (extinction). Other parameter values for equilibrium analysis were fixed at: b = .025, m = .001, s = 1/6, c = 0. Special points from bifurcations analyses are listed in Supplementary file 3. In fitting our theoretical model to in vitro data, we estimated the within-host virus transmission rate (b) and the rate(s) of cellular acquisition to antiviral status (r or r + ") ( Table 1 ; Supplementary file 4). Under absent immune assumptions, r and " were fixed at 0 while b was estimated; under induced immune assumptions, " was fixed at 0 while r and b were estimated; and under constitutive immune assumptions, all three parameters (r, ", and b) were simultaneously estimated for each cell-virus combination. Best fit parameter estimates for MOI=0.001 data are visualized in conjunction with br and b -" bifurcations in (r) and (B) the constitutive immunity rate of antiviral acquisition ("). Panels show variation in the extent of immunity, from absent (left) to high (right). Branch point curves are represented as solid lines and Hopf curves as dashed lines. White space indicates endemic equilibrium (persistence), gray space indicates limit cycling, and black space indicates no infection (extinction). Other parameter values for equilibrium analysis were fixed at: b = .025, m = .001, s = 1/6, a = 1/6, c = 0. Special points from bifurcations analyses are listed in Supplementary file 3. space corresponding to theoretical limit cycles, consistent with observed virus-induced epidemic extinctions in stochastic tissue cultures. In contrast to Vero cells, the induced immunity model offered the best fit to all RoNi/7.1 data, consistent with reported patterns in the literature and our own validation by qPCR ( Table 1; Arnold et al., 2018; Kuzmin et al., 2017; Biesold et al., 2011; Pavlovich et al., 2018) . As in Vero cell trials, we estimated highest b values for rVSV-G infections on RoNi/7.1 cell lines but here recovered higher b estimates for rVSV-MARV than for rVSV-EBOV. This reversal was balanced by a higher estimated rate of acquisition to antiviral status (r) for rVSV-EBOV versus rVSV-MARV. In general, we observed that more rapid rates of antiviral acquisition (either induced, r, constitutive, ", or both) correlated with higher transmission rates (b). When offset by r, b values estimated for RoNi/7.1 infections maintained the same amplitude as those estimated for immune-absent Vero cell lines but caused gentler epidemics and reduced cellular mortality (Figure 1) . RoNi/7.1 parameter estimates localized in the region corresponding to endemic equilibrium for the deterministic, theoretical model (Figure 4) , yielding less acute epidemics which nonetheless went extinct in stochastic experiments. Finally, rVSV-G and rVSV-EBOV trials on PaKiT01 cells were best fit by models assuming constitutive immunity, while rVSV-MARV infections on PaKiT01 were matched equivalently by models assuming either induced or constitutive immunity-with induced models favored over constitutive in AIC comparisons because one fewer parameter was estimated (Figure 1-figure supplements 4-5; Supplementary file 4). For all virus infections, PaKiT01 cell lines yielded b estimates a full order of magnitude higher than Vero or RoNi/7.1 cells, with each b balanced by an immune response (either r, or r combined with ") also an order of magnitude higher than that recovered for the other cell lines ( Figure 4 ; Table 1 ). As in RoNi/7.1 cells, PaKiT01 parameter fits localized in the region corresponding to endemic equilibrium for the deterministic theoretical model. Because constitutive immune processes can actually prohibit initial pathogen invasion, constitutive immune fits to rVSV-MARV infections on PaKiT01 cell lines consistently localized at or below the Branch point threshold for virus invasion (R 0 ¼ 1). During model fitting for optimization of ", any parameter tests of " values producing R 0 <1 resulted in no infection and, consequently, produced an exceedingly poor fit to infectious time series data. In all model fits assuming constitutive immunity, across all cell lines, antiviral contributions from " prohibited virus from invading at all. The induced immune model thus produced a more parsimonious recapitulation of these data because virus invasion was always permitted, then rapidly controlled. In order to compare the relative contributions of each cell line's disparate immune processes to epidemic dynamics, we next used our mean field parameter estimates to calculate the initial 'antiviral rate'-the initial accumulation rate of antiviral cells upon virus invasion for each cell-virus-MOI combination-based on the following equation: where P E was calculated from the initial infectious dose (MOI) of each infection experiment and P S was estimated at disease-free equilibrium: Because and " both contribute to this initial antiviral rate, induced and constitutive immune assumptions are capable of yielding equally rapid rates, depending on parameter fits. Indeed, under fully induced immune assumptions, the induced antiviral acquisition rate (r) estimated for rVSV-MARV infection on PaKiT01 cells was so high that the initial antiviral rate exceeded even that estimated under constitutive assumptions for this cell-virus combination (Supplementary file 4) . In reality, we know that NPC1 receptor incompatibilities make PaKiT01 cell lines constitutively refractory to rVSV-MARV infection (Ng and Chandrab, 2018, Unpublished results) and that PaKiT01 cells also constitutively express the antiviral cytokine, IFN-a. Model fitting results suggest that this constitutive expression of IFN-a may act more as a rapidly inducible immune response following virus invasion than as a constitutive secretion of functional IFN-a protein. Nonetheless, as hypothesized, PaKiT01 cell lines were by far the most antiviral of any in our study-with initial antiviral rates estimated several orders of magnitude higher than any others in our study, under either induced or constitutive assumptions ( Table 1 ; Supplementary file 4). RoNi/7.1 cells displayed the second-most-pronounced signature of immunity, followed by Vero cells, for which the initial antiviral rate was essentially zero even under forced assumptions of induced or constitutive immunity ( Table 1 ; Supplementary file 4). Using fitted parameters for b and ", we additionally calculated R 0 , the basic reproduction number for the virus, for each cell line-virus-MOI combination ( Table 1 ; Supplementary file 4). We found that R 0 was essentially unchanged across differing immune assumptions for RoNi/7.1 and Vero cells, for which the initial antiviral rate was low. In the case of PaKiT01 cells, a high initial antiviral rate under either induced or constitutive immunity resulted in a correspondingly high estimation of b (and, consequently, R 0 ) which still produced the same epidemic curve that resulted from the much lower estimates for b and R 0 paired with absent immunity. These findings suggest that antiviral immune responses protect host tissues against virus-induced cell mortality and may facilitate the establishment of more rapid within-host transmission rates. Total monolayer destruction occurred in all cell-virus combinations excepting rVSV-EBOV infections on RoNi/7.1 cells and rVSV-EBOV and rVSV-MARV infections on PaKiT01 cells. Monolayer destruction corresponded to susceptible cell depletion and epidemic turnover where R-effective (the product of R 0 and the proportion susceptible) was reduced below one ( Figure 5) . For rVSV-EBOV infections on RoNi/7.1, induced antiviral cells safeguarded remnant live cells, which birthed new susceptible cells late in the time series. In rVSV-EBOV and rVSV-MARV infections on PaKiT01 cells, this antiviral protection halted the epidemic ( Figure 5 ; R-effective <1) before susceptibles fully declined. In the case of rVSV-EBOV on PaKiT01, the birth of new susceptibles from remnant live cells protected by antiviral status maintained late-stage transmission to facilitate long-term epidemic persistence. Importantly, under fixed parameter values for the infection incubation rate (s) and infectioninduced mortality rate (a), models were unable to reproduce the longer-term infectious time series captured in data from rVSV-EBOV infections on PaKiT01 cell lines without incorporation of cell births, an assumption adopted in previous modeling representations of IFN-mediated viral dynamics in tissue culture (Howat et al., 2006) . In our experiments, we observed that cellular reproduction took place as plaque assays achieved confluency. Finally, because the protective effect of antiviral cells is more clearly observable spatially, we confirmed our results by simulating fitted time series in a spatially-explicit, stochastic reconstruction of our mean field model. In spatial simulations, rates of antiviral acquisition were fixed at fitted values for r and " derived from mean field estimates, while transmission rates (b) were fixed at values ten times greater than those estimated under mean field conditions, accounting for the intensification of parameter thresholds permitting pathogen invasion in local spatial interactions (see Materials and methods; Videos 1-3; Figure 5-figure supplement 3; Supplementary file 5; Webb et al., 2007) . In immune capable time series, spatial antiviral cells acted as 'refugia' which protected live cells from infection as each initial epidemic wave 'washed' across a cell monolayer. Eventual birth of new susceptibles from these living refugia allowed for sustained epidemic transmission in cases where some infectious cells persisted at later timepoints in simulation (Videos 1-3; Figure 5-figure supplement 3 ). Bats are reservoirs for several important emerging zoonoses but appear not to experience disease from otherwise virulent viral pathogens. Though the molecular biological literature has made great progress in elucidating the mechanisms by which bats tolerate viral infections (Zhou et al., 2016; Ahn et al., 2019; Xie et al., 2018; Pavlovich et al., 2018; Zhang et al., 2013) , the impact of unique bat immunity on virus dynamics within-host has not been well-elucidated. We used an innovative combination of in vitro experimentation and within-host modeling to explore the impact of unique bat immunity on virus dynamics. Critically, we found that bat cell lines demonstrated a signature of enhanced interferon-mediated immune response, of either constitutive or induced form, which allowed for establishment of rapid within-host, cell-to-cell virus transmission rates (b). These results were supported by both data-independent bifurcation analysis of our mean field theoretical model, as well as fitting of this model to viral infection time series established in bat cell culture. Additionally, we demonstrated that the antiviral state induced by the interferon pathway protects live cells from mortality in tissue culture, resulting in in vitro epidemics of extended duration that enhance the probability of establishing a long-term persistent infection. Our findings suggest that viruses evolved in bat reservoirs possessing enhanced IFN capabilities could achieve more rapid within-host transmission rates without causing pathology to their hosts. Such rapidly-reproducing viruses would likely generate extreme virulence upon spillover to hosts lacking similar immune capacities to bats. To achieve these results, we first developed a novel, within-host, theoretical model elucidating the effects of unique bat immunity, then undertook bifurcation analysis of the model's equilibrium properties under immune absent, induced, and constitutive assumptions. We considered a cell line to be constitutively immune if possessing any number of antiviral cells at disease-free equilibrium but allowed the extent of constitutive immunity to vary across the parameter range for ", the constitutive rate of antiviral acquisition. In deriving the equation for R 0 , the basic reproduction number, which defines threshold conditions for virus invasion of a tissue (R 0 >1), we demonstrated how the invasion threshold is elevated at high values of constitutive antiviral acquisition, ". Constitutive immune processes can thus prohibit pathogen invasion, while induced responses, by definition, can only control infections post-hoc. Once thresholds for pathogen invasion have been met, assumptions of constitutive immunity will limit the cellular mortality (virulence) incurred at high transmission rates. Regardless of mechanism (induced or constitutive), interferon-stimulated antiviral cells appear to play a key role in maintaining longer term or persistent infections by safeguarding susceptible cells from rapid infection and concomitant cell death. Fitting of our model to in vitro data supported expected immune phenotypes for different bat cell lines as described in the literature. Simple target cell models that ignore the effects of immunity best recapitulated infectious time series derived from IFN-deficient Vero cells, while models assuming induced immune processes most accurately reproduced trials derived from RoNi/7.1 (Rousettus aegyptiacus) cells, which possess a standard virusinduced IFN-response. In most cases, models assuming constitutive immune processes best recreated virus epidemics produced on PaKiT01 (Pteropus alecto) cells, which are known to constitutively express the antiviral cytokine, IFN-a (Zhou et al., 2016) . Model support for induced immune assumptions in fits to rVSV-MARV infections on PaKiT01 cells suggests that the constitutive IFN-a expression characteristic of P. alecto cells may represent more of a constitutive immune priming process than a perpetual, functional, antiviral defense. Results from mean field model fitting were additionally confirmed in spatially explicit stochastic simulations of each time series. As previously demonstrated in within-host models for HIV (Coffin, 1995; Perelson et al., 1996; Nowak et al., 1995; Bonhoeffer et al., 1997; Ho et al., 1995) , assumptions of simple target-cell depletion can often provide satisfactory approximations of viral dynamics, especially those reproduced in simple in vitro systems. Critically, our model fitting emphasizes the need for incorporation of top-down effects of immune control in order to accurately reproduce infectious time series derived from bat cell tissue cultures, especially those resulting from the robustly antiviral PaKiT01 P. alecto cell line. These findings indicate that enhanced IFN-mediated immune pathways in bat reservoirs may promote elevated within-host virus replication rates prior to cross-species emergence. We nonetheless acknowledge the limitations imposed by in vitro experiments in tissue culture, especially involving recombinant viruses and immortalized cell lines. Future work should extend these cell culture studies to include measurements of multiple state variables (i.e. antiviral cells) to enhance epidemiological inference. The continued recurrence of Ebola epidemics across central Africa highlights the importance of understanding bats' roles as reservoirs for virulent zoonotic disease. The past decade has born witness to emerging consensus regarding the unique pathways by which bats resist and tolerate highly virulent infections (Brook and Dobson, 2015; Xie et al., 2018; Zhang et al., 2013; Ahn et al., 2019; Zhou et al., 2016; Ng et al., 2015; Pavlovich et al., 2018) . Nonetheless, an understanding of the mechanisms by which bats support endemic pathogens at the population level, or promote the evolution of virulent pathogens at the individual level, remains elusive. Endemic maintenance of infection is a defining characteristic of a pathogen reservoir (Haydon et al., 2002) , and bats appear to merit such a title, supporting long-term persistence of highly transmissible viral infections in isolated island populations well below expected critical community sizes (Peel et al., 2012) . Researchers debate the relative influence of population-level and within-host mechanisms which might explain these trends (Plowright et al., 2016) , but increasingly, field data are difficult to reconcile without acknowledgement of a role for persistent infections (Peel et al., 2018; Brook et al., 2019) . We present general methods to study cross-scale viral dynamics, which suggest that within-host persistence is supported by robust antiviral responses characteristic of bat immune processes. Viruses which evolve rapid replication rates under these robust antiviral defenses may pose the greatest hazard for cross-species pathogen emergence into spillover hosts with immune systems that differ from those unique to bats. All experiments were carried out on three immortalized mammalian kidney cell lines: Vero (African green monkey), RoNi/7.1 (Rousettus aegyptiacus) (Kühl et al., 2011; Biesold et al., 2011) and PaKiT01 (Pteropus alecto) (Crameri et al., 2009) . The species identifications of all bat cell lines was confirmed morphologically and genetically in the publications in which they were originally described (Kühl et al., 2011; Biesold et al., 2011; Crameri et al., 2009) . Vero cells were obtained from ATCC. Monolayers of each cell line were grown to 90% confluency (~9Â10 5 cells) in 6-well plates. Cells were maintained in a humidified 37˚C, 5% CO 2 incubator and cultured in Dulbecco's modified Eagle medium (DMEM) (Life Technologies, Grand Island, NY), supplemented with 2% fetal bovine serum (FBS) (Gemini Bio Products, West Sacramento, CA), and 1% penicillin-streptomycin (Life Technologies). Cells were tested monthly for mycoplasma contamination while experiments were taking place; all cells assayed negative for contamination at every testing. Previous work has demonstrated that all cell lines used are capable of mounting a type I IFN response upon viral challenge, with the exception of Vero cells, which possess an IFN-b deficiency (Desmyter et al., 1968; Rhim et al., 1969; Emeny and Morgan, 1979) . RoNi/7.1 cells have been shown to mount idiosyncratic induced IFN defenses upon viral infection (Pavlovich et al., 2018; Kuzmin et al., 2017; Arnold et al., 2018; Kühl et al., 2011; Biesold et al., 2011) , while PaKiT01 cells are known to constitutively express the antiviral cytokine, IFN-a (Zhou et al., 2016) . This work is the first documentation of IFN signaling induced upon challenge with the particular recombinant VSVs outlined below. We verified known antiviral immune phenotypes via qPCR. Results were consistent with the literature, indicating a less pronounced role for interferon defense against viral infection in RoNi/7.1 versus PaKiT01 cells. Replication-capable recombinant vesicular stomatitis Indiana viruses, expressing filovirus glycoproteins in place of wild type G (rVSV-G, rVSV-EBOV, and rVSV-MARV) have been previously described (Wong et al., 2010; Miller et al., 2012) . Viruses were selected to represent a broad range of anticipated antiviral responses from host cells, based on a range of past evolutionary histories between the virus glycoprotein mediating cell entry and the host cell's entry receptor. These interactions ranged from the total absence of evolutionary history in the case of rVSV-G infections on all cell lines to a known receptor-level cell entry incompatibility in the case of rVSV-MARV infections on PaKiT01 cell lines. To measure infectivities of rVSVs on each of the cell lines outlined above, so as to calculate the correct viral dose for each MOI, NH 4 Cl (20 mM) was added to infected cell cultures at 1-2 hr postinfection to block viral spread, and individual eGFP-positive cells were manually counted at 12-14 hr post-infection. Previously published work indicates that immortalized kidney cell lines of Rousettus aegyptiacus (RoNi/7.1) and Pteropus alecto (PaKiT01) exhibit different innate antiviral immune phenotypes through, respectively, induced (Biesold et al., 2011; Pavlovich et al., 2018; Kühl et al., 2011; Arnold et al., 2018) and constitutive (Zhou et al., 2016 ) expression of type I interferon genes. We verified these published phenotypes on our own cell lines infected with rVSV-G, rVSV-EBOV, and rVSV-MARV via qPCR of IFN-a and IFN-b genes across a longitudinal time series of infection. Specifically, we carried out multiple time series of infection of each cell line with each of the viruses described above, under mock infection conditions and at MOIs of 0.0001 and 0.001-with the exception of rVSV-MARV on PaKiT01 cell lines, for which infection was only performed at MOI = 0.0001 due to limited viral stocks and the extremely low infectivity of this virus on this cell line (thus requiring high viral loads for initial infection). All experiments were run in duplicate on 6well plates, such that a typical plate for any of the three viruses had two control (mock) wells, two MOI = 0.0001 wells and two MOI = 0.001 wells, excepting PaKiT01 plates, which had two control and four MOI = 0.0001 wells at a given time. We justify this PaKiT01 exemption through the expectation that IFN-a expression is constitutive for these cells, and by the assumption that any expression exhibited at the lower MOI should also be present at the higher MOI. For these gene expression time series, four 6-well plates for each cell line-virus combination were incubated with virus for one hour at 37˚C. Following incubation, virus was aspirated off, and cell monolayers were washed in PBS, then covered with an agar plaque assay overlay to mimic conditions under which infection trials were run. Plates were then harvested sequentially at timepoints of roughly 5, 10, 15, and 20 hr post-infection (exact timing varied as multiple trials were running simultaneously). Upon harvest of each plate, agar overlay was removed, and virus was lysed and RNA extracted from cells using the Zymo Quick RNA Mini Prep kit, according to the manufacturer's instructions and including the step for cellular DNA digestion. Post-extraction, RNA quality was verified via nanodrop, and RNA was converted to cDNA using the Invitrogen Superscript III cDNA synthesis kit, according to the manufacturer's instructions. cDNA was then stored at 4˚C and as a frozen stock at À20˚C to await qPCR. We undertook qPCR of cDNA to assess expression of the type I interferon genes, IFN-a and IFNb, and the housekeeping gene, b-Actin, using primers previously reported in the literature (Supplementary file 6) . For qPCR, 2 ml of each cDNA sample was incubated with 7 ml of deionized water, 1 ml of 5 UM forward/reverse primer mix and 10 ml of iTaq Universal SYBR Green, then cycled on a QuantStudio3 Real-Time PCR machine under the following conditions: initial denaturation at 94 C for 2 min followed by 40 cycles of: denaturation at 95˚C (5 s), annealing at 58˚C (15 s), and extension at 72˚C (10 s). We report simple d-Ct values for each run, with raw Ct of the target gene of interest (IFN-a or IFN-b) subtracted from raw Ct of the b-Actin housekeeping gene in Figure 1 -figure supplement 6. Calculation of fold change upon viral infection in comparison to mock using the d-d-Ct method (Livak and Schmittgen, 2001) was inappropriate in this case, as we wished to demonstrate constitutive expression of IFN-a in PaKiT01 cells, whereby data from mock cells was identical to that produced from infected cells. After being grown to~90% confluency, cells were incubated with pelleted rVSVs expressing eGFP (rVSV-G, rVSV-EBOV, rVSV-MARV). Cell lines were challenged with both a low (0.0001) and high (0.001) multiplicity of infection (MOI) for each virus. In a cell monolayer infected at a given MOI (m), the proportion of cells (P), infected by k viral particles can be described by the Poisson distribution: P k ð Þ ¼ e Àm m k k! , such that the number of initially infected cells in an experiment equals: 1 À e Àm . We assumed that a~90% confluent culture at each trial's origin was comprised of~9x10 5 cells and conducted all experiments at MOIs of 0.0001 and 0.001, meaning that we began each trial by introducing virus to, respectively,~81 or 810 cells, representing the state variable 'E' in our theoretical model. Low MOIs were selected to best approximate the dynamics of mean field infection and limit artifacts of spatial structuring, such as premature epidemic extinction when growing plaques collide with plate walls in cell culture. Six-well plates were prepared with each infection in duplicate or triplicate, such that a control well (no virus) and 2-3 wells each at MOI 0.001 and 0.0001 were incubated simultaneously on the same plate. In total, we ran between 18 and 39 trials at each cell-virus-MOI combination, excepting r-VSV-MARV infections on PaKiT01 cells at MOI = 0.001, for which we ran only eight trials due to the low infectivity of this virus on this cell line, which required high viral loads for initial infection. Cells were incubated with virus for one hour at 37˚C. Following incubation, virus was aspirated off, and cell monolayers were washed in PBS, then covered with a molten viscous overlay (50% 2X MEM/Lglutamine; 5% FBS; 3% HEPES; 42% agarose), cooled for 20 min, and re-incubated in their original humidified 37˚C, 5% CO 2 environment. After application of the overlay, plates were monitored periodically using an inverted fluorescence microscope until the first signs of GFP expression were witnessed (~6-9.5 hr post-infection, depending on the cell line and virus under investigation). From that time forward, a square subset of the center of each well (comprised of either 64-or 36-subframes and corresponding to roughly 60% and 40% of the entire well space) was imaged periodically, using a CellInsight CX5 High Content Screening (HCS) Platform with a 4X air objective (ThermoFisher, Inc, Waltham, MA). Microscope settings were held standard across all trials, with exposure time fixed at 0.0006 s for each image. One color channel was imaged, such that images produced show GFP-expressing cells in white and non-GFP-expressing cells in black (Figure 1-figure supplement 1) . Wells were photographed in rotation, as frequently as possible, from the onset of GFP expression until the time that the majority of cells in the well were surmised to be dead, GFP expression could no longer be detected, or early termination was desired to permit Hoechst staining. In the case of PaKiT01 cells infected with rVSV-EBOV, where an apparently persistent infection established, the assay was terminated after 200+ hours (8+ days) of continuous observation. Upon termination of all trials, cells were fixed in formaldehyde (4% for 15 min), incubated with Hoechst stain (0.0005% for 15 min) (ThermoFisher, Inc, Waltham, MA), then imaged at 4X on the CellInsight CX5 High Content Screening (HCS) Platform. The machine was allowed to find optimal focus for each Hoechst stain image. One color channel was permitted such that images produced showed live nuclei in white and dead cells in black. Hoechst stain colors cellular DNA, and viral infection is thought to interfere with the clarity of the stain (Dembowski and DeLuca, 2015) . As such, infection termination, cell fixation, and Hoechst staining enables generation of a rough time series of uninfectious live cells (i.e. susceptible + antiviral cells) to complement the images which produced time series of proportions infectious. Due to uncertainty over the exact epidemic state of Hoechst-stained cells (i.e. exposed but not yet infectious cells may still stain), we elected to fit our models only to the infectious time series derived from GFPexpressing images and used Hoechst stain images as a post hoc visual check on our fit only ( Figure 5 ; Figure 5 -figure supplements 1-2). Images recovered from the time series above were processed into binary ('infectious' vs. 'non-infectious' or, for Hoechst-stained images, 'live' vs. 'dead') form using the EBImage package (Pau et al., 2010) in R version 3.6 for MacIntosh, after methods further detailed in Supplementary file 7. Binary images were then further processed into time series of infectious or, for Hoechst-stained images, live cells using a series of cell counting scripts. Because of logistical constraints (i.e. many plates of simultaneously running infection trials and only one available imaging microscope), the time course of imaging across the duration of each trial was quite variable. As such, we fitted a series of statistical models to our processed image data to reconstruct reliable values of the infectious proportion of each well per hour for each distinct trial in all cell line-virus-MOI combinations (Figure 1 To derive the expression for R 0 , the basic pathogen reproductive number in vitro, we used Next Generation Matrix (NGM) techniques (Diekmann et al., 1990; Heffernan et al., 2005) , employing Wolfram Mathematica (version 11.2) as an analytical tool. R 0 describes the number of new infections generated by an existing infection in a completely susceptible host population; a pathogen will invade a population when R 0 >1 (Supplementary file 2). We then analyzed stability properties of the system, exploring dynamics across a range of parameter spaces, using MatCont (version 2.2) (Dhooge et al., 2008) for Matlab (version R2018a) (Supplementary file 3). The birth rate, b, and natural mortality rate, m, balance to yield a population-level growth rate, such that it is impossible to estimate both b and m simultaneously from total population size data alone. As such, we fixed b at. 025 and estimated m by fitting an infection-absent version of our mean field model to the susceptible time series derived via Hoechst staining of control wells for each of the three cell lines (Figure 1-figure supplement 7) . This yielded a natural mortality rate, m, corresponding to a lifespan of approximately 121, 191, and 84 hours, respectively, for Vero, RoNi/7.1, and PaKiT01 cell lines (Figure 1-figure supplement 7) . We then fixed the virus incubation rate, s, as the inverse of the shortest observed duration of time from initial infection to the observation of the first infectious cells via fluorescent microscope for all nine cell line -virus combinations (ranging 6 to 9.5 hours). We fixed a, the infection-induced mortality rate, at 1/6, an accepted standard for general viral kinetics (Howat et al., 2006) , and held c, the rate of antiviral cell regression to susceptible status, at 0 for the timespan (<200 hours) of the experimental cell line infection trials. We estimated cell line-virus-MOI-specific values for b, r, and " by fitting the deterministic output of infectious proportions in our mean field model to the full suite of statistical outputs of all trials for each infected cell culture time series (Figure 1-figure supplements 2-3) . Fitting was performed by minimizing the sum of squared differences between the deterministic model output and cell linevirus-MOI-specific infectious proportion of the data at each timestep. We optimized parameters for MOI = 0.001 and 0.0001 simultaneously to leverage statistical power across the two datasets, estimating a different transmission rate, b, for trials run at each infectious dose but, where applicable, estimating the same rates of r and " across the two time series. We used the differential equation solver lsoda() in the R package deSolve (Soetaert et al., 2010) to obtain numerical solutions for the mean field model and carried out minimization using the 'Nelder-Mead' algorithm of the optim() function in base R. All model fits were conducted using consistent starting guesses for the parameters, b (b = 3), and where applicable, r (r = 0.001) and " (" = 0.001). In the case of failed fits or indefinite hessians, we generated a series of random guesses around the starting conditions and continued estimation until successful fits were achieved. All eighteen cell line-virus-MOI combinations of data were fit by an immune absent (" = r = 0) version of the theoretical model and, subsequently, an induced immunity (" = 0; r >0) and constitutive immunity (" >0; r >0) version of the model. Finally, we compared fits across each cell line-virus-MOI combination via AIC. In calculating AIC, the number of fitted parameters in each model (k) varied across the immune phenotypes, with one parameter (b) estimated for absent immune assumptions, two (b and r) for induced immune assumptions, and three (b, r, and ") for constitutive immune assumptions. The sample size (n) corresponded to the number of discrete time steps across all empirical infectious trials to which the model was fitted for each cell-line virus combination. All fitting and model comparison scripts are freely available for download at the following FigShare repository: DOI: 10.6084/m9.figshare.8312807. Finally, we verified all mean field fits in a spatial context, in order to more thoroughly elucidate the role of antiviral cells in each time series. We constructed our spatial model in C++ implemented in R using the packages Rcpp and RcppArmadillo (Eddelbuettel and Francois, 2011; Eddelbuettel and Sanderson, 2017) . Following Nagai and Honda (2001) and Howat et al. (2006) , we modeled this system on a two-dimensional hexagonal lattice, using a ten-minute epidemic timestep for cell state transitions. At the initialization of each simulation, we randomly assigned a duration of natural lifespan, incubation period, infectivity period, and time from antiviral to susceptible status to all cells in a theoretical monolayer. Parameter durations were drawn from a normal distribution centered at the inverse of the respective fixed rates of m, s, a, and c, as reported with our mean field model. Transitions involving the induced (r) and constitutive (") rates of antiviral acquisition were governed probabilistically and adjusted dynamically at each timestep based on the global environment. As such, we fixed these parameters at the same values estimated in the mean field model, and multiplied both r and " by the global proportion of, respectively, exposed and susceptible cells at a given timestep. In contrast to antiviral acquisition rates, transitions involving the birth rate (b) and the transmission rate (b) occurred probabilistically based on each cell's local environment. The birth rate, b, was multiplied by the proportion of susceptible cells within a six-neighbor circumference of a focal dead cell, while b was multiplied by the proportion of infectious cells within a thirty-six neighbor vicinity of a focal susceptible cell, thus allowing viral transmission to extend beyond the immediate nearestneighbor boundaries of an infectious cell. To compensate for higher thresholds to cellular persistence and virus invasion which occur under local spatial conditions (Webb et al., 2007) , we increased the birth rate, b, and the cell-to-cell transmission rate, b, respectively, to six and ten times the values used in the mean field model (Supplementary file 4) . We derived these increases based on the assumption that births took place exclusively based on pairwise nearest-neighbor interactions (the six immediately adjacent cells to a focal dead cell), while viral transmission was locally concentrated but included a small (7.5%) global contribution, representing the thirty-six cell surrounding vicinity of a focal susceptible. We justify these increases and derive their origins further in Supplementary file 5. We simulated ten stochastic spatial time series for all cell-virus combinations under all three immune assumptions at a population size of 10,000 cells and compared model output with data in . Transparent reporting form Data availability All data generated or analysed during this study are included in the manuscript and supporting files. All images and code used in this study have been made available for download at the following Figshare

Why may the bats have this unique adaptation?

Development of an ELISA-array for simultaneous detection of five encephalitis viruses https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3305475/ SHA: ef2b8f83d5a3ab8ae35e4b51fea6d3ed9eb49122 Authors: Kang, Xiaoping; Li, Yuchang; Fan, Li; Lin, Fang; Wei, Jingjing; Zhu, Xiaolei; Hu, Yi; Li, Jing; Chang, Guohui; Zhu, Qingyu; Liu, Hong; Yang, Yinhui Date: 2012-02-27 DOI: 10.1186/1743-422x-9-56 License: cc-by Abstract: Japanese encephalitis virus(JEV), tick-borne encephalitis virus(TBEV), and eastern equine encephalitis virus (EEEV) can cause symptoms of encephalitis. Establishment of accurate and easy methods by which to detect these viruses is essential for the prevention and treatment of associated infectious diseases. Currently, there are still no multiple antigen detection methods available clinically. An ELISA-array, which detects multiple antigens, is easy to handle, and inexpensive, has enormous potential in pathogen detection. An ELISA-array method for the simultaneous detection of five encephalitis viruses was developed in this study. Seven monoclonal antibodies against five encephalitis-associated viruses were prepared and used for development of the ELISA-array. The ELISA-array assay is based on a "sandwich" ELISA format and consists of viral antibodies printed directly on 96-well microtiter plates, allowing for direct detection of 5 viruses. The developed ELISA-array proved to have similar specificity and higher sensitivity compared with the conventional ELISAs. This method was validated by different viral cultures and three chicken eggs inoculated with infected patient serum. The results demonstrated that the developed ELISA-array is sensitive and easy to use, which would have potential for clinical use. Text: Japanese encephalitis virus(JEV), tick-borne encephalitis virus(TBEV), eastern equine encephalitis virus (EEEV), sindbis virus(SV), and dengue virus(DV) are arboviruses and cause symptoms of encephalitis, with a wide range of severity and fatality rates [1] . Establishment of an accurate and easy method for detection of these viruses is essential for the prevention and treatment of associated infectious diseases. Currently, ELISA and IFA are the methods which are clinically-available for the detection of encephalitis viral antigens, but they could only detect one pathogen in one assay [2, 3] . There are a variety of different methods available for identifying multiple antigens in one sample simultaneously, such as two-dimensional gel electrophoresis , protein chip, mass spectrometry, and suspension array technology [4] [5] [6] . However, the application of these techniques on pathogen detection is still in an early phase, perhaps due to the complicated use and high cost. Antibody arrays for simultaneous multiple antigen quantification are considered the most accurate methods [7] [8] [9] [10] . Liew [11] validated one multiplex ELISA for the detection of 9 antigens; Anderson [12] used microarray ELISA for multiplex detection of antibodies to tumor antigens in breast cancer, and demonstrated that ELISA-based array assays had the broadest dynamic range and lowest sample volume requirements compared with the other assays. However, the application of ELISA-based arrays is currently limited to detection of cancer markers or interleukins; no detection of pathogens has been reported. In this study, we developed an ELISA-based array for the simultaneous detection of five encephalitis viruses. Seven specific monoclonal antibodies were prepared against five encephalitis viruses and used to establish an ELISA-array assay. The assay was validated using cultured viruses and inoculated chicken eggs with patient sera. The results demonstrated that this method combined the advantage of ELISA and protein array (multiplex and ease of use) and has potential for the identification of clinical encephalitis virus. Monoclonal antibodies were prepared from hybridoma cell lines constructed by Prof. Zhu et al. Purification was conducted by immunoaffinity chromatography on protein G affinity sepharose [13] . Specific monoclonal antibodies (4D5 against JEV, 2B5 against TBEV, 1F1 against SV, 2B8 against serotype 2 DV, 4F9 against serotype 4 DV, 4E11 against EEEV, and 2A10 against Flavivirus) were selected for this study. All of the antibodies were raised according to standard procedures. Using 4D5, 2B5, 1F1, 2B8, 4F9, and 4E11 as capture antibodies, detection antibodies (2A10, 1 F1, and 4E11) were coupled to biotin-NHS ester(Pierce, Germany) at 4°C for 3 h according to the manufacturer's instructions. Unincorporated biotin was removed by Desalt spin column (Pierce). Immunologic reactions were reported by Streptavidin-HRP (CWBIO, Beijing, China) and Super Signal ELISA Femto Maximum sensitive substrate. Purified goat-anti mouse antibody was used as a positive control. JEV and DV were cultured in C6/36 cells; SV, TBEV, and EEEV were cultured in BHK-21 cells. The culture of TBEV and EEEV was conducted in biosafety level 3 facility, however, JEV, DV and SV were conducted in biosafety level 2 facility. Viral titers were determined by the 50% tissue culture infectious dose (TCID 50 ) method. All the cultures were inactivated by 0.025% β-propionolactone at 4°C overnight, then 37°C for 1 h to decompose β-propionolactone. Antibodies were spotted using a BIODOT machine (BD6000;California, USA) on ELISA plates (30 nl/dot). The plates were blocked with 3% BSA-PBS in 37°C for 1 h, followed by washing 3 times with PBS containing 0.1% Tween-20 for 2 min each. Then, the plates were dried, sealed, and stored at 4°C before use [11] . When spotting, different spotting buffers and concentrations of capture monoclonal antibodies were evaluated to optimize the ELISA-array assay. The optimization was evaluated by dot morphology and signal intensity. The tested spotting buffers included 1 × phosphate buffer saline (PBS), PBS +20% glycerol, and 1 × PBS + 20% glycerol+0.004% Triton-X100. A range of monoclonal antibody concentrations (0.0125, 0.025, 0.05, 0.1, and 0.2 mg/ml) were compared. Following a double antibody sandwich format, printed plates were incubated sequentially with inactivated viral cultures, biotin-labeled detecting antibody, HPR-labeled avidin, and substrate, followed by signal evaluation. Antigen binding was performed in PBS(containing 0.1% Tween-20 and 5% FCS) at 37°C for 2 h, followed by washing 3 times(1 × PBS containing 0.1% Tween-20). Incubation of ELISA plates with biotinylated detecting antibody cocktails was performed in PBS (containing 0.1% Tween-20 and 5% FCS) at 37°C for 2 h. After washing, specific binding of the detecting antibodies was reported by streptavidin-HRP and stained with Super Signal ELISA Femto Maximum sensitive substrate (Thermo scientific, Rockford, USA) [11, 14, 15] . Visualization of the plate was performed in AE 1000 cool CCD image analyzer(Beijing BGI GBI Biotech Company., LTD, China). The signal intensity and background of each spot was read out and recorded with "Monster"software. The positive signals were defined as a signal value > 400 and a signal value (sample)/signal value (negative) > 2. The identical antibodies used in the ELISA-array format were also tested in a conventional ELISA format to determine the difference in sensitivity and specificity of the two methods. The conventional ELISAs were performed at the same time as the ELISA-array assays to ensure similar reaction conditions. The conventional ELISAs were performed in an identical maner to the ELISA-array, except that antibodies were coated at a concentration of 2 μg/mL in PBS (pH 7.4), and substrate TMB was used instead of Super Signal ELISA Femto Maximum sensitive substrate [16, 17] . Three serum samples were collected from patients with nervous system symptoms and histories of tick bites. The serum samples were treated with penicillin and streptomycin, then inoculated into the allantoic cavities of chicken eggs. 3 days later, the liquid was collected and divided into two portions (one for inactivation and one for RNA extraction). The RNA and inactivated samples were stored at -70°C before use. RNA was extracted from the inoculated chicken eggs using a RNeasy mini kit (Qiagen Inc., Valencia, CA, USA) according to the manufacturer's instructions. All RNA extraction procedures were conducted at BSL-3 facilities. The primers and probes were used as previously described [18] . The real-time RT-PCR was conducted with a Quti-teck q-RT-PCR Kit (Qiagen Inc,). The reaction consisted of 10 μL of 2 × reaction buffer (0.2 μL reverse transcription enzyme, and 250 nmol/l primers and probes). RNA and deionized water were added to a final volume of 20 μl. PCR was performed with a LightCycler 2.0 (Roche, Switzerland) [19] . Optimization of the ELISA-array assay The spotted array layout is depicted in Figure 1 and the efficacy of three different spotting buffers on the quality of the printed ELISA-arrays were investigated by spot morphology observation and signal intensity comparison. The spotting concentration of the capture antibodies varied from 0.2 to 0.0125 mg/ml (each was serially diluted 2-fold). The efficacy of the spotting concentration of the capture antibodies was evaluated by virus culture detection, the proper spotting concentration was determined by a combination of minimized cross reaction and higher signal intensity. Figure 1 illustrates the array layout and Figure 2 demonstrates the result of the three spotting buffers and spot concentration of antibody 2B5 by TBE virus culture detection. Cross reaction detection was also conducted by applying JEV, YF, and DV cultures. Spot morphology observation (Figures 2a, b , and 2c) demonstrated that spotting buffer containing PBS with 20% glycerol produced tailed spot morphology; buffers containing PBS alone and PBS with 20% glycerol +0.004% Triton-X100 gave good spot morphology (round and full). Buffers containing PBS with 20% glycerol and PBS with 20% glycerol+0.004% Triton-X100 produced higher signal intensities than PBS alone. Thus, PBS with 20% glycerol+0.004% Triton-X100 was adopted as the optimized spotting buffer for subsequent experiments. Simultaneously, the spot concentration evaluation suggested that 0.05 mg/ml was optimal. At this concentration, the signal intensity was higher and the cross-reaction did not appear (Figure 2d ). Consequently, spotting concentration optimization of other capture antibodies (4D5, 1F1, 4E11, and 2B8) demonstrated that 0.05 mg/ml was also suitable(data not shown). The optimized ELISA array layout is shown in Figure 3 , which was applied in the following experiments. Successful detection of viral pathogens requires a test with high sensitivity and specificity. To evaluate the performance of the designed antibody arrays, the specificity and sensitivity of the individual analytes were examined. By testing serially-diluted viral cultures, including DV-2, DV-4, JEV, TBE, SV, and EEEV, the sensitivity of ELISAarray and the identical conventional ELISA were compared ( Table 1 ). The detection limit of the two methods was compared and demonstrated. The cross-reactivity test was conducted using BHK-21 and vero cell lysate, Yellow fever virus (YFV) cultures (5 × 10 5 TCID 50 /ml, West Nile virus(WNV) cultures(2 × 10 6 TCID 50 /ml), and Western equine encephalitis virus(1 × 10 7 TCID 50 /ml). The results demonstrated that neither the ELISA-array nor traditional ELISA displayed cross-reactivity. Equal volumes of cultured TBEV, JEV, DV-2, DV-4, SV, and EEEV were prepared for single sample detection; two or three of the cultures were mixed for multiplex detection. A cocktail of biotin conjugated antibody (2A10, 4E11, and 1F1) was used in all tests. The results demonstrated that for all virus combinations, each virus was detected specifically, with no false-positive or-negative results (Figures 4 and 5) . Chicken eggs inoculated with infected human serum were used for validation of the ELISA-array assay. All samples showed high reaction signals with capture antibody 2B5, which was specific for TBEV ( Figure 6b ). The ELISA-array assay suggested that the three patients were all infected with TBEV. To verify the results tested by ELISA-array, RNA extracted from chicken eggs was applied to a real time-RT-PCR assay using primers and probes targeting TBEV. The results were also positive (Figure 6a) . The consensus detection results confirmed that the ELISAarray assay was reliable. To be widely used in the clinical setting, the detection system should be easy to use and can be performed by untrained staff with little laboratory and experimental experience. Moreover, when the volume of the clinical samples is limited and an increasing number of pathogens per sample needs to be tested, the detecting system should be high-throughput to allow detection of multiple pathogens simultaneously [6, 20, 21] . Multiple detection, easy to use, and affordability are requirements for detection methods in the clinical setting. Thus, an ELISA-array, which combines the advantages of ELISA and protein array, meets the above requirements. It has been reported that an ELISA-array has been used in the diagnosis of cancer and auto-allergic disease [7, 12] ; however, No study has reported the detection of viral pathogens. In this study, we developed a multiplex ELISA-based method in a double-antibody sandwich format for the simultaneous detection of five encephalitis-associated viral pathogens. The production of a reliable antibody chip for identification of microorganisms requires careful screening of capture of antibodies [14] . Cross-reactivity must be minimized and the affinity of the antibody is as important as the specificity. First, we prepared and screened 23 monoclonal antibodies against eight viruses and verified the specificity and affinity to the target viruses by an immunofluorescence assay. Then, the antibodies were screened by an ELISA-array with a double-antibody sandwich ELISA format. The antibodies which produced cross-reactivity and low-positive signals were excluded. Finally, six antibodies were selected as capture antibodies. Another monoclonal antibody, 2A10, which could specifically react with all viruses in the genus Flavivirus was used for detecting antibody against DV, JEV, and TBEV. For the detection of EEEV and SV, although the detecting and trapping antibodies were the same (1F1 and 4E11, respectively), the antibodies produced excellent positive signals. The epitope was not defined; however, we suspect that the antibodies both target the surface of the virions. As one virion exits as, many with the same epitope appear, thus no interference occurred using the same antibody in the double-antibody sandwich format assay. Currently, the availability of antibodies suitable for an array format diagnostic assay is a major problem. In the ELISA-array assay, this problem exists as well. Because of the limitation of available antibodies, this assay could only detect 5 pathogens. In the future, with increasing numbers of suitable antibodies, especially specific antibodies against Flavivirus, this ELISAarray might be able to test more pathogens and be of greater potential use. To make the assay more amenable to multiple virus detection, the assay protocol was optimized. In addition to the dotting buffer, the capture antibody concentration and the different virus inactivation methods (heating and β-propiolactone) were also compared and evaluated. Heat inactivation was performed by heating the viral cultures at 56°C for 1 h, and β-propiolactone inactivation was performed by adding β-propiolactone into the retains better antigenicity than the heat-inactivation method. Thus, β-propiolactone treatment was chosen as the virus-inactivation method. A conventional ELISA is a standard method in many diagnostic laboratories. We compared the ELISA-array with a conventional ELISA and confirmed that the advantage of the ELISA-array was evident with comparable specificity and higher sensitivity than ELISA. The time required for the ELISA-array is significantly less than for conventional ELISA (4 h vs. a minimum of 6 h, respectively). Furthermore, less IgG is required for printing than for coating ELISA plates. Coating of a single well in microtiter plate requires 100 μl of a 1 μg/ml antibody solution, which is equivalent to 100 ng of IgG. For the ELISA-array, only 30 nl of a 50 μg/ml antibody solution is required for each spot, which is equivalent to 1.5 ng of IgG. With the characteristics of ease of use, sensitivity, specificity, and accuracy, the ELISA-array assay would be widely accepted for clinical use.

What capture antibodies were used in the study?

Whole genome sequencing and phylogenetic analysis of human metapneumovirus strains from Kenya and Zambia https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6941262/ SHA: f5ae3f66face323615df39d838e056ab5fcc98df Authors: Kamau, Everlyn; Oketch, John W.; de Laurent, Zaydah R.; Phan, My V. T.; Agoti, Charles N.; Nokes, D. James; Cotten, Matthew Date: 2020-01-02 DOI: 10.1186/s12864-019-6400-z License: cc-by Abstract: BACKGROUND: Human metapneumovirus (HMPV) is an important cause of acute respiratory illness in young children. Whole genome sequencing enables better identification of transmission events and outbreaks, which is not always possible with sub-genomic sequences. RESULTS: We report a 2-reaction amplicon-based next generation sequencing method to determine the complete genome sequences of five HMPV strains, representing three subgroups (A2, B1 and B2), directly from clinical samples. In addition to reporting five novel HMPV genomes from Africa we examined genetic diversity and sequence patterns of publicly available HMPV genomes. We found that the overall nucleotide sequence identity was 71.3 and 80% for HMPV group A and B, respectively, the diversity between HMPV groups was greater at amino acid level for SH and G surface protein genes, and multiple subgroups co-circulated in various countries. Comparison of sequences between HMPV groups revealed variability in G protein length (219 to 241 amino acids) due to changes in the stop codon position. Genome-wide phylogenetic analysis showed congruence with the individual gene sequence sets except for F and M2 genes. CONCLUSION: This is the first genomic characterization of HMPV genomes from African patients. Text: Human metapneumovirus (HMPV) is a single-stranded RNA virus in the family Paramyxoviridae and closely related to human respiratory syncytial virus (RSV) [1] . HMPV causes respiratory disease similar to RSV, ranging from mild upper respiratory infection to bronchiolitis and pneumonia [2] . HMPV infections are seasonal and coinfection with other respiratory pathogens is common [1] . The HMPV genome is approximately 13 kb and comprises eight open reading frames (ORFs) encoding nucleoprotein (N), phosphoprotein (P), matrix protein (M), fusion glycoprotein (F), transcription enhancer protein (M2), small hydrophobic protein (SH), attachment glycoprotein (G), and large polymerase protein (L) [3] . The membrane glycoproteins F and G sequences are used to define two major genotypes or groups, A and B, which are further classified into four subgroups (A1, A2, B1, and B2). HMPV A2, the most frequently observed subgroup, is further divided into two proposed sub-lineages (A2a and A2b) [3] . HMPV is reported to have an important contribution to acute respiratory infections (ARI) in Africa. For instance, HMPV-associated hospitalization was estimated at 6.5 per 1000 person years in infants in Soweto, South Africa [4] ; at 4% in hospitalized children with severe ARI during a 2-year period in Cameroon [5] ; and in rural western Kenya, incidence of HMPV associated with ARI cases in outpatient clinic visits was estimated at 0.43 per 100 person-years among outpatients [6] . In Kilifi coastal Kenya, between January 2007 to December 2011, children under 6 months of age accounted for 44% of HMPV positive cases, while 74% were children under 1 year, and 1.3% (2/160) were children > 36 months [7] . In Dadaab and Kakuma refugee camps in Kenya, HMPV was detected in 5.7% hospitalizations, and virus-positive crude hospitalization rate (per 1000 children < 5 years old) was 4 for HMPV [8] . In Mali, contribution of HMPV to pneumonia had a population attributable fraction of 9% (95% CI: 7-11%) [9] ; while in Morocco [10] , 8 .9% of children < 5 years admitted with severe pneumonia were infected with HMPV. HMPV prevalence and incidence elsewhere globally, is indicated in Additional file 4: Table S1 . Of note is that the variations in incidence rates could be attributed to study population, seasonality and even detection methods. Nonetheless, genomic epidemiology of HMPV in Africa is inadequately reported, and comparison of genetic similarity and differences between African and global strains is not documented. Genome sequences provide valuable resources for characterizing viral evolution and disease epidemiology, and for identifying transmission events and outbreaks, which is not always possible with sub-genomic fragments [11] [12] [13] . The increased number of phylogenetically informative variant sites obtained from full genomes may allow better linking of cases and aid public health interventions in real time during epidemics [14, 15] . PCR approaches for targeted whole genome sequencing, in contrast to random amplification, can preferentially amplify the target virus over host or environmental nucleic acids [16, 17] potentially focusing sequencing on the virus of interest. To date, the largest dataset of HMPV whole genomes (n = 61) sequenced from any tropical country is from three Peruvian cities, Lima, Piura and Iquitos [18] . In Africa, apart from one metapneumovirus genome identified from a wild mountain gorilla in Rwanda (GenBank accession number HM197719), there are no HMPV genomes reported according to the NIAID Virus Pathogen Database and Analysis Resource (ViPR, http://www.viprbrc. org/, accessed April 30, 2019). This has led to limited understanding of the genetic and genomic diversity of HMPV in the continent. This work describes a whole genome sequencing (WGS) approach for HMPV from a small number of HMPV positive clinical samples collected at Kilifi County Hospital in Kilifi, Kenya and University Teaching Hospital in Lusaka, Zambia. The genomes were generated by sequencing overlapping PCR amplicons spanning the entire genome. These are the first reported complete genome sequences of locally circulating HMPV strains obtained directly from clinical samples in Africa. We also combined the new genomes with publicly available sequences to examine patterns in global HMPV genetic diversity. Whole genome sequencing was successful for all 5 clinical samples that were attempted. A single genomic sequence was obtained from each sample, and the length of the 5 new HMPV genomes ranged from 13,097 to 13, 134 nt (> 95% length coverage). Sequencing and data assembly parameters, including coverage depth are shown in Table 1 . Sequence annotation of the full-length genomes using Geneious R8.1.5 (https://www.geneious.com) identified the expected eight coding ORFs and non-coding genomic regions. The overall nucleotide identity (i.e., identical sites averaging over all sequence pairs and excluding positions containing gaps) between all 143 genome sequences analyzed (5 new genomes plus 138 from ViPR) was 58.2%. Nucleotide sequence identity was 71.3% within HMPV-A and 80% within HMPV-B. Intrasubgroup, A1, A2, B1 and B2 genomes shared 92.1% (10 sequences), 76.8% (88 sequences), 91% (24 sequences) and 89.6% (21 sequences) amino acid sequence identity. For the 143 HMPV genomes, we checked sequence conservation at transcriptional control regions, at the termini of each gene, as well as the lengths of intergenic sequences between gene boundaries. The length of the F-M2 intergenic region was different between group A and B viruses, that is, 13 nt and 2 nt, respectively. The SH-G and G-L intergenic regions were the longest, up to 125 nt and to 190 nt, respectively. Consensus nucleotides (9 to 19 length) at the putative start and end regions flanking the ORF of the viral genes are shown in Fig. 1 . The gene-start and -end regions of N and P were conserved (> 90% average pairwise identity) in both HMPV groups, and the M2 and M gene-start and -end were also conserved in HMPV group A and B, respectively. The putative ATG start codon was consistently located at positions 14-16 upstream of a gene start motif (consensus: GG/AGAC/TAAA/GTnnnnATG), except for the internal M2-2. An additional ATG start codon upstream of the gene-start motif was observed in the SH gene for the B1 and B2 strains. In five of the eight annotated genes (N, P, F, M2, and G (B1 and B2 strains only)), the intergenic regions were short and the ORFs for these 5 genes terminated within the propositioned gene-end motifs. We combined the five genome sequences from Kenya and Zambia with available global sequences, aligned individual genes and calculated the percent nucleotide (nt) and amino acid (aa) identity ( Table 2) . The coding sequences of N, M, F, M2-1, M2-2, and L genes were conserved at nucleotide and amino acid levels, by sharing > 85% between-subgroup nucleotide identity and 90% protein identity ( Table 3 ). The nucleoprotein gene was the most conserved among all subgroups at the nt and aa levels. SH and G glycoprotein genes were more divergent between the HMPV subgroups at the nucleotide level with 76 and 63% identity, respectively. The SH protein length was variable between group A and B strains due to a nucleotide substitution (CAA ➔ TAA) at gene position 532 in group B, resulting in protein lengths of 178 and 180 aa, respectively. The predicted G protein length also varied among the different HMPV subgroups, between 219 and 241 aa, due to different positions of the Stop codon. Amino acid sequence diversity for G and SH glycoproteins is depicted in Fig. 2 and Additional file 2: Figure S2 , respectively. The diversity of the complete nucleotide sequences of SH and G genes is depicted in phylogenetic trees in Fig. 3 . We evaluated phylogenetic classification and relationship between the 5 new genomes obtained in this study and previously published genomes (Fig. 3) . Full genome Figure S3 . There was phylogenetic congruence with the individual gene sequence sets as with the full genome dataset, except for F and M2 gene (Additional file 3: Figure S3 ). Variant or drifted viral strains may lower the sensitivity of detection resulting in a decreased quantitation of the viral load and underestimation of disease incidence [19] . We checked the new HMPV genomes for nucleotide differences in the genomic regions targeted by our diagnostic rRT-PCR primers and probes (Additional file 7: Table S4 ) used for HMPV detection. Up to eight primer-and probetemplate mismatches were identified (Fig. 4) : one mismatch in the forward primer region in HMPV group A (F gene-based rRT-PCR assay, Fig. 4a ); one mismatch in each of the forward and probe target regions in group B (F gene-based rRT-PCR assay, Fig. 4b) ; and 5 different mismatches with the N-gene based rRT-PCR assay (Fig. 4c) . Note, the F gene-based rRT-PCR assays are different or specific to the two HMPV groups. HMPV causes respiratory illness presenting as mild upper respiratory tract infection or life-threatening severe bronchiolitis and pneumonia primarily in children, sometimes adults as well as immunocompromised individuals [2] . However, HMPV genome sequence data from Africa is sparse and information on genome-wide diversity is limited. In the present study, the whole genome sequences of five HMPV strains from Kenya and Zambia were determined and compared with the genomes published previously from around the world. Comparative sequence analysis indicated fairly conserved positioning of the gene-start and -end regions as well as translational start and -end codons. Variation in genestart and -end sequences can have significant impact on transcription initiation and termination efficiency so that there is more selective pressure preventing changes in these regions [20] , and this likely explains our observation. The additional ATG start codon found upstream of the gene-start motif of the SH gene was consistent with a previous report [21] , though its role in gene expression is yet to be identified. These observed sequence conservation in N, M, F, M2-1, M2-2, and L genes is not unusual and is suggestive of functional and structural constraints on diversity, but less expected of the F gene because of its status as a neutralization and protective antigen, similar to its close 'relative' RSV [22] . It has also been suggested that the low diversity in F gene might make a substantial contribution to cross-neutralization and cross-protection between the HMPV subgroups [21] . The relatively high frequency of amino acid diversity in G (and to a lesser extent SH) could be attributable to selective pressure for amino acid change coming from host immunity; and the ability of the protein to tolerate substitutions, which might be due to its proposed extended, unfolded nature [22] . The phylogenetic incongruence observed between whole genome tree and the F and G gene trees, is as reported previously for HMPV [23] , and could be attributed to differential rates of evolution, selection pressure or past recombination events [24] . The prevalence of HMPV in hospitalized pediatric population in Kilifi county in coastal Kenya has been reported [7, 25] . However, it is notable that in recent years, HMPV has been detected at low prevalence in Kilifi (unpublished observations from hospital-based pneumonia surveillance). Whether this low prevalence is due to reduced virus transmission, or decreased sensitivity of our HMPV molecular diagnostic assay due to progressive primer/probe mismatches, is yet to be established. We present the first full genome sequences of circulating HMPV strains from sub-Saharan Africa. A limitation of our sequencing method, as is common with amplicon sequencing protocols [26, 27] , was absent coverage at the 3′ leader and 5′ trailer regions not captured by these primers. Our results demonstrate the application of amplicon sequencing to generate full length HMPV genomes directly from clinical samples. The observed diversity of the individual genes is comparable to that described previously [20] [21] [22] . This method and data provide a useful reference for design of local molecular diagnostics and for studies aimed at understanding HMPV epidemiology and evolution in Africa. Nasopharyngeal and oropharyngeal (NP-OP) swab samples were collected from children (1-59 months) hospitalized with pneumonia, four of whom were enrolled in the PERCH study [18] in 2012. The fifth sample was collected from a child enrolled in the routine pneumonia surveillance study at Kilifi County Hospital, Kenya, in 2015. The samples were tested for HMPV by multiplex semi-quantitative real-time reverse transcription PCR (rRT-PCR) assays. The rRT-PCR primers and probes used, cycling conditions and assay set up have been described elsewhere [28, 29] . Fusion (F) and glycoprotein (G) encoding genes of the HMPV positive samples were amplified in a one-step RT-PCR assay (OneStep RT-PCR kit, QIAGEN), as described previously [7] . Partial G or F nucleotide sequences were analyzed by maximum likelihood (ML) phylogenetic trees using IQ-TREE [30] , together with reference strains of HMPV subgroups (accession numbers AF371337.2, FJ168779, AY297749, AY530095, JN184401 and AY297748). Five HMPV positive samples from the Kenya and Zambia study sites, belonging to the A2a (n = 1), A2b (n = 2), B1 (n = 1) and B2 (n = 1) genetic subgroups based on their G and F gene sequences, were selected for whole genome sequencing. Data on age, sex and clinical assessment information collected at the time of sample collection, for the five selected samples, are shown in Table 3 . The sequencing protocol consisted of four steps as follows: (i) primer design, (ii) preparation of primer mixes, (iii) cDNA and PCR (iv) Illumina sequencing and data analysis. All human metapneumovirus (HMPV) full genome sequences were retrieved from GenBank (January 2018) using the query (txid162145 (Organism) AND 12000(SLEN): 14000(SLEN) NOT patent). Sequence entries with gaps larger than 6 nt were excluded to generate a set of yielding 178 genomes. All possible 23 nt sequences were generated from the genomes dataset and trimmed to a final calculated melting temperature (Tm) of 47.9-49.5°C. Sequences with homology to rRNA sequences, with GC content outside < 0.3 or > 0.75 or with a single nucleotide fractional content of > 0.6 were discarded. The primer set was then made nonredundant yielding 60,746 potential primers. All potential primers were mapped against the 178 HMPV full genomes and the number of perfect matches (frequency score) was determined as a measure of primer sequence conservation. To select primers, the HMPV genome sequences were divided into amplicons with 222 nt overlap spanning the virus genome. Potential primers that mapped within the terminal 5′ and 3′ 222 nt of each amplicon were identified and the sequence with the highest frequency score was selected, and primers mapping to the reverse bins were reverse complemented. In this manner, 24 primers were selected for each of the 4 HMPV genotype representative genomes (GenBank accession number HMPV A1: AF371337, HMPV A2: FJ168779; HMPV B1: AY525843, and HMPV B2: FJ168778). Because of conservation between genotypes, there was primer redundancy which was removed. The final set of 65 primer sequences, their lengths, calculated Tm, fractional GC content and mapping position on the HMPV genome are presented in Additional file 5: Table S2 . The primers were computationally tested against each of the 4 HMPV subgroups. A graphical representation of the primer target sites is presented in Additional file 1: Figure S1 . Amplification was performed in two reactions. To avoid generating small products from adjacent forward and reverse primers, amplicons were assigned to alternate Table 3 ). Bootstrap support values (evaluated by 1000 replicates) are indicated along the branches. Genetic subgroups A1, A2a, A2b, B1, and B2, are indicated. Multiple sequence alignment was done using MAFFT and the ML phylogeny inferred using GTR + Γ nucleotide substitution model and ultrafast bootstrap approximation in IQ-TREE. The genotype B2 Sabana strain sequence (GenBank accession number HM197719) reported from a wild mountain gorilla in Rwanda is marked in blue. The scaled bar indicates nucleotide substitutions per site reactions, with reaction 1 containing primers for amplicons 1,3,5,7,9,11; reaction 2 containing primers for amplicons 2,4,6,8,10,12. Each reverse transcription used Forward Primer Mixes (FPMs) made with 3.0 μl of each reverse primer (100 pmol/μl) plus water to 200 μl to generate a primer concentration of 24 pmol/μl. Two microlitre of the FPM is then used in a 20 μl reverse transcription reaction (2.4 pmol/μl final concentration in reaction or 2.4 μM/primer). For PCR amplification, each amplicon reaction used a separate PCR Primer Mix (PPM) containing 1.5 μl of each 100 pmol/μl forward primer and 1.5 μl of each reverse primer (5.3-5.5 pmol/μl total primer in the PPM). 2 μl PPM was used per 25 μl PCR reaction = 0.5 pmol/μl in reaction (= 500 nM). Viral nucleic acids were extracted from the original samples using QIAamp Viral RNA Mini kit (QIAGEN). RNA (5 μl) was reverse transcribed into cDNA using SuperScript III (200 U, Invitrogen), RT buffer (1X final concentration, Invitrogen), and 2 μl of FPM in 20 μl reactions. An aliquot of cDNA (5 μl) was amplified in 35 cycles using Phusion Highfidelity PCR kit (New England Biolabs) and 2 μl of PPM in a 25 μl reaction. The PCR mixture was incubated at 98°C for 30 s, followed by 35 cycles of 98°C for 10 s, 43°C for 30 s, and 72°C for 90s and a final extension of 72°C for 10 min. Expected PCR products for each amplicon were approximately 1500 bp. PCR products from the two reactions for each sample were pooled for Illumina library preparation. Fig. 4 Mismatches between the rRT-PCR diagnostic primers and probes and their expected binding sites in the five genomes from Kenya and Zambia. 'Fwd primer' = Forward primer and 'Rev primer' = Reverse primer. Two rRT-PCR assays were used for HMPV detection. The colored bars in the figure indicate nucleotide differences (mismatches) between (a) three HMPV-A genomes and HMPV-A specific primers and probes targeting fusion gene, (b) two HMPV-B genomes and HMPV-B specific primers and probes also targeting fusion gene, and (c) all five genomes reported here and specific primers and probes targeting nucleoprotein gene. The sequences of the rRT-PCR primers and probes checked against the African HMPV genomes are listed in Additional file 7: Table S4 Illumina sequencing and data analysis Libraries were prepared using Nextera XT kit (Illumina) and pair-end sequencing (2 × 300 base pairs) with the MiSeq Reagent V3 kit (Illumina), following the manufacturer's instructions. The Nextera enzyme mix was used to simultaneously fragment input DNA and tag with universal adapters in a single tube reaction, followed by 12-cycle PCR reaction for dual indexing. Agencourt AMPure XP beads (Beckman Coulter) were used for all purification steps and libraries were quantified and quality-checked using the Qubit (Thermo Fisher) and Bioanalyzer (Agilent). Adapter trimming, quality filtering, kmer normalization of sequencing reads, de novo assembly, calculation of mean genome coverage was as previously described [31] . A dataset of HMPV genome sequences was retrieved from ViPR in order to infer relationship between HMPV viruses from Kenya and Zambia and viral populations sampled globally. The dataset included 138 sequence entries (> 13,000 nt) that included date (year) and location of sample Table S3 ). Sequence alignment was done using MAFFT v.7.221 [32] using the parameters 'localpair -maxiterate 1000'. IQ-TREE was used to infer maximum likelihood (ML) trees of the complete genome and individual genes under general time-reversible (GTR) substitution model with gamma-distributed among-site rate heterogeneity. A summary of the methodology outlined here is depicted in Fig. 5 .

How many open reading frames are in the HMPV genome?

2019-nCoV: The Identify-Isolate-Inform (3I) Tool Applied to a Novel Emerging Coronavirus https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7081861/ SHA: f323af9a07cc54faf9bdbabadaacb0e8b46f99a2 Authors: Koenig, Kristi L.; Beÿ, Christian K.; McDonald, Eric C. Date: 2020-01-31 DOI: 10.5811/westjem.2020.1.46760 License: cc-by Abstract: 2019 Novel Coronavirus (2019-nCoV) is an emerging infectious disease closely related to MERS-CoV and SARS-CoV that was first reported in Wuhan City, Hubei Province, China in December 2019. As of January 2020, cases of 2019-nCoV are continuing to be reported in other Eastern Asian countries as well as in the United States, Europe, Australia, and numerous other countries. An unusually high volume of domestic and international travel corresponding to the beginning of the 2020 Chinese New Year complicated initial identification and containment of infected persons. Due to the rapidly rising number of cases and reported deaths, all countries should be considered at risk of imported 2019-nCoV. Therefore, it is essential for prehospital, clinic, and emergency department personnel to be able to rapidly assess 2019-nCoV risk and take immediate actions if indicated. The Identify-Isolate-Inform (3I) Tool, originally conceived for the initial detection and management of Ebola virus and later adjusted for other infectious agents, can be adapted for any emerging infectious disease. This paper reports a modification of the 3I Tool for use in the initial detection and management of patients under investigation for 2019-nCoV. After initial assessment for symptoms and epidemiological risk factors, including travel to affected areas and exposure to confirmed 2019-nCoV patients within 14 days, patients are classified in a risk-stratified system. Upon confirmation of a suspected 2019-nCoV case, affected persons must immediately be placed in airborne infection isolation and the appropriate public health agencies notified. This modified 3I Tool will assist emergency and primary care clinicians, as well as out-of-hospital providers, in effectively managing persons with suspected or confirmed 2019-nCoV. Text: 2019 Novel Coronavirus (2019-nCoV) is a novel respiratory disease first reported in Wuhan, Hubei Province, China in December 2019. 1 Chinese health officials were originally investigating a sudden increase in cases of pneumonia which were later determined to be linked to 2019-nCoV. While most cases originated within mainland China, the disease spread to neighboring countries including Taiwan, Thailand, South Korea, and Japan, and later to the United States, Europe, and Australia. A near real-time updated tracking website for cases and locations worldwide, along with reported deaths is available. 2 Chinese health authorities have sequenced 2019-nCoV and freely shared its genetic profile online. 3, 4 Additionally, on January 28, 2020, an Australian laboratory reported growing the virus from a patient sample. As of January 30, 2020, there have been at least 9,776 persons infected and 213 verified deaths. 2 These numbers are likely underestimates due to the limited information available regarding incubation time, transmissibility, and virus origin. The What was the research question? Investigators adapted the "Identify, Isolate, Inform" (3I) Tool for use in suspected cases of 2019-nCoV. What was the major finding of the study? A novel 2019-nCoV 3I Tool is designed for frontline clinicians in the management of suspected patients. This 2019-nCoV 3I adaptation will aid healthcare providers most likely to encounter the disease in the containment and effective treatment of patients. age distribution of these verified deaths is currently not available. One preliminary, small-scale study of 41 patients in Wuhan China, reported 6 deaths (15% mortality) with a median age of 49.0 years. 5 Additionally, transmission of the virus has reportedly occurred in healthcare facilities in Wuhan City, raising concerns of spread to healthcare workers, as was seen during prior outbreaks of the novel coronaviruses, Middle Eastern Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). Due to the dynamic nature of the outbreak, exposure criteria may change depending on where new cases of 2019-nCoV are detected, the degree of transmissibility, and when additional information regarding the origin of the virus is discovered and reported. On January 15, 2020, the Centers for Disease Control and Prevention (CDC) confirmed the first known imported case of 2019-nCoV in the US state of Washington. The patient had recently returned from Wuhan City, where he likely contracted the disease. Chicago health authorities reported a second US case on January 24, 2020. This was quickly followed by additional imported cases reported in Orange and Los Angeles Counties, California on January 26, 2020. Additional suspected cases continue to be evaluated. On January 30, 2020, the CDC reported the first local transmission in the US between members in a household. On the same day, the World Health Organization declared 2019-nCoV to be a Public Health Emergency of International Concern (PHEIC). 6 On January 31, 2020, the US Department of Health and Human Services declared coronavirus a public health emergency. 7 Healthy individuals and those with mild illness may be asymptomatic, while others may have more pronounced symptoms of fever or lower respiratory illness. Upon identification of a suspected patient, that individual should immediately be isolated with airborne precautions. Further workup and laboratory confirmation can then proceed. Emergency physicians (EPs), emergency medical services (EMS) personnel, and other healthcare workers who encounter patients with suspected 2019-nCoV infection must inform the appropriate authorities, including but not limited to hospital infection control and local or state public health agencies. Healthcare workers must follow on-going developments related to the outbreak, especially new information concerning detection and management. 8, 9 The 3I Tool outlined in this paper is consistent with current US CDC guidelines and can be applied in a variety of settings such as those in emergency departments, urgent-care clinics, physicians' offices, and prehospital settings. This paper will first briefly review 2019-nCoV and then present the novel 2019-nCoV 3I Tool as modified from its initial conception for Ebola virus disease 10,11 and later adapted for measles, 12 MERS, 13 mumps, 14 Zika virus disease, 15 hepatitis A, 16 pertussis, 17 and scabies. 18 Coronavirus 2019-nCoV infection commonly presents with signs and symptoms of pneumonia or as a nonspecific lower respiratory illness, with coughing or difficulty breathing accompanied by fever. 5, 19, 20 Fever and cough constitute the most common presentations. However, patients may have other respiratory symptoms, sore throat, nasal congestion, malaise, myalgia, and headache. Bilateral infiltrates may be seen on chest X-ray. Severe cases may present with sepsis and even shock. Conversely, some patients may present as only mildly ill or asymptomatic altogether. 21 To date, patients with underlying medical conditions and the elderly are more likely to become severely ill, require hospitalization, and ultimately die. 22 Early predictions for incubation time are between 2 and 14 days, based on data from similar coronaviruses. The 14-day criterion for epidemiological risk assumes the longest estimated incubation time. 23 In addition, the World Health Organization (WHO) has created its own interim case definition. 24 By definition, the main features of a novel virus, for example, how it is transmitted, will not be immediately known. However, as with the development of any 3I Tool, it is essential to understand specific characteristics of the disease. In the case of a novel virus such as 2019-CoV, this is challenging since information is rapidly evolving and the science is not yet fully understood. It is possible that the virus will undergo mutations over time that could substantially change its The Identify-Isolate-Inform (3I) Tool Applied to a Novel Emerging Coronavirus Koenig et al. features. Nevertheless, an appreciation of the key concepts that drive evidence-based management is beneficial (Table 1) . Management guidance will likely change over time. With the initial discovery of a new potential public health threat, it will likely be unclear how patients become sick. For example, rather than a contagion, there could be a contaminant or a toxin responsible for signs and symptoms. In this case, the possibility of an environmental toxin in the Wuhan Market was a consideration early on when limited to no human-tohuman transmission was reported. The mode of transmission has implications for the types of personal protective equipment (PPE) needed to protect healthcare providers in the prehospital, clinic, and hospital settings. 25 In addition, patients may need decontamination after exposure to certain toxins. 26 Another important consideration for application of the 3I Tool is whether the disease is contagious prior to symptom onset (like measles) or only after symptoms develop (like Ebola). A January 30, 2020 letter to the New England Journal of Medicine describes a purported confirmed instance of transmission from an asymptomatic individual. Researchers state that, before symptom onset, the primary case infected two individuals, one of which infected two additional colleagues. 27 Subsequent investigation suggested that the source patient did have mild symptoms and had taken an antipyretic, calling this reported asymptomatic transmission into question. While quarantine may not be feasible and can have unintended consequences, 28, 29, 30 it is a public health tool that can be considered in cases when disease is transmissible before symptom onset. 30 Conversely, if a disease is known not to be transmissible prior to symptom onset, asymptomatic exposed patients must be monitored, but do not require quarantine or isolation unless they develop symptoms. Initially, it may be unclear whether an infectious agent occurred naturally or was deliberately or accidentally released. In this case, a BSL-4 laboratory studying coronaviruses was located approximately 32 kilometers away from the market where initial exposures were felt to occur. 31 Recall that in 2001, the anthrax letter attacks were initially thought to be naturally occurring. Once determined to be bioterrorism, management of the event was similar to that for a chemical exposure with a sudden impact, defined scene, and need for a rapid response and decontamination on site. This differed from the WHO's modeling predicting an aerosolized release that would result in an incubation period with 100,000 or more persons exposed rather than the 22 people who contracted anthrax in 2001. 32 By understanding the key features of a novel disease, healthcare workers can take evidence-based measures to protect themselves, optimize individual patient management, and prevent further disease spread. It is currently unclear how 2019-nCoV is spread, but it is suspected to be transmitted through contact with infected respiratory secretions, like other known coronaviruses. There are instances of sustained human-to-human transmission across generations of cases, especially near the epicenter in Wuhan City. 21 Current evidence suggests that close contact with an infected person is a major factor in disease transmission. CDC defines "close contact" 33 as being in or within two meters of an area with a confirmed patient or being directly exposed to infectious secretions without appropriate PPE. Healthcare facilities in China have reported spread from person to person. In addition, some mildly ill or potentially even asymptomatic patients may have a higher chance of spreading the disease to others as they may be less likely to seek medical care. 34 The possibility that patients may be infectious prior to symptom onset further compounds the difficulty of containing the virus and effectively preventing transmission. The current majority of 2019-nCoV cases have been within China and its bordering countries. 2 Persons with recent travel (within 14 days) to Wuhan City or another region with widespread disease, or exposure to a patient under investigation, are considered to have an epidemiologic risk factor and should be assessed for signs and symptoms of a viral illness such as fever and respiratory symptoms. Coronavirus is a zoonotic virus The Identify-Isolate-Inform (3I) Tool Applied to a Novel Emerging Coronavirus that is transmitted to humans via contact with infected animals. Preliminary reports suggest the disease may have originated in a seafood and live animal market in Wuhan City, but it is still unknown how or whether such transmission occurred. Clinicians working with local public health departments must arrange to have specimens from patients under investigation (PUIs) sent to the CDC laboratory. At this time, the CDC has the only laboratory that can definitively test for 2019-nCoV, though laboratory testing capacity is being rapidly expanded. Polymerase chain reaction (PCR) assays conducted on samples from a patient's upper and lower respiratory tracts will be used to confirm potential cases. In addition, serum antibody titers can be analyzed for confirmation of infection or evidence of immunity. Up-to-date information about the needed specimens and handling requirements to test for 2019-nCoV are available on the CDC website. 35 Like other related coronaviruses, patients with 2019-nCoV frequently present with non-specific symptoms resembling that of influenza. Physicians may consider differential diagnoses related to a wide variety of respiratory infections. In order to relate these symptoms to 2019-nCoV, it is imperative that the identification of a potential exposure event (epidemiologic risk factor) within 14 days of symptom onset is made so that a more focused work-up for 2019-nCoV can be completed. Although the likelihood of coinfection of 2019-nCoV and another respiratory virus is thought to be low, a positive finding of another respiratory pathogen does not exclude the diagnosis of 2019-nCoV. Many commercially available respiratory panels include "coronavirus" in the results, but neither a positive nor a negative finding on these panels should be used to include or exclude a diagnosis of 2019-nCoV. Supportive care with appropriate infection control is the mainstay of current CDC treatment guidelines for 2019-nCoV. There are not yet any approved antiviral treatments for 2019-nCoV. Emergency Use Authorizations (EUA) for compassionate use cases may be forthcoming from the US federal government for normally unapproved treatments. Supportive treatment predominantly includes respiratory support, hydration, and antipyretics. General treatment for severe cases should focus on the preservation of vital organ function. In the future, antiviral medications may be available. If a secondary bacterial infection such as pneumonia develops, targeted antibiotics are indicated. Prevention of 2019-nCoV transmission, like any other infectious agent, involves minimizing risk of exposure. Vaccines are under accelerated development and may be useful in the future for post-exposure prophylaxis. Healthcare personnel are at increased risk and should practice standard, droplet, and airborne precautions when encountering an infected person, a PUI, or any symptomatic close contacts. Healthcare workers handling specimens should also adhere to CDC guidelines and should not attempt to perform any virus isolation or characterization. Fever screening has been implemented at numerous airports, including major international hubs within Asia and the US. The efficacy of this intervention is not well documented, however, as some infected persons may be afebrile and disease transmission might occur prior to symptom onset. 27 In addition, people can artificially lower their temperature readings, e.g., by applying ice to their foreheads. As outlined above, admission criteria for 2019-nCoV are similar to that of other patients. If patients do not meet medical criteria for hospitalization, they may be discharged home with isolation precautions and continued observation. EPs must notify local public health authorities so appropriate monitoring and community protective measures can be instituted. The Identify-Isolate-Inform (3I) Tool was initially developed for Ebola virus disease 10,11 and later adapted for measles, 12 MERS, 13 mumps, 14 Zika virus disease, 15 hepatitis A, 16 pertussis, 17 and scabies. 18 This novel tool for suspected 2019-nCoV patients ( Figure 1 ) provides frontline clinicians with a simple algorithm to manage an emerging disease. Identification of exposed patients with an epidemiologic risk factor within 14 days of symptom onset is a crucial first step. An automatic prompt in the electronic health record can be useful in assisting clinicians with early identification of patients at risk. Case definitions promulgated by the WHO 24 and CDC 33 provide useful comprehensive definitions that have been incorporated into the 3I Tool. The 2019-nCoV Tool provides an accurate, summarized algorithm to immediately, and effectively manage suspected patients until additional resources can be consulted. Patients who do not have an exposure risk or any symptoms may be triaged normally. However, before making patient contact, providers must first apply the Vital Sign Zero concept. 36 Vital Sign Zero is a preliminary, non-contact assessment (i.e., performed prior to touching a patient to take traditional vital signs) to first determine whether specific PPE is indicated before the examination commences. By taking the additional time to complete this assessment, risk of exposure and further transmission can be minimized. while in the treatment facility should be started and maintained to assist with the possibility of contact tracing. Following isolation, physicians should immediately inform the appropriate authorities. Patients who do not meet medical criteria for admission can be isolated at home during the evaluation phase. 37 Health department officials can help prevent transmission in isolated patients by providing in-home monitoring and implementing appropriate exposure-control measures. Providers in the prehospital setting who have a high likelihood of encountering 2019-nCoV patients, such as those near international ports of entry, should adhere to established exposure control guidelines. 38 Along with appropriate PPE, providers should also carry thermometers to quantify any fever. In the US, providers should contact the appropriate CDC quarantine station upon isolation of infected or suspected patients, especially those from Wuhan, China or other regions with widespread disease, who report symptoms in the last 14 days. As for other infectious diseases, assessing travel history is essential. Dispatch protocols have been instituted to facilitate identification of callers to 911 or the country-equivalent emergency number prior to prehospital personnel arrival. 39 In addition, CDC has promulgated EMS guidelines for prehospital PPE, transportation of PUIs, vehicle decontamination, and 911 Public Safety Answering Points (PSAPs) for 2019-nCoV. 40 2019-nCoV is an emerging infectious disease with rapidly evolving features, the full scope of which will be defined over time. Prior outbreaks of coronaviruses can help inform needed actions in the short term to assist with both treatment of individual patients and prevention of global disease spread. This adaptation of the Identify-Isolate-Inform Tool serves as a resource for healthcare workers who need to make clear, rapid assessments when confronted with potential patients. The concise nature of the 2019-nCoV 3I Tool allows for the rapid and effective management of a novel disease by healthcare providers.

What symptoms might people experience with COVID19?

MERS coronavirus: diagnostics, epidemiology and transmission https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4687373/ SHA: f6fcf1a99cbd073c5821d1c4ffa3f2c6daf8ae29 Authors: Mackay, Ian M.; Arden, Katherine E. Date: 2015-12-22 DOI: 10.1186/s12985-015-0439-5 License: cc-by Abstract: The first known cases of Middle East respiratory syndrome (MERS), associated with infection by a novel coronavirus (CoV), occurred in 2012 in Jordan but were reported retrospectively. The case first to be publicly reported was from Jeddah, in the Kingdom of Saudi Arabia (KSA). Since then, MERS-CoV sequences have been found in a bat and in many dromedary camels (DC). MERS-CoV is enzootic in DC across the Arabian Peninsula and in parts of Africa, causing mild upper respiratory tract illness in its camel reservoir and sporadic, but relatively rare human infections. Precisely how virus transmits to humans remains unknown but close and lengthy exposure appears to be a requirement. The KSA is the focal point of MERS, with the majority of human cases. In humans, MERS is mostly known as a lower respiratory tract (LRT) disease involving fever, cough, breathing difficulties and pneumonia that may progress to acute respiratory distress syndrome, multiorgan failure and death in 20 % to 40 % of those infected. However, MERS-CoV has also been detected in mild and influenza-like illnesses and in those with no signs or symptoms. Older males most obviously suffer severe disease and MERS patients often have comorbidities. Compared to severe acute respiratory syndrome (SARS), another sometimes- fatal zoonotic coronavirus disease that has since disappeared, MERS progresses more rapidly to respiratory failure and acute kidney injury (it also has an affinity for growth in kidney cells under laboratory conditions), is more frequently reported in patients with underlying disease and is more often fatal. Most human cases of MERS have been linked to lapses in infection prevention and control (IPC) in healthcare settings, with approximately 20 % of all virus detections reported among healthcare workers (HCWs) and higher exposures in those with occupations that bring them into close contact with camels. Sero-surveys have found widespread evidence of past infection in adult camels and limited past exposure among humans. Sensitive, validated reverse transcriptase real-time polymerase chain reaction (RT-rtPCR)-based diagnostics have been available almost from the start of the emergence of MERS. While the basic virology of MERS-CoV has advanced over the past three years, understanding of the interplay between camel, environment, and human remains limited. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12985-015-0439-5) contains supplementary material, which is available to authorized users. Text: An email from Dr Ali Mohamed Zaki, an Egyptian virologist working at the Dr Soliman Fakeeh Hospital in Jeddah in the Kingdom of Saudi Arabia (KSA) announced the first culture of a new coronavirus to the world. The email was published on the website of the professional emerging diseases (ProMED) network on 20 th September 2012 [1] (Fig. 1) and described the first reported case, a 60 year old man from Bisha in the KSA. This information led to the rapid discovery of a second case of the virus, this time in an ill patient in the United Kingdom, who had been transferred from Qatar for care [2] . The new virus was initially called novel coronavirus (nCoV) and subsequentlty entitled the Middle East respiratoy syndrome coronavirus (MERS-CoV). As of 2 nd of September 2015, there have been 1,493 detections of viral RNA or virus-specific antibodies across 26 countries (Additional file 1: Figure S1 ) confirmed by the World Health Organization (WHO), with over a third of the positive people dying (at least 527, 35 %) [3] . Since that first report, a slow discovery process over the following two to three years revealed a virus that had infected over 90 % of adult dromedary camels (DC; Camelus dromedarius) in the KSA [4] , also DCs across the Arabian Peninsula and parts of Africa that are a source of DC imports for the KSA [5] . To date, MERS-CoV has not been detected in DCs tested in zoos or herds from other parts of the world [6] [7] [8] [9] . Occasionally, virus is transmitted from infected DCs to exposed humans. Subsequent transmission to other humans requires relatively close and prolonged exposure [10] . The first viral isolate was patented and concerns were raised that this would restrict access to both the virus and to viral diagnostics [11, 12] . However, sensitive, validated reverse transcriptase real-time polymerase chain reaction (RT-rtPCR)-based diagnostics were quickly described and virus was made freely available subject to routine biosafety considerations [13] . Subsequent epidemiology and research has identified the cell receptor as exopeptidase dipeptidyl peptidase 4 (DPP4; also called CD26); that MERS-CoV has a broad tropism, replicating better in some cells lines and eliciting a more proinflammatory response than SARS-CoV; is widespread in DCs; has the potential to infect other animals and that MERS kills its human host more often than SARS did (20-40 % versus 9 % for SARS [14] ) [15] [16] [17] [18] [19] . In humans, overt disease was given the name Middle East respiratory syndrome, with the acronym MERS. From intermittent animal-to-human spill-over events, the MERS-CoV spreads sporadically among people, causing more severe disease among older adults, especially males, with pre-existing diseases. The spread of MERS-CoV among humans has often been associated with outbreaks in hospitals, with around 20 % of all cases to date involving healthcare workers (HCWs). Although DCs appear to suffer the equivalent of a 'common cold' from MERS-CoV infection, in humans, the virus can be a more serious and opportunistic pathogen associated with the death of up to 40 % of reported cases. It has yet to be established whether infections thought to have been acquired from an animal source produce a more severe outcome than those spread between humans [20] . Studies have established that the mean incubation period for MERS is five to six days, ranging from two to 16 days, with 13 to 14 days between when illness begins in one person and subsequently spreads to another [21] [22] [23] [24] . Among those with progressive illness, the median time to death is 11 to 13 days, ranging from five to 27 days [23, 24] . Fever and gastrointestinal symptoms may form a prodrome, after which symptoms decline, only to be followed by a more severe systemic and respiratory syndrome [25, 26] . The first WHO case definition [27] defined probable cases of MERS based on the presence of febrile illness, cough and requirement for hospitalization with suspicion of lower respiratory tract (LRT) involvement. It also included roles for contact with a probable or confirmed case or for travel or residence within the Arabian Peninsula. If strictly adhered to, only the severe syndrome would be subject to laboratory testing, which was the paradigm early on [21] . From July 2013, the revised WHO case definition included the importance of seeking out and understanding the role of asymptomatic cases and from June 2014, the WHO definition more clearly stated that a confirmed case included any person whose sample was RT-PCR positive for MERS-CoV, or who produced a seroconversion, irrespective of clinical signs and symptoms. [28] [29] [30] Apart from the WHO and the KSA Ministry of Health reports, asymptomatic or subclinical cases of MERS-CoV infection were documented in the scientific literature although not always as often as occurred early on [31, 32] . The KSA definition of a case became more strict on 13 th May 2014, relying on the presence of both clinical features and laboratory confirmation [33] . Testing of asymptomatic people was recommended against from December 2014 [34] , reinforced by a case definition released by the KSA Ministry of Health in June 2015 [35] . The KSA has been the source of 79 % of human cases. Severe MERS is notable for its impact among older men with comorbid diseases including diabetes mellitus, cirrhosis and various lung, renal and cardiac conditions [36] [37] [38] . Interestingly in June 2015, an outbreak in South Korea followed a similar distribution [39, 40] . Among laboratory confirmed cases, fever, cough and upper respiratory tract (URT) signs and symptoms usually occur first, followed within a week by progressive LRT distress and lymphopaenia [37] . Patients often present to a hospital with pneumonia, or worse, and secondary bacterial infections have been reported [37, 41] . Disease can progress to acute respiratory distress syndrome and multiorgan system failure [37] . MERS has reportedly killed approximately 35 % of all reported cases, 42 % of cases in the KSA, yet only 19 % of cases in South Korea, where mortality ranged from 7 % among younger age groups to 40 % among those aged 60 years and above [42] ; all may be inflated values with asymptomatic or mild infections sometimes not sought or not reported [34] . General supportive care is key to managing severe cases [43] . Children under the age of 14 years are rarely reported to be positive for MERS-CoV, comprising only 1.1 % (n = 16) of total reported cases. Between 1 st September 2012 and 2 nd December 2013, a study described the then tally of paediatric cases in the KSA, which stood at 11 (two to 16 years of age; median 13 years); nine were asymptomatic (72 %) and one infant died [44] . In Amman, Jordan, 1,005 samples from hospitalized children under the age of two years with fever and/or respiratory signs and symptoms were tested but none were positive for MERS-CoV RNA, despite being collected at a similar time to the first known outbreak of MERS-CoV in the neighbouring town of Al-Zarqa [45] . A second trimester stillbirth occurred in a pregnant woman during an acute respiratory illness and while not RT-rtPCR positive, the mother did subsequently develop antibodies to MERS-CoV, suggestive of recent infection [46] . Her exposure history to a MERS-CoV RT-rtPCR positive relative and an antibody-reactive husband, her incubation period and her symptom history met the WHO criteria for being a probable MERS-CoV case [46] . Diagnostic methods were published within days of the ProMED email announcing the first MERS case [47] , including several now gold standard in-house RT-rtPCR assays (Fig. 2 ) as well as virus culture in Vero and LLC-MK2 cells [18, 47, 48] . A colorectal adenocarcinoma (Caco-2) epithelial cell line has since been recommended for isolation of infections MERS-CoV [49] . We previously [18] .). Open reading frames are indicated as yellow rectangles bracketed by terminal untranslated regions (UTR; grey rectangles). FS-frame-shift. Predicted regions encompassing recombination break-points are indicated by orange pills. Created using Geneious v8.1 [211] and annotated using Adobe Illustrator. Beneath this is a schematic depicting the location of RT-PCR primers (blue arrows indicate direction) and oligoprobes (green rectangles) used in the earliest RT-rtPCR screening assays and conventional, semi-nested (three primers) RT-PCR confirmatory sequencing assays [47, 48] . Publication order is noted by first [27 th September 2012; red] and second [6 th December 2012; orange] coloured rectangles; both from Corman et al. [47, 48] Those assays recommended by the WHO are highlighted underneath by yellow dots [53] . The NSeq reverse primer has consistently contained one sequence mismatch with some MERS-CoV variants. An altered version of that from Mackay IM, Arden KE. Middle East respiratory syndrome: An emerging coronavirus infection tracked by the crowd. Virus Res 2015 Vol 202:60-88 with permission from Elsevier [5] reviewed the broad tropism of MERS-CoV [5] . However, as is well described, cell culture is a slow, specialised and insensitive method [50] while PCR-based techniques are the preferred method for MERS-CoV detection. The first open reading frames (ORF 1a and 1b; Fig. 2 ) have become a key diagnostic and taxonomic target for CoV species identification. With less than 80 % identity between the amino acid sequence of MERS ORF 1ab and betacoronavirus relatives, Tylonycteris bat HKU4 and Pipistrellus bat HKU5, it can be concluded that it is a novel and distinct virus. MERS-CoV is predicted to encode ten open reading frames with 5' and 3' untranslated regions [51] . The structural proteins include the spike (S), envelope (E), membrane (M) and nucleocapsid (N) [52] . The products of ORF1a and ORF1b are predicted to encode nonstructural proteins. The majority of specimen testing to date has employed validated RT-rtPCR assays shown to be sensitive and specific [47, 48, 53] . The RealStar® kit uses these WHOrecommended assays [54] . The target sequences of these screening assays have not changed among genomes examined until at least mid-2015 (IMM observation). Other RT-rtPCR assays have been developed and validated for use as laboratory-based diagnostic tools [55] [56] [57] . Additionally, loop-mediated [58, 59] or recombinase polymerase [60] isothermal assays have been designed for field deployment. The detection of MERS-CoV antigen has not been common to date but the combination of short turnaround time from test to result, high throughput and identification of viral proteins makes this an attractive option. Detection of viral proteins rather than viral RNA indicates the likely presence of infectious virus. The first rapid immunochromatographic tool described could detect recombinant MERS-CoV nucleocapsid protein from DC nasal swabs with 94 % sensitivity and 100 % specificity compared to RT-rtPCR [61] . A different approach used a monoclonal antibody-based capture ELISA targeting the MERS-CoV nucleocapsid protein with a sensitivity of 10 3 TCID 50 and 100 % specificity [62] . Demonstration of a seroconversion to a MERS-CoV infection meets the current WHO definition of a case so optimized and thoroughly validated sero-assays employed alongside good clinical histories are useful to both identify prior MERS-CoV infection and help support transmission studies. Because serology testing is, by its nature, retrospective, it is usual to detect a viral footprint, in the form of antibodies, in the absence of any signs or symptoms of disease and often in the absence of any viral RNA [63] . Strategic, widespread sero-surveys of humans using samples collected after 2012 are infrequent. Much of the Arabian Peninsula and all of the Horn of Africa lack baseline data describing the proportion of the community who may have been infected by a MERS-CoV. However, sero-surveys have had widespread use in elucidating the role of DCs as a transmission source for MERS-CoV. Because of the identity shared between DC and human MERS-CoV (see Molecular epidemiology: using genomes to understand outbreaks), serological assays for DC sero-surveys should be transferrable to human screening with minimal re-configuration. Also, no diagnostically relevant variation in neutralization activity have been found from among a range of circulating tested MERS-CoV isolates and sera, so whole virus or specific protein-based sero-assays should perform equivalently in detecting serological responses to the single MERS-CoV serotype [49] . The development of robust serological assays requires reliable panels of wellcharacterized animal or human sera, including those positive for antibodies specific to MERS-CoV, as well as to likely sources of cross-reaction [64] . Obtaining these materials was problematic and slowed the development and commercialization of antibody detection assays for human testing [64] . A number of commercial ELISA kits, immunofluorescent assays (IFA) kits, recombinant proteins and monoclonal antibodies have been released [31, [65] [66] [67] [68] . Initially, conventional IFAs were used for human sero-surveys. These relied on MERS-CoV-infected cell culture as an antigen source, detecting the presence of human anti-MERS-CoV IgG, IgM or neutralizing antibodies in human samples [18, 48, 69] . No sign of MERS-CoV antibodies was found among 2,400 sera from patients visiting Hospital in Jeddah, from 2010 through 2012, prior to the description of MERS-CoV [18] . Nor did IFA methods detect any sign of prior MERS-CoV infection among a small sample of 130 healthy blood donors from another Hospital in Jeddah (collected between Jan and Dec 2012) [70] . Of 226 slaughterhouse workers, only eight (3.5 %) were positive by IFA, and those sera could not be confirmed by virus neutralization (NT) test. The study indicated that HCoV-HKU1 was a likely source of crossreactive antigen in the whole virus IFA [70] . Whole virus MERS-CoV IFA also suffered from some cross-reactivity with convalescent SARS patient sera and this could not be resolved by an NT test which was also cross-reactive [71] . IFA using recombinant proteins instead of whole-virus IFA, has been shown to be a more specific tool [31] . Since asymptomatic zoonoses have been posited [72] , an absence of antibodies to MERS-CoV among some humans who have regular and close contact with camels may reflect the rarity of actively infected animals at butcheries, a limited transmission risk associated with slaughtering DCs [70] , a pre-existing cross-protective immune status or some other factor(s) resulting in a low risk of disease and concurrent seroconversion developing after exposure in this group. IFA using recombinant proteins instead. Some sero-assays have bypassed the risks of working with infectious virus by creating transfected cells expressing recombinant portions of the MERS-CoV nucleocapsid and spike proteins [48, 73] , or using a recombinant lentivirus expressing MERS-CoV spike protein and luciferase [74, 75] . A pseudo particle neutralization (ppNT) assay has seen widespread used in animal studies and was at least as sensitive as the traditional microneutralization (MNT) test. [10, 74, [76] [77] [78] ] Studies using small sample numbers and ppNT found no evidence of MERS-CoV neutralizing antibody in sera from 158 children with LRT infections between May 2010 and May 2011, 110 sera from 19 to 52 year old male blood donors and 300 selfidentified animal workers from the Jazan Region of the KSA during 2012 [79, 80] . Similarly, a study of four herdsmen in contact with an infected DC herd in Al-Ahsa, eight people who had intermittent contact with the herd, 30 veterinary surgeons and support staff who were not exposed to the herd, three unprotected abattoir workers in Al-Ahsa and 146 controls who were not exposed to DCs in any professional role, found none with serological evidence of past MERS-CoV infection using the ppNT assay [10] . A delay in the neutralizing antibody response to MERS-CoV infection was associated with increased disease severity in South Korea cases with most responses detectable by week three of illness while others, even though disease was severe, did not respond for four or more weeks [81] . The implications for our ability to detect any response in mild or asymptomatic cases was not explored but may be a signifcant factor in understanding exposure in the wider community. A Jordanian outbreak of acute LRT disease in a hospital in 2012 was retrospectively found to be associated with MERS-CoV infection, initially using RT-rtPCR, but subsequently, and on a larger scale, through positivity by ELISA and IFA or MNT test. [46, 82, 83] This outbreak predated the first case of MERS in the KSA. The ELISA used a recombinant nucleocapsid protein from the group 2 betacoronavirus bat-CoV HKU5 to identify antibodies against the equivalent crossreactive MERS-CoV protein [71] . It was validated using 545 sera collected from people with prior HCoV-OC43, HCoV-229E, SARS-CoV, HCoV-NL63, HRV, HMPV or influenza A(H1N1) infections but was reportedly less specific than the recombinant IFA discussed above. It was still considered an applicable tool for screening large sample numbers [82] . A protein microarray expressing the S1 protein subunit has also been validated and widely used for DC testing [5, 84] . Detection of MERS-CoV infection using ELISA or S1 subunit protein microarray [84] is usually followed by confirmatory IFA and/ or a plaque-reduction neutralization (PRNT) [69, 70, 85] or MNT test. [74, 85, 86] This confirmatory process aims toensure the antibodies detected are able to specifically neutralize the intended virus and are not more broadly reactive to other coronaviruses found in DCs (bovine CoV, BCoV) or humans (HCoV-OC43, HCoV-229E, HCoV-NL63, HCoV-HKU1, SARS-CoV). In the largest study of human sera, a tiered diagnostic process assigned both recombinant IFA and recombinant ELISA positive sera to 'stage 1' seropositivity. A stage 2 seropositive result additionally required a suitably titred PRNT result [87] . The study found 15 sera collected in 2012 to 2013 from 10,009 (0.2 %) people in 13 KSA provinces contained MERS-CoV antibodies, but significantly higher proportions in occurred in camel shepherds (two of 87; 2.3 %) and slaughterhouse workers (five of 140; 3.6 %) [87] . Contemporary surveys are needed. MERS-CoV does not appear to be easily transmitted from DCs to humans, or perhaps it is [72] , but generally does not trigger a detectable immune response if only mild disease or asymptomatic infection results. Serology assays are in need of further validation in this area so care is required when moving newly developed diagnostic serology algorithms from a research setting to one that informs public health decisions. This was reinforced when a false positive US case, purported to have been infected after a handshake and two face-to-face meetings, did not withstand further confirmatory analysis using a more specific, NT assay and was subsequently retracted [88, 89] . The WHO recommends sampling from the LRT for MERS-CoV RT-rtPCR testing, especially when sample collection is delayed by a week or more after onset of symptoms. [53] LRT samples are also best for attempting isolation of infectious virus, although the success of culture is reduced when disease persists [49] . Recommended sample types include bronchoalveolar lavage (BAL), tracheal/tracheobronchial aspirate, pleural fluid and sputum [53, 90] . Fresh samples yield better diagnostic results than refrigerated material [69] and if delays in testing of ≥72 h are likely, samples (except for blood) should be frozen at −70°C [90] . If available, lung biopsy or autopsy tissues can also be tested [53] . The URT is a less invasive and more convenient sampling site however, and an oropharyngeal and throat swab or a nasopharyngeal aspirate/wash are recommended when URT sampling is to be conducted [90] . Paired sera, collected two to three weeks apart are preferable for serological testing while a single sample is suggested to be sufficient if collected two weeks after onset of disease or a single serum collected during the first 10-12 days if conducting RT-rtPCR [53, 90] . Human urine and stool have been found to contain MERS-CoV RNA 12 to 26 days after symptom onset [25, 69, 91] and are listed as samples that should be considered [53, 90] . In two cases that arrived in the Netherlands, urine was RT-rtPCR negative but faeces was weakly positive and sera were RT-rtPCR positive for five days or more [25] . The finding of MERS-CoV viral RNA in serum provides an avenue for retrospective PCR-based studies if respiratory samples are unavailable [83] . RNAaemia may also correlate with disease severity; signs of virus were cleared from the serum of a recovered patient, yet lingered until the death of another [92] . Clinically suspected MERS cases may return negative results by RT-rtPCR. Data have shown one or more negative URT samples may be contradicted by further URT sampling or the use of LRT samples, which is preferred [2, 43, 93] . Higher viral loads occur in the LRT compared to the URT. [22, 69, 88, 94] This fits with the observation that the majority of disease symptoms are reported to manifest as systemic and LRT disease [21] . However, on occasion, even LRT specimens from MERS cases may initially be negative, only to later become positive by RT-PCR [95] . This may be due to poor sampling when a cough is absent or non-productive or because the viral load is low [95] . Despite this both the largest human MERS-CoV studies [32, [96] [97] [98] and smaller ones [22, 25, 99] , use samples from the URT. It is then noteworthy that one study reported an association between higher loads in the URT and worse clinical outcome including intensive care and death [94] . At writing, no human data exist to define whether the virus replicates solely or preferentially in the LRT or URT, or replicates in other human tissues in vivo although MERS-CoV RNA has been detected from both the URT and LRT in a macaque monkey model [100] .The distribution of DPP4 in the human upper airways is also not well described. Individual human case studies report long periods of viral shedding, sometimes intermittently and not necessarily linked to the presence of disease symptoms. [25, 69, 99, 101] In one instance, a HCW shed viral RNA for 42 days in the absence of disease [99] . It is an area of high priority to better understand whether such cases are able to infect others. Over three quarters of MERS cases shed viral RNA in their LRT specimens (tracheal aspirates and sputum) for at least 30 days, while only 30 % of contacts were still shedding RNA in their URT specimens [91, 102] . In the only study to examine the effect of sample type on molecular analysis, 64 nasopharyngeal aspirates (NPA; an URT sample), 30 tracheal aspirates, 13 sputa and three BAL were examined. The tracheal aspirates and BAL returned the highest viral load values followed by NPA and sputum. Unsurprisingly, higher viral loads generally paralleled whole genome sequencing and culture success and, in NPA testing, were significantly correlated with severe disease and death [49, 94, 103] . This study demonstrated the importance of LRT sampling for whole genome sequencing. When tested, samples positive for MERS-CoV are often negative for other pathogens [2, 25, 93, 104] . However, many studies make no mention of additional testing for endemic human respiratory viruses [21, 23, 73, 105] . When viruses are sought, they have included human herpesvirus (HHV), rhinoviruses (HRV), enteroviruses (EV), respiratory syncytial virus (RSV), parainfluenzavirus types 1, 2 and 3 (PIVs),influenzaviruses (IFVs), endemic HCoVs, adenoviruses (AdVs) metapneumovirus (MPV) and influenza A\H1N1 virus; co-detections with MERS-CoV have been found on occasion [2, 22, 37, 69, 97] . Bacterial testing is sometimes included (for example, for Legionella and Pneumococcus) but the impact of bacterial co-presence is also unclear [22, [104] [105] [106] . Further testing of the LRT sample from the first MERS case used IFA to screen for some viruses (negative for IFV, PIVs, RSV and AdVs) and RT-PCR for others (negative for AdV, EVs, MPV and HHVs) [18] . RT-PCR also detected MERS-CoV. The WHO strongly recommends testing for other respiratory pathogens [53] but with this recommendation often discounted, there are limited data to address the occurrence and impact of co-infections or alternative viral diagnoses among both MERS cases and their contacts. Little is known of other causes of MERS-like pneumonia in the KSA or of the general burden of disease due to the known classical respiratory viruses. Testing of adult pilgrims performing the Hajj in 2012 to 2014 has not detected any MERS-CoV. In 2012, nasal swabs from 154 pilgrims collected prior to leaving for or departing from the KSA were tested [47] . In 2013, testing was significantly scaled up with 5,235 nasopharyngeal swabs from 3,210 incoming pilgrims and 2,025 swabs from outgoing pilgrims tested [98] . It should be noted that most pilgrims arrived from MERS-free countries. A further 114 swabs were taken from pilgrims with influenza-like illness [96, 107] . In earlier Hajj gatherings, it was found that influenza viruses circulated widely, whilst other viruses, often rhinoviruses, circulated more selectively, interpreted as indicating their importation along with foreign pilgrims. [107] [108] [109] Over time, increased influenza vaccination has been credited for a fall in the prevalence of influenza like illnesses among Hajj pilgrims. [110] A LRT sample is often not collected for these studies [98, 107, 109] , so false negative findings are a possibility although little is known about the initial site of MERS-CoV infection and replication; it may have been assumed it was the LRT because disease was first noticed there but the URT may be the site of the earliest replication. In Jeddah between March and July 2014 (hereafter called the Jeddah-2014 outbreak; Fig. 3 ), there was a rapid increase in MERS cases, accompanied by intense screening; approximately 5,000 samples from in and around the region were tested in a month yielding around 140 MERS-CoV detections (~3 % prevalence) [111] . Among 5,065 individuals sampled and tested across the KSA between October 2012 and September 2013,108 (2.1 %) detections were made in a hospital-centric population which included hospitalized cases (n = 2,908; 57.4 %), their families (n = 462; 9.1 %) and associated HCWs (n = 1,695; 33.5 %) [32] . Among the detections, 19 (17.8 %) were HCWs and 10 (9.3 %) were family contacts [32] . The 2-3 % prevalence of active MERS-CoV infections is not dissimilar to the hospital-based prevalence of other human CoVs. [112] However, the proportion of deaths among those infected with MERS-CoV is much higher than that known for the HCoVs NL63, HKU1, 229E or OC43 in other countries, and even above that for SARS-CoV; it is not a virus that could reasonably be described as a "storm in a teacup". It is the low transmission rate that has prevented worldwide spread, despite many "opportunities". Very early in the MERS outbreak, some animals were highly regarded as either the reservoir or intermediate host(s) of MERS-CoV with three of the first five cases having contact with DCs [73, 113, 114] . Today, animal MERS-CoV infections must be reported to the world organization for animal health as an emerging disease [115] . A summary of the first MERS cases reported by the WHO defined animal contact with humans as being direct and within 10 days prior to symptom onset [20] . This definition made no specific allowance for acquisition from DCs through a droplet-based route, which is very likely route for acquisition of a virus that initially and predominantly causes respiratory disease [23] . Camels are known to produce high levels of MERS-CoV RNA in their URT and lungs [116] . Providing support for a droplet transmission route and perhaps indicating the presence of RNA in smaller, drier droplet nuclei, MERS-CoV RNA was identified in a high volume air sample collected from a barn housing an infected DC [117] . The precise source from which humans acquire MERS-CoV remains poorly studied but it seems likely that animal and human behavioural factors may play roles (Fig. 3) [118] . These factors may prove important for human cases who do not describe any DC contact [119] nor any contact with a confirmed case. Whether the WHO definition of animal contact is sufficient to identify exposure to this respiratory virus remains unclear. Wording focuses on consumption of DC products but does not specifically ascribe risk to a droplet route for acquisition of MERS-CoV from DC [120] . Some MERS patients are listed in WHO disease notices as being in proximity to DCs or farms, but the individuals have not described coming into contact with the animals. No alternative path for acquiring infection is reported in many of these instances. What constitutes a definition of "contact" during these interviews has been defined for one study [72] . Despite this lack of clarity, the WHO consider that evidence linking MERS-CoV transmission between DCs to humans is irrefutable (Fig. 4) [120] . The possibility that bats were an animal host of MERS-CoV was initially widely discussed because of the existing diversity of coronaviruses known to reside among them [121] [122] [123] [124] . Conclusive evidence supporting bats as a source for human infections by MERS-CoV has yet to be found, but bats do appear to host ancestral representatives [53, 125] . However, these are not variants of the same virus nor always within the same phylogenetic lineage as MERS-CoV; they are each a genetically distinct virus. Bat-to-human infection by MERS-CoV is a purely speculative event. The only piece of MERS-CoV-specific evidence pointing to bats originates from amplification of a 190 nt fragment of the RNAdependent RNA polymerase gene of the MERS-CoV genome, identified in a faecal pellet from an insectivorous Emballonuridae bat, Taphozous perforatus found in Bisha, the KSA [121] . While very short, the sequence of the fragment defined it as a diagnostic discovery. Subsequently a link to DCs was reported [85] and that link has matured into a verified association [38, 126] (Fig. 4) . (See figure on previous page.) Fig. 3 Monthly detections of MERS-CoV (blue bars) and of cases who died (red bars) with some dates of interest marked for 2012 to 4 th September 2015. An approximation of when DC calving season [128] and when recently born DCs are weaned is indicated. Spring (green) and summer (orange) in the Arabian Peninsula are also shaded. Note the left-hand y-axis scale for 2014 and 2015 which is greater than for 2012/13. Sources of these public data include the WHO, Ministries of Health and FluTrackers [207] [208] [209] . Earlier and subsequent versions of this chart are maintained on a personal blog [210] . Modified and reprinted from Mackay IM, Arden KE. Middle East respiratory syndrome: An emerging coronavirus infection tracked by the crowd. Virus Res 2015 Vol 202:60-88 with permission from Elsevier [5] DCs, which make up 95 % of all camels, have a central presence in the Arabian Peninsula where human-DC contact ranges from little to close [119] . Contact may be commonplace and could occur in variety of ways (Fig. 4a) . There are several large well-attended festivals, races, sales and parades which feature DCs and DCs are also kept and bred close to populated areas in the KSA [127, 128] . DC milk and meat are widely consumed and the older DC is an animal of ritual significance after the Hajj pilgrimage [129] . However, MERS-CoV infection frequency is reportedly much lower than is the widespread and frequent habit of eating, drinking and preparing DC products. Daily ingestion of fresh unpasteurized DC milk is common among the desert Bedouin and many others in the KSA. DC urine is also consumed or used for supposed health benefits. Despite camel butchery being a local occupation, neither butchers nor other at-risk groups are identifiable among MERS cases; this may simply be a reporting issue rather than an unexplainable absence of MERS. A small case-control study published in 2015 identified direct DC contact, and not ingestion of products, to be associated with onset of MERS [38] . The first sero-survey of livestock living in the Middle East region was conducted during 2012-2013 [85] . DCs were sampled from a mostly Canary Island-born herd and from Omani DCs (originally imported from the Horn of Africa) [85] . A neutralising antibody assay found only 10 % of strongly seropositive Canary Island [5] . b Camel-to-human infections appear to be infrequent, while human-to-human spread of infection is regularly facilitated by poor IPC in healthcare settings where transmission is amplified, accounting for the bulk of cases. There are human MERS cases that do not fall into either category of source and it is unclear if these acquired infection through some entirely separate route, or from cases that escaped diagnosis. c Hypothetical ways in which subclinical (when infection may not meet a previously defined clinical threshold of signs and/or symptoms) or asymptomatic (no obvious signs or measured, noticed or recalled symptoms of illness) MERS-CoV infection may be implicated in transmission DC sera could neutralise MERS-CoV while all Omani DC sera had high levels of specific MERS-CoV neutralizing antibody [85] . This indicated that DCs had in the past been infected by MERS-CoV, or a very similar virus. Since this study, a host of peer-reviewed reports have looked at both DCs and other animals, and the possibility that they may host MERS-CoV infection. Seropositive DCs have been found throughout the Arabian Peninsula including Oman, the KSA, Qatar, Jordan, the United Arab Emirates (UAE), Kuwait as well as Sudan, Somalia, Egypt, Tunisia, Nigeria, Kenya and Ethiopia in Africa and the Canary Islands [85, [130] [131] [132] [133] [134] . Other animals tested include sheep, cows, pigs, horses, donkeys, mules, birds, water buffalo, goats, Bactrian camels, llamas and guanaco (south American camelids) but none had detectable neutralising antibody against MERS-CoV [4, 74, 78, 85, 86, 135, 136] . No virology or serology studies of human samples from areas in Africa where there are camels with a history of MERS-CoV have been reported to date. However,an absence of unexplained pneumonia that may be attributable to MERS-CoV infection may not signal the absence of virus among humans in each country but simply reflect a lack of expensive epidemiology studies conducted by resource-poor countries. It is thus unclear whether MERS-CoV, or an antigenically related CoV, is an unrecognized pathogen in these regions, perhaps circulating for even longer than it has been known in the Arabian Peninsula [133] . MERS-CoV RNA has also been detected in DC samples, and recovery of infectious virus has also been achieved from DC samples [4, 77, 117, 132, [137] [138] [139] [140] [141] . From some of these, full or majority length genomes of MERS-CoV have been sequenced [77, 137, 138] . DC versions of MERS-CoV were found to be as similar to each other, as were variants detected from different humans over time and across distance. Antibody screening assays have also detected crossreactive antibodies in sera. These were identified as such by screening sera against similar viruses, for example BCoV or HCoV-OC43 (as an antigenic facsimile for BCoV). It is possible that other MERS-CoV-like viruses also reside within DCs, but this does not detract from the definitive finding of MERS-CoV genetic sequences in both DCs and humans [117, 142, 143] . Screening studies have shown that juvenile DCs are more often positive for virus or viral RNA while older DCs are more likely to be seropositive and RNA or virus negative [76, 77, 144] . In adult DCs, MERS-CoV RNA has been detected among animals with pre-existing antibody, suggesting re-infection is possible [77, 144] . Viral loads among positive DCs can be very high [4, 76, 77, 139, 144] and DCs have been found positive both when ill with URT respiratory signs [77, 117, 142, 145] or when apparently healthy [137] . These findings indicate DCs host natural MERS-CoV infections. Furthermore, stored DC sera have revealed signs of MERS-CoV in DCs which date back over three decades (the earliest collected in 1983) [4, 133, 135] . Older sera have not been tested and so precisely how long DCs have been afflicted by MERS-CoV, whether the virus is enzootic among them, introduced to them decades or centuries ago from bats in Africa or the Arabian Peninsula, or they are the subject of regular but short-lived viral incursions from an as yet unknown host, cannot be answered. Researchers sought to determine a direction for infection; were DCs transmitting virus to humans or were humans infecting DCs? At a Qatari site, a farm owner and his employee became ill in mid-October 2013 and tested positive for MERS-CoV RNA in a sputum and throat swab sample, respectively. RT-rtPCRs found MERS-CoV RNA in 11 of 14 positive DC nasal swabs at the farm; six (43 %) positive by two or more assays [138] . The results indicated a recent outbreak had occurred in this herd; the first indication of MERS-CoV RNA found within DCs with a temporal association to human infections. Three positive DC samples were confirmed by sequencing a 358 nt portion of the spike gene; these sequences were identical to each other, again with close homology to other human and DC MERS-CoV sequences [138] . The DCs and human contacts yielded ORF1a and ORF4b sequences differing by only a single nucleotide each, clustering closely with the Hafr-Al-Batin_1_2013 variant [138] . Subsequent case studies found evidence of a concurrent human and DC infection and the direction of that infection was inferred to be from the ill DCs and to their human owners [117, 142, 146] . Partial genome sequences indicated that a human and a MERS-CoV RT-rtPCR positive DC had been infected by a variant of the same virus, harbouring the same distinct pattern of nucleotide polymorphisms. [142] All nine DC in the owner's herd, serially sampled, reacted in a recombinant S1 antigen ELISA, with the two animals that had been RT-rtPCR positive showing a small, verifiable rise in antibody titre [142] . A rise in titre theoretically begins 10 to 21 days after DC infection [142] . The authors suggested that the rise in titre in DC sera which occurred alongside a declining RNA load, while the patient was actively ill and hospitalized, indicated that the DCs were infected first followed by the owner [117, 142] . BCoV antibodies were also present, and rising in one of the two RT-rtPCR positive animals but no animal's antibodies could neutralise BCoV infection [142] . Camel calving season occurs in the winter months (between late October and late February; Fig. 3 ) and this may be a time when there is increased risk to humans of spill-over due to new infections among naïve DC populations [128] . What role maternal camel antibody might play in delaying infection of calves remains unknown [128, 142] . Juvenile DCs appear to host active infection more often than adult DCs and thus the sacrificial slaughter of DCs, which must be five years of age or older (termed a thane), may not be accompanied by significant risk of exposure to infection. In contrast to earlier results, slaughterhouse workers who kill both younger and older DCs, may be an occupational group with significantly higher incidence of seropositivity to MERS-CoV when animals have active MERS-CoV infections [129, 139, [147] [148] [149] . Expanded virological investigations of African DCs may lead to more seropositive animals and geographic areas in which humans may be at risk. It is possible that there are areas where humans already harbour MERS-CoV infections that have not been identified because of an absence of laboratory surveillance. Virological investigations of bats may lead to findings of ancestral viruses and viral 'missing links' and identifying any other animal sources of zoonotic spread is important to inform options for reducing human exposures [56, 76] . Infectious MERS-CoV added to DC, goat or cow milk and stored at 4°C could be recovered at least 72 h later and, if stored at 22°C, recovery was possible for up to 48 h [150] . MERS-CoV titre decreased somewhat when recovered from milk at 22°C but pasteurization completely ablated MERS-CoV infectivity [150] . In a subsequent study, MERS-CoV RNA was identified in the milk, nasal secretion and faeces of DCs from Qatar [151] . A single study has examined the ability of MERS-CoV to survive in the environment [150] . Plastic or steel surfaces were inoculated with 10 6 TCID 50 of MERS-CoV at different temperature and relative humidity (RH) and virus recovery was attempted in cell culture. At high ambient temperature (30°C) and low RH (30 %) MERS-CoV remained viable for 24 h [150] . By comparison, a well known and efficently transmitted respiratory virus, influenza A virus, could not be recovered in culture beyond four hours under any conditions [150] . Aerosol experiments found MERS-CoV viability only decreased 7 % at low RH at 20°C. In comparison, influenza A virus decreased by 95 % [150] . MERS-CoV survival is inferior to that previously demonstrated for SARS-CoV [152] . For context, pathogenic bacteria can remain viable and airborne for 45 min in a coughed aerosol and can spread 4 m. MERS-CoV's ability to remain viable over long time periods gives it the capacity to thoroughly contaminate a room's surfaces when occupied by an infected and symptomatic patient [153] . Whether MERS-CoV can remain adrift and infectious for extended periods (truly airborne) remains unknown. Such findings expand our understanding of the possibilities for droplets to transmit respiratory viruses in many settings, including hospital waiting rooms, emergency departments, treatment rooms, open intensive care facilities and private patient rooms. The nature and quality of air exchange, circulation and filtration are important variables in risk measurement and reduction as is the use of negative pressure rooms to contain known cases. Droplet spread between humans is considered the mechanism of human-to-human transmission and the need for droplet precautions was emphasized after the Al-Ahsa hospital, the KSA and the South Korean outbreaks [21, 23, 154, 155] . By extrapolation, aerosol-generating events involving DCs (urination, defecation, and preparation and consumption of DC products) should be factored into risk measurement and reduction efforts and messaged using appropriate context. The provision of evidence supporting the best formulation of personal protective equipment to be worn by HCWs who receive, manage or conduct procedures on infectious cases remains a priority. MERS-CoV was found and characterized because of its apparent association with severe, and therefore more obvious, illness in humans; we were the canaries in the coal mine. Sero-assays and prospective cohort studies have yet to determine the extent to which milder or asymptomatic cases contribute to MERS-CoV transmission chains. However, transmission of MERS-CoV is defined as sporadic (not sustained), intra-familial, often healthcare associated, inefficient and requiring close and prolonged contact [22, 31, 63, 93, 97, 102, 156] In a household study, 14 of 280 (5 %) contacts of 26 MERS-CoV positive index patients were RNA or antibody positive; the rate of general transmission, even in outbreaks is around 3 % [31] . It seems that the majority of human cases of MERS-CoV, even when numbers appear to increase suddenly, do not readily transmit to more than one other human so to date, the localized epidemic of MERS-CoV has not been self-sustaining [157] [158] [159] [160] [161] . That is to say, the basic reproduction number (R 0 ) -the average number of infections caused by one infected individual in a fully susceptible populationhas been close to one throughout various clusters and outbreaks. If R 0 was greater than 1, a sustained increase in case numbers would be expected. Some R o calculations may be affected by incomplete case contact tracing, limited community testing and how a case is defined. That MERS has had a constant presence in the Arabian Peninsula since 2012 is due to ongoing, sporadic spill-over events from DCs amplified by poorly controlled hospital outbreaks. The first known MERS human-to-human transmission event was one characterized by acute LRT disease in a healthcare setting in Jordan. In stark contrast, a sero-survey of HCW who were sometimes in close and prolonged contact with the first, fatal MERS-CoV case in 2012 [162] , found none of the HCW had seroconverted four months later, despite an absence of eye protection and variable compliance with required PPE standards [162] . Early on in the MERS story, samples for testing were mostly collected from patients with severe illness and not those with milder acute respiratory tract infections. Contacts of confirmed MERS cases were often observed for clinical illness, but not tested. These omissions may have confounded our understanding of MERS-CoV transmission and biased early data towards higher numbers of seriously ill and hospitalized patients, inflating the apparent proportion of fatal cases. Case-control studies were not a focus. As testing paradigms changed and contacts were increasingly tested, more asymptomatic and mild infections were recognized [163] . A rise in the cases termed asymptomatic (which enlarge the denominator for calculations of the proportion of fatal cases, defined in [164] ) resulted in a drop in the proportion of fatal cases during the Jeddah-2014 outbreak. Historically, such rises are consistent with changing definitions and laboratory responses and clinical management of a newly discovered virus infection that was first noted only among the severely ill. Upon follow-up, over three-quarters of such MERS-CoV RNA positive people did recall having one or more symptoms at the time, despite being reported as asymptomatic [165] raising some question over the reliability of other reported data. The proportion of fatal MERS cases within the KSA compared to outside the KSA, as well as the age, and sex distribution change in different ways when comparing MERS outbreaks. Approximately 43 % of MERS cases (549 of 1277) in the KSA were fatal betwen 2012 and December 2015 while 21 % (72 of 330) died among those occurring outside of the KSA. The total number of male cases always outnumber females and the proportion of male deaths is always greater than the proportion of females who die. However the proportion of male deaths from total males with MERS is a similar figure to that for females. In the KSA, there is a greater proportion of younger males among cases and deaths than were observed from the 2015 South Korean or the Jeddah-2014 outbreaks (Additional file 2: Figure S2 ). Why these aspects have differed may be due to differences in the time to presentation and diagnosis, the nature and quality of supportive care, the way a person became infected (habits, exposure to a human or zoonotic source, viral load, route of infection) or the extent to which different populations are burdened by underlying diseases [40] . As a group, HCWs comprised 16 % of MERS cases in the KSA and South Korea. It is apparent that the weekly proportion of infected HCWs increases alongside each steep rise in overall detections (Fig. 5) . In May 2013, the WHO published guidelines for IPC during care of probable or confirmed cases of MERS-CoV infection in a healthcare setting [166] . This is explainable because to date, each case rise has been intimately associated with healthcare-facility related outbreaks [118] . These rises in MERS-CoV detections can decrease the average age during each event because HCWs are usually younger than inpatients with MERS. Healthcare facilities have been a regular target for suggested improvements aimed at improving infection prevention and control (IPC) procedures [115, 118] . Most of the analysis of MERS-CoV genetics has been performed using high throughput or "deep" sequencing methods for complete genome deduction [167] [168] [169] . MERS-CoV was the first subject of such widespread use of deep sequencing to study an emerging viral outbreak with global reach. The technique can produce genomic [207] [208] [209] . Earlier and subsequent versions of this chart are maintained on a personal blog [210] length coverage in a single experiment with highly repetitious measurement of each nucleotide position [52, 140] . Despite assays having been published early on, subgenomic sequencing, once the mainstay of viral outbreak studies, has less often been published during MERS-CoV characterization [48] . As more genomes from both humans and DCs have been characterized, two clades have become apparent; A and B (Fig. 6) . Clade A contains only human-derived MERS-CoV genomes from Jordan, while Clade B comprises the majority of human and camel genomes deduced thus far [168] . Two studies during 2015, one looking at Jeddah-2014 MERS-CoV variants and another looking at a variant exported from South Korea to China, have now identified signs of genetic recombination among MERS-CoV variants. While human and camel whole genome sequences have retained >99 % identity with each other, members of genetically distinct lineages can and do swap genetic material when suitable conditions and coinfections co-occur [170] [171] [172] . Shared identity implies that the major source for human acquisition is the DC, rather than another animal, although more testing of other animal species is needed to confirm that conclusion. Over a month, a DC virus sequenced on different occasions did not change at all indicating a degree of genomic stability in its host, supporting that DCs are the natural, rather than intermediate, host for the MERS-CoV we know today [77] . To date, recombination has been localised to breakpoints near the boundary between ORF1a and ORF1b regions, within the spike gene [170] and in the ORF1b region (Fig. 2) [172] . It is not unexpected that recombination should occur since it is well known among other CoVs [124] and because the majority of MERS-CoV whole genomes collected from samples spanning three years (2012-2015) and from humans, camels and different countries have shown close genetic identity to each other, with just enough subtle variation to support outbreak investigations so long as whole genome sequencing is applied [52, 77, 135, 138, 168, [173] [174] [175] . Changes in genome sequence may herald alterations to virus transmissibility, replication, persistence, lethality or response to future drugs. If we have prior knowledge of the impact of genetic changes because of thorough characterization studies, we can closely Fig. 6 The genetic relationship between MERS-CoV nucleotide sequences (downloaded from GenBank using the listed accession numbers and from virological.org [212] ). This neighbour joining tree was created in MEGA v6 using an alignment of human and DCderived MERS-CoV sequences (Geneious v8.1 [211] ). Clades are indicated next to dark (Clade A) or pale (Clade B) blue vertical bars. Camel icons denote genomes from DCs. Healthcare or community outbreaks are boxed and labelled using previously described schemes [212, 213] monitor the genomic regions and better understand any changes in transmission or disease patterns as they occur. Genetic mutations noted during the largest of human outbreaks, Jeddah-2014, did not impart any major replicative or immunomodulatory changes when compared to earlier viral variants in vitro [156, 176] . However, we understand very little of the phenotypic outcomes that result from subtle genetic change in MERS-CoV genomes. To date no clinical relevance or obvious in vivo changes to viral replication, shedding or transmission has been reported or attributed to mutations or to new recombinant viruses [156] . But vigilance and larger, more contemporary and in vivo studies are needed. Genome sequence located to a distinct clade were identified from an Egyptian DC that was probably imported from Sudan. This does not fit into either of the current clades [125, 168, 177] . A virus sequenced from a Neoromicia capensis bat was more closely related to MERS-CoV than other large bat-derived sequences had been to that point, but the genome of a variant of a MERS-CoV has yet to be discovered and deduced from any bat [125] . Analyses of MERS-CoV genomes have shown that most single nucleotide differences among variants were located in the last third of the genome (Fig. 2) , which encodes the spike protein and accessory proteins [168] . At least nine MERS-CoV genomes contained amino acid substitutions in the receptor binding domain (RBD) of the spike protein and codons 158 (N-terminal region), 460 (RBD), 1020 (in heptad repeat 1), 1202 and 1208 bear investigation as markers of adaptive change [140, 169] . The spike protein had not changed in the recombinant MERS-CoV genome identified in China in 2015 but was reported to have varied at a higher rate than that for complete MERS-CoV genomes, among South Korean variants [172, 178] . This highlights that subgenomic regions may not always contain enough genetic diversity to prove useful for differentiating viral variants. Despite this, one assay amplifying a 615 nucleotide fragment of the spike S2 domain gene for Sanger sequencing agreed with the results generated by the sequencing of a some full genomes and was useful to define additional sequence groupings [177] . Genomic sequence can also be used to define the geographic boundaries of a cluster or outbreak and monitor its progress, based on the similarity of the variants found among infected humans and animals when occurring together, or between different sites and times (Fig. 6 ) [169] . This approach was employed when defining the geographically constrained MERS hospital outbreak in Al-Ahsa, which occurred between 1 st April and 23 rd May 2013, as well as clusters in Buraidah and a community outbreak in Hafr Al-Batin, the KSA. Genomic sequencing identified that approximately 12 MERS-CoV detections from a community outbreak in Hafr Al-Batin between June and August 2013 may have been triggered by an index case becoming infected through DC contact [175] . Sequencing MERS-CoV genomes from the 2013 Al-Ahsa hospital outbreak indicated that multiple viral variants contributed to the cases but that most were similar enough to each other to be consistent with human-tohuman transmission. Molecular epidemiology has revealed otherwise hidden links in transmission chains encompassing a period of up to five months [179] . However, most outbreaks have not continued for longer than two to three months and so opportunities for the virus to adapt further to humans through co-infection and sustained serial passage have been rare [169] . In Riyadh-2014, genetic evidence supported the likelihood of multiple external introductions of virus, implicating a range of healthcare facilities in an event that otherwise looked contiguous [23, 168, 179] . Riyadh is a nexus for camel and human travel and has had more MERS cases than any other region of the KSA to date but also harbours a wide range of MERS-CoV variants [128, 167, 179] . However the South Korean outbreak originated from a single infected person, resulting in three to four generations of cases [180, 181] . Studies of this apparently recombinant viral variant did not find an increased evolutionary rate and no sign of virus adaptation thus the outbreak seems to have been driven by circumstance rather than circumstance together with mutation [181] . For many MERS cases detected outside the Arabian Peninsula, extensive contact tracing has been performed and the results described in detail. Contact tracing is essential to contain the emergence and transmission of a new virus and today it is supported by molecular epidemiology. Although it is an expensive and time consuming process, contact tracing can identify potential new infections and through active or passive monitoring, react more rapidly if disease does develop. Results of contact tracing to date have found that onward transmission among humans is an infrequent event. For example, there were 83 contacts, both symptomatic and asymptomatic, of a case treated in Germany who travelled from the UAE but no sign of virus or antibody were found in any of them [73] . The very first MERS case had made contact with 56 HCWs and 48 others, but none developed any indication of infection [162] . In a study of 123 contacts of a case treated in France, only seven matched the definition for a possible case and were tested; one who had shared a 20 m 2 hospital room while in a bed 1.5 m away from the index case for a prolonged period was positive [26] . None of the contacts of the first two MERS cases imported into the USA in 2014 contained any MERS-CoV footprint [182] and none of the 131 contacts of two travellers returning to the Netherlands developed MERS-CoV antibodies or tested RNA positive [25, 183] . Analyses of public data reveal many likely instances of nosocomial acquisition of infection in the Arabian Peninsula and these data may be accompanied by some details noting contact with a known case or facility. One example identified the likely role of a patient with a subclinical infection, present in a hospital during their admission for other reasons, as the likeliest index case triggering a family cluster [93] . Contact tracing was a significant factor in the termination of a 2015 outbreak involving multiple South Korean hospitals [184] . Such studies demonstrate the necessity of finding and understanding a role for mild and asymptomatic cases, together with restricting close contact or prolonged exposure of infected people to others, especially older family members and friends with underlying disease (Fig. 4c) . The hospital-associated outbreak in Jeddah in 2014 was the largest and most rapid accumulation of MERS-CoV detections to date. The greatest number of MERS-CoV detections of any month on record occurred in Jeddah in April. The outbreak was mostly (>60 % of cases) associated with human-to-human spread within hospital environments and resulted from a lack of, or breakdown in, infection prevention and control [37, 185, 186] . A rise in fatalities followed the rapid increase in case numbers. In 2015 two large outbreaks occurred. South Korea was the site of the first large scale outbreak outside the Arabian Peninsula and produced the first cases in both South Korea and China, occurring between May and July 2015. This was closely followed by a distinct outbreak in Ar Riyad province in the KSA which appeared to come under control in early November. After staying in Bahrain for two weeks, a 68 year old male (68 M) travelled home to South Korea via Qatar, arriving free of symptoms on the 4 th May 2015 [187] . He developed fever, myalgia and a cough nearly a week later (11 th ). He visited a clinic as an outpatient between the 12 th and 15 th of May and was admitted to Hospital A on the 15 th [188] . He was discharged from Hospital A on the 17 th then visited and was admitted to the emergency department of Hospital B on the 18 th . During this second stay, a sputum sample was taken and tested positive for MERS-CoV on the 20 th [187, 188] , triggering transfer to the designated isolation treatment facility. Over a period of 10 days, the index case was seen at three different hospitals, demonstrating a key feature of "hospital shopping" that shaped the South Korean outbreak. Approximately 34 people were infected during this time [187] . In total 186 cases were generated in this outbreak, all linked through a single transmission chain to 68 M; 37 cases died [189] . In South Korea, the national health insurance system provides for relatively low cost medical care, defraying some costs by making family members responsible for a portion of the ministration of the sick, resulting in them sometimes staying for long periods in the rooms that often have more than four beds in them [24] . Other factors thought to have enabled this outbreak included unfamiliarity of local clinicians with MERS, ease with which the public can visit and be treated by tertiary hospitals, the custom of visiting sick friends and relatives in hospitals, the hierarchical nature of Korean society, crowded emergency rooms, poor IPC measures, a lack of negative pressure isolation rooms and poor inter-hospital communication of patient disease histories [24, [190] [191] [192] . All of the reported transmission occurred across three or four generations and apart from one unknown source, were all hospital-acquired [24, 120, 181, [193] [194] [195] . Few clinical details about these cases have been reported to date and detail on transmission and contact tracing is minimal. The hospitals involved were initially not identified, governmental guidance and actions produced confusing messages and there was very limited communication at all early on which resulted in unnecessary concern, distrust and a distinct economic impact [191, [196] [197] [198] . Early in the outbreak, a infected traveller, the son of an identified case in South Korea, passed through Hong Kong on his way to China where he was located, isolated and cared for in China [91, 199, 200] . No contacts became ill. The outbreak was brought under control in late July/ early August [201] after improved IPC measures were employed, strong contact tracing monitoring and quarantine, expanded laboratory testing, hospitals were better secured, specialized personnel were dispatched to manage cases and international cooperation increased [202, 203] . A review of public data showed that, as for MERS in the KSA, older age and the presence of underlying disease were significantly associated with a fatal outcome in South Korea. [40] Even though R 0 is <1, super-spreading events facilitated by circumstances created in healthcare settings and characterized by cluster sizes over 150, such as this one, are not unexpected from MERS-CoV infection [204] . The dynamic of an outbreak depends on the R 0 and an individual's viral shedding patterns, contact type and frequency, hospital procedures and population structure and density [204] . In the region of Ar Riyad, including the capital city of Riyadh, a hospital based cluster began, within a single hospital, from late June 2015 [205] . By mid-September there had been approximately170 cases reported but the outbreak appeared to been brought under control in November. It became apparent early on that MERS-CoV spread relatively ineffectively from human-to-human. Despite ongoing and possibly seasonal introduction of virus to the human population via infected DCs and perhaps other animals yet to be identified, the vast majority of MERS-CoV transmission has occurred from infected to uninfected humans in close and prolonged contact through circumstances created by poor infection control in health care settings. This opportunistic virus has had its greatest impact on those with underlying diseases and such vulnerable people, sometimes suffering multiple comorbidities, have been most often associated with hospitals, creating a perfect storm of exposure, transmission and mortality. It remains unclear if this group are uniquely affected by MERS-CoV or if other respiratory virus infections, including those from HCoVs, produce a similarly serious impact. In South Korea, a single imported case created an outbreak of 185 cases and 36 deaths that had a disproportionate impact on economic performance, community behaviour and trust in government and the health care system. Household human-to human transmission occurs but is also limited. Educational programs will be essential tools for combatting the spread of MERS-CoV both within urban and regional communities and for the health care setting. Vigilance remains important for containment since MERS-CoV is a virus with a genetic makeup that has been observed for only three years and is not stable. Among all humans reported to be infected, nearly 40 % have died. Continued laboratory testing, sequencing, analysis, timely data sharing and clear communication are essential for such vigilance to be effective. Global alignment of case definitions would further aid accurate calculation of a case fatality ratio by including subclinical case numbers. Whole genome sequencing has been used extensively to study MERS-CoV travel and variation and although it remains a tool for experts, it appears to be the best tool for the job. MERS and SARS have some clinical similarities but they also diverge significantly [206] . Defining characteristics include the higher PFC among MERS cases (above 50 % in 2013 and currently at 30-40 %; well above the 9 % of SARS) and the higher association between fatal MERS and older males with underlying comorbidities. For the viruses, MERS-CoV has a broader tropism, grows more rapidly in vitro, more rapidly induces cytopathogenic change, triggers distinct transcriptional responses, makes use of a different receptor, induces a more proinflammatory state and has a delayed innate antiviral response compared to SARS-CoV. There appears to be a 2-3 % prevalence of MERS-CoV in the KSA with a 5 % chance of secondary transmission within the household. There is an increased risk of infection through certain occupations at certain times and a much greater chance for spread to other humans during circumstances created by humans, which drives more effective transmission than any R 0 would predict on face value. Nonetheless, despite multiple mass gatherings that have afforded the virus many millions of opportunities to spread, there have remarkably been no reported outbreaks of MERS or MERS-CoV during or immediately after these events. There is no evidence that MERS-CoV is a virus of pandemic concern. Nonetheless, hospital settings continue to describe MERS cases and outbreaks in the Arabian Peninsula. As long as we facilitate the spread of MERS-CoV among our most vulnerable populations, the world must remain on alert for cases which may be exported more frequently when a host country with infected camel reservoirs is experiencing human clusters or outbreaks. The MERS-CoV appears to be an enzootic virus infecting the DC URT with evidence of recent genetic recombination. It may once have had its origins among bats, but evidence is lacking and the relevance of that to today's ongoing epidemic is academic. Thanks to quick action, the sensitive and rapid molecular diagnostic tools required to achieve rapid and sensitive detection goal have been in place and made widely available since the virus was reported in 2012. RT-PCR testing of LRT samples remains the gold standard for MERS-CoV confirmation. Serological tools continue to emerge but they are in need of further validation using samples from mild and asymptomatic infections and a densely sampled cohort study to follow contacts of new cases may address this need. Similarly, the important question of whether those who do shed MERS-CoV RNA for extended periods are infectious while appearing well, continues to go unanswered. It is even unclear just how many 'asymptomatic' infections have been described and reported correctly which in turn raises questions about the reliability of other clinical data collection to date. While the basic virology of MERS-CoV has advanced over the course of the past three years, understanding what is happening in, and the interplay between, camel, environment and human is still in its infancy. Additional file 1: Figure S1 . The

Compared to severe acute respiratory syndrome (SARS) and another sometimes- fatal zoonotic coronavirus disease, how does MERS affect the patients?

Genome Sequences of Porcine Epidemic Diarrhea Virus: In Vivo and In Vitro Phenotypes https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4056290/ SHA: f6d6d7efc1686a7d219ecfc55f9a48ce72d4fb00 Authors: Lawrence, Paulraj K.; Bumgardner, Eric; Bey, Russell F.; Stine, Douglas; Bumgarner, Roger E. Date: 2014-06-12 DOI: 10.1128/genomea.00503-14 License: cc-by Abstract: Since the outbreak of porcine epidemic diarrhea virus (PEDV) in May 2013, U.S. swine producers have lost almost five million baby pigs. In an attempt to understand the evolution of PEDV in the United States and possibly develop a control strategy, we compared the genome sequences of a PEDV strain isolated from an infected piglet against its in vitro adapted version. The original PEDV strain was grown in Vero cells and passed 10 times serially in a MARC145 cell line. The sequence analysis of the native PEDV strain and in vitro passaged virus shows that the cell culture adaptation specifically modifies PEDV spike protein whereas the open reading frame 1a/b (ORF1a/b)-encoded polyprotein, the nucleoprotein, NS3B (ORF3), and membrane and envelope proteins remain unchanged. Text: highly contagious swine disease. While older pigs have a chance of survival, 80 to 100 percent of PEDV-infected piglets die within 24 h of being infected. PEDV spreads primarily through fecal-oral contact (1, 2) . Once the virus is internalized, it destroys the lining of piglets' intestines, making them incapable of digesting and deriving nutrition from milk and feed (1) . The virus causes diarrhea, vomiting, and death from dehydration and starvation (2) . PEDV is a member of the Coronavirinae subfamily and belongs to the Alphacoronavirus genus. Its genomic size ranges from approximately 26 to 32 kb, which is relatively large for an RNA virus. Although vaccines for PEDV exist in China, Japan, and South Korea, there is no approved vaccine in the United States or Europe (3) . Furthermore, PEDV is still evolving within the U.S. swine population. This report briefly describes the comparison of genome sequences of a PEDV strain isolated from small intestine samples of an infected piglet and its in vitro adapted version. The original PEDV strain was dubbed NPL-PEDV/2013, grown in Vero cells, and passed 10 times in a MARC145 cell line. The serial in vitro passage strain was named NPL-PEDV/2013/P10. The total viral RNA was extracted by TRIzol LS reagent and sequenced by Sanger dideoxy sequencing using a primer walking technique. The raw sequences were imported into the Geneious assembler (Biomatters, CA), assembled, annotated, and compared against each other using USA/Colorado/2013 (GenBank accession no. KF272920) as a reference sequence. The whole-genome sequences of NPL-PEDV/2013 and NPL-PEDV/2013/P10 contain 28,038 and 28,025 nucleotides (nt), respectively, including the 5= and 3= untranslated regions (UTR). The NPL-PEDV/2013 genome shares 99% identity with all the U.S. isolates sequenced to date and many Chinese isolates as well. The top three BLAST hits were against U.S. isolates, USA/Colora-do/2013 (GenBank accession no. KF272920), IA1 (GenBank accession no. KF468753.1), and an isolate from Iowa, 13-019349 (GenBank accession no. KF267450.1). The NPL-PEDV/2013 isolate also shares 99% identity with the Chinese outbreak isolate AH2012 (GenBank accession no. KC210145). When the NPL-PEDV/2013/P10 strain was compared against NPL-PEDV/2013 , the open reading frame 1a/b (ORF1a/b) polyprotein, the nucleoprotein, NS3B, and membrane and envelope proteins were found to be 100% identical at the amino acid level. In contrast, the spike gene contains six nonsynonymous single nucleotide polymorphisms, resulting in amino acid (aa) substitutions in the following positions: 375 (F¡L), 486 (T¡P), 856 (D¡E), 1081 (A¡V), 1099 (A¡S), and 1253 (Y¡D). The S1 domain of spike protein contains 2 aa substitutions, whereas the S2 domain contains 4 aa substitutions. PEDV has been shown to use porcine aminopeptidase N (pAPN) as the major receptor for cell entry (4, 5) . However, Vero and MARC145 cells lack pAPN, clearly indicating that other receptors or receptor-independent pathways may be used for entry (6) . The spike protein in its trimeric conformation interacts with the cell receptor and contains numerous neutralizing antibody binding epitopes (7) . Analysis of the spike by PeptideCutter (http://web.expasy.org/ peptide_cutter/) shows that the native spike protein of NPL-PEDV/2013 has 63 trypsin and 2 chymotrypsin cleavage sites at 100% efficiency whereas NPL-PEDV/2013/P10 has lost one trypsin cleavage site but the number of chymotrypsin sites remain unchanged. This indicates that cell culture adaptation specifically modifies the PEDV spike protein; however, the immunological implications are unknown. Nucleotide sequence accession numbers. The whole-genome sequences of the NPL-PEDV/2013 and NPL-PEDV/2013/P10 strains have been deposited at DDBJ/EMBL/GenBank under accession no. KJ778615 and KJ778616.

How does PEDV cause illness?

A mathematical model for simulating the phase-based transmissibility of a novel coronavirus https://doi.org/10.1186/s40249-020-00640-3 SHA: 018269476cd191365d6b8bed046078aea07c8c01 Authors: Yin, Tian-Mu Chen; Jia, Rui; Qiu-Peng, Wang; Ze-Yu, Zhao; Jing-An, Cui; Ling Date: 2020 DOI: 10.1186/s40249-020-00640-3 License: cc-by Abstract: Background As reported by the World Health Organization, a novel coronavirus (2019-nCoV) was identified as the causative virus of Wuhan pneumonia of unknown etiology by Chinese authorities on 7 January, 2020. The virus was named as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by International Committee on Taxonomy of Viruses on 11 February, 2020. This study aimed to develop a mathematical model for calculating the transmissibility of the virus. Methods In this study, we developed a Bats-Hosts-Reservoir-People transmission network model for simulating the potential transmission from the infection source (probably be bats) to the human infection. Since the Bats-Hosts-Reservoir network was hard to explore clearly and public concerns were focusing on the transmission from Huanan Seafood Wholesale Market (reservoir) to people, we simplified the model as Reservoir-People (RP) transmission network model. The next generation matrix approach was adopted to calculate the basic reproduction number (R 0) from the RP model to assess the transmissibility of the SARS-CoV-2. Results The value of R 0 was estimated of 2.30 from reservoir to person and 3.58 from person to person which means that the expected number of secondary infections that result from introducing a single infected individual into an otherwise susceptible population was 3.58. Conclusions Our model showed that the transmissibility of SARS-CoV-2 was higher than the Middle East respiratory syndrome in the Middle East countries, similar to severe acute respiratory syndrome, but lower than MERS in the Republic of Korea. Text: On 31 December 2019, the World Health Organization (WHO) China Country Office was informed of cases of pneumonia of unknown etiology (unknown cause) detected in Wuhan City, Hubei Province of China, and WHO reported that a novel coronavirus (2019-nCoV), which was named as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by International Committee on Taxonomy of Viruses on 11 February, 2020, was identified as the causative virus by Chinese authorities on 7 January [1] . It is reported that the virus might be bat origin [2] , and the transmission of the virus might related to a seafood market (Huanan Seafood Wholesale Market) exposure [3, 4] . The genetic features and some clinical findings of the infection have been reported recently [4] [5] [6] . Potentials for international spread via commercial air travel had been assessed [7] . Public health concerns are being paid globally on how many people are infected and suspected. Therefore, it is urgent to develop a mathematical model to estimate the transmissibility and dynamic of the transmission of the virus. There were several researches focusing on mathematical modelling [3, 8] . These researches focused on calculating the basic reproduction number (R 0 ) by using the serial intervals and intrinsic growth rate [3, 9, 10] , or using ordinary differential equations and Markov Chain Monte Carlo methods [8] . However, the bat origin and the transmission route form the seafood market to people were not considered in the published models. In this study, we developed a Bats-Hosts-Reservoir-People (BHRP) transmission network model for simulating the potential transmission from the infection source (probably be bats) to the human infection. Since the Bats-Hosts-Reservoir network was hard to explore clearly and public concerns were focusing on the transmission from Huanan Seafood Wholesale Market (reservoir) to people, we simplified the model as Reservoir-People (RP) transmission network model, and R 0 was calculated based on the RP model to assess the transmissibility of the SARS-CoV-2. The reported cases of SARS-CoV-2, which have been named as COVID-19, were collected for the modelling study from a published literature [3] . As reported by Li et al. [3] , the onset date of the first case was on 7 December, 2020, and the seafood market was closed on 1 January, 2020 [11] . The epidemic curve from 7 December, 2019 to 1 January, 2020 was collected for our study, and the simulation time step was 1 day. fourth-order Runge-Kutta method, with tolerance set at 0.001, was used to perform curve fitting. While the curve fitting is in progress, Berkeley Madonna displays the root mean square deviation between the data and best run so far. The coefficient of determination (R 2 ) was employed to assess the goodness-of-fit. SPSS 13.0 (IBM Corp., Armonk, NY, USA) was employed to calculate the R 2 . The Bats-Hosts-Reservoir-People (BHRP) transmission network model The BHRP transmission network model was posted to bioRxiv on 19 January, 2020 [12] . We assumed that the virus transmitted among the bats, and then transmitted to unknown hosts (probably some wild animals). The hosts were hunted and sent to the seafood market which was defined as the reservoir of the virus. People exposed to the market got the risks of the infection (Fig. 1) . The BHRP transmission network model was based on the following assumptions or facts: a) The bats were divided into four compartments: susceptible bats (S B ), exposed bats (E B ), infected bats (I B ), and removed bats (R B ). The birth rate and death rate of bats were defined as n B and m B . In this model, we set Ʌ B = n B × N B as the number of the newborn bats where N B refer to the total number of bats. The incubation period of bat infection was defined as 1/ω B and the infectious period of bat infection was defined as 1/γ B . The S B will be infected through sufficient contact with I B , and the transmission rate was defined as β B . b) The hosts were also divided into four compartments: susceptible hosts (S H ), exposed hosts (E H ), infected hosts (I H ), and removed hosts (R H ). The birth rate and death rate of hosts were defined as n H and m H . In this model, we set Ʌ H = n H × N H where N H refer to the total number of hosts. The incubation period of host infection was defined as 1/ω H and the infectious period of host infection was defined as 1/γ H . The S H will be infected through sufficient contact with I B and I H , and the transmission rates were defined as β BH and β H , respectively. c) The SARS-CoV-2 in reservoir (the seafood market) was denoted as W. We assumed that the retail purchases rate of the hosts in the market was a, and that the prevalence of SARS-CoV-2 in the purchases was I H /N H , therefore, the rate of the SARS-CoV-2 in W imported form the hosts was aWI H /N H where N H was the total number of hosts. We also assumed that symptomatic infected people and asymptomatic infected people could export the virus into W with the rate of μ P and μ' P , although this assumption might occur in a low probability. The virus in W will subsequently leave the W compartment at a rate of εW, where 1/ε is the lifetime of the virus. d) The people were divided into five compartments: susceptible people (S P ), exposed people (E P ), symptomatic infected people (I P ), asymptomatic infected people (A P ), and removed people (R P ) including recovered and death people. The birth rate and death rate of people were defined as n P and m P . In this model, we set Ʌ P = n P × N P where N P refer to the total number of people. The incubation period and latent period of human infection was defined as 1/ω P and 1/ω' P . The infectious period of I P and A P was defined as 1/γ P and 1/γ' P . The proportion of asymptomatic infection was defined as δ P . The S P will be infected through sufficient contact with W and I P , and the transmission rates were defined as β W and β P , respectively. We also assumed that the transmissibility of A P was κ times that of I P , where 0 ≤ κ ≤ 1. The parameters of the BHRP model were shown in Table 1 . We assumed that the SARS-CoV-2 might be imported to the seafood market in a short time. Therefore, we added the further assumptions as follows: a) The transmission network of Bats-Host was ignored. b) Based on our previous studies on simulating importation [13, 14] , we set the initial value of W as following impulse function: In the function, n, t 0 and t i refer to imported volume of the SARS-CoV-2 to the market, start time of the simulation, and the interval of the importation. Therefore, the BHRP model was simplified as RP model and is shown as follows: During the outbreak period, the natural birth rate and death rate in the population was in a relative low level. However, people would commonly travel into and out from Wuhan City mainly due to the Chinese New Year holiday. Therefore, n P and m P refer to the rate of people traveling into Wuhan City and traveling out from Wuhan City, respectively. In the model, people and viruses have different dimensions. Based on our previous research [15] , we therefore used the following sets to perform the normalization: In the normalization, parameter c refers to the relative shedding coefficient of A P compared to I P . The normalized RP model is changed as follows: The transmissibility of the SARS-CoV-2 based on the RP model In this study, we used the R 0 to assess the transmissibility of the SARS-CoV-2. Commonly, R 0 was defined as the expected number of secondary infections that result from introducing a single infected individual into an otherwise susceptible population [13, 16, 17] . If R 0 > 1, the outbreak will occur. If R 0 < 1, the outbreak will toward an end. In this study, R 0 was deduced from the RP model by the next generation matrix approach [18] . The multiple of the transmissibility of A P to that of I P . The parameters were estimated based on the following facts and assumptions: a) The mean incubation period was 5.2 days (95% confidence interval [CI]: 4.1-7.0) [3] . We set the same value (5.2 days) of the incubation period and the latent period in this study. Thus, ω P = ω' P = 0.1923. b) There is a mean 5-day delay from symptom onset to detection/hospitalization of a case (the cases detected in Thailand and Japan were hospitalized from 3 to 7 days after onset, respectively) [19] [20] [21] . The duration from illness onset to first medical visit for the 45 patients with illness onset before January 1 was estimated to have a mean of 5.8 days (95% CI: 4.3-7.5) [3] . In our model, we set the infectious period of the cases as 5.8 days. Therefore, γ P = 0.1724. c) Since there was no data on the proportion of asymptomatic infection of the virus, we simulated the baseline value of proportion of 0.5 (δ P = 0.5). d) Since there was no evidence about the transmissibility of asymptomatic infection, we assumed that the transmissibility of asymptomatic infection was 0.5 times that of symptomatic infection (κ = 0.5), which was the similar value as influenza [22] . We assumed that the relative shedding rate of A P compared to I P was 0.5. Thus, c = 0.5. e) Since 14 January, 2020, Wuhan City has strengthened the body temperature detection of passengers leaving Wuhan at airports, railway stations, long-distance bus stations and passenger terminals. As of January 17, a total of nearly 0.3 million people had been tested for body temperature [23] . In Wuhan, there are about 2.87 million mobile population [24] . We assumed that there was 0.1 million people moving out to Wuhan City per day since January 10, 2020, and we believe that this number would increase (mainly due to the winter vacation and the Chinese New Year holiday) until 24 January, 2020. This means that the 2.87 million would move out from Wuhan City in about 14 days. Therefore, we set the moving volume of 0.2 million per day in our model. Since the population of Wuhan was about 11 million at the end of 2018 [25] , the rate of people traveling out from Wuhan City would be 0.018 (0.2/11) per day. However, we assumed that the normal population mobility before January 1 was 0.1 times as that after January 10. Therefore, we set the rate of people moving into and moving out from Wuhan City as 0.0018 per day (n P = m P = 0.0018). f) The parameters b P and b W were estimated by fitting the model with the collected data. g) At the beginning of the simulation, we assumed that the prevalence of the virus in the market was 1/100000. h) Since the SARS-CoV-2 is an RNA virus, we assumed that it could be died in the environment in a short time, but it could be stay for a longer time (10 days) in the unknown hosts in the market. We set ε = 0.1. In this study, we assumed that the incubation period (1/ ω P ) was the same as latent period (1/ω' P ) of human infection, thus ω P = ω' P . Based on the equations of RP model, we can get the disease free equilibrium point as: In the matrix: By the next generation matrix approach, we can get the next generation matrix and R 0 for the RP model: The R 0 of the normalized RP model is shown as follows: Our modelling results showed that the normalized RP model fitted well to the reported SARS-CoV-2 cases data (R 2 = 0.512, P < 0.001) (Fig. 2) . The value of R 0 was estimated of 2.30 from reservoir to person, and from person to person and 3.58 from person to person which means that the expected number of secondary infections that result from introducing a single infected individual into an otherwise susceptible population was 3.58. In this study, we developed RP transmission model, which considering the routes from reservoir to person and from person to person of SARS-CoV-2 respectively. We used the models to fit the reported data in Wuhan City, China from published literature [3] . The simulation results showed that the R 0 of SARS-CoV-2 was 3.58 from person to person. There was a research showed that the R 0 of SARS-CoV-2 was 2.68 (95% CI: 2.47-2.86) [8] . Another research showed that the R 0 of SARS-CoV-2 was 2.2 (95% CI: 1.4-3.9) [3] . The different values might be due to the different methods. The methods which Li et al. employed were based on the epidemic growth rate of the epidemic curve and the serial interval [3] . Our previous study showed that several methods could be used to calculate the R 0 based on the epidemic growth rate of the epidemic curve and the serial interval, and different methods might result in different values of R 0 [26] . Our results also showed that the R 0 of SARS-CoV-2 was 2.30 from reservoir to person which was lower than that of person to person. This means that the transmission route was mainly from person to person rather than from reservoir to person in the early stage of the transmission in Wuhan City. However, this result was based on the limited data from a published literature, and it might not show the real situation at the early stage of the transmission. Researches showed that the R 0 of severe acute respiratory syndrome (SARS) was about 2.7-3.4 or 2-4 in Hong Kong, China [27, 28] . Another research found that the R 0 of SARS was about 2.1 in Hong Kong, China, 2.7 in Singapore, and 3.8 in Beijing, China [29] . Therefore, we believe that the commonly acceptable average value of the R 0 of SARS might be 2.9 [30] . The transmissibility of the Middle East respiratory syndrome (MERS) is much lower than SARS. The reported value of the R 0 of MERS was about 0.8-1.3 [31] , with the inter-human transmissibility of the disease was about 0.6 or 0.9 in Middle East countries [32] . However, MERS had a high transmissibility in the outbreak in the Republic of Korea with the R 0 of 2.5-7.2 [33, 34] . Therefore, the transmissibility of SARS-CoV-2 might be higher than MERS in the Middle East countries, similar to SARS, but lower than MERS transmitted in the Republic of Korea. To contain the transmission of the virus, it is important to decrease R 0 . According to the equation of R 0 deduced from the simplified RP model, R 0 is related to many parameters. The mainly parameters which could be changed were b P , b W , and γ. Interventions such as wearing masks and increasing social distance could decrease the b P , the intervention that close the seafood market could decrease the b W , and shorten the duration form symptoms onset to be diagnosed could decrease 1/γ. All these interventions could decrease the effective reproduction number and finally be helpful to control the transmission. Since there are too many parameters in our model, several limitations exist in this study. Firstly, we did not use the detailed data of the SARS-CoV-2 to perform the estimation instead of using the data from literatures [3] . We simulated the natural history of the infection that the proportion of asymptomatic infection was 50%, and the transmissibility of asymptomatic infection was half of that of symptomatic infection, which were different to those of MERS and SARS. It is known that the proportion of asymptomatic infection of MERS and SARS was lower than 10%. Secondly, the parameters of population mobility were not from an accurate dataset. Thirdly, since there was no data of the initial prevalence of the virus in the seafood market, we assumed the initial value of 1/100 000. This assumption might lead to the simulation been under-or over-estimated. In addition, since we did not consider the changing rate of the individual's activity (such as wearing masks, increasing social distance, and not to travel to Wuhan City), the estimation of importation of the virus might not be correct. All these limitations will lead to the uncertainty of our results. Therefore, the accuracy and the validity of the estimation would be better if the models fit the first-hand data on the population mobility and the data on the natural history, the epidemiological characteristics, and the transmission mechanism of the virus. By calculating the published data, our model showed that the transmissibility of SARS-CoV-2 might be higher than MERS in the Middle East countries, similar to SARS, but lower than MERS in the Republic of Korea. Since the objective of this study was to provide a mathematical model for calculating the transmissibility of SARS-CoV-2, the R 0 was estimated based on limited data which published in a literature. More data were needed to estimate the transmissibility accurately.

What compartments were the host animals divided into?

Chikungunya: A Potentially Emerging Epidemic? https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2860491/ SHA: f7c3160bef4169d29e2a8bdd79dd6e9056d4774c Authors: Thiboutot, Michelle M.; Kannan, Senthil; Kawalekar, Omkar U.; Shedlock, Devon J.; Khan, Amir S.; Sarangan, Gopalsamy; Srikanth, Padma; Weiner, David B.; Muthumani, Karuppiah Date: 2010-04-27 DOI: 10.1371/journal.pntd.0000623 License: cc-by Abstract: Chikungunya virus is a mosquito-borne emerging pathogen that has a major health impact in humans and causes fever disease, headache, rash, nausea, vomiting, myalgia, and arthralgia. Indigenous to tropical Africa, recent large outbreaks have been reported in parts of South East Asia and several of its neighboring islands in 2005–07 and in Europe in 2007. Furthermore, positive cases have been confirmed in the United States in travelers returning from known outbreak areas. Currently, there is no vaccine or antiviral treatment. With the threat of an emerging global pandemic, the peculiar problems associated with the more immediate and seasonal epidemics warrant the development of an effective vaccine. In this review, we summarize the evidence supporting these concepts. Text: Chikungunya virus (CHIKV), a mosquito-borne pathogen listed by National Institute of Allergy and Infectious Diseases (NIAID) as a Category C Priority Pathogen that causes Chikungunya fever (CHIKF), has been spreading throughout Asia, Africa, and parts of Europe in recent times [1, 2, 3] . CHIKV is an arthropod-borne virus (arbovirus) and is transmitted to humans primarily by Aedes aegypti, the infamous yellow fever propagator [4, 5] . CHIKV infection is marked by severe joint pain, contorting its victims into unusual postures [6] . The disease gets its name from the Kimakonde vernacular language of Tanzania and Mozambique, and the word chikungunya means ''that which contorts or bends up'' and translates in Swahili to ''the illness of the bended walker'' [7, 8, 9] . In Africa, CHIKV is maintained in a sylvatic cycle among forest-dwelling Aedes spp. mosquitoes, wild primates, squirrels, birds, and rodents ( Figure 1 ) [10] . In Asia, the disease is vectored by Ae. aegypti and Ae. albopictus [11] . Transmission in Asia occurs in an urban cycle whereby the mosquito spreads the disease from an infected human to an uninfected human, following an epidemiological pattern similar to dengue fever [12] . The 2005-2006 epidemic of CHIKV in La Reunion islands in the Indian Ocean, spurred the discovery of a new vector species, Ae. albopictus [5] . Wrecking over one-third of the island's population, this epidemic peaked its devastation between January and February 2006, when over 46,000 cases came into light every week, including 284 deaths [5, 13] . Ae. albopictus is common in urban areas of the United States and is already flourishing in 36 states, raising grave concerns to the immunologically naive populace of the United States [14] . Accordingly, this review elaborately details the epidemiology and global expansion of CHIKV, describes its clinical features and pathogenesis and its symptoms and complications, and finally nominates a possible vaccine approach against CHIKV infection. CHIKV has been isolated into three genotypes based on phylogenetic studies. These genotypes, based on the gene sequences of an Envelope protein (E1), are Asian, East/Central/ South African, and West African [4, 11, 15] . Using phylogenetic models, Cherian et al. estimate that the Asian genotype of CHIKV emerged between 50 and 310 y ago, and the West and East African genotypes diverged between 100 and 840 y ago [15] . Since then, CHIKV has come a long way, with several mutations incorporated, and has continued to wreak epidemics in several regions. Recent activities of CHIKV include the Indian epidemic in 2005-2006, which was followed by a sudden explosion of cases in 2007. An estimated 1.3 million people across 13 states were reported to be infected in India [12, 16] , and CHIKV was also widespread in Malaysia, Sri Lanka, and Indonesia [17] . In July-August of 2007, CHIKV was reported in Italy, probably brought in by travelers from CHIKV-prone regions of India, Africa, and Indian Ocean islands such as Mauritius, Madagascar, and Seychelles. Few of the Italian isolates were found to have evolved from the Kerala isolate, which was associated with a A226V shift in E1 gene that represents a successful evolutionary adaptation in the mosquito vector similar to the ones observed in Reunion Island [2, 18, 19] . In recent times, with an increase in global travel, the risk for spreading CHIKV to non-endemic regions has heightened [1] . Several travelers have brought CHIKV home with them after visiting areas with actively infected populations [12, 20] . Such cases have been documented in European countries, Australia, Asia, and the United States [8, 21] . The United States has already reported at least twelve cases of travel-associated CHIKV, while France has reported 850 cases, and the United Kingdom 93 [8, 14] . Beyond this, CHIKV-infected travelers have also been diagnosed in Australia, Belgium, Canada, Czech Republic, French Guiana, Germany, Hong Kong, Italy, Japan, Kenya, Malaysia, Martinique, Norway, Switzerland, and Sri Lanka [21] . Some travelers were viremic, worrying public health officials about the spread of CHIKV to new areas [1, 8] . The incubation time for CHIKV is relatively short, requiring only 2-6 d with symptoms usually appearing 4-7 d post-infection [22] . Vazeille et al. detected CHIKV in the salivary glands of Ae. albopictus only 2 d after infection [5] . Upon infection, CHIKF tends to present itself in two phases. The first stage is acute, while the second stage, experienced by most but not all, is persistent, causing disabling polyarthritis. Characteristics of the acute phase include an abrupt onset of fever, arthralgia, and in some cases, maculopapular rash [6, 23] . The acute phase causes such intense joint and muscular pain that makes movement very difficult and prostrates its victims [6, 20] . Ninety-five percent of infected adults are symptomatic after infection, and of these, most become disabled for weeks to months as a result of decreased dexterity, loss of mobility, and delayed reaction. Eighteen months after disease onset, 40% of patients are found to still have anti-CHIKV IgM [6, 18, 23, 24] . The chronic stage of CHIKF is characterized by polyarthralgia that can last from weeks to years beyond the acute stage [6] . CHIKV has been shown to attack fibroblasts, explaining the involvement of muscles, joints, and skin connective tissues. The high number of nociceptive nerve endings found within the joints and muscle connective tissues can explain pain associated with CHIKF [25, 26] . More than 50% of patients who suffer from severe CHIKF are over 65 y old, and more than 33% of them die. Most adults who suffer from severe CHIKF have underlying medical conditions [6, 24, 27] . The other group that is disproportionately affected by severe CHIKV is children. Other complications associated with CHIKV, from most common to least common, include respiratory failure, cardiovascular decompensation, meningoencephalitis, severe acute hepatitis, severe cutaneous effects, other central nervous system problems, and kidney failure [6, 18, 20, 23, 24, 26, 27] . CHIKV undertakes a complex replication cycle upon host infection (Figure 2 ), which makes its genome susceptible to mutations [28, 29] . For instance, Ae. aegypti, responsible for epidemics in Kenya, Comoros, and Seychelles, carried CHIKV with an alanine in the 226 position of the E1 gene (E1-A226) [4, 18] . However, when the virus struck La Reunion Islands, a decline in population of Ae. aegypti, due to massive dichlorodiphenyltrichloroethane usage and dearth of Ae. albopictus species' www.plosntds.org population, resulted in an ecological pressure, favoring replacement of alanine at position 226 with valine (E1-A226V) [5] . This mutation allowed CHIKV's secondary vector species, Ae. albopictus, to supplement Ae. aegypti as its primary vector [5] . Within a year, the E1-A226V mutation was present in La Reunion Island, and Ae. albopictus apparently vectored the large epidemic infecting 34% of La Reunion Island's population [5] . All of the CHIKV strains isolated from Mayotte carried the E1-A226V mutation, and the mutation was also found in Madagascar in 2007 [5] . The E1-A226V mutation was not present at the beginning of the Indian Ocean Islands outbreak (before September 2005). However, more than 90% of later viral strains found there had incorporated the mutation (December-March 2006), indicating a genotype switch during the winter season [5, 18, 20] . The E1-A226V mutation also enabled an increase in infectivity of Ae. albopictus when compared to its infectivity of Ae. aegypti [4, 11, 18, 30] , and with several factors taken together, Ae. albopictus has become the new preferred and more lethal vector for CHIKV [4, 5, 11] . In fact, Tsetsarkin et al. found that a Green Fluorescent Protein tagged E1-A226V virus was 100 times more infective to Ae. albopictus than it was to Ae. aegypti [4] . In all the Indian Ocean Islands, Ae. albopictus became the main vector for CHIKV within 1-2 y after CHIKV was introduced to the region [31] . Of note is that Ae. aegypti has most likely been established in North America for over 300 y, while Ae. albopictus has been in many areas of the US, since 1985, primarily in Florida [32] and since then has expanded its range in the country. Reiskind et al. set out to determine if Ae. aegypti and Ae. albopictus mosquitoes captured in Florida were susceptible to CHIKV infection by a La Reunion isolate [32] . Each mosquito tested was highly susceptible to infection by a full-length infectious clone of the La Réunion Island isolate, CHIKV LR2006 OPY1 strain. Even though the Ae. albopictus strains were more susceptible to infection, overall ecology and differences in human biting patterns need to be studied further Characteristically, there are two rounds of translation: (+) sense genomic RNA (49S9 = 11.7 kb) acts directly as mRNA and is partially translated (59 end) to produce non-structural proteins (nsp's). These proteins are responsible for replication and formation of a complementary (2) strand, the template for further (+) strand synthesis. Subgenomic mRNA (26 S = 4.1 kb) replication occurs through the synthesis of full-length (2) intermediate RNA, which is regulated by nsp4 and p123 precursor in early infection and later by mature nsp's. Translation of the newly synthesized sub-genomic RNA results in production of structural proteins such as Capsid and protein E2-6k-E1 (from 39 end of genome). Assembly occurs at the cell surface, and the envelope is acquired as the virus buds from the cell and release and maturation almost simultaneous occurred. Replication occurs in the cytoplasm and is very rapid (,4 h) [28, 29] . doi:10.1371/journal.pntd.0000623.g002 www.plosntds.org to gain a more accurate understanding of a potential CHIKV epidemic in the US [32] . During the 7 d preceding birth, no human mother has been reported to transmit the disease vertically. However, about 50% of newborns delivered while the mother was infected with CHIKV contracted the disease from their mother, despite the method of delivery. Furthermore, there have been instances of CHIKV transmission from mother to fetus causing congenital illness and fetal death [33] . During the 2005-2006 La Reunion Island outbreaks, Ramful et al. discovered that mothers could transmit CHIKV to their progeny during the perinatal period (Day 24 to Day +1) [33, 34] , and it is associated with a high degree of morbidity. By mean Day 4 of life, all of the neonates were symptomatic for CHIKV, exhibiting common CHIKF symptoms. Six neonates were confirmed to have contracted CHIKV and developed mengoencephalitis. Of those mothers who, during the La Reunion Island epidemic, were infected long before delivery, only three fetal deaths were reported [12, 33] . Ramful et al. theorized that motherto-child transmission most likely happens transplacentally shortly before delivery [33] . A similar study by Gerardin et al. reported nineteen cases of neonatal infection associated with intrapartum maternal viremia that progressed to develop encephalitis owing to vertical transmission from infected mothers [34] . Clinical and epidemiological similarities with dengue fever make CHIKV diagnosis difficult, which may lead physicians to misdiagnose CHIKV as dengue fever; therefore, the incidence of CHIKV may actually be higher than currently believed (Table 1 ) [6, 12, 35] . The amount of time elapsed since disease onset is the most critical parameter when choosing a diagnostic test. CHIKV can be detected and isolated by culturing with mosquito cells (C6/36), Vero cells (mammalian), or in mice [26] . However, this method can take at least a week and only achieves a high sensitivity during the viremic phase, which usually only lasts up to 48 h after the bite. Five days post-infection, the viral isolation approach has a low sensitivity but is still the preferred method for detecting the CHIKV strain [12, 26, 31, 35] . RT-PCR on the other hand is a faster and more sensitive method that can be used within the first week of disease onset [26] , and it is currently the most sensitive method for detecting and quantifying viral mRNA [4, 36] . Classic serological detection, by assays such as ELISA [37] , immunofluorescence [5, 38] , complement binding, and haemagglutination inhibition [39] , constitutes the second diagnostic tool used for biological diagnosis of CHIKV infection. These proven techniques are useful for detection of Antigen in mosquitoes during epidemiological studies. These assays detect virus-specific IgM and IgG, however the sensitivity and specificity of these assays has been poorly characterized. Viral competence, or the potential of viral infection and transmission, is an important parameter that can be quantified by ELISA, viral culture, and PCR. A study by Ng et al. showed biomarkers indicative of severe CHIKV infection [40] . They found decreased levels of RANTES and increased levels of Interleukin-6 (IL-6) and Interleukin-1b (IL-1b) that could be sued for CHIKV detection in patients as indicators of CHIKV-driven cytokine storm. Couderc et al. demonstrate another cytokine, type-I IFN, as a key player in the progression to CHIKV infection [26] . Using an IFN-a/b null mouse model, they demonstrated evidence of muscles, joints, and skin as privileged CHIKV targets, which is consistent with human pathology. Although Ng et al. concluded that RANTES levels were significantly suppressed in severe CHIKF patients [40] , interestingly, an increase in levels of RANTES has been observed in dengue infection [41] . Since the symptoms of CHIKF mimic those of dengue fever, results obtained from this study strongly suggest that RANTES could be a potential distinctive biomarker that differentiates between these two clinically similar diseases. There are no approved antiviral treatments currently available for CHIKV [1, 3, 12, 42] . Currently, CHIKF is treated symptomatically, usually with non-steroidal anti-inflammatory drugs or steroids, bed rest, and fluids. Movement and mild exercise are thought to decrease stiffness and morning arthralgia, but heavy exercise may exacerbate rheumatic symptoms. Corticosteroids may be used in cases of debilitating chronic CHIKV infection. There is a debate about the appropriateness of chloroquine as treatment for unresolved, non-steroidal anti-inflammatory drugresistant arthritis [43] . A study showed that viral production was www.plosntds.org drastically reduced at 16 h post-infection after treatment with 100 mM dec-RVKR-cmk (Decanoyl-Arg-Val-Lys-Arg-chloromethylketone), a furine inhibitor [42, 44] . Chloroquine acted by raising the pH, blocking low pH-dependent entry of virus into the cell. It is important to note that dec-RVKR-cmk or chloroquine only inhibited viral spreading from cell to cell, not CHIKV replication once it had entered the cell [43] . However, most would agree that the best weapon against CHIKV is prevention. A live CHIKV vaccine developed by the United States reached phase II clinical trial encompassing 59 healthy volunteers [45] . Eight percent of the volunteers experienced transient arthralgia, while 98% of the volunteers had seroconversion [45] . However, live CHIKV vaccines are still questionable. One cannot discount the risk of a live vaccine possibly inducing chronic rheumatism. Also, there is the question as to whether widespread use among the public could trigger mosquito transmission or lead to chronic infection or viral reversion [1] . An alternative approach would be to produce a chimeric vaccine against CHIKV. Wang et al. developed a chimeric alphavirus vaccine that is uniformly attenuated and does not cause reactogenicity in mice [3] . Three different versions of this vaccine were made using three different backbone vectors: Venezuelan equine encephalitis virus (VEEV) attenuated vaccine strain T-83, naturally attenuated eastern equine encephalitis virus (EEEV), and attenuated Sindbis virus (SINV). In short, CHIKV structural proteins were engineered into the backbones of the aforementioned vaccines to produce the chimeras [3] . These chimeras were found to stimulate a strong humoral immunity, and even at doses of 5.3-5.8 log 10 PFU, they did not trigger reactogenicity. When vaccinated mice were challenged with CHIKV, neither adult nor neonatal mice gained weight, had fever, or displayed signs of neurological illness. Upon comparison of the chimeras with the Army181/25 vaccine, the Army vaccine resulted in higher levels of viremia and replication in the joints of neonatal mice. Because the joints are known targets of CHIKV, Wang et al. noted their vaccine might avoid the negative reactogenic side effects of the Army vaccine. After being subcutaneously vaccinated with 5.3-5.8 log 10 PFU of the chimeric vaccines, mice produced strong neutralizing antibody titers. The VEEV and EEEV chimeras yielded higher neutralizing antibody titers than the SINV chimera without being more virulent. On top of this, the VEEV and EEEV CHIKV chimeras seemed to be more immunogenic than the Army vaccine despite the chimeras' lower viremia and replication in the joints of neonatal mice [3] . Tiwari et al. [46] adopted a different strategy using formalin inactivated CHIKV in combination with alhydrogel (Aluminum Hydroxide) as an adjuvant. This study clearly suggests that this vaccine elicits both humoral and cell-mediated immune responses in mice, providing its immunogenic potential. A recent study by Couderc et al. [47] showed passive immunization as a potential treatment for CHIKV infection. Using purified immunoglobulin extracted from convalescent CHIKV patients, they demonstrated effective neutralizing activity against CHIKV infection both in vitro and in vivo. This thereby establishes a potential preventive and therapeutic approach to combat CHIKV infection. Pathogenesis studies conducted with related alpha virus, like RRV, have shown the role of macrophages in persistence on infection [48] . They also demonstrated the role of RRV-specific CD8 T cells in clearing viral load in infected patients, thereby warranting similar investigations with CHIKV and the importance of investigating a cell-mediated immune response-based vaccine against CHIKV [49] . There are always certain risks associated with live attenuated or inactivated viral vaccines [50] . One way to avoid these potential problems is to construct a consensus-based DNA vaccine. DNA based vaccines have an improved safety profile as compared to live or attenuated vaccines [51, 52] . A consequence of CHIKV's rapid evolution is difficulty in constructing a vaccine that will be able to Figure 3 . Levels of CHIKV-specific IgG in mice immunized with CHIKV vaccines. Each group of C57BL/6 mice (n = 5) was immunized with 12.5 mg of pVax1 control vector or CHIKV vaccine plasmids as indicated at 0 and 2 wk. Mice were bled 2 wk after each immunization, and each group's serum pool was diluted to 1:100 and 1:500 for reaction with specific vaccine constructs. Serum was incubated for 1 h at 37uC on 96-well plates coated with 2 mg/ml of respective CHIKV peptides, and antibody was detected using anti-mouse IgG-HRP and OD was measured at 405 nm. doi:10.1371/journal.pntd.0000623.g003 www.plosntds.org effectively protect large populations from multiple strains of the virus. One of the strengths of DNA consensus vaccines is its ability to induce cross-reactive immune responses against the three distinct phylogenetic groups of CHIKV. Also DNA-based vaccines can be produced more rapidly than protein-based vaccines. Recently, Muthumani et al. constructed a vaccine that was shown to induce both humoral and cellular immunity in vivo in 3-4-wk-old female C57/BL6 mice [49] . These mice were immunized using an in vivo electroporation method to deliver the vaccine into the quadriceps muscle. The consensus construct was designed against E1, E2, and the core protein capsid. To design the construct, they aligned 21 sequences of CHIKV isolated between 1952 and 2006, using strains from differing countries, including La Reunion Island. The most common nucleotide among the sequences was chosen at each position to be used in the consensus construct, taking care not to alter the reading frame. They conducted codon and RNA optimization, added a strong Kozak sequence, and substituted signal peptide with an immunoglobulin E leader sequence to improve vaccine efficacy. After immunizing the mice, spleens were harvested along with serum and tested to determine antibody titer. After three immunizations, consensus E1, E2, and C vaccines were shown to induce T-cell immune responses leading to strong IFN-c responses and proliferation in C57/BL6 mice. Furthermore, when compared with control mice, immunized mice had higher total IgG levels as well as higher anti-E1 specific, anti-E2 specific, and anti-C specific IgG antibodies, suggesting a strong humoral immune response ( Figure 3 ) and also specificity for the antigens encoded in the vaccine constructs ( Figure 4 ). Because of its promising results and the need for a safer vaccine, this consensus DNA vaccine deserves further investigation. Determining longevity of protective effects of the vaccine and persistence of antibody and IFN-c responses could be the next step of investigation. Challenged studies of immunized mice must also be carried out. CHIKV mosquito-borne disease has caused massive outbreaks for at least half a century but is no longer confined to the www.plosntds.org developing nations. It began to encroach into the boundaries of the developing world. As a result, the NIAID has designated CHIKV as a Category C pathogen alongside the influenza and SARS-CoV viruses [3] . Realization of the potential severity of this disease is exigent; for instance, if used as a biological weapon, the world economy could be severely crippled; if enough members of the armed forces were to become infected during a military deployment, military operations could be significantly affected. Efforts to monitor the disease will only provide minimal warning in a global society, and steps to prevent the morbidity and mortality associated with pandemic are imperative [21, 31] . Despite the gravity of its infectious potency and the fear of it being a potential biological weapon, there is currently no vaccine for CHIKV infections. Live attenuated vaccine trials were carried out in 2000, but funding for the project was discontinued. Newer approaches such as DNA vaccines appear promising over conventional strategies like live attenuated or inactivated virus and thus call for further investigation. Recent advances such electroporation delivery and incorporation of adjuvants has boosted DNA vaccine efficacy [51, 53] . Despite the low antibody response to DNA vaccines, other numerous advantages have overshadowed these minor drawbacks (Table 2) , the most important one being the ability to induce both humoral and cellular immune responses [51, 54] . Judging by recent success, such as the immunogenic construct developed by Muthumani et al., DNA vaccines could play a major role in combating CHIKV [49] . Vaccines are literally a critical component of CHIKV disease control and therefore research in this area is highly encouraged. The dramatic spread of dengue viruses (DENV) throughout tropical America since 1980 via the same vectors and human hosts underscores the risk to public health in the Americas. The adverse events associated with the current live vaccine are well documented [55] . Realizing these drawbacks, earnest efforts should be taken to develop new strategies to forestall further spread and complications.

What is te chronic stage characterized by?

The Battle Against Coronavirus Disease 2019 (COVID-19): Emergency Management and Infection Control in a Radiology Department https://www.jacr.org/article/S1546-1440(20)30285-4/pdf Journal Pre-proof Zixing Huang, Shuang Zhao, Zhenlin Li, Weixia Chen, Lihong Zhao, Lipeng Deng, Bin Song PII: S1546-1440(20)30285-4 DOI: https://doi.org/10.1016/j.jacr.2020.03.011 Reference: JACR 5139 To appear in: Journal of the American College of Radiology Received Date: 24 February 2020 Revised Date: 13 March 2020 Accepted Date: 15 March 2020 Please cite this article as: Huang Z, Zhao S, Li Z, Chen W, Zhao L, Deng L, Song B, The Battle Against Coronavirus Disease 2019 (COVID-19): Emergency Management and Infection Control in a Radiology Department, Journal of the American College of Radiology (2020), doi: https://doi.org/10.1016/ j.jacr.2020.03.011. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Inc. on behalf of American College of Radiology The Battle Against Coronavirus Disease 2019 (COVID-19): Emergency Management and Infection Control in a Radiology Department Zixing Huang*, Shuang Zhao*, Zhenlin Li, Weixia Chen, Lihong Zhao, Lipeng Deng, Bin Song Department of Radiology, West China Hospital, Sichuan University, Chengdu, China *Zixing Huang and Shuang Zhao contributed equally to this work as co-first author. Corresponding Author: Bin Song, MD Address: Department of Radiology, West China Hospital, Sichuan University. No. 37, GUOXUE Alley, Chengdu, 610041, China Tel.: (+86)28 85423680, Fax: (+86)28 85582944 Email: [email protected]. Authors’ contributions ZXH: conceived the study and drafted the manuscript. ZS: conceived the study and drafted the manuscript. ZLL: The member of the emergency management and infection control team (EMICT) and was involved in the formulation of the measures. WXC: The member of the EMICT and was involved in the formulation of the measures. LHZ: The member of the EMICT and was involved in the formulation of the measures. LPD: The member of the EMICT and was involved in the formulation of the measures. BS: Leader of the EMICT, conceived the study and reviewed the manuscript. All authors read and approved the final manuscript. The authors declare no conflict of interest. The authors declare that they had full access to all of the data in this study and the authors take complete responsibility for the integrity of the data and the accuracy of the data analysis 1 The Battle Against Novel Coronavirus Pneumonia (COVID-19): Emergency Management and Infection Control in a Radiology Department Abstract Objective: To describe the strategy and the emergency management and infection control procedure of our radiology department during the COVID-19 outbreak. Methods: We set up emergency management and sensing control teams. The team formulated various measures: reconfiguration of the radiology department, personal protection and training of staff, examination procedures for patients suspected of or confirmed with COVID-19 as well as patients without an exposure history or symptoms. Those with suspected or confirmed COVID-19 infection were scanned in the designated fever-CT unit. Results: From January 21, 2020 to March 9, 2020, 3,083 people suspected of or confirmed with COVID-19 underwent fever-CT examinations. Including initial examinations and reexaminations, the total number of fever-CT examinations numbered 3,340. As a result of our precautions, none of the staff of the radiology department were infected with COVID-19. Conclusion: Strategic planning and adequate protections can help protect patients and staff against a highly infectious disease while maintaining function at a high volume capacity. Keywords: Coronavirus, COVID-19, novel coronavirus pneumonia, infection control 2 Introduction The whole world has been closely focusing on an outbreak of respiratory disease caused by a novel coronavirus that was first reported in Wuhan, China, on December 31, 2019, and that continues to spread. On February 11, 2020, the World Health Organization (WHO) named the disease “coronavirus disease 2019” (COVID-19). As of 24:00 on March 11, 2020, the National Health Commission (NHC) had received reports of 80,793 confirmed cases and 3,169 deaths on the Chinese mainland. There remain 14,831 confirmed cases (including 4,257 in serious condition) and 253 suspected cases still hospitalized. To date, 677,243 people have been identified as having had close contact with infected patients of whom13,701 are under medical observation [1]. Outside China, 44,067 laboratory-confirmed cases and 1,440 deaths have occurred in 117 countries /territories/areas according to the WHO [2]. COVID-19 poses significant threats to international health. Like the flu, COVID-19 is thought to spread mainly from person-to-person between people who are in close contact with one another through respiratory droplets produced when an infected person coughs or sneezes. In light of the infectious nature of this disease, healthcare workers are at high risk of infection of COVID-19. In China, healthcare workers account for 1,716 confirmed cases of COVID-19, including six deaths [3]. Computed tomography (CT) can play a role in both diagnosing and categorizing COVID-19 on the basis of case definitions issued by the WHO and the treatment guidelines from the NHC [4]. Suspected patients having the virus may undergo chest CT. Isolation and barrier procedures are necessary to protect both the department staff and other patients in the hospital. Note should be made that due to overlap of imaging findings with other respiratory 3 diseases, CT is not helpful as a screening tool. But it can help identify the degree of pulmonary involvement and disease course. Our hospital is a national regional medical center with 4,300 beds and a tertiary referral center in Sichuan province. The initial response started on January 21, 2020, after transmission of COVID-19 was confirmed to be human-to-human on January 20, 2020. The first suspected case of COVID-19 in Sichuan province was reported on January 21, 2020. The Sichuan provincial government immediately launched the first-level response to major public health emergencies. On the same day, our hospital was designated to care for Sichuan province patients with COVID-19. This article describes the emergency management procedure of our radiology department for situations involving severe infectious diseases, such as COVID-19, and the infection-protection experience of the department staff. Methods The hospital provided personal protective equipment (medical protective clothing, surgical cap, N95 mask, gloves, face shields, and goggles) to all its healthcare staff, erected three medical tents (fever tents) for screening of fever cases in the parking lot of the emergency department, planned an examination route and examination area for patients suspected of harboring the virus, and placed confirmed patients in an isolation ward. “Fever” was the colloquial term used to designate suspected COVID-19 based on symptoms such as a fever or with an epidemiological history of a potential exposure as well as those with confirmed COVID-19 referred for treatment. Further, during outbreak, emergency and outpatient patients 4 without fever were asked for information such as epidemiological history and sent to fever tents as long as they met suspected criteria. The radiology department has 65 diagnostic radiologists and 161 other staff members (trained technologists, nurses, engineers, and support staff). The equipment of the radiology department includes 12 magnetic resonance (MR) scanners, 14 CT scanners, 15 digital subtraction angiography (DSA) systems, 32 sets of digital radiography (DR) systems (including nine mobile bedside DR sets), and 130 imaging diagnostic workstations for picture archiving and communication systems (PACS). Most of the equipment is distributed among four buildings at the hospital main campus. 4 CT scanners, 4 MR scanners, 1 DR are located on the first floor of the first inpatient building, and 9 DR and 8 DSA are located on the second floor. 1 CT and 1 MR scanner are located in the third inpatient building. 1 CT and 1 MR scanner are located in the sixth inpatient building. 2 CT scanners, 2 MR scanners and 7 DSA are located in the technical building. The rest of the equipment is located in the seventh inpatient building in the branch campus. The first inpatient building, located next to the emergency department, was reconfigured to handle cases of COVID-19. Fever tents were set up by the emergency department in the emergency department parking lot to separate normal emergency patients from patients with symptoms or exposure history suspicious of COVID-19. We established separate means of access between fever tents and between the fever examination area of the radiology department to avoid cross-contamination. The emergency management and infection control measures, as described below and implemented in the radiology department during the outbreak, have been approved by the 5 infection control committee of hospital. These measures are in accordance with relevant laws and regulations, in order to protect patients as well as the staff. Radiology Emergency Management and Infection Control Team (EMICT) The radiology department director chaired the EMICT. Its members include the deputy director, chief technologist, head nurse, equipment engineer supervisor, and infection control nurse of the radiology department. Team responsibilities included (1) coordination between the hospital’s management and planning of infection control and radiology departments; (2) collection of the most up-to-date protection-related information to educate and train staff in the department; (3) reallocation of staff according to the actual situation; (4) establishment of the CT procedures for patients with COVID-19; and (5) establishment of an emergency management plan for the radiology department to ensure that the department would run normally. Suspected patients The suspected patients were identified according to the Diagnosis and Treatment Program of the Novel Coronavirus Pneumonia of the NHC [5], mainly based on epidemiological history. Reconfiguration of the radiology department The radiology department was divided into four areas [6]: contaminated, semicontaminated, buffer, and clean areas (Figure 1). The contaminated area is connected to the fever clinic and includes the fever accessway, the CT examination room, and the DR examination room for 6 confirmed and suspected cases. One CT scanner and one DR system closest to the emergency department are designated the fever-CT and fever-DR to examine patients with suspected and confirmed COVID-19. There is a separate dedicated access between the contaminated area and the fever screening tents. The semicontaminated area includes the fever-CT control room, fever-DR control room, and other patient examination access areas. The buffer zone includes access areas for medical personnel and a dressing area for technologists. The clean area includes the administrative office and the diagnostic room. The contaminated area was isolated from other areas using physical barricades. Directional signs were newly installed to guide patients and staff. Personal protection and training of staff For providing care for patients with confirmed and suspected COVID-19, all hospital staff are required to wear complete personal protective equipment [7]: medical protective clothing, surgical cap, N95 mask, gloves, face shields, and goggles. Wearing and removing of the equipment must be performed in accordance with the procedures and under the supervision of the infection control nurse. Because staff members working in the contaminated area are under much situational pressure, periodically taking time off could lower their physical and mental stress levels. The technologists on fever-CT duty shifts are provided a break once a week for four hours. In addition, the health of staff in the contaminated area must be monitored closely for the symptoms of COVID-19. Pregnant staff must be assigned to the clean area. 7 The EMICT formulates and continually updates guidelines and educates all staff for West China Hospital of Sichuan University. The EMICT training for staff is mainly involves documents regarding infection control and CT findings of COVID-19 and maintains an EMICT WeChat group for West China Hospital of Sichuan University. WeChat is the most widely used social media app in China. The EMICT releases the latest national and hospital-based information regarding COVID-19, guidance documents, and other notices from the hospital and radiology department in the WeChat group on a daily basis. Staff can also report to the EMICT in the WeChat group any time. Protocols for each modality and infection control instructions are posted on the walls in all examination rooms. The EMICT periodically reminds staff to undertake personal measures to reduce infection, such as wearing masks at all instances in the radiology department and N95 masks if working in the contaminated area; not touching the mask and the eyes; practicing hand hygiene; facing away from colleagues when eating, drinking, and talking; and not using personal cell phones while on duty. In addition, the chief thoracic radiologist provided lectures on all radiologists and technologists on typical CT findings of COVID-19 infection using materials developed in Wuhan, the epicenter of the outbreak in China. CT examination procedures There are two sets of procedures for CT examination: the fever-CT procedure and routine CT procedure for those not suspected of COVID-19. The fever-CT procedure for suspected or confirmed COVID-19 (Figure 2) 8 Before the fever-CT technologist operates the equipment, he or she should wear personal protective equipment according to three-level protection standard [8]. Before the CT examination of patients with suspected and confirmed COVID-19 begins, the fever tent or isolation ward notifies the radiologist in advance. The fever-CT technologist checks the equipment and prepares to disinfect the imaging equipment immediately after the examination. The patient enters the fever-CT waiting area through the fever access area. If the patient can get onto and off the examination table by themselves, the patient is allowed to do so. If the patient cannot get onto or off the examination table independently, the person accompanying the patient assists the patient, rather than the technologist. The technologist checks the patient information and, using an intercom system in the examination room, asks the patient to remove any metal ornaments on the neck and chest. Also, by intercom, the technologist trains the patient to hold his or her breath during the examination. The technologist uses a low-dose chest CT protocol to scan the patient. After scanning, the original images are reconstructed as 1 mm-thick layers. The technologist browses the images to ensure that their quality meets the diagnostic requirements and then guides the patient to leave through the fever access area. The disposable sheets for patient examination are changed after each patient. The equipment is disinfected according to the procedure below. To protect themselves, the technologists assigned to the fever-CT wear N95 mask and other personal protection as established by the EMICT. The CT procedure for regular patients (figure.3) 9 Some patients with COVID-19 have no symptoms, and they may call at the general clinic for other reasons. The following CT procedure is applicable under these circumstances: When the patient makes an appointment for examination, the staff asks the patient about their epidemiological history, symptoms, and signs. If suspected criteria are met, the patient will be sent to the fever tent for further screening. When a patient presents to the radiology department entrance, his/her temperature is measured. If the temperature is higher than 37.2 , ℃ the patient is sent to the fever tent for further investigation. Those with no exposure history, suspicious symptoms or fever are screened in one of the non-contaminated CT scanners. The technologists assigned to these scanners wear surgical masks. All patients and the person accompanying them are required to wear surgical masks. After the CT examination, the technologist browses the images quickly. If the CT appearance is typical of lung infection, the technologist immediately reports it to the chest radiologist on duty and asks the patient to wait in the CT examination room. If the chest radiologist does not suspect COVID-19 infection, the patient can leave the CT examination room. If the chest radiologist does suspect COVID-19 infection, the technologist immediately reports it to the EMICT and sends the patient to the fever tent. The floor and equipment in the CT examination room are disinfected according to regulations, and air disinfection is conducted for 30 min before examining other patients. These CT scanners are considered noncontaminated (not fever-CTs) after these sterilization procedures. Fever-DR examination procedure 10 The COVID-19 guideline of the NHC does not recommend chest DR because its ability in diagnosing COVID-19 is limited. At our hospital, we only use mobile DR units to provide bedside examination for critically ill patients. The technologist operating the mobile DR wears personal protective equipment according to the three-level protection standard and sterilizes the mobile DR according to the ward management requirements as described below. Equipment and environment disinfection procedures Routine disinfection procedure [9] 1) Object surface disinfection: Object surface is wiped with 1000mg/L chlorine-containing disinfectant, wipe twice with 75% ethanol for non-corrosion resistance, once /4 hours. 2) Equipment disinfection: The equipment in the contaminated area are wiped with 2000mg/L chlorine-containing disinfectant. The DR and CT gantry in the contaminated area are wiped with 75% ethanol. The equipment in the buffer area is wiped with 500-1000mg/L chlorine-containing disinfectant or alcohol-containing disposable disinfectant wipes twice a day. 3) Air disinfection: Turning off all central air conditioners to prevent air contamination with each other. Polluted area: open the door for ventilation, each time more than 30 minutes, once /4 hours; The air sterilizer is continuously sterilized or the ultraviolet ray is continuously used in the unmanned state for 60 minutes, four times a day, remembered to close the inner shielding door when air disinfection. Other ambient air is sprayed with 1000mg/L chlorine-containing disinfectant and ventilated twice a day 4) Ground disinfection: The ground is wiped with 1000mg/L chlorine-containing disinfectant, once /4 hours. 5) When contaminated, disinfect at any time. In case of visible contamination, disposable absorbent materials should be used first to completely remove the pollutants, and then a cloth soaked with 2000mg/L chlorine-containing disinfectant should be used for 30 minutes before wiping. 11 Fever-CT disinfection procedures after examination In addition to the above, disinfect the examination bed and ground with chlorinated disinfectant containing 2000mg/L [10]. Noncontaminated CT disinfection procedures after suspected COVID-19 case examination In addition to the above routine disinfection procedure, air disinfection is conducted for 30 min before examining other patients. Results From January 21, 2020 when screening for epidemiological history or symptoms suspicious for COVID-19, to March 9, 2020, our hospital screened a total of 7,203 individuals and confirmed 24 cases of COVID-19. Of these, 3,083 people underwent fever-CT examinations. Including the initial examination and reexamination, the total number of fever CT examination numbered 3,340. The fever-CT scanned a patient approximately every 21.5 minutes. As a result of our precautions, none of the staff of the radiology department developed symptoms suspicious for COVID-19. The fever-CT technologist, with the highest probability of exposure, remains PCR negative. Discussion It has been 17 years since the severe acute respiratory syndrome (SARS) epidemic, the last national spread of severe infectious disease, broke out. Currently, the Chinese people are panicking again. The speed and extent by which COVID-19 has spread in 2 months are 12 unprecedented, beyond those of SARS, and this has been aided by its contagious nature and rapid spread via droplets and contact. The droplet mode of transmission means that a person can be infected easily by means of casual contact or even fomites on contaminated environmental surfaces. Another theory has yet to be proved: aerosol propagation. How radiology departments respond to any infectious disease outbreak is determined primarily by the estimated risk of cross-infection to the staff and other patients. Appropriate precautions taken only by staff in direct contact with patients may be adequate when the risk is low. The strongest measures need to be implemented to limit the spread of the disease when the risk is high. With severe infectious diseases such as COVID-19, the highest level of infection control measures must be implemented; these include providing adequate standard protective equipment, training staff, and instituting proper emergency plans. Once a contagious infectious disease has been identified, the EMICT must consider four main areas of response: data gathering, collaboration, needs assessment, and expert advice [10]. Data gathering includes dissemination of up-to-date case definitions and information about confirmatory tests to all staff with direct patient contact to allow appropriate barrier precautions to be taken. All typical and atypical imaging features of the disease should be made known to all radiologists to assist in recognition of the disease on images and to allow accurate reporting of these findings. We have stored images of all probable cases of COVID-19 in the PACS so that these images were readily available for any radiologist to review, and images from previous imaging studies are also available for comparison. Collaboration with the radiology departments of other hospitals is very important because patients may initially present to different centers, depending on geographic location and travel 13 distance. These patients may be few in number at a single hospital, but if data from patients at several hospitals are available, a more accurate overall understanding of both imaging features and epidemiology can be achieved. Dissemination of this information to all healthcare facilities will also lead to early recognition of the disease, and appropriate isolation measures may be instituted. The Internet and social media apps, especially WeChat, have been used for distribution of medical information, and because the exchange of information regarding infectious disease outbreaks is almost instantaneous, it is an indispensable tool for radiologists. In fact, within a month of the outbreak, the hospital that received the most infected patients from the source of the outbreak made a PowerPoint presentation of the CT manifestations of COVID-19, which was shared via WeChat and disseminated across the country in a very short time. Subsequently, COVID-19-teaching PowerPoint presentations from various hospitals appeared and were quickly shared via WeChat. Our diagnostic process is limited as chest CT along is not diagnostic of COVID-19 because of lack of imaging specificity. But when combined with other epidemiological, clinical, laboratory and virus nucleic acid information, typical chest CT imaging findings are helpful for making the diagnosis. In our opinion, the major role of chest CT is to understand the extent and dynamic evolution of lung lesions induced by COVID-19. The reasons why we adopted the low-dose chest CT scan protocol are as follows: low-dose chest CT has been widely used in the screening of early lung cancer. It is well known that many early lung cancers are ground-glass opacities (GGO), so we believe that low-dose screening is also applicable for COVID-19. In addition, considering the rapid development of COVID-19, many CT 14 examinations may be conducted in the same individual to monitor disease progress. Low-dose scanning can reduce the radiation damage to patients. Although the processes we established minimized the exposure of hospital staff, ancillary personnel and other patients, it remains limited as follows. Sichuan province is not the center of the epidemic. The number of patients with COVID-19 whom we have treated has not been high, and most cases are from other provinces of China. However, we believe that our experience in management, the reconfiguration of our radiology department, and the workflow changes implemented in the current COVID-19 situation are useful for other radiology departments that must prepare for dealing with patients with COVID-19. While no radiology personnel developed symptoms suspicious for or were confirmed as having COVID-19, there may be asymptomatic personnel. REFERENCES 1. National Health Commission of the People’s Republic of China.(2020). March 12: Daily briefing on novel coronavirus cases in China. Retrieved from http://en.nhc.gov.cn/2020-03/12/c_77618.htm. Accessed March 11, 2020. 2. World Health Organization. (2020). Coronavirus disease 2019 (COVID-19) Situation Report-52. Retrieved from https://www.who.int/docs/default-source/coronaviruse/20200312-sitrep-52-covid-19.pdf?sfvrsn=e 2bfc9c0_2 9. Accessed March 11, 2020. 3. National Health Commission of the People’s Republic of China.(2020). Latest developments in epidemic control on Feb 15. Retrieved from http://en.nhc.gov.cn/2020-02/16/c_76622. Accessed March 11, 2020. 15 4. Health Commission of the People’s Republic of China.(2020). The notification of the trial operation based on the guideline version 6 in the coronavirus disease diagnosis and treatment. Retrieved from http://www.nhc.gov.cn/xcs/zhengcwj/202002/8334a8326dd94d329df351d7da8aefc2.shtml. Accessed March 11, 2020 5. Health Commission of the People’s Republic of China.(2020). The notification of the trial operation based on the guideline version 6 in the coronavirus disease diagnosis and treatment. Retrieved from http://www.nhc.gov.cn/xcs/zhengcwj/202002/8334a8326dd94d329df351d7da8aefc2.shtml. Accessed March 11, 2020. 6. Health Commission of the People’s Republic of China.(2009). The guideline for pathogens isolated operations in hospital. Retrieved from http://www.nhc.gov.cn/wjw/s9496/200904/40116.shtml. Accessed March 11, 2020. 7. Health Commission of the People’s Republic of China.(2017). The guideline for prevention and control of hospital acquired infections of airborne pathogens. Retrieved from http://www.nhc.gov.cn/wjw/s9496/201701/7e0e8fc6725843aabba8f841f2f585d2.shtml. Accessed March 11, 2020. 8. Health Commission of the People’s Republic of China.(2017). The guideline for prevention and control of hospital acquired infections of airborne pathogens. Retrieved from http://www.nhc.gov.cn/wjw/s9496/201701/7e0e8fc6725843aabba8f841f2f585d2.shtml. Accessed March 11, 2020. 9. Health Commission of the People’s Republic of China.(2012). The standardization for sterilization techniques in hospital. Retrieved from http://www.nhc.gov.cn/wjw/s9496/201204/54510.shtml. Accessed March 11, 2020. 10. Health Commission of the People’s Republic of China.(2012). The standardization for sterilization techniques in hospital. Retrieved from http://www.nhc.gov.cn/wjw/s9496/201204/54510.shtml. Accessed March 11, 2020. 11. Katona P. Bioterrorism Preparedness: Generic Blueprint for Health Departments, Hospitals, and Physicians. Infectious Diseases in Clinical Practice. 2002;11(3):115-122. Accessed March 11, 2020. 16 Figure Legends Figure 1. Diagram of the layout of our radiology department was divided into four areas: contaminated (shaded in black), semicontaminated (shaded in dark gray), buffer (shaded in light gray), and clean areas (shaded in white). The contaminated area was separated from other areas by barriers. Figure 2. Diagram shows CT protocol for suspected and confirmed patients with COVID-19. Figure 3. Diagram shows CT protocol for regular patients. Abbreviations: COVID-19: coronavirus disease 2019 CT: computed tomography DR: digital radiography EMICT: emergency management and infection control team NHC: National Health Commission PACS: picture archiving and communication system SARS: severe acute respiratory syndrome Sentence Summary With severe infectious diseases such as COVID-19, the highest level of infection control measures must be implemented, collaboration with the radiology departments of other hospitals be needed, and social media be employed. Take-home points 1. To response to a community infection emergency, a special emergency management team needs to be setup at the departmental level to implement infection containment and control procedures that continues to allow the imaging examination and imaging diagnosis of those with suspected infection, and to prevent intra-departmental spreading of infection (EMICT). 2. Infection control measures, such as reconfiguration of department areas, personal protection and anti-infection training of all staff, standardized procedures including contact minimization for chest CT and DR examinations, and timely disinfection of CT and DR examination rooms, should be implemented properly. 3. If there are more than one scanner in a hospital, only one of them should be assigned to suspected cases.

What must the data gathering include?

Acute Hemorrhagic Encephalitis Responding to Combined Decompressive Craniectomy, Intravenous Immunoglobulin, and Corticosteroid Therapies: Association with Novel RANBP2 Variant https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5857578/ SHA: ef6638accc1ef599ad1aafd47b3a86f2b904cc76 Authors: Alawadhi, Abdulla; Saint-Martin, Christine; Bhanji, Farhan; Srour, Myriam; Atkinson, Jeffrey; Sébire, Guillaume Date: 2018-03-12 DOI: 10.3389/fneur.2018.00130 License: cc-by Abstract: BACKGROUND: Acute hemorrhagic encephalomyelitis (AHEM) is considered as a rare form of acute disseminated encephalomyelitis characterized by fulminant encephalopathy with hemorrhagic necrosis and most often fatal outcome. OBJECTIVE: To report the association with Ran Binding Protein (RANBP2) gene variant and the response to decompressive craniectomy and high-dose intravenous methylprednisolone (IVMP) in life-threatening AHEM. DESIGN: Single case study. CASE REPORT: A 6-year-old girl known to have sickle cell disease (SCD) presented an acquired demyelinating syndrome (ADS) with diplopia due to sudden unilateral fourth nerve palsy. She received five pulses of IVMP (30 mg/kg/day). Two weeks after steroid weaning, she developed right hemiplegia and coma. Brain magnetic resonance imaging showed a left frontal necrotico-hemorrhagic lesion and new multifocal areas of demyelination. She underwent decompressive craniotomy and evacuation of an ongoing left frontoparietal hemorrhage. Comprehensive investigations ruled out vascular and infectious process. The neurological deterioration stopped concomitantly with combined neurosurgical drainage of the hematoma, decompressive craniotomy, IVMP, and intravenous immunoglobulins (IVIG). She developed during the following months Crohn disease and sclerosing cholangitis. After 2-year follow-up, there was no new neurological manifestation. The patient still suffered right hemiplegia and aphasia, but was able to walk. Cognitive/behavioral abilities significantly recovered. A heterozygous novel rare missense variant (c.4993A>G, p.Lys1665Glu) was identified in RANBP2, a gene associated with acute necrotizing encephalopathy. RANBP2 is a protein playing an important role in the energy homeostasis of neuronal cells. CONCLUSION: In any ADS occurring in the context of SCD and/or autoimmune condition, we recommend to slowly wean steroids and to closely monitor the patient after weaning to quickly treat any recurrence of neurological symptom with IVMP. This case report, in addition to others, stresses the likely efficacy of combined craniotomy, IVIG, and IVMP treatments in AHEM. RANBP2 mutations may sensitize the brain to inflammation and predispose to AHEM. Text: Acute hemorrhagic encephalomyelitis (AHEM) or acute hemorrhagic leukoencephalitis is considered a rare and extremely severe form of acute disseminated encephalomyelitis (ADEM). AHEM is characterized by an acute and rapidly progressive encephalopathy including hemorrhagic necrosis of the parenchyma of the central nervous system. It is usually fatal (1) (2) (3) . Many treatment options have been used including intravenous (IV) steroids, intravenous immunoglobulins (IVIG), and plasmapheresis (4) . There have been few reports of survival following early intervention with high-dose corticosteroid therapy and/or decompressive craniotomy (5) (6) (7) (8) (9) . RANBP2, a nuclear pore protein, has numerous roles in the cell cycle. RANBP2 is associated with microtubules and mitochondria suggesting roles in intracellular protein trafficking or energy maintenance and homeostasis of neuronal cells. RANBP2 mutations have been reported in acute necrotizing encephalopathy (ANE) which could present with coma, convulsions, and encephalopathy. The hallmark of ANE is multiple, symmetric brain lesions located in the thalami bilaterally, putamina, deep periventricular white matter, cerebellum, and brainstem. It could be triggered by a viral infection in previously healthy children (10) . We report a new case of AHEM associated to a Ran Binding Protein (RANBP)-2 variant and responsive to combined craniectomy, intravenous methylprednisolone (IVMP), and IVIG as inaugural manifestation of multisystemic autoimmunity in a girl with sickle cell disease (SCD). A 6-year-old girl known for SCD treated on folic acid and hydroxyurea was admitted for new-onset diplopia [day 0 (D0): refers to the start of the diplopia] 6 weeks after respiratory tract infection due to rhinovirus. She was diagnosed with a fourth nerve palsy secondary to an acquired demyelinating syndrome. The initial brain magnetic resonance imaging (MRI) performed at D5 after onset of neurological symptom showed left midbrain and pontine edema with expansion of the brainstem, right caudate nucleus, and scattered supratentorial white matter foci of high T2/FLAIR signal (Figure 1 ). Brain MR angiography (MRA) showed a normal appearing circle of Willis. The cerebrospinal fluid (CSF) obtained by lumber puncture was normal (WBC 1 cells/μl, RBC 0 cells/μl, glucose 2.9 mmol/L, protein 0.18 g/L, and absent oligoclonal bands). The infectious workup including blood bacterial culture, CSF bacterial and viral cultures, nasopharyngeal aspirate (tested for Influenza A, Influenza B, Parainfluenza 1-2-3, Respiratory Syncytial Virus, Adenovirus, Coronavirus 229E, Coronavirus OC43, Metapneumovirus, Enterovirus, and Rhinovirus), and serologies for Epstein-Barr virus, Mycoplasma pneumoniae, HTLV I, HTLV II, HIV1, and Lyme disease were negative. Bartonella Henselae IgG was positive (1:1,280) reflecting a previously acquired common and self-limited infection in our area. Antinuclear antibodies (ANA) were positive (1:160). B12 and folate levels were normal. Smooth muscle antibodies were negative. Anti-mitochondrial antibodies were positive. Sedimentation rate was 65 mm/h. She was treated with five doses of IVMP (30 mg/kg/day) followed by 9 days of oral prednisone (1 mg/kg/day). At discharge, her neurological exam was significant only for vertical diplopia. She presented 1 month later with 5 days of upper respiratory tract infection symptoms, fever, headache, and a rapidly progressive right-hand weakness (D30) with normal alertness. She had normal blood pressure (120/81 mmHg). She was started on cefotaxime, vancomycin, and acyclovir. White cell count was 13.4 × 10 9 /L, hemoglobin was 7.8 g/L, and platelets were 239 × 10 9 /L. While in the MRI machine (D30) she deteriorated with vomiting and reduced level of consciousness (Glasgow Coma Scale dropped from 15 to 8 over 30 min). Brain MRI showed a rapid progression over a few sequences of an active bleed involving both superficial and deep gray matter as well as subcortical white matter of the left hemisphere anterior quadrant. Brain MRA was normal (Figures 2A-F) . The patient was immediately brought out of the magnet and her physical exam demonstrated unequal dilated pupils. She received IV mannitol and hypertonic saline for the management of acute intracranial hypertension/ herniation and was taken for surgery. She underwent left frontotemporoparietal decompressive craniotomy, evacuation of left frontoparietal intracerebral hemorrhage, and insertion of an external ventricular drain (EVD). Upon opening the skull, there was significant dural tension, and on opening the dura mater, there was a large amount of bleeding, in addition to brain swelling and necrosis. Estimated blood loss was 3.5 L. She received 8 units of packed red blood cells, 3 units of cryoprecipitate, 6 units of fresh frozen plasma, and 3 units of platelets. Coagulation profile showed international normalization ratio = 3.38, prothrombin time = 51.2 s, and partial thromboplastin time = 122 s. An intraventricular pressure monitor was inserted. She returned with stable vitals to PICU. At D31, the CT scan showed extensive multi-compartmental bleed involving the left frontoparietal lobes, the interhemispheric fissure, and the left hemispheric arachnoid spaces. New white matter lesions were detected in the left posterior parietal and occipital lobes and in the left caudate head. MRI at D33 showed interval worsening with disseminated gray and white matter non-hemorrhagic lesions in the right cerebral and both cerebellar hemispheres, bilateral deep gray nuclei, as well as new necrotic non-hemorrhagic lesions in the left hemisphere (Figures 2G-I) . She was started on IVMP (30 mg/kg/ day for 5 days) and IVIG (1 g/kg/day for 2 days). Repeat MRI at D9 showed no new parenchymal hemorrhage and partial resolution of the non-hemorrhagic lesions (Figure 3) . Prednisolone was tapered course over 6 weeks. At discharge (D71), she was able to say a few words and had better power of her right side. Brain MRI performed 3 months later showed complete resolution of the non-hemorrhagic non-necrotic lesions, mainly seen in the right cerebral hemisphere and the cerebellum. Brain biopsy of the hematoma, some small vessels, cortex, and white matter showed necrotic area, reactive and non-specific findings which could be entirely explained by compressive changes adjacent to a hematoma. There was diffuse microglial activation and signs of early microinfarcts. Blood, CSF and urine culture, and PCR (HSV1/2) were negative for bacteria and for viruses. CSF obtained through craniotomy and EVD performed at D32 showed elevated proteins 2.56 g/L, glucose 3.6 mmol/L, white blood cells 9 cells/μL, and red blood cells 1,341 cells/μL. ANA and anti-DNA antibody were negative. Anti-extractable nuclear antigens (SSA-RO, SSB-LA, smith, RNP) were negative. Serum autoimmune antibodies panel (NMO, NMDAR, AMPA I/II, GAB, MAG, VGCC, MOG, YO, HU, RI) were negative but GAD antibody was slightly positive, possibly due to the IVIG infusion. EBV showed no signs of recent infection. After discharge, the patient was started on regular transfusion exchange. Six months later, the patient was diagnosed to have Crohn's disease and primary sclerosing cholangitis. Two years later, the patient still suffers right hemiparesis but is able to walk without support. She presents an expressive aphasia. Her intellectual abilities are average, or below the mean but in the normal range, except for the speed of information processing, verbal working memory, and some elaborated executive functions. A gene panel ( Table 1 ) targeting inflammatory disorders and post-infectious necrotic encephalopathies found a heterozygous RANBP2 missense mutation (NM_006267.4, c.4993A>G, p.Lys1665Glu). This mutation has not been previously reported in the HGMD database. This variant has been observed at a frequency of <0.01% across the entire Broad ExAC dataset of individuals without severe childhood onset disease (6/117,118 alleles). Analysis of amino acid conservation indicates that the wild-type amino acid Lys1665 is conserved in 59 of 60 mammals examined, including 12 of 12 primates, and in 25 of 34 nonmammalian vertebrates increasing the likelihood that a change at this position might not be tolerated. In silico tools predict that this variant is damaging (SIFT and Align GVGD). Several differential diagnoses of acute encephalopathy in a patient with sickle cell anemia can be considered. An infectious encephalitis, including herpes encephalitis, was ruled out by blood and CSF bacterial and viral cultures and negative HSV I/ II PCR. Nasopharyngeal aspirate was negative for viruses. Some infections have been previously associated with necrotizing encephalitis such as Influenza A (11) . SCD patients are prone to ischemic or hemorrhagic strokes (12) . Primary hemorrhagic stroke is uncommon in pediatric SCD. Most cases were from adults and have been described in the context of previous ischemic stroke, aneurysms, low hemoglobin, acute chest syndrome, and hypertransfusions. Moreover, although hemorrhagic stroke has been described in SCD patients receiving transfusion or corticosteroids, it was in the context of elevated blood pressure which was not present in our case (13) . This was ruled out as the MRI findings were not consistent with a specific vascular territory and normal arterial and venous flows were shown on vascular imaging. Another differential is posterior reversible encephalopathy syndrome which has been reported in SCD patients (13) (14) (15) (16) . However, it is unlikely in our case due to the severity of the brain injury and the absence of classic precipitating factors of posterior reversible encephalopathy syndrome such as high blood pressure. Macrophage activation syndrome could also lead to acute necrotic brain injury. However, it is associated to high ferritin and low triglycerides at the time of the encephalopathy, other multisystemic injuries, typical neuropathological findings, and recurrence over time, which were not noted in our patient (17) . Parvovirus B19 has been described to cause encephalopathy in sickle cell patients. It is associated with aplastic anemia. It caused punctate areas of hemorrhages in the basal ganglia, periventricular white matter, and mainly along the posterior parietal cortex. This was attributed to parvovirus B19-induced vasculitis (18) . In our patient, there was no sign of aplasia or any neuroradiological finding of parvovirus B19 infection. Finally, acute encephalitis has been observed in SCD patients in the context of arterial hypoxemia from fat embolism, pulmonary embolism, sudden anemia, or acute chest syndrome due to pneumonia (19) . This was ruled out as the patient did not have clinical or radiological signs of acute chest syndrome or embolism and there was no arterial hypoxemia. Acute hemorrhagic encephalomyelitis has been described in pediatric patients following ADEM or ADEM-like episodes (20, 21) . AHEM is the most plausible diagnosis in our patients based on the clinical and radiological presentation, the preceding ADEM-like episode, and the exclusion of other etiologies of acute encephalopathy. Other patients with AHEM have been described in the SCD context (7, 19) . Many treatment options have been used to treat AHEM; of these, IV steroids have been associated with survival following aggressive, high-dose corticosteroid therapy (5) (6) (7) (8) (9) (22) (23) (24) (25) . Autosomal dominant mutations (with incomplete penetrance) in RANBP2 have been associated with susceptibility to infectioninduced necrotizing encephalopathy (26, 27) . Previously healthy patients with pathogenic mutations in RANBP2 can present acutely with encephalopathy and convulsions in the context of an infection, with brain imaging revealing involvement of the brainstem, thalami, putamina, cerebellum and external capsules, and claustrum (10) . Our patient has a similar presentation and imaging features as infection-induced necrotizing encephalopathy, including bilateral thalamic involvement. The rare heterozygous previously unreported variant we identified in RANBP2 affects a very conserved aminoacid and is predicted deleterious using in silico tools (a prediction tool performing a fast bioinformatics analysis which can predict the pathogenicity of a variant based on the change to an amino acid). It is possible that this variant is pathogenic and responsible for the clinical phenotype. There is an overlap between the diagnostic criteria of AHEM and those of acute hemorrhagic encephalopathy (25, 26) making possible that both entities might be part of the same pathophysiological continuum. RANBP2 is a protein playing an important role in the energy homeostasis of neuronal cells (28) . Hence, RANBP2 dysfunction might make neuronal cells much vulnerable to energy failure and necrosis when exposed to inflammatory or other stresses, such as those implicated in AHEM. This study was carried out in accordance with the recommendations of our institutional ethic committee. Written informed consent was obtained from all the participants for the publication. All authors participated in gathering the data, designing the article, and discussing and editing the manuscript. aCKNoWleDgMeNts We thank Dr. S. Abish, Dr. N. Ahmed, and Mrs. C. Guiraut for their help. We are grateful to the Hoppenheim Fund from the Montreal Children Hospital Foundation. The first author of this article received a scholarship from the Hoppenheim Fund, Montreal Children Hospital Foundation (2016). This work was supported by grants from Heart and Stroke Foundation of Canada (grant number: G-14-0005756), and Foundation of Stars.

What is RANBP2?

Rotavirus A in wild and domestic animals from areas with environmental degradation in the Brazilian Amazon https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6298726/ SHA: f3c309c596c20f48f493b77e714ce957d877bdcb Authors: de Barros, Bruno de Cássio Veloso; Chagas, Elaine Nunes; Bezerra, Luna Wanessa; Ribeiro, Laila Graziela; Duarte Júnior, Jose Wandilson Barboza; Pereira, Diego; da Penha Junior, Edvaldo Tavares; Silva, Julia Rezende; Bezerra, Delana Andreza Melo; Bandeira, Renato Silva; Pinheiro, Helder Henrique Costa; Guerra, Sylvia de Fátima dos Santos; Guimarães, Ricardo José de Paula Souza e; Mascarenhas, Joana D'Arc Pereira Date: 2018-12-18 DOI: 10.1371/journal.pone.0209005 License: cc-by Abstract: Acute gastroenteritis is one of the main causes of mortality in humans and young animals. Domestic and mainly wild animals such as bats, small rodents and birds are highly diversified animals in relation to their habitats and ecological niches and are widely distributed geographically in environments of forest fragmentation in some areas of the Amazon, being considered important sources for viruses that affect humans and other animals. Due to the anthropical activities, these animals changed their natural habitat and adapted to urbanized environments, thus representing risks to human and animal health. Although the knowledge of the global diversity of enteric viruses is scarce, there are reports demonstrating the detection of rotavirus in domestic animals and animals of productive systems, such as bovines and pigs. The present study investigated the prevalence of Rotavirus A in 648 fecal samples of different animal species from the northeastern mesoregion of the state of Pará, Brazil, which is characterized as an urbanized area with forest fragments. The fecal specimens were collected from October 2014 to April 2016 and subjected to a Qualitative Real-Time Polymerase Chain Reaction (RT-qPCR), using the NSP3 gene as a target. It was observed that 27.5% (178/648) of the samples presented positive results for RVA, with 178 samples distributed in birds (23.6%), canines (21.35%), chiropterans (17.98%), bovines (14.6%), horses (8.43%), small rodents (6.74%), pigs (3.93%) and felines (3.37%), demonstrating the circulation of RVA in domestic animals and suggesting that such proximity could cause transmissions between different species and the occurrence of rearrangements in the genome of RVA as already described in the literature, associated to the traces of environmental degradation in the studied areas. Text: Emerging and reemerging infectious diseases are increasing each year in several countries, with an impact both on human populations and on domestic and wild animals living in areas with considerable forest remnants [1] . Most of these diseases are of viral origin, suggesting the emergence and reemergence of viruses that are triggered by human activities that modify the environment [2] . The populations of wild animals that inhabit forest fragments are strategic groups for studies of public health and the transmission of zoonosis, given that they act as indicators in the assistance and intervention in the human populations, aiming at the prevention of outbreaks and epidemics [3] . Acute gastroenteritis can be caused by infection in the gastrointestinal tract, caused by different infectious or parasite agents [4] [5] [6] [7] . They represent one of the main causes of mortality in humans, and in young animals, counting for about 25% of mortality [8] . Rotavirus is widely distributed in animals, which act as sources of rotavirus emergent strains, with these animals acting in the transmission between species and through reassortment leading to the emergence of new strains which have been reported in human infections [9] [10] [11] [12] . The rotavirus (RV) belongs to the Reoviridae family and comprises nine species known as Rotavirus group A to I, with a recent proposal of the J species [13, 14] . Rotavirus A (RVA) is widespread worldwide and predominantly infects humans, bovines and other mammal species, as well as birds [15] . They have a double-stranded ribonucleic acid (dsRNA) genome, divided into 11 segments coding for structural proteins (VP1-VP4, VP6 and VP7) and nonstructural (NSP1-NSP5/NSP6) proteins [16, 17] . There are records of a close relationship between Amazonian wildlife and human populations [18] , and this interaction is the effect of anthropogenic urbanization activities that result in the deforestation of forest areas, causing the degradation of previously isolated sites such as caves and small caves, a continuous and nature progressive process that has led not only to changes in wildlife habitats but also to a greater relationship with human populations in rural and urban environments, contributing to the occurrence and emergence of diseases different from what normally occurs in endemic regions [19] [20] [21] [22] . Although the results of RVA have already been described globally [12, [23] [24] [25] [26] [27] [28] [29] [30] , in Brazil, the occurrence, diversity and role of rotavirus in these animals are still poorly studied, considering the large number of present species [4, [31] [32] [33] [34] . In the Brazilian Amazon, especially in the state of Pará, the city of Belém and Northeast metropolitan mesoregions are some of the areas with the highest indexes of environmental changes [35] , which are concentrated, along with the fact that the knowledge of the global diversity of enteric virus in animals is scarce [36] . Therefore, it is important to monitor the health of domestic and wild animals in their natural habitat, especially in areas with anthropic alterations that have an interface with rural communities and enterprises, in order to investigate the occurrence of RVA in this population. These communities are ecologically complex, because they have multiple hosts and endless pathogens that may eventually circulate in contiguous urban centers, in addition to the fact that it should also be considered that there is still a lack of studies showing the significance of these viruses infecting this population, as in the context of epidemiological surveillance, these animals become important, since they can be considered as natural sources, with the possibility of transmission to humans [37] [38] [39] . The qualitative real-time polymerase chain reaction (qRT-PCR) used the NSP3 gene and the TaqMan probe from a highly conserved region of the rotavirus non-structural protein 3 (NSP3), which was previously used in samples from human origin and with low viral loads Precipitation data were obtained from The Brazilian National Institute of Meteorology (Inmethttp://www.inmet.gov.br/) for the years of capture in the Expedito Ribeiro Settlement (2014) and Açailândia (2015) of the Data Collection Platforms (PCDs) of Belém, located 50 km from Santa Bárbara do Pará, and Tracuateua, located 50 km from Peixe-Boi and 100 km from Viseu. Garmin GPSMap 64s Global Positioning System (GPS) coordinates were collected in the field. The municipal boundaries were obtained on the website of the Brazilian Institute of Geography and Statistics (IBGE) (http://www.ibge.gov.br/) and data on deforestation and land use were obtained from the PRODES [43] and TerraClass [44] Projects. PRODES has annual data in digital format since 2000 and TerraClass presents biannual data since 2004. The satellite image was generated using the sensor Sentinel 2 of the European Space Agency (ESA) (https://sentinel.esa.int/ web/sentinel/user-guides/sentinel-2-msi) with Open Access CC-BY License (http://open.esa.int/) from the years of 2017 and 2018. All the data obtained was stored in a Geographic Database (BDG). The BDG was imported/ stored in a GIS for the editing of the graphic elements, establishment of topological relations between the elements and their respective attributes, spatial analysis and visualization of the result through thematic maps. For the present study, forest fragments of similar size, shape and Phyto physiology were chosen, considering an open peri urban matrix with similar soil use. The selected fragments were distributed within the mesoregions studied, and in each selected fragment fecal samples were randomly collected from domestic and wild animals [45] . Soil use classes were obtained from the TerraClass data mosaic from 2004 to 2016, because the study sites were in an area with a high cloud presence, which prevented observation (the area was not observed). The data processing, interpretation, visualization and spatial analysis were performed in ArcGIS software (http://www.arcgis.com/). For the analysis of data related to the determination of the richness, composition and abundance of the fauna of the animals studied in the study area, considering the collection methods adopted and the species available in each city, each sample was considered as an independent sample. The richness of wild fauna and domestic animals was determined by the total number of species including all collection methods, and the similarity of species was made by the chi-square analysis between the samples of the different treatments with the aid of the EstimateS 8.0 software [46] . For the calculation of the Test T, the Statistica software was used, and the indices of infected animals in the two environments (forest fragment and peridomicile) were calculated for each treatment sampled by collection area, using the software Past 1.92. Aiming at comparing the values of the diversity indexes through the paired test, as well as the descriptive analysis of the anthropic effects [47] . The data obtained for the occurrence of RVA and the questionnaires was inserted into a database for a descriptive analysis of the epidemiological profile of the animal population in the three forest ecosystems studied. In this analysis, descriptive statistical treatments were carried out, using customized "row-columns" type charts, referring to the data, in order to characterize the sample and quantify the results using absolute frequency values using the chi-square test and the Test T. Population study, collection of clinical specimens and laboratory methodology. The flying animals (wild birds and chiroptera) were captured using mist nets which were opened at dawn (4:00 a.m.) and closed in the morning (9:00 a.m.) and were inspected every one hour until the closing, with a sampling effort of 15 days. This research was approved by National All procedures with animals were performed by veterinarians, being birds and bats identified and released at the same capture site. The fecal specimens were collected by stimulation of the rectal ampulla with the use of a "Zaragatoa", packed in cryogenic vials, identified, stored in liquid nitrogen, and later sent to the Laboratory. Wild animals (small non-flying mammals) were trapped within live-containment traps of the Tomahawk cage (size 45x16x16cm) and Sherman type aluminum (size 30x9x8cm). In each sample plot, 61 traps were distributed, 20 Shermans and 41 Tomahawks being baited with a mixture made with peanut paste, sardines, cod liver oil and corn meal, as well as fruit like banana, apple and pineapple. All the traps used were inspected daily in the morning, the baits being exchanged when necessary and later after the capture in bags of cloth and at least five specimens of each species were chosen for the collection of biological material. The wild animals were sedated with a combination of ketamine 20mg/kg and xylazine 2mg/kg intramuscularly and subsequently, euthanized with anesthetic overdose of 2% lidocaine in the foramen magnum, according to the recommendation of the National Council for the Control of Animal Experimentation (CONCEA). From October 2014 to April 2016, 1,282 fecal samples were collected from wild and domestic animals. Amongthese, 648 (50.5%) samples were randomly selected for RVA research and handled in Level Three Biosafety Laboratory (NB3). The viral genome was extracted using the TRIZOL LS REAGENT protocol (INVITRO-GEN, USA/KIT QIAGEN), following the manufacturer's recommendations, with minor adaptation according to the protocol described in the supplemental data. The qRT-PCR was conducted according to Zeng et al. [40] for the detection of RVA using the NSP3 segment of RVA as the target gene sequence. The assay was conducted in a mixture containing: RNAse-free H 2 O, TaqMan RT-PCR Mix (2x), TAqMan RT Enzyme Mix (40x), primers for the NSP3 gene, Primer NSP3 Forward (20mM), Primer NSP3 Reverse (20mM), probe NSP3 S (10nm), Template (RNA) 3μL, having a total reaction volume of 17μL and reverse transcription cycling of 50˚C, 30 minutes, denaturation of 95˚C, 10 minutes, annealing of 45 cycles of 95˚C, 15 seconds and extension of 60˚C, 1 minute. The analyzes were considered positive when presenting the cycle threshold (CT) � 40. In order to guarantee a reliable test result, the measurements of contamination control were performed with the use of positive animal control (SA11 prototype) and a negative control (ultrapure water). All RVA-positive samples were subjected to reverse transcription-polymerase chain reaction (RT-PCR) according to Mijatovic et al [41] to genotyping low viral loads samples. First round was performed with consensus primers N-VP4F1/N-VP4R1 and the Nested-PCR was conducted with N-VP4F2/N-VP4R2 primers to amplify VP4 gene. Amplicons were purified and sequencing for VP4 gene using the same primers of Nested-PCR. The sequences were collected from an automated ABI Prism 3130xl DNA sequencer (Applied Biosystems). The sequence fragments were assembled and edited using the Geneious Bioinformatics software platform v.8.1.7. Posteriorly, the data were compared with othersequences from the National Center for Biotechnology Information GenBank database using BLAST alignment tool to elucidate the RVA genotype of the samples. From October 2014 to April 2016, a total of 648 fecal samples of wild and domestic animals belonging to three forest fragments areas were tested for the NSP3 gene by qualitative qPCR, and 178 (27.5%) were positive for RVA, distributed among the species: birds (23.6%), canines (21.35%), bats (17.98%), cattle (14.6%), horses (8.43%), small rodents (6.74%), swine (3.93%) and felines (3.37%). The CT interval ranged from 28. 47 It was possible to detect viral strains in all genders of animals studied and in the harvesting period none of the animals showed signs of acute infection and / or diarrhea. Rotavirus A (RVA) detected in the present study of wild and domestic animals belonging to the three areas of forest fragment, according to Fig 2. In relation to the evaluated bovines, only in the city of Viseu, these species were studied because they were created extensively. In addition, most of the animals were young with ages varying from 1 day to 8 yearsold, history of deficient vaccination, lack of technical assistance and raised in the form of subsistence. The animals showed no symptoms of diarrhea, only low weight performance and poor sanitary management status. In relation to chiroptera, 32 (17.98%) positive samples for RVA were distributed among Carollia perspicillata species, with 12 (37.5%) being all adults, 9 (28.12%) Desmodus rotundus samples (4 young and 5 adults), 5 (15.6%) of Uroderma bilobata (15.62%), 3 (9.37%) of Artibeus lituratus and the species Artibeus Planirostus, Diaemus iyoug and Glossophagine with 1 (3.12%) each. These animals came from areas of forest fragments located near bovine and equine farms, in addition to inhabiting small chicken farms. Fig 3 shows the results obtained for all the species of animals investigated in the forest fragment as well as in the peridomicillus area. The anthropic variables were analyzed for the three cities studied, as well as the use of the soil within the range of the animals, obeying the domicile, the peridomicile and the forest fragment where the traps of small rodents, birds and various species of animals were captured (Fig 4 and Fig 5) . Considering the factors related to the anthropic activities in the three studied areas within the three cities of the present study, it was observed that the city of Santa Bárbara is the one that has a better area of preserved forest and the city of Viseu a smaller area. However, in the city of Santa Bárbara, a greater concentration of occupations was observed around the area of forest fragment. It was observed in this chosen area of the city, the presence of different families living in a rural settlement, surviving from the exploitation of forest resources and the creation of small animals for subsistence, such as poultry and fish farming, as well as family farming products. The breeding of animals in native pastures was only observed in the cities of Peixe Boi and Viseu. Extensive livestock farming was practiced with beef cattle, equines for work and small animals (swine and goats). In relation to the most preserved pasture area, the city of Peixe Boi had the largest area, according to the data shown in Fig 5, however, in the city of Viseu, a higher regeneration was observed in the pastures during the period of the study, with significant secondary vegetation. When comparing the climates of the three areas it was observed that the predominant climate is megathermal and humid with average annual temperature around 27˚C. The months of October, November and December are the hottest, with temperatures between 32˚C and 34˚C and absolute maximums around 41˚C. Annual rainfall is quite high, generally around 2,350 mm, but strongly concentrated from January to June (80%). From September to December, on the contrary, rainfall is rare, about 7%, with a short dry season, of moderate water deficit in those months. The relative humidity of the average air oscillates around 85%, as shown in Fig 6 [48] . The description of the accumulated precipitation in the year of capture of the fecal specimens compared to the Climatological Normals (CLINO) for the period from 1961-1990 of the PCDs closest to the locations of the Expedito Ribeiro / Santa Bárbara settlement (Belém PCD), Vila Ananim / Peixe-Boi and Açaiteua / Viseu (Tracauateua PCD) show the frequency of rainfall in the regions, which facilitates the renewal of the pastures and the regeneration of the impacted forests, being an important indicator of the reduction of the damages caused by deforestation in the region. The average deforestation index in the three study areas was calculated from data obtained from INPE information systems. It was observed that in the years of 2013 to 2014 there were no changes in these regions; in the period from 2014 to 2015 about 4.1% of the city of Viseu was changed and 1.6% of the city of Peixe Boi. In relation to the period of 2016, great changes were observed in Peixe Boi (79%) and in the city of Viseu (70%), thus demonstrating that changes in the natural ecosystem may be associated with the frequencies for RVA in the studied areas, according to Fig 7. When assessing the infected animals in relation to the uninfected animals in both the forest fragment and the peridomicile, considering as animals of the forest fragment the birds, the chiroptera and the small rodents and as animals of the peridomicile the canines, bovines, pigs, felines and horses, a percentage of 37.07% infected peri domestic animals (86/232) and 22.12% infected forest fragment animals (92/416) were obtained. Applying the selected statistical analysis, a Pearson x2 Chi-square value was obtained: 16.7159, df = 1 and p <0.001, meaning that the hypothesis was corroborated, that is, the greater the degradation of the environment, the more likely it will be the search for food by wild animals in adjacent areas, or in the edge of the forest or even in the peri domiciliary region. In this sense, the possibility of contagion with other species of animals, even humans, should be considered because of the capacity of the rotavirus to be transmitted via the fecal / oral route or through direct contact with the environment. It is important to point out that the animals detected in this study are important sources of viral strains. A total of 80 stool samples were selected, reextracted and analyzed using PCR for the VP4 gene. Eight strains (10%) were positive for VP4 gene, being 2 strains bellowed to P [6] genotype and 6 to P[4]-type, according to In the present study, RVA was detected circulating in 27.5% of the animals; 36% in domestic animals and 64% in wild animals, providing a unique dataset with qRT-PCR detecting a low viral load of RVA in different species, which further correlates with the deforestation index. These data are important because there is a lack of tests for RVA diagnosis in animals, since the current methods of RVA detection does not always detect in these populations [8] . With the advent of real-time PCR (qPCR), there was an exponential growth, compared to conventional PCR essays, since its superior accuracy, sensitivity and specificity is remarkable, and it is Rotavirus A in wild and domestic animals possible to detect RVA in a variety of animal species using NSP3 gene [49] . The sensitivity of RT-qPCR significantly improved the rate of RVA detection in clinical samples from animals and in this context, the present study proposed an interesting study metrics using virus spreading in the wild animals which inhabit forest fragments to indicate human population interventions, with the goal of preventing the virus outbreaks leveraged on the unique geographic characteristics of Brazil and its large number of species in Amazon. Currently, no data have been described in the literature regarding the RVA detection using real-time qPCR technique in a wide variety of wild animal species. However, a study by Soltan et al. [50] conducted with horses and cattle detected RVA by RT-PCR, commercial RT-PCR and RT-qPCR in 36.7%, 51.4% and 56.9% respectively, differently from the present study that showed higher positivity for chiropterans (17.98%), canines (21.35%), birds (23.6%) and cattle (14.6%). The first description of RVA in chiroptera was recorded in feces of Eidolon helvum caught in Vihiga, Kenya [51] . Afterwards, several strains of RVA were detected by different molecular techniques involving chiroptera, in several countries, including Kenya (E. helvum), China (Rhinolophus hipposideros and Aselliscus stoliczkanus), France (Myotis mystacinus), Cameroon (E. helvum) and Brazil [31, [51] [52] [53] [54] [55] . The present study shows the occurrence of RVA in 17.98% of the chiroptera, being among the species Carollia perspicillata (37.5%), Desmodus rotundus (28.12%), Uroderma bilobata (15.6%), Artibeus lituratus (9.37%), Artibeus Planirostus (3.12%), Diaemus iyoug and Glossophagine (3.12%). Barquez et al. [56] reported that Desmodus rotundus is one of the three hematophagous species of the Phyllostomidae family, found throughout South America, Central America and Mexico. Of the positive chiroptera for RVA in the present study, a prevalence of 28.12% was of Desmodus rotundus. This species feeds on birds, can feed on mammals, mostly medium or large, facilitating the dissemination of viral spores among the community within the habitat, as observed in the present study. These findings show the importance of epidemiological data on the studied species due to the lack of studies involving species of neotropical chiroptera, and it is not possible to establish comparative parameters for these animals. Regarding the circulation of RVA in canines and birds, the prevalence was 53% and 29%, respectively. Although in the Amazon region there are records of RVA, RVD, RVF and RVG that infect birds [57] [58] and RVD in migratory birds [59] , all were detected by RT-PCR assays differently from the present study which detected the RVA by RT-qPCR involving a variety of animal species. On the other hand, the prevalence in felines (16%) and pigs (22%) was lower, probably because there are few animals of these species in the region, as well as few creations. The study detected the presence of RVA in different species of animals both in areas near the home and in areas located in fragments of forest, characterized as forest remnants, since they were located in cities that suffered high environmental impacts due to vegetal extractivism, pasture formation for cattle breeding, exploitation of natural resources, and direct reflexes on the habitats of wild animals that can serve as virus sources, thus facilitating the dispersion of RVA among communities of coexisting animals. It is worth emphasizing that these animals have a greater contact with the human populations of the studied areas since they cohabit with the humans in the region, besides having a high flow of movement between the forest extracts and environments chosen for the present study. However, it is noteworthy that only in the communities of Santa Bárbara and Viseu were collected fecal specimens from asymptomatic humans for diarrhea and tested for RVA, but all were negative. It is notorious yet, the existence of different levels of degradation in the studied environments, considering the presented data. The fragmentation of the forest generates many consequences on the Amazonian biota, being able to alter the diversity and the composition of the animal communities in the fragments and even to interfere in the ecological processes, without considering that the fragments of forest in the Amazon are influenced by the climate, possibly facilitating the dispersion of pathogens by the environment, since the wild animals detected in the present study are asymptomatic and have low viral load for RVA. The occurrence of RVA in this population of animals may explain the possibility of dispersion of viral strains, since there is a proximity to the human population, besides the biological characteristics of these species that may represent important sources for gastroenteric viruses, along with the fact that all animals were asymptomatic for diarrhea. Wild birds have unlimited flight capacity, were captured in an interface region between the peridomicile and forest fragments and it is believed that this region has not been influenced by anthropic activities such as those observed in the area of the present study. On the other hand, the breeding method for poultry and canines close to homes and the forest ecosystem, as they are created in the communities surveyed, probably facilitates direct contact with possible sources of contamination, since in the areas the use of septic tanks is deficient and sometimes non-existent, which may facilitate or even increase the risk of viral dispersion throughout the environment. The high rates of increase and the analysis of land use in the researched areas may be important indicators of how these animals interact, since with deforestation, the populations of wild animals seek refuge in nearby communities facilitating the dispersal of infectious agents and the possible occurrence of carrier animals by direct contact or contamination of the local environment. To our knowledge, this is the first study in which a real-time PCR assay was applied for the detection of RVA involving a wide variety of domestic and wild animals, facilitating practical utility in epidemiological and molecular studies and assisting in a perspective in the elaboration of sanitary control and monitoring, preventing possible outbreaks in the studied communities. The detection of positive animals was useful to monitor the infection of the agent in the animal population and to provide an early warning signal to predict an impending epidemic and a favorable risk for the human population, given the evidence of RVA circulation in the different forest fragments. In addition, the RT-qPCR assay may be a useful alternative for the differential diagnosis of RV in possible coexisting mixed infections clinically indistinguishable such as those caused by other viral strains that cause gastroenteritis such as: astrovirus, coronavirus, picobirnavirus, calicivirus, among others as observed in the studies of Jing et al. [60] and Waruhiu et al. [61] . Diarrhea associated with RVA infections in pigs is an important cause of increased mortality and economic losses in Europe. The most prevalent genotypes isolated from feces of Belgian diarrheal and non-diarrheal piglets in 2012 [62] demonstrate a wide range of combinations of genotypes G / P including; G3P [6] , G4P [6] , G5P [6] , G4P [7] , G5P [7] , G9P [7] , G9P [13] and G9P [23] . On the other hand, in the present study it was possible to detect only P [6] genotype, since majority of samples was asymptomatic for diarrhea. Finding shows that different P genotypes of RVA strains interact with distinct blood group histological antigens (HBGA, ABOH, Lewis) and sialic acids via VP4 providing insight into the regional prevalence and increased zoonotic potential of some RVA of origin swine [63] . The genotype P [6] was identified in piglets in Brazil [64] and in Italy and Japan resembling genotype P [6] human [65, 66] . In the population of animals studied the zoonotic transmission can be frequent, since the animals live in contact with humans and in precarious sanitary conditions. In Brazil, this genotype was described in animal and human populations in studies of Luchs et al. [32] ; Honma et al. [67] ; Araújo et al. [68] ; Mascarenhas et al. [69] and Lorenzetti et al. [70] such studies corroborate the importance of continuing to monitor genotypes to verify if uncommon strains or new strains are emerging and can infect animal populations or inter-species transmissions. Regarding the genotype P [4] , itwas most detected in our samples in bats, dogs, swine and feline. This genotype is not common in animals, being more detected in human and environmental samples in various parts of the world and included our region [71] . It is important to emphasize that the indicators of environmental contamination in Brazil are significant and contribute to the possibility of human-animal transmission [71] . Such data need further investigation in later work to better characterize the interspecies transmission, since the occurrence of enteric viruses in different matrices demonstrates the anthropogenic impact of the exposed population around and points to the potential risk of infection by the possible exposure of individuals susceptible. Our findings may be useful for tracking fecal contamination in the environment using animals as possible sources thus minimizing the risk of infection by exposure to susceptible individuals, in this case different animal species or even human populations. RVA were detected in wild and domestic animals using a RT-qPCR assay that analyzed samples that had low viral load for RVA. Although the samples are asymptomatic for diarrhea, it is necessary to conduct strategies for the monitoring and control of the animals in the areas studied in the human population as well as in other species of animals, as well as the implementation of preventive measures aimed at future outbreaks in communities animals in the resident population in these impacted areas. Therefore, the present study is unprecedented in the region and in the country in relation to the research of RVA in wild animals. It is noteworthy that, although the quality of the analyzed samples is characterized as low detectable viral load, the technique presented a good analytical response in the detection of the source animals for RVA, facilitating the selection of the samples for future genetic characterization tests.

Is Rotavirus single or double-stranded?

1918 Influenza: the Mother of All Pandemics Jeffery K. Taubenberger" and David M. Morens1- The “Spanish" influenza pandemic of 1918—1919, which caused :50 million deaths worldwide, remains an ominous warning to public health. Many questions about its origins, its unusual epidemiologic features, and the basis of its pathogenicity remain unanswered. The public health implications of the pandemic therefore remain in doubt even as we now grapple with the feared emergence of a pandemic caused by H5N1 or other virus. However, new information about the 1918 virus is emerging, for example, sequencing of the entire genome from archival autopsy tis- sues. But, the viral genome alone is unlikely to provide answers to some critical questions. Understanding the 1918 pandemic and its implications for future pandemics requires careful experimentation and in-depth historical analysis. ”Curiouser and curiouser/ ” criedAlice Lewis Carroll, Alice’s Adventures in Wonderland, 1865 An estimated one third of the world’s population (or z500 million persons) were infected and had clinical- ly apparent illnesses (1,2) during the 191871919 influenza pandemic. The disease was exceptionally severe. Case- fatality rates were >2.5%, compared to <0.1% in other influenza pandemics (3,4). Total deaths were estimated at z50 million (577) and were arguably as high as 100 mil- lion (7). The impact of this pandemic was not limited to 191871919. All influenza A pandemics since that time, and indeed almost all cases of influenza A worldwide (except- ing human infections from avian Viruses such as H5N1 and H7N7), have been caused by descendants of the 1918 Virus, including “drifted” H1N1 Viruses and reassorted H2N2 and H3N2 Viruses. The latter are composed of key genes from the 1918 Virus, updated by subsequently-incor— porated avian influenza genes that code for novel surface *Armed Forces Institute of Pathology, Rockville, Maryland, USA; and TNational Institutes of Health, Bethesda, Maryland, USA proteins, making the 1918 Virus indeed the “mother” of all pandemics. In 1918, the cause of human influenza and its links to avian and swine influenza were unknown. Despite clinical and epidemiologic similarities to influenza pandemics of 1889, 1847, and even earlier, many questioned whether such an explosively fatal disease could be influenza at all. That question did not begin to be resolved until the 1930s, when closely related influenza Viruses (now known to be H1N1 Viruses) were isolated, first from pigs and shortly thereafter from humans. Seroepidemiologic studies soon linked both of these viruses to the 1918 pandemic (8). Subsequent research indicates that descendants of the 1918 Virus still persists enzootically in pigs. They probably also circulated continuously in humans, undergoing gradual antigenic drift and causing annual epidemics, until the 1950s. With the appearance of a new H2N2 pandemic strain in 1957 (“Asian flu”), the direct H1N1 Viral descen- dants 0f the 1918 pandemic strain disappeared from human circulation entirely, although the related lineage persisted enzootically in pigs. But in 1977, human H1N1 Viruses suddenly “reemerged” from a laboratory freezer (9). They continue to circulate endemically and epidemically. Thus in 2006, 2 major descendant lineages of the 1918 H1N1 Virus, as well as 2 additional reassortant lineages, persist naturally: a human epidemic/endemic H1N1 line- age, a porcine enzootic H1N1 lineage (so-called classic swine flu), and the reassorted human H3N2 Virus lineage, which like the human H1N1 Virus, has led to a porcine H3N2 lineage. None of these Viral descendants, however, approaches the pathogenicity of the 1918 parent Virus. Apparently, the porcine H1N1 and H3N2 lineages uncom- monly infect humans, and the human H1N1 and H3N2 lin- eages have both been associated with substantially lower rates ofillness and death than the virus of 1918. In fact, cur- rent H1N1 death rates are even lower than those for H3N2 lineage strains (prevalent from 1968 until the present). H1N1 Viruses descended from the 1918 strain, as well as H3N2 Viruses, have now been cocirculating worldwide for 29 years and show little evidence of imminent extinction. Trying To Understand What Happened By the early 1990s, 75 years of research had failed to answer a most basic question about the 1918 pandemic: why was it so fatal? No Virus from 1918 had been isolated, but all of its apparent descendants caused substantially milder human disease. Moreover, examination of mortality data from the 1920s suggests that within a few years after 1918, influenza epidemics had settled into a pattern of annual epidemicity associated with strain drifting and sub- stantially lowered death rates. Did some critical Viral genet- ic event produce a 1918 Virus of remarkable pathogenicity and then another critical genetic event occur soon after the 1918 pandemic to produce an attenuated H1N1 Virus? In 1995, a scientific team identified archival influenza autopsy materials collected in the autumn of 1918 and began the slow process of sequencing small Viral RNA fragments to determine the genomic structure of the causative influenza Virus (10). These efforts have now determined the complete genomic sequence of 1 Virus and partial sequences from 4 others. The primary data from the above studies (11717) and a number of reviews covering different aspects of the 1918 pandemic have recently been published ([8720) and confirm that the 1918 Virus is the likely ancestor of all 4 of the human and swine H1N1 and H3N2 lineages, as well as the “extinct” H2N2 lineage. No known mutations correlated with high pathogenicity in other human or animal influenza Viruses have been found in the 1918 genome, but ongoing studies to map Virulence factors are yielding interesting results. The 1918 sequence data, however, leave unanswered questions about the ori- gin of the Virus (19) and about the epidemiology of the pandemic. When and Where Did the 1918 Influenza Pandemic Arise? Before and after 1918, most influenza pandemics developed in Asia and spread from there to the rest of the world. Confounding definite assignment of a geographic point of origin, the 1918 pandemic spread more or less simultaneously in 3 distinct waves during an z12-month period in 191871919, in Europe, Asia, and North America (the first wave was best described in the United States in March 1918). Historical and epidemiologic data are inade- quate to identify the geographic origin of the Virus (21), and recent phylogenetic analysis of the 1918 Viral genome does not place the Virus in any geographic context ([9). Although in 1918 influenza was not a nationally reportable disease and diagnostic criteria for influenza and pneumonia were vague, death rates from influenza and pneumonia in the United States had risen sharply in 1915 and 1916 because of a major respiratory disease epidemic beginning in December 1915 (22). Death rates then dipped slightly in 1917. The first pandemic influenza wave appeared in the spring of 1918, followed in rapid succes- sion by much more fatal second and third waves in the fall and winter of 191871919, respectively (Figure 1). Is it pos- sible that a poorly-adapted H1N1 Virus was already begin- ning to spread in 1915, causing some serious illnesses but not yet sufficiently fit to initiate a pandemic? Data consis- tent with this possibility were reported at the time from European military camps (23), but a counter argument is that if a strain with a new hemagglutinin (HA) was caus- ing enough illness to affect the US national death rates from pneumonia and influenza, it should have caused a pandemic sooner, and when it eventually did, in 1918, many people should have been immune or at least partial- ly immunoprotected. “Herald” events in 1915, 1916, and possibly even in early 1918, if they occurred, would be dif- ficult to identify. The 1918 influenza pandemic had another unique fea- ture, the simultaneous (or nearly simultaneous) infection of humans and swine. The Virus of the 1918 pandemic like- ly expressed an antigenically novel subtype to which most humans and swine were immunologically naive in 1918 (12,20). Recently published sequence and phylogenetic analyses suggest that the genes encoding the HA and neu- raminidase (NA) surface proteins of the 1918 Virus were derived from an avianlike influenza Virus shortly before the start of the pandemic and that the precursor Virus had not circulated widely in humans or swine in the few decades before (12,15, 24). More recent analyses of the other gene segments of the Virus also support this conclu- sion. Regression analyses of human and swine influenza sequences obtained from 1930 to the present place the ini- tial circulation of the 1918 precursor Virus in humans at approximately 191571918 (20). Thus, the precursor was probably not circulating widely in humans until shortly before 1918, nor did it appear to have jumped directly from any species of bird studied to date (19). In summary, its origin remains puzzling. Were the 3 Waves in 1918—1 919 Caused by the Same Virus? If So, How and Why? Historical records since the 16th century suggest that new influenza pandemics may appear at any time of year, not necessarily in the familiar annual winter patterns of interpandemic years, presumably because newly shifted influenza Viruses behave differently when they find a uni- versal or highly susceptible human population. Thereafter, confronted by the selection pressures of population immu- nity, these pandemic Viruses begin to drift genetically and eventually settle into a pattern of annual epidemic recur- rences caused by the drifted Virus variants. Figure 1. Three pandemic waves: weekly combined influenza and pneumonia mortality, United Kingdom, 1918—1919 (21). In the 1918-1919 pandemic, a first or spring wave began in March 1918 and spread unevenly through the United States, Europe, and possibly Asia over the next 6 months (Figure 1). Illness rates were high, but death rates in most locales were not appreciably above normal. A sec- ond or fall wave spread globally from September to November 1918 and was highly fatal. In many nations, a third wave occurred in early 1919 (21). Clinical similari- ties led contemporary observers to conclude initially that they were observing the same disease in the successive waves. The milder forms of illness in all 3 waves were identical and typical of influenza seen in the 1889 pandem- ic and in prior interpandemic years. In retrospect, even the rapid progressions from uncomplicated influenza infec- tions to fatal pneumonia, a hallmark of the 191871919 fall and winter waves, had been noted in the relatively few severe spring wave cases. The differences between the waves thus seemed to be primarily in the much higher fre- quency of complicated, severe, and fatal cases in the last 2 waves. But 3 extensive pandemic waves of influenza within 1 year, occurring in rapid succession, with only the briefest of quiescent intervals between them, was unprecedented. The occurrence, and to some extent the severity, of recur- rent annual outbreaks, are driven by Viral antigenic drift, with an antigenic variant Virus emerging to become domi- nant approximately every 2 to 3 years. Without such drift, circulating human influenza Viruses would presumably disappear once herd immunity had reached a critical threshold at which further Virus spread was sufficiently limited. The timing and spacing of influenza epidemics in interpandemic years have been subjects of speculation for decades. Factors believed to be responsible include partial herd immunity limiting Virus spread in all but the most favorable circumstances, which include lower environ- mental temperatures and human nasal temperatures (bene- ficial to thermolabile Viruses such as influenza), optimal humidity, increased crowding indoors, and imperfect ven- tilation due to closed windows and suboptimal airflow. However, such factors cannot explain the 3 pandemic waves of 1918-1919, which occurred in the spring-sum- mer, summer—fall, and winter (of the Northern Hemisphere), respectively. The first 2 waves occurred at a time of year normally unfavorable to influenza Virus spread. The second wave caused simultaneous outbreaks in the Northern and Southern Hemispheres from September to November. Furthermore, the interwave peri- ods were so brief as to be almost undetectable in some locales. Reconciling epidemiologically the steep drop in cases in the first and second waves with the sharp rises in cases of the second and third waves is difficult. Assuming even transient postinfection immunity, how could suscep- tible persons be too few to sustain transmission at 1 point, and yet enough to start a new explosive pandemic wave a few weeks later? Could the Virus have mutated profoundly and almost simultaneously around the world, in the short periods between the successive waves? Acquiring Viral drift sufficient to produce new influenza strains capable of escaping population immunity is believed to take years of global circulation, not weeks of local circulation. And hav- ing occurred, such mutated Viruses normally take months to spread around the world. At the beginning of other “off season” influenza pan- demics, successive distinct waves within a year have not been reported. The 1889 pandemic, for example, began in the late spring of 1889 and took several months to spread throughout the world, peaking in northern Europe and the United States late in 1889 or early in 1890. The second recurrence peaked in late spring 1891 (more than a year after the first pandemic appearance) and the third in early 1892 (21 ). As was true for the 1918 pandemic, the second 1891 recurrence produced of the most deaths. The 3 recur- rences in 1889-1892, however, were spread over >3 years, in contrast to 191871919, when the sequential waves seen in individual countries were typically compressed into z879 months. What gave the 1918 Virus the unprecedented ability to generate rapidly successive pandemic waves is unclear. Because the only 1918 pandemic Virus samples we have yet identified are from second-wave patients ([6), nothing can yet be said about whether the first (spring) wave, or for that matter, the third wave, represented circulation of the same Virus or variants of it. Data from 1918 suggest that persons infected in the second wave may have been pro- tected from influenza in the third wave. But the few data bearing on protection during the second and third waves after infection in the first wave are inconclusive and do lit- tle to resolve the question of whether the first wave was caused by the same Virus or whether major genetic evolu- tionary events were occurring even as the pandemic exploded and progressed. Only influenza RNAipositive human samples from before 1918, and from all 3 waves, can answer this question. What Was the Animal Host Origin of the Pandemic Virus? Viral sequence data now suggest that the entire 1918 Virus was novel to humans in, or shortly before, 1918, and that it thus was not a reassortant Virus produced from old existing strains that acquired 1 or more new genes, such as those causing the 1957 and 1968 pandemics. On the con- trary, the 1918 Virus appears to be an avianlike influenza Virus derived in toto from an unknown source (17,19), as its 8 genome segments are substantially different from contemporary avian influenza genes. Influenza Virus gene sequences from a number offixed specimens ofwild birds collected circa 1918 show little difference from avian Viruses isolated today, indicating that avian Viruses likely undergo little antigenic change in their natural hosts even over long periods (24,25). For example, the 1918 nucleoprotein (NP) gene sequence is similar to that ofviruses found in wild birds at the amino acid level but very divergent at the nucleotide level, which suggests considerable evolutionary distance between the sources of the 1918 NP and of currently sequenced NP genes in wild bird strains (13,19). One way of looking at the evolutionary distance of genes is to com- pare ratios of synonymous to nonsynonymous nucleotide substitutions. A synonymous substitution represents a silent change, a nucleotide change in a codon that does not result in an amino acid replacement. A nonsynonymous substitution is a nucleotide change in a codon that results in an amino acid replacement. Generally, a Viral gene sub- jected to immunologic drift pressure or adapting to a new host exhibits a greater percentage of nonsynonymous mutations, while a Virus under little selective pressure accumulates mainly synonymous changes. Since little or no selection pressure is exerted on synonymous changes, they are thought to reflect evolutionary distance. Because the 1918 gene segments have more synony- mous changes from known sequences of wild bird strains than expected, they are unlikely to have emerged directly from an avian influenza Virus similar to those that have been sequenced so far. This is especially apparent when one examines the differences at 4-fold degenerate codons, the subset of synonymous changes in which, at the third codon position, any of the 4 possible nucleotides can be substituted without changing the resulting amino acid. At the same time, the 1918 sequences have too few amino acid difierences from those of wild-bird strains to have spent many years adapting only in a human or swine intermedi- ate host. One possible explanation is that these unusual gene segments were acquired from a reservoir of influenza Virus that has not yet been identified or sampled. All of these findings beg the question: where did the 1918 Virus come from? In contrast to the genetic makeup of the 1918 pandem- ic Virus, the novel gene segments of the reassorted 1957 and 1968 pandemic Viruses all originated in Eurasian avian Viruses (26); both human Viruses arose by the same mech- anismireassortment of a Eurasian wild waterfowl strain with the previously circulating human H1N1 strain. Proving the hypothesis that the Virus responsible for the 1918 pandemic had a markedly different origin requires samples of human influenza strains circulating before 1918 and samples of influenza strains in the wild that more closely resemble the 1918 sequences. What Was the Biological Basis for 1918 Pandemic Virus Pathogenicity? Sequence analysis alone does not ofier clues to the pathogenicity of the 1918 Virus. A series of experiments are under way to model Virulence in Vitro and in animal models by using Viral constructs containing 1918 genes produced by reverse genetics. Influenza Virus infection requires binding of the HA protein to sialic acid receptors on host cell surface. The HA receptor-binding site configuration is different for those influenza Viruses adapted to infect birds and those adapted to infect humans. Influenza Virus strains adapted to birds preferentially bind sialic acid receptors with 01 (273) linked sugars (27729). Human-adapted influenza Viruses are thought to preferentially bind receptors with 01 (2%) link- ages. The switch from this avian receptor configuration requires of the Virus only 1 amino acid change (30), and the HAs of all 5 sequenced 1918 Viruses have this change, which suggests that it could be a critical step in human host adaptation. A second change that greatly augments Virus binding to the human receptor may also occur, but only 3 of5 1918 HA sequences have it (16). This means that at least 2 H1N1 receptor-binding vari- ants cocirculated in 1918: 1 with high—affinity binding to the human receptor and 1 with mixed-affinity binding to both avian and human receptors. No geographic or chrono- logic indication eXists to suggest that one of these variants was the precursor of the other, nor are there consistent dif- ferences between the case histories or histopathologic fea- tures of the 5 patients infected with them. Whether the Viruses were equally transmissible in 1918, whether they had identical patterns of replication in the respiratory tree, and whether one or both also circulated in the first and third pandemic waves, are unknown. In a series of in Vivo experiments, recombinant influen- za Viruses containing between 1 and 5 gene segments of the 1918 Virus have been produced. Those constructs bearing the 1918 HA and NA are all highly pathogenic in mice (31). Furthermore, expression microarray analysis performed on whole lung tissue of mice infected with the 1918 HA/NA recombinant showed increased upregulation of genes involved in apoptosis, tissue injury, and oxidative damage (32). These findings are unexpected because the Viruses with the 1918 genes had not been adapted to mice; control experiments in which mice were infected with modern human Viruses showed little disease and limited Viral replication. The lungs of animals infected with the 1918 HA/NA construct showed bronchial and alveolar epithelial necrosis and a marked inflammatory infiltrate, which suggests that the 1918 HA (and possibly the NA) contain Virulence factors for mice. The Viral genotypic basis of this pathogenicity is not yet mapped. Whether pathogenicity in mice effectively models pathogenicity in humans is unclear. The potential role of the other 1918 pro- teins, singularly and in combination, is also unknown. Experiments to map further the genetic basis of Virulence of the 1918 Virus in various animal models are planned. These experiments may help define the Viral component to the unusual pathogenicity of the 1918 Virus but cannot address whether specific host factors in 1918 accounted for unique influenza mortality patterns. Why Did the 1918 Virus Kill So Many Healthy Young Ad ults? The curve of influenza deaths by age at death has histor- ically, for at least 150 years, been U-shaped (Figure 2), exhibiting mortality peaks in the very young and the very old, with a comparatively low frequency of deaths at all ages in between. In contrast, age-specific death rates in the 1918 pandemic exhibited a distinct pattern that has not been documented before or since: a “W—shaped” curve, similar to the familiar U-shaped curve but with the addition of a third (middle) distinct peak of deaths in young adults z20410 years of age. Influenza and pneumonia death rates for those 1534 years of age in 191871919, for example, were 20 times higher than in previous years (35). Overall, near- ly half of the influenza—related deaths in the 1918 pandem- ic were in young adults 20410 years of age, a phenomenon unique to that pandemic year. The 1918 pandemic is also unique among influenza pandemics in that absolute risk of influenza death was higher in those <65 years of age than in those >65; persons <65 years of age accounted for >99% of all excess influenza—related deaths in 191871919. In com- parison, the <65-year age group accounted for 36% of all excess influenza—related deaths in the 1957 H2N2 pandem- ic and 48% in the 1968 H3N2 pandemic (33). A sharper perspective emerges when 1918 age-specific influenza morbidity rates (21) are used to adj ust the W- shaped mortality curve (Figure 3, panels, A, B, and C [35,37]). Persons 65 years of age in 1918 had a dispro- portionately high influenza incidence (Figure 3, panel A). But even after adjusting age-specific deaths by age-specif— ic clinical attack rates (Figure 3, panel B), a W—shaped curve with a case-fatality peak in young adults remains and is significantly different from U-shaped age-specific case- fatality curves typically seen in other influenza years, e.g., 192871929 (Figure 3, panel C). Also, in 1918 those 5 to 14 years of age accounted for a disproportionate number of influenza cases, but had a much lower death rate from influenza and pneumonia than other age groups. To explain this pattern, we must look beyond properties of the Virus to host and environmental factors, possibly including immunopathology (e.g., antibody-dependent infection enhancement associated with prior Virus exposures [38]) and exposure to risk cofactors such as coinfecting agents, medications, and environmental agents. One theory that may partially explain these findings is that the 1918 Virus had an intrinsically high Virulence, tem- pered only in those patients who had been born before 1889, e.g., because of exposure to a then-circulating Virus capable of providing partial immunoprotection against the 1918 Virus strain only in persons old enough (>35 years) to have been infected during that prior era (35). But this the- ory would present an additional paradox: an obscure pre- cursor Virus that left no detectable trace today would have had to have appeared and disappeared before 1889 and then reappeared more than 3 decades later. Epidemiologic data on rates of clinical influenza by age, collected between 1900 and 1918, provide good evi- dence for the emergence of an antigenically novel influen- za Virus in 1918 (21). Jordan showed that from 1900 to 1917, the 5- to 15-year age group accounted for 11% of total influenza cases, while the >65-year age group accounted for 6 % of influenza cases. But in 1918, cases in Figure 2. “U-” and “W—” shaped combined influenza and pneumo- nia mortality, by age at death, per 100,000 persons in each age group, United States, 1911—1918. Influenza- and pneumonia- specific death rates are plotted for the interpandemic years 1911—1917 (dashed line) and for the pandemic year 1918 (solid line) (33,34). Incidence male per 1 .nao persunslage group Mortality per 1.000 persunslige group + Case—fataiity rale 1918—1919 Case fatalily par 100 persons ill wilh P&I pel age group Figure 3. Influenza plus pneumonia (P&l) (combined) age-specific incidence rates per 1,000 persons per age group (panel A), death rates per 1,000 persons, ill and well combined (panel B), and case-fatality rates (panel C, solid line), US Public Health Service house-to-house surveys, 8 states, 1918 (36). A more typical curve of age-specific influenza case-fatality (panel C, dotted line) is taken from US Public Health Service surveys during 1928—1929 (37). the 5 to 15-year-old group jumped to 25% of influenza cases (compatible with exposure to an antigenically novel Virus strain), while the >65-year age group only accounted for 0.6% of the influenza cases, findings consistent with previously acquired protective immunity caused by an identical or closely related Viral protein to which older per- sons had once been exposed. Mortality data are in accord. In 1918, persons >75 years had lower influenza and pneumonia case-fatality rates than they had during the prepandemic period of 191171917. At the other end of the age spectrum (Figure 2), a high proportion of deaths in infancy and early childhood in 1918 mimics the age pat- tern, if not the mortality rate, of other influenza pandemics. Could a 1918-like Pandemic Appear Again? If So, What Could We Do About It? In its disease course and pathologic features, the 1918 pandemic was different in degree, but not in kind, from previous and subsequent pandemics. Despite the extraordi- nary number of global deaths, most influenza cases in 1918 (>95% in most locales in industrialized nations) were mild and essentially indistinguishable from influenza cases today. Furthermore, laboratory experiments with recombi- nant influenza Viruses containing genes from the 1918 Virus suggest that the 1918 and 1918-like Viruses would be as sensitive as other typical Virus strains to the Food and Drug Administrationiapproved antiinfluenza drugs riman- tadine and oseltamivir. However, some characteristics of the 1918 pandemic appear unique: most notably, death rates were 5 7 20 times higher than expected. Clinically and pathologically, these high death rates appear to be the result of several factors, including a higher proportion of severe and complicated infections of the respiratory tract, rather than involvement of organ systems outside the normal range of the influenza Virus. Also, the deaths were concentrated in an unusually young age group. Finally, in 1918, 3 separate recurrences of influenza followed each other with unusual rapidity, resulting in 3 explosive pandemic waves within a year’s time (Figure 1). Each of these unique characteristics may reflect genetic features of the 1918 Virus, but understand- ing them will also require examination of host and envi- ronmental factors. Until we can ascertain which of these factors gave rise to the mortality patterns observed and learn more about the formation of the pandemic, predictions are only educated guesses. We can only conclude that since it happened once, analogous conditions could lead to an equally devastating pandemic. Like the 1918 Virus, H5N1 is an avian Virus (39), though a distantly related one. The evolutionary path that led to pandemic emergence in 1918 is entirely unknown, but it appears to be different in many respects from the cur- rent situation with H5N1. There are no historical data, either in 1918 or in any other pandemic, for establishing that a pandemic “precursor” Virus caused a highly patho- genic outbreak in domestic poultry, and no highly patho- genic avian influenza (HPAI) Virus, including H5N1 and a number of others, has ever been known to cause a major human epidemic, let alone a pandemic. While data bearing on influenza Virus human cell adaptation (e.g., receptor binding) are beginning to be understood at the molecular level, the basis for Viral adaptation to efficient human-to- human spread, the chief prerequisite for pandemic emer- gence, is unknown for any influenza Virus. The 1918 Virus acquired this trait, but we do not know how, and we cur- rently have no way of knowing whether H5N1 Viruses are now in a parallel process of acquiring human-to-human transmissibility. Despite an explosion of data on the 1918 Virus during the past decade, we are not much closer to understanding pandemic emergence in 2006 than we were in understanding the risk of H1N1 “swine flu” emergence in 1976. Even with modern antiviral and antibacterial drugs, vaccines, and prevention knowledge, the return of a pan- demic Virus equivalent in pathogenicity to the Virus of 1918 would likely kill >100 million people worldwide. A pandemic Virus with the (alleged) pathogenic potential of some recent H5N1 outbreaks could cause substantially more deaths. Whether because of Viral, host or environmental fac- tors, the 1918 Virus causing the first or ‘spring’ wave was not associated with the exceptional pathogenicity of the second (fall) and third (winter) waves. Identification of an influenza RNA-positive case from the first wave could point to a genetic basis for Virulence by allowing differ- ences in Viral sequences to be highlighted. Identification of pre-1918 human influenza RNA samples would help us understand the timing of emergence of the 1918 Virus. Surveillance and genomic sequencing of large numbers of animal influenza Viruses will help us understand the genet- ic basis of host adaptation and the extent of the natural reservoir of influenza Viruses. Understanding influenza pandemics in general requires understanding the 1918 pan- demic in all its historical, epidemiologic, and biologic aspects. Dr Taubenberger is chair of the Department of Molecular Pathology at the Armed Forces Institute of Pathology, Rockville, Maryland. His research interests include the molecular patho- physiology and evolution of influenza Viruses. Dr Morens is an epidemiologist with a long-standing inter- est in emerging infectious diseases, Virology, tropical medicine, and medical history. Since 1999, he has worked at the National Institute of Allergy and Infectious Diseases. References 1. Frost WH. Statistics of influenza morbidity. Public Health Rep. 19203558497. 2. Bumet F, Clark E. Influenza: a survey ofthe last 50 years in the light of modern work on the Virus of epidemic influenza. Melbourne: MacMillan; 1942. 3. Marks G, Beatty WK. Epidemics. New York: Scribners, 1976. 4. Rosenau MJ, Last JM. Maxcy-Rosenau preventative medicine and public health. New York: Appleton-Century-Crofts; 1980. 5. Crosby A. America’s forgotten pandemic. Cambridge (UK): Cambridge University Press;1989. 6. Patterson KD, Pyle GF. The geography and mortality of the 1918 influenza pandemic. Bull Hist Med. 1991;65:4–21. 7. Johnson NPAS, Mueller J. Updating the accounts: global mortality of the 1918–1920 “Spanish” influenza pandemic. Bull Hist Med 2002;76:105–15. 8. Shope RE. The incidence of neutralizing antibodies for swine influenza virus in the sera of human beings of different ages. J Exp Med. 1936;63:669–84. 9. Kendal AP, Noble GR, Skehel JJ, Dowdle WR. Antigenic similarity of influenza A (H1N1) viruses from epidemics in 1977–1978 to “Scandinavian” strains isolated in epidemics of 1950–1951. Virology. 1978;89:632–6. 10. Taubenberger JK, Reid AH, Krafft AE, Bijwaard KE, Fanning TG. Initial genetic characterization of the 1918 “Spanish” influenza virus. Science. 1997;275:1793–6. 11. Basler CF, Reid AH, Dybing JK, Janczewski TA, Fanning TG, Zheng H, et al. Sequence of the 1918 pandemic influenza virus nonstructural gene (NS) segment and characterization of recombinant viruses bearing the 1918 NS genes. Proc Natl Acad Sci U S A 2001;98:2746–51. 12. Reid AH, Fanning TG, Hultin JV, Taubenberger JK. Origin and evolution of the 1918 “Spanish” influenza virus hemagglutinin gene. Proc Natl Acad Sci U S A 1999;96:1651–6. 13. Reid AH, Fanning TG, Janczewski TA, Lourens RM, and Taubenberger JK. Novel origin of the 1918 pandemic influenza virus nucleoprotein gene segment. J Virol. 2004;78:12462–70. 14. Reid AH, Fanning TG, Janczewski TA, McCall S, Taubenberger JK. Characterization of the 1918 “Spanish” influenza virus matrix gene segment. J Virol. 2002;76:10717–23. 15. Reid AH, Fanning TG, Janczewski TA, Taubenberger JK. Characterization of the 1918 “Spanish” influenza virus neuraminidase gene. Proc Natl Acad Sci U S A 2000;97:6785–90. 16. Reid AH, Janczewski TA, Lourens RM, Elliot AJ, Daniels RS, Berry CL, et al. 1918 influenza pandemic caused by highly conserved viruses with two receptor-binding variants. Emerg Infect Dis. 2003;9:1249–53. 17. Taubenberger JK, Reid AH, Lourens RM, Wang R, Jin G, Fanning TG. Characterization of the 1918 influenza virus polymerase genes. Nature. 2005;437:889–93. 18. Reid AH, Taubenberger JK. The 1918 flu and other influenza pandemics: “over there” and back again. Lab Invest. 1999;79:95–101. 19. Reid AH, Taubenberger JK, Fanning TG. Evidence of an absence: the genetic origins of the 1918 pandemic influenza virus. Nat Rev Microbiol. 2004;2:909–14. 20. Taubenberger JK, Reid AH, Fanning TG. The 1918 influenza virus: a killer comes into view. Virology. 2000;274:241–5. 21. Jordan E. Epidemic influenza: a survey. Chicago: American Medical Association, 1927. 22. Capps J, Moody A. The recent epidemic of grip. JAMA. 1916;67:1349–50. 33. Oxford JS, Sefton A, Jackson R, Innes W, Daniels RS, Johnson NP. World War I may have allowed the emergence of “Spanish” influenza. Lancet Infect Dis. 2002;2:111–4. 24. Fanning TG, Slemons RD, Reid AH, Janczewski TA, Dean J, Taubenberger JK. 1917 avian influenza virus sequences suggest that the 1918 pandemic virus did not acquire its hemagglutinin directly from birds. J Virol. 2002;76:7860–2. 25. Reid AH, Fanning TG, Slemons RD, Janczewski TA, Dean J, Taubenberger JK. Relationship of pre-1918 avian influenza HA and NP sequences to subsequent avian influenza strains. Avian Dis. 2003;47:921–5. 26. Bean W, Schell M, Katz J, Kawaoka Y, Naeve C, Gorman O, et al. Evolution of the H3 influenza virus hemagglutinin from human and nonhuman hosts. J Virol. 1992;66:1129–38. 27. Weis W, Brown JH, Cusack S, Paulson JC, Skehel JJ, Wiley DC. Structure of the influenza virus haemagglutinin complexed with its receptor, sialic acid. Nature. 1988;333:426–31. 28. Gambaryan AS, Tuzikov AB, Piskarev VE, Yamnikova SS, Lvov DK, Robertson JS, et al. Specification of receptor-binding phenotypes of influenza virus isolates from different hosts using synthetic sialylglycopolymers: non-egg-adapted human H1 and H3 influenza A and influenza B viruses share a common high binding affinity for 6′-sialyl(N-acetyllactosamine). Virology. 1997;232: 345–50. 29. Matrosovich M, Gambaryan A, Teneberg S, Piskarev VE, Yamnikova SS, Lvov DK, et al. Avian influenza A viruses differ from human viruses by recognition of sialyloigosaccharides and gangliosides and by a higher conservation of the HA receptor-binding site. Virology. 1997;233:224–34. 30. Glaser L, Stevens J, Zamarin D, Wilson IA, Garcia-Sastre A, Tumpey TM, et al. A single amino acid substitution in the 1918 influenza virus hemagglutinin changes the receptor binding specificity. J Virol. 2005;79:11533–6. 31. Kobasa D, Takada A, Shinya K, Hatta M, Halfmann P, Theriault S, et al. Enhanced virulence of influenza A viruses with the haemagglutinin of the 1918 pandemic virus. Nature. 2004;431:703–7. 32. Kash JC, Basler CF, Garcia-Sastre A, Carter V, Billharz R, Swayne DE, et al. Global host immune response: pathogenesis and transcriptional profiling of type A influenza viruses expressing the hemagglutinin and neuraminidase genes from the 1918 pandemic virus. J Virol. 2004;78:9499–511. 33. Grove RD, Hetzel AM. Vital statistics rates in the United States: 1940–1960. Washington: US Government Printing Office, 1968. 34. Linder FE, Grove RD. Vital statistics rates in the United States: 1900–1940. Washington: US Government Printing Office, 1943. 35. Simonsen L, Clarke MJ, Schonberger LB, Arden NH, Cox NJ, Fukuda K. Pandemic versus epidemic influenza mortality: a pattern of changing age distribution. J Infect Dis 1998;178:53–60. 36. Frost WH. The epidemiology of influenza. Public Health Rep. 1919;34:1823–61. 37. Collins SD. Age and sex incidence of influenza and pneumonia morbidity and mortality in the epidemic of 1928-1929 with comparative data for the epidemic of 1918–1919. Public Health Rep. 1931;46:1909–37. 38. Majde JA. Influenza: Learn from the past. ASM News. 1996;62:514. 39. Peiris JS, Yu WC, Leung CW, Cheung CY, Ng WF, Nicholls JM, et al. Re-emergence of fatal human influenza A subtype H5N1 disease. Lancet. 2004;363:617–9. Address for correspondence: Jeffery K. Taubenberger, Department of Molecular Pathology, Armed Forces Institute of Pathology, 1413 Research Blvd, Bldg 101, Rm 1057, Rockville, MD 20850-3125, USA; fax. 301-295-9507; email: [email protected] The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the Centers for Disease Control and Prevention or the institutions with which the authors are affiliated.

Are the modern day Influenza viruses related to the 1918 Spanish Influenza virus?

Pre-existing immunity against vaccine vectors – friend or foe? https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3542731/ SHA: f5bdf18567bb3760e1ce05008135f0270badbd5c Authors: Saxena, Manvendra; Van, Thi Thu Hao; Baird, Fiona J.; Coloe, Peter J.; Smooker, Peter M. Date: 2013-01-27 DOI: 10.1099/mic.0.049601-0 License: cc-by Abstract: Over the last century, the successful attenuation of multiple bacterial and viral pathogens has led to an effective, robust and safe form of vaccination. Recently, these vaccines have been evaluated as delivery vectors for heterologous antigens, as a means of simultaneous vaccination against two pathogens. The general consensus from published studies is that these vaccine vectors have the potential to be both safe and efficacious. However, some of the commonly employed vectors, for example Salmonella and adenovirus, often have pre-existing immune responses in the host and this has the potential to modify the subsequent immune response to a vectored antigen. This review examines the literature on this topic, and concludes that for bacterial vectors there can in fact, in some cases, be an enhancement in immunogenicity, typically humoral, while for viral vectors pre-existing immunity is a hindrance for subsequent induction of cell-mediated responses. Text: In the fields of medicine and veterinary medicine, there are numerous live, attenuated bacterial and viral vaccines in use today worldwide. The safety and efficacy of such vaccines is well established and allows further development as vector systems to deliver antigen originating from other pathogens. Various attenuated bacteria, including Escherichia coli, Vibrio cholerae, lactic acid bacteria (LAB), specifically Lactococcus lactis, Mycobacterium, Listeria, Shigella and Salmonella, have been tested for the targeted delivery of heterologous antigens of bacterial, viral and parasitic origin into a variety of animal hosts (Bahey-El-Din et al., 2010; Innocentin et al., 2009; Johnson et al., 2011; Tobias et al., 2008 Tobias et al., , 2010 Tobias & Svennerholm, 2012) . Bacteria such as E. coli and lactic acid bacteria have recently gained favour, as E. coli is a commensal and lactic acid bacteria are present in most fermented food items and are therefore naturally present in the host. They are also a much safer option than traditional attenuated vaccines in children and immunecompromised people. As this review discusses the effects of pre-existing immune responses to attenuated vaccines, further discussion of LAB and E. coli as potential vectors will not be undertaken; however, the reader is directed to several interesting reviews (Bermú dez-Humarán et al., 2011; Wells & Mercenier, 2008) . Intracellular bacteria from the genera Mycobacterium (Guleria et al., 1996) , Listeria (Gentschev et al., 2001) , Shigella (Levine et al., 1997) and Salmonella (Dougan et al., 1987) are considered to be suitable candidates for the delivery of vaccine antigens due to their capability to induce robust T cell immune responses (Alderton et al., 1991; Lo et al., 1999; Mastroeni et al., 2001; Mittrücker & Kaufmann, 2000; Nauciel, 1990) . Salmonella is one genus that has been well examined as a vector, building on the extensive research available on the micro-organism's physiology and pathogenesis (Basso et al., 2000; Killeen & DiRita, 2000; Sirard et al., 1999; Ward et al., 1999) . There exist several commercial vaccines that are used as anti-Salmonella vaccines in humans and animals (e.g. Ty21a for typhoid fever in humans, several Salmonella serovars against salmonellosis in chickens and other animals). The general strategy for vectoring heterologous antigen is depicted in Fig. 1 . The first clinical trial of a recombinant, which was conducted over 20 years ago using an attenuated Salmonella as a delivery vector, led to the widespread testing of this bacterium as a mucosal delivery system for antigens from non-Salmonella pathogens (Dougan et al., 1987) . These studies have demonstrated the utility of live bacteria to deliver expressed antigens and DNA vaccines to the host immune system (Atkins et al., 2006; Husseiny & Hensel, 2008; Jiang et al., 2004; Kirby et al., 2004) . Since then several other intracellular bacterial vectors have been successfully tested for their capability to deliver a variety of antigens from various pathogens, as well as vaccination against cancer. One genus which has been widely tested as vector is Listeria. Listeria species are Gram-positive intracellular food-borne pathogens. The advantages of Listeria are that it can invade a variety of cells, including antigen presenting cells (APCs). After invading the host cell, Listeria resides inside the phagosome; however, it can escape the phagosome with the help of listeriolysin O (LLO; Hly) and reside in the cytoplasm of the cells, thereby efficiently presenting antigen to both CD8 and CD4 T cells (Cossart & Mengaud, 1989; Kaufmann, 1993; Pamer et al., 1997) . Several studies have demonstrated the effectiveness and ease of using Listeria monocytogenes to deliver heterologous vaccine antigens and DNA vaccines Jensen et al., 1997; Johnson et al., 2011; Peters et al., 2003; Shen et al., 1995; Yin et al., 2011) . Similarly, various viral vectors have been successfully tested for their capability to deliver heterologous vaccine antigens, and this generally results in the induction of strong CTL immune responses. In the veterinary field, there are numerous viral vector vaccines that are currently licensed for use in livestock and domesticated animals. These recombinant vaccines are based on both DNA viruses (such as fowlpox virus-based vaccines which target avian influenza virus and fowlpox virus, or vaccinia virusbased vectors against the rabies virus in wildlife) and RNA viruses [such as Newcastle disease virus-based vaccines to be used in poultry or yellow fever virus (YFV)-based vaccines to be used in horses against West Nile virus] (Draper & Heeney, 2010) . Based on the safety record in the veterinary field, many viruses have been studied for human use as a vector in vaccine development (Beukema et al., 2006; Esteban, 2009; Schirrmacher & Fournier, 2009; Stoyanov et al., 2010; Weli & Tryland, 2011) . Amongst them, YFV (YF-17D strain) was the first to be licensed for use in humans, where the cDNAs encoding the envelope proteins of YFV were replaced with the corresponding genes of an attenuated Japanese encephalitis virus strain, SA14-14-2 (Appaiahgari & Vrati, 2010; Rollier et al., 2011) . Poxviruses are also studied extensively as candidate vectors for human use, among which attenuated derivatives of vaccinia virus [such as modified vaccinia virus Ankara (MVA) and New York attenuated vaccinia virus NYVAC strains] are the most promising vectors (Esteban, 2009; Gó mez et al., 2008; Rimmelzwaan & Sutter, 2009 ). They are ideal candidate vectors due to their large DNA-packing capacity and their thermal and genetic stability (Minke et al., 2004) . The NYVAC vector has been shown to induce CD4 + T cell-dominant responses, and MVA induces both CD4 + and CD8 + T cell responses (Mooij et al., 2008) . The adenovirus (Ad) vector is another of the most widely evaluated vectors to date to express heterologous antigens, due to ease of production, safety profile, genetic stability, the ease of DNA genome manipulation, and the ability to stimulate both innate and adaptive immune responses and induce both T and B cell responses (Alexander et al., 2012; Fitzgerald et al., 2003; Gabitzsch & Jones, 2011; Lasaro & Ertl, 2009; Vemula & Mittal, 2010; Weyer et al., 2009) . They have been extensively examined as a delivery vector in several preclinical and clinical studies for infectious diseases such as anthrax, hepatitis B, human immunodeficiency virus (HIV)-1, influenza, measles, severe acute respiratory syndrome (SARS), malaria and tuberculosis M. Saxena and others (Chengalvala et al., 1994; Gao et al., 2006; Hashimoto et al., 2005; Hsu et al., 1992; Limbach & Richie, 2009; Radosevic et al., 2007; Shiver et al., 2002) . However, before vectored vaccines can be used in the human population they need to satisfy several important criteria. Safety is a major concern, as even a low level of toxicity is unacceptable (of course the minor discomfort that accompanies many vaccinations is normal). Secondly, a vaccine should be inexpensive, so that it can be administered to a large population at minimal cost, and this is particularly important in resource-poor countries (Killeen & DiRita, 2000) . Similar constraints apply to veterinary vaccines, with cost often an even more important consideration. Finally, long-lasting cellular and (where appropriate) humoral immune responses to the vectored antigen must be induced following administration of these vaccines, preferably with a single dose (Atkins et al., 2006) . As some of the vectors in use will have been seen by the host immune system prior to vaccination, whether the presence of pre-existing immune responses is detrimental for the further development of a vector-based vaccine scheme, or can augment responses to the vectored antigen, needs to be considered in detail. This is the subject of this review. In discussing the possible effects on pre-existing immunity, the natural immunity to the vector needs to be considered. Therefore, considering a vector such as Salmonella, if a host has previously been infected there will exist robust B and T memory responses, and as such, when a vaccination is delivered, an anamnestic response to the Salmonella antigens will be induced (while the response to the vectored antigen will be a primary response). This will theoretically reduce the exposure of the heterologous antigen to the immune system, as the vector is rapidly cleared. Surprisingly, as will be seen in some of the examples given below, this can have results that differ depending on the magnitude of the response to the vectored antigen. Similarly, for virally vectored antigens, the existence of pre-existing immunity to the vector (particularly neutralizing antibody) will restrict delivery of the virus into cells, thereby effectively reducing the dose of the vectored antigen. Again, this might be expected to result in a reduction in the antigenicity of the vectored antigen. In the case of bacterial vectors, the effect of pre-existing immune responses has only been tested using Salmonella serovars and Listeria spp. Concern that prior immunological experience of the host with either the homologous Salmonella vector strain or a related strain might compromise its ability to deliver heterologous vaccine antigen was first raised in 1987 (Dougan et al., 1987) . Bao and Clements subsequently reported experimental evidence of the consequences of prior exposure of animals to the vector strain (Bao & Clements, 1991) . This work showed that both serum and mucosal antibody responses against the foreign antigen were in fact upregulated in animals with prior exposure to the vector strain. Whittle & Verma (1997) reported similar findings. Mice immunized via the intra-peritoneal route with a Salmonella dublin aroA mutant expressing heterologous antigen after being exposed to the same vector showed a higher immune response to the vectored antigen in comparison to mice without any immunological memory against the vector. Subsequently, several studies have been conducted to examine the effect of pre-existing immunity in the host against Salmonella. These results are summarized in Table 1 . The various reports are contradictory in their findings and seem to paint a rather confusing picture. Some studies concluded that pre-existing immunity against the Salmonella vector leads to stronger immune responses against the delivered antigen (Bao & Clements, 1991; Jespersgaard et al., 2001; Kohler et al., 2000a, b; Metzger et al., 2004; Saxena et al., 2009; Sevil Domènech et al., 2008; Whittle & Verma, 1997) , with others considering pre-existing immunity to be a limiting factor in the long-term use of Salmonella as an efficient vector for antigen delivery (Attridge et al., 1997; Gahan et al., 2008; Roberts et al., 1999; Sevil Domènech et al., 2007; Vindurampulle & Attridge, 2003a, b) . A slight majority of the studies listed in Table 1 (10 versus eight) indicate the upregulation of immune responses after animals have been exposed to either homologous or related strains before the delivery of heterologous antigen using a Salmonella vector. A study by Metzger and co-workers on human volunteers using Salmonella Typhi as a vector suggested that there was no change in the T cell immune response against the heterologous antigen in human volunteers who were exposed to empty vector in comparison with volunteers who were immunologically naive of the vector strain (Metzger et al., 2004) . In these subjects, humoral responses were moderately elevated in preexposed individuals. Similarly, Saxena et al. (2009) indicated higher humoral and T cell responses in mice pre-exposed to homologous or heterologous Salmonella strains. The interleukin 4 (IL4) response was significantly higher when the animal host was exposed to the homologous strain, whereas pre-exposure to a related species did not have such an impact on IL4 responses. Conversely interferon (IFN)-c responses were higher, irrespective of the strain to which mice were pre-exposed. This study also indicated that the presence of homologous or heterologous opsonizing antibodies leads to a higher uptake of Salmonella by macrophages in vitro, which may explain the higher immune responses in exposed mice. As may be expected, uptake was higher when homologous sera were used as the opsonin rather than heterologous sera. This is depicted in Fig. 2 . Conversely, there are reports that indicate that pre-existing immunity against the bacterial vector downregulates immune responses against the delivered heterologous antigen using similar or related vectors. Attridge and coworkers reported that the presence of immunity against the bacterial vector prior to the delivery of vectored antigenic Microbiology 159 protein can downregulate immune responses in mice against the delivered antigen (Attridge et al., 1997) . Similar results were reported by Roberts et al. (1999) and Vindurampulle & Attridge (2003a, b) . However, the latter authors found that the hypo-responsiveness could be largely eliminated by exposing animals to the foreign antigen prior to vectorpriming (Vindurampulle & Attridge, 2003b) . Unfortunately, this would appear to be impractical for an immunization regimen! A study presented by Gahan et al. (2008) immunized mice with S. Typhimurium expressing C fragment of tetanus toxin antigen from an expression plasmid or as a DNA vaccine. Vaccinated mice developed humoral responses to LPS and tetC (for the plasmid-bearing vaccines). Animals from all groups (including a previously unvaccinated group) were immunized on day 182 with Salmonella expressing tetC. At this time, the anti-LPS and tetC titres were beginning to wane. Fourteen days after the second immunization, the colonization of various mouse organs was assessed. The ability to colonize was found to be significantly reduced in groups that had been previously vaccinated with Salmonella. In view of this finding, it was perhaps not surprising that at day 210 the LPS titres were not significantly different between groups receiving one or two vaccinations. More interestingly, mice that had been primed with Salmonella alone, and then boosted with Salmonella expressing tetC, induced much lower anti-tetC responses than mice that had not been primed. This argues strongly that prior immunological immunity to the vector can seriously dampen subsequent antigen-specific humoral responses. Whether the same is true for cellular responses was not evaluated. Other studies have evaluated cellular responses. A study by Sevil Domènech and colleagues reported that pre-existing anti-vector immunity seriously compromises CD8 + responses in mice when exposed to a similar strain used as vector (Sevil Domènech et al., 2007) . In contrast, another study by the same authors reported that animals exposed to related vectors induce much higher CD8 + responses when compared with animals which do not have any pre-existing Salmonella immunity (Sevil Domènech et al., 2008) . The difference between these two studies was that in the first, the prime and boost were with identical serovars, while in the second study, different serovars were used. This may point to a way of avoiding downregulation of CD8 responses by pre-existing immunity. This is important, as one of the advantages of using Salmonella (an intracellular pathogen) is that strong cellular immune responses can be induced. It must be noted that in the case of Salmonella vaccines, effects other than strictly immunological responses (particularly adaptive responses) should be considered. In the context of innate immunity, it was shown that administration of non-virulent Salmonella to gnobiotic pigs eliminated disease following challenge with a virulent strain (Foster et al., 2003) . Interestingly, protection was not by competitive exclusion, as the virulent strain was in high numbers in the gut but did not distribute systemically. The protection was proposed to be mediated by the infiltration of a large number of polymorphonuclear leukocytes into the gut, and although perhaps impractical as a general prophylactic (as the time between vaccination and infection is short), this may be an option for short-term or perhaps therapeutic vaccination (as reviewed by Foster et al., 2012) . Chickens (Gallus gallus) are a natural animal reservoir for Salmonella, which makes them an important source of Salmonella-associated gastroenteritis in humans. The ability to use oral Salmonella vaccines to immunize against heterologous pathogens would be of enormous benefit to Uptake of STM-1 by J774 macrophages, relative to the highest uptake percentage. X, Opsonized with naive sera; m, opsonized with serum from mice exposed to Salmonella enteriditis; &, opsonized with serum from mice exposed to STM-1. Pre-existing immunity against vaccine vectors the poultry industry in both broiler and layer flocks. Both vertical and horizontal transmission is associated with Salmonella in chickens (Liljebjelke et al., 2005) . Vertical transmission via in ovo transmission is particularly important, because if there is prior exposure to the vaccine strain, subsequent vaccination using an oral Salmonella vector could be severely compromised. A considerable number of studies on cross-protective immunity and competitive exclusion have been undertaken in chickens. Protective cross-reactive immunity against Salmonella strains has been demonstrated against both homologous and heterologous challenges (Beal et al., 2006) , although cross-serogroup protection was not strong. Furthermore, a recent study reported that pretreatment of newly hatched chickens with different Salmonella strains could produce a complete invasioninhibition effect on any subsequent exposure to both homologous and heterologous strains (Methner et al., 2010) . Pre-exposure with a highly invasive form of Salmonella Enteritidis caused a large influx of heterophils to the caecal mucosa in 1-day-old chicks, and subsequent heterologous caecal colonization was inhibited for a period of 48 h (Methner et al., 2010) . The implications of this kind of colonization-inhibition study on the immunological status of the affected chickens are yet to be fully elucidated. It should be noted that the studies listed in Tables 1 and 2 are controlled laboratory studies, with the possibility of a competitive exclusion component to immunity not discussed. Similarly studies of L. monocytogenes and the effects of preexisting immune responses indicate conflicting results. A study by Bouwer et al. (1999) indicates that pre-existing immune responses against the Listeria vector do not diminish immune responses against the delivered heterologous antigen, and a similar study by Starks et al. (2004) also concluded that prior exposure of mice to the empty Listeria vector did not influence anti-cancer immune responses when a similar mutant was used as a carrier of a melanoma cancer antigen. Similar findings were reported by Whitney et al. (2011) in rhesus macaques in which L. monocytyogens was used as a carrier of gag-HIV antigen. Conversely, studies by Stevens et al. (2005) in which L. monocytogens was used to deliver feline immunodeficiency virus (FIV) gag protein and as a carrier of DNA vaccines to vaccinate cats against FIV envelope protein indicated lower immune responses against the delivered antigen in cats exposed to empty Listeria vector in comparison with naive animals (Stevens et al., 2005) . Similar findings have been reported by Tvinnereim et al. (2002) and Leong et al. (2009) . However, taken together, these studies conclude that prior exposure of host animals to empty vector does not abrogate immune responses to the vectored antigen, but only reduces them somewhat. Only the study by Vijh et al. (1999) indicated that exposure to the empty vector may completely abrogate immune responses against the delivered antigens (Vijh et al., 1999) . However, these studies also indicate that downregulation of antigenspecific immune responses is highly dependent on dose and time. Leong et al. (2009) also demonstrated that the negative impact of vector-specific immune responses can also be countered by repeated immunization with the same vaccine and dose; this in effect leads to higher priming of naive T cells against the delivered antigen. Of course, such repeated vaccination may not be practicable in real-world situations. Despite the many advantages which viral vectoring can offer, pre-existing immunity is a major obstacle of many viralvectored vaccines, such as Ad serotype 5 or herpes simplex virus type 1 (HSV-1), where the rate of seroprevalence to these viruses is very high [40-45 % and 70 % (or more) of the US population, respectively] (Hocknell et al., 2002; Pichla-Gollon et al., 2009) . Vector-specific antibodies may impede the induction of immune responses to the vaccine-encoded antigens, as they may reduce the dose and time of exposure of the target cells to the vaccinated antigens (Pichla-Gollon et al., 2009; Pine et al., 2011) . In a large-scale clinical trial (STEP) of an Ad serotype 5 (AdHu5)-based HIV-1 vaccine, the vaccines showed a lack of efficacy and tended to increase the risk of HIV-1 infection in vaccine recipients who had pre-existing neutralizing antibodies to AdHu5 (Buchbinder et al., 2008) . For an HSV-1-based vector vaccine, it has been demonstrated that pre-existing anti-HSV-1 immunity reduced, but did not abolish, humoral and cellular immune responses against the vaccine-encoded antigen (Hocknell et al., 2002; Lauterbach et al., 2005) . However, Brockman and Knipe found that the induction of durable antibody responses and cellular proliferative responses to HSVencoded antigen were not affected by prior HSV immunity (Brockman & Knipe, 2002) . Similarly, pre-existing immunity to poliovirus has little effect on vaccine efficacy in a poliovirus-vectored vaccine (Mandl et al., 2001) . Different effects of pre-existing immunity on the efficacy of recombinant viral vaccine vectors are summarized in Table 2 . There are several approaches to avoiding pre-existing vector immunity, such as the use of vectors derived from nonhuman sources, using human viruses of rare serotypes (Kahl et al., 2010; Lasaro & Ertl, 2009) , heterologous prime-boost approaches (Liu et al., 2008) , homologous reimmunization (Steffensen et al., 2012) and removing key neutralizing epitopes on the surface of viral capsid proteins (Gabitzsch & Jones, 2011; Roberts et al., 2006) . The inhibitory effect of pre-existing immunity can also be avoided by masking the Ad vector inside dendritic cells (DCs) (Steffensen et al., 2012) . In addition, mucosal vaccination or administration of higher vaccine doses can overcome pre-existing immunity problems (Alexander et al., 2012; Belyakov et al., 1999; Priddy et al., 2008; Xiang et al., 2003) . As we search for new vaccine approaches for the array of pathogens for which none is yet available, revisiting proven vaccines and developing these further has gained M. Saxena and others momentum. Hence, attenuated bacteria and viruses which have a long history of efficacy and safety are being brought into use. While very attractive, a common theme in these experimental approaches has been the limitations that preexisting immunity to the vector may pose. However, as this examination of the relevant literature shows, there is a rather confusing picture, with some studies in fact indicating that pre-existing immunity may be a friend, rather than foe. Few studies using viral vectors have reported on the influence of pre-existing immunity on humoral responses. Generally speaking, for bacterial-delivered antigens, the humoral responses were influenced by pre-existing immunity, with slightly more studies finding augmentation rather than diminution. Why is there variation? This may be due to several factors, including the type of Salmonella used and its invasiveness. Dunstan and colleagues tested the ability of six isogenic Salmonella serovar Typhimurium strains harbouring different mutations for their ability to induce immune responses against the C fragment of tetanus toxin and concluded that the strain which had the least ability to colonize Peyer's patches induced the lowest immune responses (Dunstan et al., 1998) . Similarly, the boosting time and nature of the antigen used might be important. Attridge and colleagues indicated the importance of boosting time. In one experiment, boosting mice at 10 weeks led to complete inhibition of antibody responses against the delivered heterologous antigen; however, when the mice were boosted at 4 weeks, the downregulation of antibody responses was not so prominent (Attridge et al., 1997) . A similar study conducted by Kohlers and colleagues shows that boosting at 7 weeks after pre-exposing animals to empty vector leads to lower antigen-specific IgG and secretory IgA responses; however, boosting at 14 weeks leads to higher IgG and secretory IgA responses (Kohler et al., 2000b) . This is in conflict with the above result, although it should be mentioned that they used different Salmonella species. Vindurampulle and Attridge also examined the impact of the Salmonella strain and the nature of the antigens used. In their study, they used S. Dublin and Salmonella Stanley aroA mutants to deliver E. coli K88 and LT-B antigens, and concluded that the effect of pre-existing immunity depends on both the strain used and the type of antigen delivered (Vindurampulle & Attridge, 2003b) . All these studies on the effect of pre-existing immunity discuss the impact on humoral responses. Sevil Domenech and colleagues reported that pre-exposing animals to the homologous Salmonella vector leads to a significant reduction in CD8 + responses; however, exposure of animals to a heterologous strain leads to significantly higher CD8 + responses (Sevil Domènech et al., 2007 , 2008 . Saxena and colleagues also reported that antigenspecific T cell responses were either similar or significantly higher, with no downregulation in T cell responses observed after pre-exposing mice to either homologous or heterologous strains (Saxena et al., 2009) . For viral vectors, the impact of cell-mediated immunity was more pronounced, and as depicted in Table 2 , almost always resulted in a reduction in the subsequent immune response. Presumably this is because viruses will induce neutralizing antibody on the first dose, and in subsequent doses this antibody will limit the number of transduced cells, therefore limiting the responses. This is particularly a problem with a common viral vector such as Ad, where a large proportion of the population will have immunological memory against common serotypes (Lasaro & Ertl, 2009) . As these authors conclude, it will be possible to utilize such vectors only by developing vaccines from alternative serotypes. It may be that a vector such as Pre-existing immunity against vaccine vectors attenuated influenza virus, with the ability to easily develop reassortants, will be useful in this context. In addition, immunological memory in the form of opsonizing antibody certainly plays an important role in the early uptake of Salmonella by macrophages and DC. This may be beneficial, as the live bacterial vector used for delivery purposes harbours mutations in genes encoding proteins responsible for their survival in the animal host. This not only encumbers their ability to cause disease, making them safe live vectors, but also limits the number of replications. The presence of opsonizing antibodies should mean a higher level of bacterial uptake, leading to higher presentation to the immune system and therefore a better immune response. We have previously shown that this is indeed the case (Saxena et al., 2009 ) (depicted in Fig. 2 ). It would be of great benefit to address these issues not only in mice but also in other organisms such as chickens, which are the most likely host to be targeted for the use of live Salmonella vectors, specifically where the vaccines are developed for use in livestock and poultry. To summarize, bacterial vectors such as Salmonella and viral vectors such as Ad show great promise as delivery vehicles for heterologous antigens; however, prior exposure to the vector must be considered. By judicious selection of the strain/serotype it will be possible to avoid the negative effects and it may indeed be possible to positively influence the response, particularly for humoral immunity.

What are methods to avoid the effect of vector immune response on the efficacy of vaccination?

A missense mutation in Katnal1 underlies behavioural, neurological and ciliary anomalies https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5761721/ SHA: f4cebabd74b16e710fb41a737d8ef84b7d565d8d Authors: Banks, G; Lassi, G; Hoerder-Suabedissen, A; Tinarelli, F; Simon, M M; Wilcox, A; Lau, P; Lawson, T N; Johnson, S; Rutman, A; Sweeting, M; Chesham, J E; Barnard, A R; Horner, N; Westerberg, H; Smith, L B; Molnár, Z; Hastings, M H; Hirst, R A; Tucci, V; Nolan, P M Date: 2017-04-04 DOI: 10.1038/mp.2017.54 License: cc-by Abstract: Microtubule severing enzymes implement a diverse range of tissue-specific molecular functions throughout development and into adulthood. Although microtubule severing is fundamental to many dynamic neural processes, little is known regarding the role of the family member Katanin p60 subunit A-like 1, KATNAL1, in central nervous system (CNS) function. Recent studies reporting that microdeletions incorporating the KATNAL1 locus in humans result in intellectual disability and microcephaly suggest that KATNAL1 may play a prominent role in the CNS; however, such associations lack the functional data required to highlight potential mechanisms which link the gene to disease symptoms. Here we identify and characterise a mouse line carrying a loss of function allele in Katnal1. We show that mutants express behavioural deficits including in circadian rhythms, sleep, anxiety and learning/memory. Furthermore, in the brains of Katnal1 mutant mice we reveal numerous morphological abnormalities and defects in neuronal migration and morphology. Furthermore we demonstrate defects in the motile cilia of the ventricular ependymal cells of mutants, suggesting a role for Katnal1 in the development of ciliary function. We believe the data we present here are the first to associate KATNAL1 with such phenotypes, demonstrating that the protein plays keys roles in a number of processes integral to the development of neuronal function and behaviour. Text: Microtubule severing enzymes are a family of AAA-ATPase proteins that participate in fundamental cellular processes such as mitosis, ciliary biogenesis and growth cone motility. In neurons this family is known to control such processes as axonal elongation 1 and synaptic development. 2 In addition, mutations in microtubule severing enzyme genes SPG4, KATNB1 and KATNAL2 are associated with hereditary spastic paraplegia, cerebral malformations and autism, respectively, [3] [4] [5] [6] and mutations in Fign cause a range of phenotypes in mice. 7 Currently the microtubule severing enzyme KATNAL1 is poorly characterised and it is not yet understood how the enzyme functions in the nervous system. Recent evidence from genetic characterisation of human patients suggests that haploinsufficiency of KATNAL1 is linked with a number of symptoms including intellectual disability (ID) and craniofacial dysmorphologies. 8, 9 It is also notable that a very rare KATNAL1 mutation has been associated with schizophrenia 10 (http://atgu.mgh.harvard.edu/~spurcell/genebook/gene book.cgi?user = guest&cmd = verb-gene&tbox = KATNAL1) and that Peters syndrome and autism have both been associated with the chromosomal region containing the KATNAL1 locus. 11, 12 Although such association studies strongly suggest that KATNAL1 plays a fundamental role in the central nervous system (CNS), additional studies using cellular or animals models are required to understand how the gene may be causative for disease. Here we present the first study describing neural and behavioural deficits associated with a loss of function allele of Katnal1 in the mouse. This mutant mouse line was independently identified in two parallel phenotyping screens, which demonstrated that mutant mice showed both male sterility and circadian phenotypes. Subsequent behavioural investigations demonstrated that this mutation is associated with anxiety and memory deficits. Underlying these behavioural phenotypes, we identified histopathological abnormalities in the brains of Katnal1 1H/1H mutants, including disordered cellular layers in the hippocampus and cortex and substantially larger ventricles. Further investigations demonstrated that Katnal1 1H/1H mice show neuronal migration and ciliary function deficits suggesting KATNAL1 plays an essential role in these processes. These findings are the first to our knowledge to conclusively show that mutations in Katnal1 lead to behavioural and neuronal disturbances and provide insight regarding the clinical associations that have been linked to the gene. performed on mouse cohorts that were partially or completely congenic on the C57BL/6 J background. Circadian wheel running was performed as previously described. 14 Sleep assessment by electroencephalography and electromyography Electroencephalography and electromyography was performed as previously described. 15 Behavioural phenotyping Spontaneous alternation. Mice were placed in a walled T-maze (black polyvinyl chloride, lined with sawdust; stem = 88 × 13 cm; arms = 32 × 13 cm) and allowed to enter a goal arm of their choice. The mouse was confined in the goal arm for 30 s, before being allowed a second free choice of goal arm. An alternation was recorded if the second choice differed from that of the first. One trial was performed per day for 10 days. Open field behaviour. Mice were placed into a walled arena (grey polyvinyl chloride; 45 × 45 cm) and allowed to explore for 20 min. Animals were monitored by EthoVision XT analysis software (Noldus, Wageningen, Netherlands). Video tracking in the home cage. Activity in the home cage was recorded by video tracking as previously described. 16 Morris water maze and ultrasonic vocalisation. These tests were performed as previously described. 17 Brain histology and immunofluorescence Brains were mounted in OCT (VWR) and 12 μm coronal sections taken. Sections were stained with hematoxylin and eosin, or immunolabelled following standard protocols. In vivo neuronal migration assessment was performed as previously described 18 using embryos at either E13 or E15 (three mothers per age group) and pups at P9. Cell counts were performed using ImageJ (NIH, Bethesda, MD, USA). In vitro neuronal migration assessment was performed using a Boyden chamber migration protocol as previously described. 19 Micro-computed tomography scanning Micro-computed tomography was performed using a Skyscan 1172 at 90 kV, 112 μA using an aluminium and copper filter, a rotation step of 0.250 degrees and a pixel size of 4.96 μm. Segmentation, volume calculation and 3D modelling was performed using ITK-SNAP version 3.0.0 (ref. 20) and 3DSlicer. 21 Golgi-Cox staining of neurons Golgi-Cox neuronal staining was performed using the FD Rapid GolgiStain Kit (FD NeuroTechnologies, Columbia, MD, USA). Neurons were analysed using ImageJ. Brains from P2 mice were dissected, and the dorsal cerebral half was sectioned (250 μm) through the floor of the lateral and 3rd ventricle using a vibratome. Ciliary beat frequency and pattern was analysed as previously described. 22 Electron microscopy For Scanning Electron Microscopy the ependymal lining of the lateral ventricle was fixed in 2.5% glutaraldehyde, 2% paraformaldehyde in 0.1 M phosphate buffer, incubated in 2% osmium tetroxide, and dehydrated through ethanol solutions. Samples were critical point dried using an Emitech K850 (Quorum Technologies, East Sussex, UK), coated with platinum using a Quorom Q150R S sputter coater (Quorum Technologies). and visualised using a JEOL LSM-6010 scanning electron microscope (Jeol, Herts, UK). Transmission electron microscopy was performed as previously described. 22 Statistical analysis Data was analysed using two-tailed students T test or AVOVA using SPSS (IBM) or GraphPad Prism 5.0 (GraphPad Software, La Jolla, CA, USA). Significance level for all analysis was set at Po 0.05. All graphs are presented showing mean ± s.e.m. Additional and more detailed methods can be found in supplementary information. Identification and cloning of the Katnal1 1H mutation To identify novel gene mutations affecting circadian behaviour we undertook a circadian running wheel screen of pedigrees of N-ethyl-N-nitrosourea mutagenised mice. 13 In one pedigree 17.65% of animals showed a short circadian period in constant darkness (o 23 h observed in 12 out of 68 animals screened). An outcross using an affected female produced no affected animals (33 animals screened). In subsequent intercross screens 15.5% of animals were affected (53 out of 342 animals screened), suggesting that the pedigree carries a mutation causing a recessive circadian phenotype which is 60% penetrant. We found no gender bias in affected animals (proportion of affected animals: male = 47.2%; female = 52.8%). Concurrently a male sterility phenotype was identified within the same pedigree. 23 Genome-wide SNP linkage analysis mapped the circadian and sterility phenotypes to the same region on chromosome 5 and subsequent sequencing identified the causative mutation as a T to G single point mutation within exon seven of the Katnal1 gene. For full details of mapping and identification of the mutation see reference 23. This mutant allele was designated Katnal1 1H , and results in a leucine to valine substitution at residue 286 of the protein. In vitro functional analysis demonstrated that the mutation is a recessive loss-offunction allele. 23 3D modelling of the protein suggests that this loss of function is due to hydrophobic changes in the AAA domain of the enzyme (Supplementary Figure S1 ). Genotyping confirmed that the mutation was homozygous in affected circadian animals and wild type or heterozygous in unaffected animals, confirming that Katnal1 1H was causative for the circadian phenotype. Circadian and sleep anomalies in Katnal1 1H/1H mice More extensive circadian phenotyping conducted on Katnal1 homozygotes (Katnal1 1H/1H ) and wild-type littermates (Katnal1 +/+ ) confirmed that Katnal1 1H/1H mice had a shorter free-running circadian period (Figures 1a-c) and furthermore revealed that Katnal1 1H/1H animals were more active in the light phase of the light/dark cycle (Figure 1d ), showed increased anticipation of light to dark transitions and greater shift in activity onset when released from light/dark cycles to constant darkness ( Figure 1e ). Data and cohort details are given in Supplementary Table S1 . Bioluminescence recordings performed using PER2::LUCIFERASE reporter mice carrying the Katnal1 1H mutation revealed that these circadian changes were not due to changes to the core molecular clock of the suprachiasmatic nucleus (the site of the master circadian clock in the brain; Supplementary Figure S2 ). Circadian disruptions are often associated with deficits in sleep homeostasis. Therefore to complement our circadian studies we conducted wireless electroencephalography recordings over a baseline period of 24 h and following a 6 h period of sleep deprivation. A detailed summary of electroencephalography analysis is given in Supplementary Table S1. Compared to wildtype littermates, the non-REM delta power of Katnal1 1H/1H mice was higher in the dark phase of baseline sleep (mixed ANOVA, interaction factors 'genotype X time, F(1,88) = 8.91, P = 0.0175) ( Figure 1f ) and in both the light and dark phases of recovery sleep (mixed ANOVA, interaction factors 'genotype X time', F(1,136) = 11.93, P = 0.0086; Figure 1g ). All other sleep parameters were unaffected in Katnal1 1H/1H animals. Katnal1 1H/1H mice display a spectrum of behavioural deficits Human patients carrying a heterozygous deletion incorporating the Katnal1 locus show a number of cognitive deficits including ID and a delay in language acquisition. 8, 9 We therefore investigated whether these deficits were modelled in Katnal1 1H/1H mice by subjecting animal cohorts to a battery of behavioural tests. Data and cohort details are given in Supplementary Table S2 . Both working memory and spatial memory were significantly poorer in Katnal1 1H/1H mice, as evidenced by reduced spontaneous alternations in a T-maze ( Figure 2a ) and in the Morris water maze where mutants take longer to find the platform in acquisition trials (Figure 2b Compared to wild-type littermates, Katnal1 1H/1H animals have a shorter period (c), are more active in the light phase of the light/dark cycle (d) and show an earlier onset of activity in light/dark transitions and in the transition from light/dark cycles to constant darkness (e). In EEG recordings during sleep, Katnal1 1H/1H mice show increased non-REM delta power in the dark phase of the light/dark cycle (f) and following sleep deprivation (g). *P ⩽ 0.05; **P ⩽ 0.01; ***P ⩽ 0.001. EEG, electroencephalography; DD, constant darkness; LD, light/dark cycle. type = 164 ± 12 m, Katnal1 1H/1H = 243 ± 20 m, P = 0.02; distance travelled in periphery of open field: wild type = 4.3 ± 0.2 m, Katnal1 1H/1H = 6 ± 0.3 m, P = 0.004). Conversely when mouse activity was recorded in the home cage, we found no difference between genotypes (distance travelled over 24 h: wild type = 399 ± 77 m, Katnal1 1H/1H = 418 ± 41 m, P = 0.833) suggesting that the former activity differences were due to the novel environment of the open field rather than generalised hyperactivity in Katnal1 1H/1H animals. Finally, in certain conditions (such as maternal separation) mice emit ultrasonic vocalisations (USVs). To test whether Katnal1 1H/1H animals vocalised differently to wild types, we separated pups at postnatal days 7-8 (the age at which mice show peak of USV emission 24 ) and recorded their USVs. In these tests, compared to wild types, Katnal1 1H/1H pups produced fewer ( Figure 2g ) and shorter (Figure 2h ) vocalisations, containing fewer phrases (Figure 2i ). Gross brain morphological abnormalities in Katnal1 1H/1H mice Since we observed a number of behavioural phenotypes in Katnal1 1H/1H mice, we performed histological analysis to ascertain whether differences in brain histology underlied these behaviours. Data and cohort details are given in Supplementary Table S3 . Analysis of hematoxylin and eosin stained brain sections revealed that, compared to wildtype littermates, Katnal1 1H/1H animals had less tightly packed pyramidal cell layers in the hippocampus (Figures 3a and b) and a narrower cortical layer 1 and wider cortical layer 6 (Figures 3c-e) . To confirm these cortical layer differences, immunofluorescence was performed using the (Figures 3l and m) . Quantification of fluorescence intensity demonstrated that in Katnal1 1H/1H cortex both calbindin and CUX1 labelling was more intense closer to the cortical surface, which is consistent with the reduction in the size of layer 1 (two-way analysis of variance (ANOVA), interaction factors 'genotype X distance of fluorescence from cortical surface', calbindin: F(75,988) = 16.8, P o 0.0005; CUX1: F(93,372 = 2.17, P = 0.001; Figures 3h and k) . Similar quantification revealed that FOXP2 labelling extended further from layer 6b (as labelled by CTGF) in the Katnal1 1H/1H cortex, which is consistent with an increase in the size of layer 6 (two-way ANOVA, interaction factors 'genotype X distance of fluorescence from CTGF labelling:' F(93,372) = 1.32, P = 0.038; Figure 3n ). Finally, three dimensional models of the ventricular system were constructed from brain micro-computed tomography scans (Figures 3o and p) . Volumetric analysis revealed that Katnal1 1H/1H mice had substantially larger ventricles than wild types (Figure 3q ). Neuronal migration and morphology defects in Katnal1 1H/1H brains The histological phenotypes of Katnal1 1H/1H mouse brains described above are suggestive of neuronal migration defects. 18 We therefore investigated whether Katnal1 1H/1H mice showed abnormal neuronal migration using BrdU labelling of E13 and E15 embryos and quantified labelled cells in the cortex of P9 pups (described in reference 18). At both ages Katnal1 1H/1H animals had greater numbers of labelled neurons in bins close to the cortical surface neurons positioned closer to the cortical surface compared to wild type. To confirm these results we used a Boyden chamber 19 and performed in vitro neuronal migration analysis in E13.5 primary cortical neuronal cultures. Here we found that a greater proportion of Katnal1 1H/1H cortical neurons migrated to the base of the cell culture insert compared to wildtype controls (Supplementary Figure S3) . Since in both BrdU labelling and the Boyden assay neurons from Katnal1 1H/1H animals migrated further than those of wild-type littermates, these results suggest that Katnal1 1H/1H cortical neurons show defects in the termination of cortical neuronal migration. Given its role in cytoskeletal organisation, we also hypothesised that neuronal morphology is modulated by Katnal1. Analysis of golgi stained neurons from layers 2-3 of the cortex (Figures 4g and i) demonstrated that, compared to wild-type littermates, Katnal1 1H/1H neurons had larger soma (Figure 4k) , and shorter and thinner axons (Figures 4l and m) (data and cohort details are given in Supplementary Table S3 ). Furthermore, analysis at higher magnification (Figures 4h and j) , demonstrated that the number of synaptic spines on Katnal1 1H/1H neurons was significantly reduced compared to wild type (Figure 4n ). Recent studies have demonstrated that mutations in some microtubule severing enzymes can cause defects in cilia. 5 Since such ciliary defects could underlie the phenotypes described above we studied the motile cilia of the ependymal lining of the lateral ventricle in sections of postnatal day 2 mouse brains from both Katnal1 1H/1H (n = 4) and wild-type animals (n = 3). We found that the ciliary beat frequency (CBF) of Katnal1 1H/1H animals was significantly attenuated compared to wild-type (CBF: wildtype = 22.39 ± 0.94 Hz, Katnal1 1H/1H = 14.25 ± 0.92 Hz, P = 0.0001; Figure 5a , Supplementary Movies S1). This reduction in CBF in Katnal1 1H/1H animals was also associated with an increased proportion of cilia with an abnormal beat pattern (ciliary dyskinesia) (proportion of dyskinetic cilia: wild type = 17%, Katnal1 1H/1H = 75%) (Figure 5b and Supplementary Movies S1). Visual inspection of the cilia identified a number of ciliary abnormalities such as a swollen ciliary tip (Supplementary Movie S3) or extremely long cilia (Supplementary Movie S4) scattered throughout the field of cilia in Katnal1 1H/1H ventricles. These abnormalities were observed in approximately 25% of Katnal1 1H/1H brain slices. The abnormal cilia always showed a dyskinetic beat pattern and lower beat frequency. To further investigate ciliary morphology we performed scanning electron microscopy upon the ependymal lining of the lateral ventricles of both Katnal1 1H/1H (n = 3) and wild-type animals (n = 3; Figures 5c and d) . Cilia measurements showed no significant differences in average cilia length between genotypes (average cilia length: wild type = 6.22 ± 0.86 μm, Katnal1 1H/1H = 6.54 ± 0.94 Hz, P = 0.303). However in Katnal1 1H/1H samples we noted the presence of both long and short cilia (Figures 5e and f ; defined as two standard deviations longer or shorter than the average cilia length) that were not present in wild-type samples. In addition, inspection of Katnal1 1H/1H cilia identified ciliary abnormalities including bifurcated cilia (Figure 5g) , abnormal kinks and bends in the cilia (Figure 5h ) and swellings along the length of the cilia (Figure 5i ). Transmission electron microscopy of ependymal cilia found that vesicular aggre- Katnal1 disruption affects CNS functions G Banks et al gates were present within the ciliary swellings described above (Figure 5j ). Although these abnormalities were present in only a small proportion (o1%) of Katnal1 1H/1H cilia, they were notably absent from wild-type cilia. Microtubule severing enzymes play diverse roles in the nervous system. 1, 2 However, at present the microtubule severing enzyme Katnal1 is poorly defined in the context of CNS development and function. Here we present a detailed phenotypic analysis of Katnal1 1H and show that the mutation is associated with changes in circadian rhythms, sleep and behaviour. Furthermore we demonstrate that defects in brain histopathology, neuronal migration and neuronal morphology underlie these phenotypes. Finally we also demonstrate that Katnal1 1H causes a range of defects in the motile cilia of ventricular ependymal cells. The data we present here are the first to associate KATNAL1 with such dysfunctions with important implications for clinical association studies. The Katnal1 1H mutation was initially identified with a circadian deficit including a short free-running period and advanced activity onset. However subsequent ex vivo experiments using SCN slices of animals carrying the PER2::LUC reporter gene demonstrated no defects in SCN cellular rhythms, suggesting that the core circadian clock was unperturbed by the mutation. Phenotypes in circadian running wheel rhythms that are not associated with changes to the core clock mechanism have also been reported in mouse models of schizophrenia. 25 Here it has been suggested that the wheel running changes observed are the result in defects in output pathways from the SCN circadian clock. Similarly, in Katnal1 1H/1H mice we hypothesise that the defects we demonstrate in neuronal anatomy and neuronal morphology may disrupt output signals from the SCN. Alternatively given that various neuropeptides such as oxytocin are secreted in a circadian manner from ependymal cells lining the third ventricle of the brain, 26 altered ventricular morphology and ciliary function in Katnal1 1H/1H mice may disrupt the circulation of factors secreted by the ciliated ventricular ependymal cells and contribute to the disruption of the behavioural rhythms observed. The behavioural consequences of microtubule severing enzyme dysfunction in mouse models have been poorly characterised. Currently the phenotypes described are limited to motor dysfunction in mice lacking the Spg4 gene 27 and head shaking and circling in the Fign mutant. 7, 28, 29 In contrast, here we demonstrate that loss of function of Katnal1 is associated with a range of behavioural phenotypes, including changes in circadian activity, poor learning and memory, hyperactivity in a novel environment (the open field) and deficits in USVs. Notably the learning and memory, anxiety and vocalisation phenotypes reprise the clinical symptoms of ID, increased anxiety in novel situations and delays in language acquisition reported in human patients who carry microdeletions incorporating haploinsufficiency of KATNAL1. 8, 9 While it is also worth noting that mutant mice spend more time the centre of the open field than wild types (implying that Katnal1 1H/1H animals show reduced anxiety), we suggest that this result is confounded by the hyperactivity in novel environments phenotype we also describe in mutant mice. This observation is backed up by the fact that mutant animals showed increased activity in all regions of the open field rather than just the anxiolytic periphery. Here we also highlight defects in Katnal1 1H/1H mice such as compromised neuronal migration and morphology which may underpin such phenotypes. In Drosophila, the homologue of Katnal1 (kat-60L1) has been demonstrated to play a critical role in neuronal morphology during development, 30 however the data that we present here is the first to demonstrate a similar phenotype in mammals and furthermore suggests how subtle perturbations to KATNAL1 function may contribute to specific neural and behavioural conditions. For example, defects in neuronal migration, synaptic spines and neuronal morphology such as those we have demonstrated here, have been suggested to underpin ID in conditions such as lissencephaly, 18 Down's syndrome 31 and Rett syndrome. 32 While we are not suggesting that Katnal1 is causative for these conditions, similarities in symptoms and neuronal phenotypes between these conditions and those linked to Katnal1 dysfunction should be appreciated. Furthermore a rare mutation in KATNAL1 has been associated with schizophrenia 10 (http://atgu.mgh.harvard.edu/~spurcell/gene book/genebook.cgi?user = guest&cmd = verb-gene&tbox = KATNAL1) and KATNAL1 has been shown to interact with the schizophrenia associated gene DISC1. 33 In line with these observations we note that increases in ventricular volume and reductions in synaptic spines have been reported in schizophrenic patients 34, 35 and our data demonstrates the same phenotypes in Katnal1 1H/1H mice. Thus the range of phenotypes associated with defects in the function of Katnal1 strongly suggests that the gene should be considered in the pathology of disorders such as ID and schizophrenia. We do note one key genetic difference between the human patients and Katnal1 1H/1H animals. While the human patients were all heterozygous for the Katnal1 deletion, we found no phenotype in heterozygous mutant mice (data not shown) suggesting that while haploinsufficiency is causative for phenotypes in humans, mice require complete loss of KATNAL1 function to show similar effects. A similar discrepancy between humans and mice has also been noted for the intellectual disability candidate gene CTNNB1. 17 While heterozygous loss of function mutations in CTNNB1 are causative for intellectual disability in humans, conditional knock outs for CTNNB1 have no reported behavioural or craniofacial phenotypes. 36, 37 These differences demonstrate that while mouse models of intellectual disability are of great use in our understanding of the causative mechanisms which underlie the condition, there are still genetic and neurodevelopmental differences between species which also must be taken into account. We also note that while the Katnal1 1H mutation shows a loss of catalytic function in both HEK293 cells and Sertoli cells, 23 this loss of function has not been verified in neuronal cells. However, given that our data demonstrates that the Katnal1 1H mutation lies in an essential catalytic domain and that we show neuronal phenotypes in Katnal1 1H/1H mice, we would expect to see the same loss of catalytic function in neurons. The data we present here also demonstrate defects in motile cilia in Katnal1 1H/1H mice. Ciliary disruptions in humans (ciliopathies) include Bardet-Biedl and Joubert syndrome. 38 While there is currently limited data available regarding the behavioural phenotypes of mouse models of ciliopathies, we note that ciliary dysfunction in mice has been linked with learning and memory 39 and vocalisation phenotypes, 40 both of which were disturbed in the Katnal1 1H/1H mice described here. It is also notable that the neuronal migration and enlarged ventricle phenotypes that we describe in Katnal1 1H/1H mice recapitulate features associated with known ciliopathy gene mutations. [41] [42] [43] [44] Furthermore in Bardet-Biedl syndrome mouse models ciliary defects such as reduced CBF 45 and structural defects such as abnormal lengthening and swellings along their length 41 have been described, that are similar to those we describe in Katnal1 1H/1H mice. There is strong evidence that ciliopathy associated genes play a number of roles in neuronal development by affecting processes such as progenitor proliferation or maintenance of the radial glia scaffold. 43 However it is also clear that defects in microtubule organisation also affect synaptic structure. 2 At present it is difficult to disentangle the relative contributions of defects in microtubule severing and ciliary abnormalities to the overall phenotypes we observe in Katnal1 1H/1H mice. Further investigations are required to clarify the impacts of these two processes. However it is notable that while defects in cilia structure may contribute to the phenotypes we describe in Katnal1 1H/1H mice, they are far less prominent in Katnal1 1H/1H mice than in other mouse ciliopathy models, 41 suggesting that the ciliary component of KATNAL1 dysfunction may be mild compared to other ciliopathies. Similarly while hydrocephalus has been suggested to be a component of some ciliopathy mouse models, 46 Katnal1 1H/1H mice showed only increased ventricle size rather than an increased incidence of hydrocephalus, further suggesting the ciliary defects in these animals are mild compared to other ciliopathies. In summary the data presented here clearly demonstrate that KATNAL1 plays an important role in a variety of neuronal processes including neuronal migration, neuronal morphology and ependymal ciliary function. The downstream effect of these defects leads in turn to a number of behavioural changes including in learning and memory, reaction to anxiogenic situations and circadian rhythms. These data therefore highlight how perturbations in KATNAL1 may play a role in neuronal dysfunction and demonstrates that the enzyme is a novel candidate in the study of behavioural and neurodevelopmental disorders. The authors declare no conflict of interest.

What genetic mutation is associated with hereditary spastic paraplegia?

Severe Acute Respiratory Syndrome Coronavirus Viroporin 3a Activates the NLRP3 Inflammasome https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6361828/ SHA: f02d0c1e8b0109648e578662dc250abe349a033c Authors: Chen, I-Yin; Moriyama, Miyu; Chang, Ming-Fu; Ichinohe, Takeshi Date: 2019-01-29 DOI: 10.3389/fmicb.2019.00050 License: cc-by Abstract: Nod-like receptor family, pyrin domain-containing 3 (NLRP3) regulates the secretion of proinflammatory cytokines interleukin 1 beta (IL-1β) and IL-18. We previously showed that influenza virus M2 or encephalomyocarditis virus (EMCV) 2B proteins stimulate IL-1β secretion following activation of the NLRP3 inflammasome. However, the mechanism by which severe acute respiratory syndrome coronavirus (SARS-CoV) activates the NLRP3 inflammasome remains unknown. Here, we provide direct evidence that SARS-CoV 3a protein activates the NLRP3 inflammasome in lipopolysaccharide-primed macrophages. SARS-CoV 3a was sufficient to cause the NLRP3 inflammasome activation. The ion channel activity of the 3a protein was essential for 3a-mediated IL-1β secretion. While cells uninfected or infected with a lentivirus expressing a 3a protein defective in ion channel activity expressed NLRP3 uniformly throughout the cytoplasm, NLRP3 was redistributed to the perinuclear space in cells infected with a lentivirus expressing the 3a protein. K(+) efflux and mitochondrial reactive oxygen species were important for SARS-CoV 3a-induced NLRP3 inflammasome activation. These results highlight the importance of viroporins, transmembrane pore-forming viral proteins, in virus-induced NLRP3 inflammasome activation. Text: Severe acute respiratory syndrome coronavirus (SARS-CoV), a member of the genus Betacoronavirus within the family Coronaviridae, is an enveloped virus with a single-stranded positive-sense RNA genome of approximately 30 kb in length. The 5 two-thirds of the genome encodes large polyprotein precursors, open reading frame (ORF) 1 and ORF1b, which are proteolytically cleaved to generate 16 non-structural proteins (Tan et al., 2005) . The 3 one-third of the genome encodes four structural proteins, spike (S), envelope (E), matrix (M) and nucleocapsid (N), and non-structural proteins, along with a set of accessory proteins (3a, 3b, 6, 7a, 7b, 8a, 8b, and 9b) (Perlman and Dandekar, 2005; Tan et al., 2005) . SARS-CoV is the etiological agent of SARS (Drosten et al., 2003; Fouchier et al., 2003; Ksiazek et al., 2003; Kuiken et al., 2003; Peiris et al., 2003) . At least 8,098 laboratory-confirmed cases of human infection, with a fatality rate of 9.6%, were reported to the World Health Organization from November 2002 to July 2003. High levels of proinflammatory cytokines, including tumor necrosis factor (TNF)-α, interleukin (IL)-1β, and IL-6, were detected in autopsy tissues from SARS patients (He et al., 2006) . Although dysregulation of inflammatory cytokines may be involved in lung injury and the pathogenesis of SARS-CoV, the underlying molecular mechanisms are not fully understood. The innate immune systems utilizes pattern recognition receptors (PRRs) to detect pathogen-associated molecular patterns (Medzhitov, 2001; Kawai and Akira, 2010) . Recognition of virus infection plays an important role in limiting virus replication at the early stages of infection. Nod-like receptor family, pyrin domain-containing 3 (NLRP3) is activated by a wide variety of stimuli, including virus infection (Bauernfeind et al., 2011) . Four models describing activation of the NLRP3 inflammasome have been proposed thus far (Hornung and Latz, 2010; Schroder et al., 2010; Tschopp and Schroder, 2010) . First, the disturbances in intracellular ionic concentrations, including K + efflux and Ca 2+ influx, play an important role (Fernandes-Alnemri et al., 2007; Petrilli et al., 2007; Arlehamn et al., 2010; Ichinohe et al., 2010; Ito et al., 2012; Murakami et al., 2012; Munoz-Planillo et al., 2013) . Second, cathepsin B and L, which are specific lysosomal cysteine proteases, are though to play a role after phagocytosis of cholesterol crystals (Duewell et al., 2010) , fibrillar peptide amyloid-beta , silica crystals, and aluminum salts . Third is the release of reactive oxygen species (ROS) or mitochondrial DNA from damaged mitochondria (Zhou et al., , 2011 Nakahira et al., 2011; Shimada et al., 2012) . Finally, viral RNA or RNA cleavage products generated by RNase L activate the NLRP3 inflammasome via the DExD/H-box helicase, DHX33 (Allen et al., 2009; Mitoma et al., 2013; Chen et al., 2014; Chakrabarti et al., 2015) . Upon activation, the NLRP3 is recruited to the mitochondria via association with mitochondrial antiviral signaling (MAVS) or mitofusin 2 expressed on the outer mitochondrial membrane Subramanian et al., 2013) ; these molecules then recruit the apoptosis-associated speck-like protein containing a caspase recruitment domain (ASC) and pro-caspase-1 to form the NLRP3 inflammasome. This event activates the downstream molecule, caspase-1, which catalyzes the proteolytic processing of pro-IL-1β and pro-IL-18 into their active forms and stimulates their secretion (Kayagaki et al., 2015; Shi et al., 2015) . It is increasingly evident that NLRP3 detects RNA viruses by sensing the cellular damage or distress induced by viroporins (Ichinohe et al., 2010; Ito et al., 2012; Triantafilou et al., 2013; Nieto-Torres et al., 2015) , transmembrane pore-forming proteins, encoded by certain RNA viruses; these proteins alter membrane permeability to ions by forming membrane channels (Tan et al., 2005; Chen and Ichinohe, 2015) . A recent study shows that the SARS-CoV E protein, which comprise only 76 amino acids, forms Ca 2+ -permeable ion channels and activates the NLRP3 inflammasome (Nieto-Torres et al., 2015) . Although the E and 3a proteins of SARS-CoV, which comprise 274 amino acids and contain three transmembrane domains (Zeng et al., 2004; Lu et al., 2006) , are thought to act as Na + /K + and K + channels, respectively (Wilson et al., 2004; Lu et al., 2006; Torres et al., 2007; Parthasarathy et al., 2008; Pervushin et al., 2009; Wang et al., 2011) , the role of the 3a protein in activating the NLRP3 inflammasome remains unknown. Here, we examined the role of the 3a protein in activating the NLRP3 inflammasome. Six-week-old female C57BL/6 mice were purchased from The Jackson Laboratory. All animal experiments were approved by the Animal Committees of the Institute of Medical Science (The University of Tokyo). Bone marrow-derived macrophages (BMMs) were prepared as described previously (Ichinohe et al., 2009) . In brief, bone marrow was obtained from the tibia and femur by flushing with Dulbecco's modified Eagle's medium (DMEM; Nacalai Tesque). Bone marrow cells were cultured for 5 days in DMEM supplemented with 30% L929 cell supernatant containing macrophage colony-stimulating factor, 10% heat-inactivated fetal bovine serum (FBS), and L-glutamine (2 mM) at 37 • C/5% CO 2 . HEK293FT cells (a human embryonic kidney cell line) and HeLa cells (a human epithelial carcinoma cell line) were maintained in DMEM supplemented with 10% FBS, penicillin (100 units/ml), and streptomycin (100 µg/ml) (Nacalai Tesque). MDCK cells (Madin-Darby canine kidney cells) and HT-1080 cells (a human fibrosarcoma cell line) were grown in Eagle's minimal essential medium (E-MEM; Nacalai Tesque) supplemented with 10% FBS, penicillin (100 units/ml), and streptomycin (100 µg/ml) (Nacalai Tesque). Influenza A virus strain A/PR8 (H1N1) was grown at 35 • C for 2 days in the allantoic cavities of 10-day-old fertile chicken eggs (Ichinohe et al., 2009) . The viral titer was quantified in a standard plaque assay using MDCK cells (Pang et al., 2013) . Plasmids cDNAs encoding the E and M proteins of SARS-CoV Frankfurt 1 strain (Matsuyama et al., 2005) were obtained by reverse transcription and PCR of total RNA extracted from SARS-CoVinfected Vero cells, followed by PCR amplification using specific primers. pcDNA3.1D-3a-V5His was provided by Ming-Fu Chang (National Taiwan University College of Medicine, Taipei, Taiwan). To generate the plasmids pLenti6-E-V5His, pLenti6-3a-V5His, and pLenti-M-V5His, cDNA fragments of E, 3a, and M were amplified from pcDNA3.1D-E-V5His, pcDNA3.1D-3a-V5His, and pcDNA3.1D-M-V5His using specific primer sets and then ligated into pLenti6-TOPO vectors (Invitrogen). To generate plasmids pCA7-flag-E, pCA7-flag-3a, and pCA7flag-M, pCA7-HA-E, pCA7-HA-3a, and pCA7-HA-M, cDNA fragments of E, 3a, and M were amplified from pcDNA3.1D-E-V5His, pcDNA3.1D-3a-V5His, and pcDNA3.1D-M-V5His using specific primer sets, digested with EcoR I and Not I, and subcloned into the EcoR I-Not I sites of the pCA7-flag-ASC plasmid or pCA7-HA-M2 plasmid, respectively (Ito et al., 2012) . To construct plasmids expressing the E mutant V25F, the mutated E fragments were amplified by inverse PCR with wildtype E-containing plasmids and specific primer sets. The PCR products were cleaved by Dpn I, ligated in a ligase-and T4 kinase-containing reaction and then transformed into DH5α competent cells (TOYOBO). To construct plasmids expressing the 3a mutant 3a-CS, fragments were amplified from wildtype 3a-containing plasmids using 3a-specific primer sets and transformed as described above. HEK293FT cells were seeded in 24-well cluster plates and transfected with 1 µg pLenti6-E/3a/M-V5His, pLenti-GFP (green fluorescent protein), or pLenti-M2 using polyethylenimine (PEI) Max. At 24 h post-transfection, the cells were lysed with RIPA buffer (50 mM Tris-HCl, 1% NP-40, 0.05% sodium dodecyl sulfate (SDS), 150 mM NaCl and 1 mM EDTA). And the lysates were subjected to SDS-polyacrylamide gel electrophoresis (PAGE) followed by electroblotting onto polyvinylidene difluoride (PVDF) membranes. The membranes were incubated over night with mouse anti-V5-tag (R960-25, Invitrogen), mouse anti-influenza A virus M2 (14C2, Abcam), mouse anti-GFP (GF200, Nacalai Tesque), or rabbit antitubulin (DM1A, Santa Cruz) antibodies, followed by horseradish peroxide-conjugated anti-mouse IgG (Jackson Immuno Research Laboratories) or anti-rabbit IgG (Invitrogen). After washing 3 times with washing buffer (0.05% Tween-20/PBS), the membranes were exposed using Chemi-Lumi One Super (Nacalai Tesque), and the chemiluminescent signals were captured by an ImageQuant LAS-4000 mini apparatus (GE Healthcare). To generate lentiviruses expressing V5-tagged SARS-CoV E, 3a, and M proteins, the full-length cDNA encoding each viral protein was cloned into the pLenti6.3/V5-TOPO vector (Invitrogen) using the following primers: SARS-CoV E forward, 5 -caccatgtactcattcgtttcgga-3 , and reverse, 5 -gaccagaagatcaggaactc-3 ; SARS-CoV 3a forward, 5caccatggatttgtttatgagatt-3 , and reverse, 5 -caaaggcacgctagtagtcg-3 ; SARS-CoV M forward, 5 -caccatggcagacaacggtactat-3 , and reverse, 5 -ctgtactagcaaagcaatat-3 . Sub-confluent monolayers of HEK293FT cells seeded in a collagen-coated dish (10 cm in diameter) were transfected with 3 µg of pLenti6.3/V5-TOPO vector expressing each viral protein or EGFP together with ViraPower Packaging Mix (Invitrogen) using Lipofectamine 2000 (Invitrogen). The supernatants containing lentiviruses were harvested and filtered through a 0.45 µm filter (Millipore) at 72-96 h post-transfection (Ito et al., 2012) . The lentiviral titer was then quantified using HT-1080 cells as described previously . Bone marrow-derived macrophages were plated at a density of 8 × 10 5 in 24-well plate and infected with A/PR8 influenza virus or lentivirus at a multiplicity of infection (MOI) of 5 or 0.2 for 1 h, respectively. Then, BMMs were stimulated with 1 µg/ml of LPS and cultured for additional 23 h in complete media. Supernatants were collected at 24 h post-infection and centrifuged to remove cell debris. The amount of IL-1β in the supernatants was measured in an enzyme-linked immunosorbent assay (ELISA) using paired antibodies (eBioscience) (Ichinohe et al., 2010 . To clarify the cellular localization of the wild-type and mutant 3a proteins of SARS-CoV, HeLa cells were cultured on coverslips and transfected with 1 µg of pCA7-flag-3a or pCD7-flag-3a-CS together with 0.5 µg of ER-mCherry or DsRed-Golgi (Ito et al., 2012) . At 24 h post-transfection, cells were fixed with 4% paraformaldehyde and permeabilized with 1% Triton X-100/PBS. After washing with PBS and blocking with 4% BSA/PBS, the cells were incubated with a mouse anti-flag antibody (M2, Sigma) followed by incubation with Alexa Fluor 488-conjugated goat anti-mouse IgG (H+L) (Life Technologies). To observe the cellular distribution of NLRP3 in the E-or 3a-expressing cells, HeLa cells were cultured on coverslips and transfected with 1 µg of pCA7-HA-E, pCA7-HA-EV25F, pCA7-HA-3a, pCA7-HA-3a-CS, or pCA7 control vector together with 0.5 µg of pCA7-NLRP3. At 24 h post-transfection, cells were fixed and permeabilized with 4% paraformaldehyde and 1% Triton X-100/PBS. After washing and blocking, the cells were incubated with rabbit anti-HA (561, MBL) and mouse anti-NLRP3 (Cryo-2; AdipoGen) antibodies, followed by Alexa Fluor 488-conjugated goat anti-rabbit IgG (H+L) and Alexa Fluor 568-conjugated goat anti-mouse IgG (H+L) (Life Technologies). Fluorescent signals were observed by confocal microscopy (A1R + , Nikon). Statistical significance was tested using a two-tailed Student's t-test. P-values < 0.05 were considered statistically significant. We previously demonstrated that the influenza virus M2 protein (a proton-selective ion channel), its H37G mutant (which has lost its proton selectivity and enables the transport of other cations such as Na + and K + ), and the EMCV 2B protein (a Ca 2+ channel) stimulates NLRP3 inflammasome-mediated IL-1β secretion (Ichinohe et al., 2010; Ito et al., 2012) . In addition, the SARS-CoV E protein acts as a Ca 2+ -permeable ion channels that activates the NLRP3 inflammasome (Nieto- Torres et al., 2015) . The fact that 3a protein of SARS-CoV acts as viroporin prompted us to examine whether it also triggers inflammasome activation. Thus, we first generated lentivirus plasmids expressing V5-tagged proteins and confirmed their expression in HEK293FT cells by immunoblot analysis (Figures 1A-C) . We next transduced lipopolysaccharide (LPS)-primed BMMs with the lentiviruses expressing the SARS-CoV E, 3a, M, influenza virus M2, or EMCV 2B proteins. Consistent with previous reports (Ichinohe et al., Figure 1D) . Similarly, the lentiviruses expressing the SARS-CoV E or 3a proteins stimulated IL-1β release from LPS-primed BMMs ( Figure 1D) . Furthermore, IL-1β secretion from LPSprimed BMMs co-infected with E-and 3a-expressing lentiviruses was significantly higher than that from SARS-CoV E-expressing lentivirus-infected cells ( Figure 1E) . These data indicated that the expression of SARS-CoV viroporin 3a is sufficient to stimulate IL-1β secretion by LPS-primed BMMs. Previous studies demonstrated that the N-terminal 40 amino acids of the SARS-CoV E protein are important for ion channel formation, and that mutations N15A and V25F [located in the transmembrane domain (from amino acid residues 7-38)] prevent ion conductivity (Wilson et al., 2004; Torres et al., 2007; Verdia-Baguena et al., 2012) . In addition, the SARS-CoV 3a protein contains a cysteine-rich domain (amino acid residues 127-133) that is involved in the formation of a homodimer to generate the ion channel (Lu et al., 2006; Chan et al., 2009) . Thus, mutation of the cysteine-rich domain blocks the ion conductivity by the 3a protein (Chan et al., 2009) . To this end, we substituted amino acids Cys-127, Cys-130, and Cys-133 within the cysteine-rich domain of the SARS-CoV 3a protein with serine to generate a lentivirus expressing the ion channel activity-loss mutant, 3a-CS (Chan et al., 2009; Figure 2A) . To test whether the ion channel activity of the SARS-CoV 3a protein is required to stimulate secretion of IL-1β, we transduced LPSprimed BMMs with lentiviruses expressing the SARS-CoV E, V25F, 3a, 3a-CS, or M proteins. Consistent with a previous report (Nieto -Torres et al., 2015) , we found that the V25F mutant lentivirus failed to stimulate IL-1β release from BMMs ( Figure 2B) . Notably, the 3a-CS mutant completely abrogated IL-1β secretion (Figure 2B) , suggesting that the ion channel activity of the 3a protein is required for SARS-CoV 3a-induced IL-1β secretion. FIGURE 4 | NLRP3 inflammasome activation by SARS-CoV 3a. HeLa cells were transfected with the expression plasmid encoding NLRP3 and that encoding HA-tagged SARS-CoV 3a, 3a-CS, E, or V25F, and by with a confocal microscope. Scale bars, 10 µm. Data are representative of at least three independent experiments. Next, we determined the subcellular localization of the SARS-CoV 3a protein using confocal microscopy. When the SARS-CoV Cell-free supernatants were collected at 24 h (lentiviruses) or 6 h (ATP) post-infection or stimulation, and analyzed for IL-1β by ELISA. Data are representative of at least three independent experiments, and indicate the mean ± SD; * * P < 0.01 and * * * P < 0.001. 3a protein was expressed in HeLa cells, we observed two main distribution patterns. Consistent with previous reports (Yu et al., 2004; Yuan et al., 2005) , the 3a protein localized to the Golgi apparatus ( Figure 3A ). In addition, the 3a proteins concentrated in spot structures, which mainly localized to the endoplasmic reticulum (ER) (Figure 3B ). By contrast, the 3a-CS mutant was concentrated in the Golgi apparatus rather than in the ER and did not form spot structures (Figures 3A,B) . We next examined the intracellular localization of NLRP3. Activation of the NLRP3 inflammasome led to a redistribution from the cytosol to the perinuclear space, a process considered as a hallmark of NLRP3 activation (Zhou et al., 2011; Ito et al., 2012; Johnson et al., 2013; Moriyama et al., 2016) . Although cells expressing the ion channel activity-loss mutants 3a-CS or V25F uniformly expressed NLRP3 throughout the cytoplasm, it was redistributed to the perinuclear region in SARS-CoV 3a-or E-expressing cells (Figure 4) . Together, these data provide evidence that the ion channel activity of the SARS-CoV 3a protein is essential for triggering the NLRP3 inflammasome. Both K + Efflux and ROS Production Are Involved in the IL-1β Release Induced by the SARS-CoV 3a Protein Finally, we investigated the mechanism by which SARS-CoV 3a triggers NLRP3 inflammasome activation. A previous study showed that the 3a protein of SARS-CoV acts as a K + channel (Lu et al., 2006) . In addition, K + efflux is a well-known activator of the NLRP3 inflammasome (Mariathasan et al., 2006; Petrilli et al., 2007) . These observations prompted us to examine whether K + efflux is required for 3a-mediated IL-1β secretion. To this end, BMMs in K + -rich medium were infected with influenza A virus or lentiviruses expressing the SARS-CoV E or 3a proteins. In agreement with a previous result (Ichinohe et al., 2010) , we found that IL-1β secretion caused by influenza virus was completely blocked when the extracellular K + concentration was increased to 130 mM ( Figure 5A) . The inhibitory effect of the K + -rich medium was also observed when cells were stimulated with lentiviruses expressing the SARS-CoV E or 3a proteins ( Figure 5B ). Since mitochondrial ROS are important for NLRP3 inflammasome activation (Nakahira et al., 2011; Zhou et al., 2011) , we next stimulated BMMs with extracellular ATP or lentiviruses expressing the SARS-CoV E or 3a proteins in the presence or absence of the antioxidant, Mito-TEMPO, a scavenger that is specific for mitochondrial ROS Trnka et al., 2009) . As reported previously (Nakahira et al., 2011; Ito et al., 2012) , treatment of BMMs with Mito-TEMPO completely blocked IL-1β secretion in response to ATP ( Figure 6A) . Similarly, IL-1β release induced by the SARS-CoV E and 3a proteins was significantly inhibited by Mito-TEMPO ( Figure 6B) . These observations indicate that the SARS-CoV 3a protein disrupts intracellular ionic concentrations and causes mitochondrial damages, thereby activating the NLRP3 inflammasome. In summary, we found that the ion channel activity of SARS-CoV 3a protein is essential for activation of the NLRP3 inflammasome. In addition, both K + efflux and mitochondrial ROS production are required for SARS-CoV 3a-mediated IL-1β secretion. Thus far, several models have been proposed to explain NLRP3 inflammasome activation by RNA viruses. First, viral RNA or RNA cleavage products generated by RNase L activate the NLRP3 inflammasome via the DExD/H-box helicase, DHX33 (Allen et al., 2009; Mitoma et al., 2013; Chen et al., 2014; Chakrabarti et al., 2015) . Second, viroporins encoded by RNA viruses activates the NLRP3 inflammasome (Ichinohe et al., 2010; Ito et al., 2012; Triantafilou et al., 2013; Nieto-Torres et al., 2015) . In the case of influenza virus, the proton-selective M2 ion channel in the acidic trans-Golgi network activates the NLRP3 inflammasome (Ichinohe et al., 2010) . Interestingly, an M2 mutant in which histidine was substituted with glycine at position 37 (H37G), causing loss of proton selectivity, enables transport of other cations (i.e., Na + and K + ), thereby leading to enhanced secretion of IL-1β from LPS-primed BMMs and dendritic cells when compared with the wild-type M2 protein. In addition, the 2B proteins of EMCV, poliovirus, enterovirus 71 (EV71), and human rhinovirus (a member of the Picornaviridae family) triggers NLRP3 inflammasome activation by inducing Ca 2+ flux from the ER and Golgi compartments (Ito et al., 2012; Triantafilou et al., 2013) . Furthermore, hepatitis C virus stimulates NLRP3 inflammasome-mediated IL-1β production though its p7 viroporin (Negash et al., 2013; Farag et al., 2017) . Third, a recent study has demonstrated that the 3D protein of EV71 directly interacts with NLRP3 to facilitate the assembly of NLRP3 inflammasome complex (Wang et al., 2017) . In the case of SARS-CoV, the viroporin E forms forms Ca 2+permeable ion channels and activates the NLRP3 inflammasome (Nieto-Torres et al., 2015) . In addition, another viroporin 3a was found to induce NLRP3 inflammasome activation (Yue et al., 2018) . Although alanine substitution at Cys-133, which is required for dimer or tetramer formation (Lu et al., 2006) , still allows activation of the NLRP3 inflammasome by interacting with caspase-1 (Yue et al., 2018) , the ion channel activity-loss mutant 3a-CS (Cys-to-Ser substitution at positions Cys-127, Cys-130, and Cys-133) (Chan et al., 2009 ) completely abrogated IL-1β secretion from LPS-primed BMMs, suggesting that the 3a protein of SARS-CoV has the ability to induce the NLRP3 inflammasome activation by multiple mechanisms. Previous studies show that the 3a protein of SARS-CoV is localized to the plasma membrane (Minakshi and Padhan, 2014) and acts as a K + channel (Lu et al., 2006) , thereby (presumably) stimulating the K + efflux at the plasma membrane. Indeed, we found that IL-1β secretion caused by the 3a protein was significantly inhibited when the extracellular K + concentration increased to 130 mM. Although it remains unclear whether another viroporin 8a of SARS-CoV (Castano-Rodriguez et al., 2018) activates the NLRP3 inflammasome, these data highlights the importance of viroporins in SARS-CoV-induced NLRP3 inflammasome activation. A better understanding of the mechanism that governs the NLRP3 inflammasome will facilitate the development of more effective interventions for the treatment of infectious diseases and increase our understanding of viral pathogenesis.

How does NLRP3 detect RNA viral infection?

Accelerated viral dynamics in bat cell lines, with implications for zoonotic emergence https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7064339/ SHA: f2cc0d63ff2c4aaa127c4caae21d8f3a0067e3d5 Authors: Brook, Cara E; Boots, Mike; Chandran, Kartik; Dobson, Andrew P; Drosten, Christian; Graham, Andrea L; Grenfell, Bryan T; Müller, Marcel A; Ng, Melinda; Wang, Lin-Fa; van Leeuwen, Anieke Date: 2020-02-03 DOI: 10.7554/elife.48401 License: cc-by Abstract: Bats host virulent zoonotic viruses without experiencing disease. A mechanistic understanding of the impact of bats’ virus hosting capacities, including uniquely constitutive immune pathways, on cellular-scale viral dynamics is needed to elucidate zoonotic emergence. We carried out virus infectivity assays on bat cell lines expressing induced and constitutive immune phenotypes, then developed a theoretical model of our in vitro system, which we fit to empirical data. Best fit models recapitulated expected immune phenotypes for representative cell lines, supporting robust antiviral defenses in bat cells that correlated with higher estimates for within-host viral propagation rates. In general, heightened immune responses limit pathogen-induced cellular morbidity, which can facilitate the establishment of rapidly-propagating persistent infections within-host. Rapidly-transmitting viruses that have evolved with bat immune systems will likely cause enhanced virulence following emergence into secondary hosts with immune systems that diverge from those unique to bats. Text: Bats have received much attention in recent years for their role as reservoir hosts for emerging viral zoonoses, including rabies and related lyssaviruses, Hendra and Nipah henipaviruses, Ebola and Marburg filoviruses, and SARS coronavirus (Calisher et al., 2006; Wang and Anderson, 2019) . In most non-Chiropteran mammals, henipaviruses, filoviruses, and coronaviruses induce substantial morbidity and mortality, display short durations of infection, and elicit robust, long-term immunity in hosts surviving infection (Nicholls et al., 2003; Hooper et al., 2001; Mahanty and Bray, 2004) . Bats, by contrast, demonstrate no obvious disease symptoms upon infection with pathogens that are highly virulent in non-volant mammals (Schountz et al., 2017) but may, instead, support viruses as longterm persistent infections, rather than transient, immunizing pathologies (Plowright et al., 2016) . Recent research advances are beginning to shed light on the molecular mechanisms by which bats avoid pathology from these otherwise virulent pathogens (Brook and Dobson, 2015) . Bats leverage a suite of species-specific mechanisms to limit viral load, which include host receptor sequence incompatibilities for some bat-virus combinations (Ng et al., 2015; Takadate et al., 2020) and constitutive expression of the antiviral cytokine, IFN-a, for others (Zhou et al., 2016) . Typically, the presence of viral RNA or DNA in the cytoplasm of mammalian cells will induce secretion of type I interferon proteins (IFN-a and IFN-b), which promote expression and translation of interferon-stimulated genes (ISGs) in neighboring cells and render them effectively antiviral (Stetson and Medzhitov, 2006) . In some bat cells, the transcriptomic blueprints for this IFN response are expressed constitutively, even in the absence of stimulation by viral RNA or DNA (Zhou et al., 2016) . In non-flying mammals, constitutive IFN expression would likely elicit widespread inflammation and concomitant immunopathology upon viral infection, but bats support unique adaptations to combat inflammation (Zhang et al., 2013; Ahn et al., 2019; Xie et al., 2018; Pavlovich et al., 2018) that may have evolved to mitigate metabolic damage induced during flight (Kacprzyk et al., 2017) . The extent to which constitutive IFN-a expression signifies constitutive antiviral defense in the form of functional IFN-a protein remains unresolved. In bat cells constitutively expressing IFN-a, some protein-stimulated, downstream ISGs appear to be also constitutively expressed, but additional ISG induction is nonetheless possible following viral challenge and stimulation of IFN-b (Zhou et al., 2016; Xie et al., 2018) . Despite recent advances in molecular understanding of bat viral tolerance, the consequences of this unique bat immunity on within-host virus dynamics-and its implications for understanding zoonotic emergence-have yet to be elucidated. The field of 'virus dynamics' was first developed to describe the mechanistic underpinnings of long-term patterns of steady-state viral load exhibited by patients in chronic phase infections with HIV, who appeared to produce and clear virus at equivalent rates (Nowak and May, 2000; Ho et al., 1995) . Models of simple target cell depletion, in which viral load is dictated by a bottom-eLife digest Bats can carry viruses that are deadly to other mammals without themselves showing serious symptoms. In fact, bats are natural reservoirs for viruses that have some of the highest fatality rates of any viruses that people acquire from wild animals -including rabies, Ebola and the SARS coronavirus. Bats have a suite of antiviral defenses that keep the amount of virus in check. For example, some bats have an antiviral immune response called the interferon pathway perpetually switched on. In most other mammals, having such a hyper-vigilant immune response would cause harmful inflammation. Bats, however, have adapted anti-inflammatory traits that protect them from such harm, include the loss of certain genes that normally promote inflammation. However, no one has previously explored how these unique antiviral defenses of bats impact the viruses themselves. Now, Brook et al. have studied this exact question using bat cells grown in the laboratory. The experiments made use of cells from one bat species -the black flying fox -in which the interferon pathway is always on, and another -the Egyptian fruit bat -in which this pathway is only activated during an infection. The bat cells were infected with three different viruses, and then Brook et al. observed how the interferon pathway helped keep the infections in check, before creating a computer model of this response. The experiments and model helped reveal that the bats' defenses may have a potential downside for other animals, including humans. In both bat species, the strongest antiviral responses were countered by the virus spreading more quickly from cell to cell. This suggests that bat immune defenses may drive the evolution of faster transmitting viruses, and while bats are well protected from the harmful effects of their own prolific viruses, other creatures like humans are not. The findings may help to explain why bats are often the source for viruses that are deadly in humans. Learning more about bats' antiviral defenses and how they drive virus evolution may help scientists develop better ways to predict, prevent or limit the spread of viruses from bats to humans. More studies are needed in bats to help these efforts. In the meantime, the experiments highlight the importance of warning people to avoid direct contact with wild bats. up resource supply of infection-susceptible host cells, were first developed for HIV (Perelson, 2002) but have since been applied to other chronic infections, including hepatitis-C virus (Neumann et al., 1998) , hepatitis-B virus (Nowak et al., 1996) and cytomegalovirus (Emery et al., 1999) . Recent work has adopted similar techniques to model the within-host dynamics of acute infections, such as influenza A and measles, inspiring debate over the extent to which explicit modeling of top-down immune control can improve inference beyond the basic resource limitation assumptions of the target cell model (Baccam et al., 2006; Pawelek et al., 2012; Saenz et al., 2010; Morris et al., 2018) . To investigate the impact of unique bat immune processes on in vitro viral kinetics, we first undertook a series of virus infection experiments on bat cell lines expressing divergent interferon phenotypes, then developed a theoretical model elucidating the dynamics of within-host viral spread. We evaluated our theoretical model analytically independent of the data, then fit the model to data recovered from in vitro experimental trials in order to estimate rates of within-host virus transmission and cellular progression to antiviral status under diverse assumptions of absent, induced, and constitutive immunity. Finally, we confirmed our findings in spatially-explicit stochastic simulations of fitted time series from our mean field model. We hypothesized that top-down immune processes would overrule classical resource-limitation in bat cell lines described as constitutively antiviral in the literature, offering a testable prediction for models fit to empirical data. We further predicted that the most robust antiviral responses would be associated with the most rapid within-host virus propagation rates but also protect cells against virus-induced mortality to support the longest enduring infections in tissue culture. We first explored the influence of innate immune phenotype on within-host viral propagation in a series of infection experiments in cell culture. We conducted plaque assays on six-well plate monolayers of three immortalized mammalian kidney cell lines: [1] Vero (African green monkey) cells, which are IFN-defective and thus limited in antiviral capacity (Desmyter et al., 1968) ; [2] RoNi/7.1 (Rousettus aegyptiacus) cells which demonstrate idiosyncratic induced interferon responses upon viral challenge (Kuzmin et al., 2017; Arnold et al., 2018; Biesold et al., 2011; Pavlovich et al., 2018) ; and [3] PaKiT01 (Pteropus alecto) cells which constitutively express IFN-a (Zhou et al., 2016; Crameri et al., 2009) . To intensify cell line-specific differences in constitutive immunity, we carried out infectivity assays with GFP-tagged, replication-competent vesicular stomatitis Indiana viruses: rVSV-G, rVSV-EBOV, and rVSV-MARV, which have been previously described (Miller et al., 2012; Wong et al., 2010) . Two of these viruses, rVSV-EBOV and rVSV-MARV, are recombinants for which cell entry is mediated by the glycoprotein of the bat-evolved filoviruses, Ebola (EBOV) and Marburg (MARV), thus allowing us to modulate the extent of structural, as well as immunological, antiviral defense at play in each infection. Previous work in this lab has demonstrated incompatibilities in the NPC1 filovirus receptor which render PaKiT01 cells refractory to infection with rVSV-MARV (Ng and Chandrab, 2018, Unpublished results) , making them structurally antiviral, over and above their constitutive expression of IFN-a. All three cell lines were challenged with all three viruses at two multiplicities of infection (MOI): 0.001 and 0.0001. Between 18 and 39 trials were run at each cell-virus-MOI combination, excepting rVSV-MARV infections on PaKiT01 cells at MOI = 0.001, for which only eight trials were run (see Materials and methods; Figure 1 -figure supplements 1-3, Supplementary file 1). Because plaque assays restrict viral transmission neighbor-to-neighbor in two-dimensional cellular space (Howat et al., 2006) , we were able to track the spread of GFP-expressing virus-infected cells across tissue monolayers via inverted fluorescence microscopy. For each infection trial, we monitored and re-imaged plates for up to 200 hr of observations or until total monolayer destruction, processed resulting images, and generated a time series of the proportion of infectious-cell occupied plate space across the duration of each trial (see Materials and methods). We used generalized additive models to infer the time course of all cell culture replicates and construct the multi-trial dataset to which we eventually fit our mechanistic transmission model for each cell line-virus-specific combination ( Figure 1; Figure 1 -figure supplements 1-5). All three recombinant vesicular stomatitis viruses (rVSV-G, rVSV-EBOV, and rVSV-MARV) infected Vero, RoNi/7.1, and PaKiT01 tissue cultures at both focal MOIs. Post-invasion, virus spread rapidly across most cell monolayers, resulting in virus-induced epidemic extinction. Epidemics were less severe in bat cell cultures, especially when infected with the recombinant filoviruses, rVSV-EBOV and rVSV-MARV. Monolayer destruction was avoided in the case of rVSV-EBOV and rVSV-MARV infections on PaKiT01 cells: in the former, persistent viral infection was maintained throughout the 200 hr duration of each experiment, while, in the latter, infection was eliminated early in the time series, preserving a large proportion of live, uninfectious cells across the duration of the experiment. We assumed this pattern to be the result of immune-mediated epidemic extinction (Figure 1) . Patterns from MOI = 0.001 were largely recapitulated at MOI = 0.0001, though at somewhat reduced total proportions (Figure 1-figure supplement 5 ). A theoretical model fit to in vitro data recapitulates expected immune phenotypes for bat cells We next developed a within-host model to fit to these data to elucidate the effects of induced and constitutive immunity on the dynamics of viral spread in host tissue ( Figure 1 ). The compartmental within-host system mimicked our two-dimensional cell culture monolayer, with cells occupying five distinct infection states: susceptible (S), antiviral (A), exposed (E), infectious (I), and dead (D). We modeled exposed cells as infected but not yet infectious, capturing the 'eclipse phase' of viral integration into a host cell which precedes viral replication. Antiviral cells were immune to viral infection, in accordance with the 'antiviral state' induced from interferon stimulation of ISGs in tissues adjacent to infection (Stetson and Medzhitov, 2006) . Because we aimed to translate available data into modeled processes, we did not explicitly model interferon dynamics but instead scaled the rate of cell progression from susceptible to antiviral (r) by the proportion of exposed cells (globally) in the system. In systems permitting constitutive immunity, a second rate of cellular acquisition of antiviral status (") additionally scaled with the global proportion of susceptible cells in the model. Compared with virus, IFN particles are small and highly diffusive, justifying this global signaling assumption at the limited spatial extent of a six-well plate and maintaining consistency with previous modeling approximations of IFN signaling in plaque assay (Howat et al., 2006) . To best represent our empirical monolayer system, we expressed our state variables as proportions (P S , P A , P E , P I , and P D ), under assumptions of frequency-dependent transmission in a wellmixed population (Keeling and Rohani, 2008) , though note that the inclusion of P D (representing the proportion of dead space in the modeled tissue) had the functional effect of varying transmission with infectious cell density. This resulted in the following system of ordinary differential equations: We defined 'induced immunity' as complete, modeling all cells as susceptible to viral invasion at disease-free equilibrium, with defenses induced subsequent to viral exposure through the term r. By contrast, we allowed the extent of constitutive immunity to vary across the parameter range of " > 0, defining a 'constitutive' system as one containing any antiviral cells at disease-free equilibrium. In fitting this model to tissue culture data, we independently estimated both r and "; as well as the cell-to-cell transmission rate, b, for each cell-virus combination. Since the extent to which constitutively-expressed IFN-a is constitutively translated into functional protein is not yet known for bat hosts (Zhou et al., 2016) , this approach permitted our tissue culture data to drive modeling inference: even in PaKiT01 cell lines known to constitutively express IFN-a, the true constitutive extent of the system (i.e. the quantity of antiviral cells present at disease-free equilibrium) was allowed to vary through estimation of ": For the purposes of model-fitting, we fixed the value of c, the return rate of antiviral cells to susceptible status, at 0. The small spatial scale and short time course (max 200 hours) of our experiments likely prohibited any return of antiviral cells to susceptible status in our empirical system; nonetheless, we retained the term c in analytical evaluations of our model because regression from antiviral to susceptible status is possible over long time periods in vitro and at the scale of a complete organism (Radke et al., 1974; Rasmussen and Farley, 1975; Samuel and Knutson, 1982) . Before fitting to empirical time series, we undertook bifurcation analysis of our theoretical model and generated testable hypotheses on the basis of model outcomes. From our within-host model system (Equation 1-5), we derived the following expression for R 0 , the pathogen basic reproduction number (Supplementary file 2): Pathogens can invade a host tissue culture when R 0 >1. Rapid rates of constitutive antiviral acquisition (") will drive R 0 <1: tissue cultures with highly constitutive antiviral immunity will be therefore resistant to virus invasion from the outset. Since, by definition, induced immunity is stimulated following initial virus invasion, the rate of induced antiviral acquisition (r) is not incorporated into the equation for R 0 ; while induced immune processes can control virus after initial invasion, they cannot prevent it from occurring to begin with. In cases of fully induced or absent immunity (" ¼ 0), the R 0 equation thus reduces to a form typical of the classic SEIR model: At equilibrium, the theoretical, mean field model demonstrates one of three infection states: endemic equilibrium, stable limit cycles, or no infection ( Figure 2) . Respectively, these states approximate the persistent infection, virus-induced epidemic extinction, and immune-mediated epidemic extinction phenotypes previously witnessed in tissue culture experiments ( Figure 1 ). Theoretically, endemic equilibrium is maintained when new infections are generated at the same rate at which infections are lost, while limit cycles represent parameter space under which infectious and susceptible populations are locked in predictable oscillations. Endemic equilibria resulting from cellular regeneration (i.e. births) have been described in vivo for HIV (Coffin, 1995) and in vitro for herpesvirus plaque assays (Howat et al., 2006) , but, because they so closely approach zero, true limit cycles likely only occur theoretically, instead yielding stochastic extinctions in empirical time series. Bifurcation analysis of our mean field model revealed that regions of no infection (pathogen extinction) were bounded at lower threshold (Branch point) values for b, below which the pathogen was unable to invade. We found no upper threshold to invasion for b under any circumstances (i.e. b high enough to drive pathogen-induced extinction), but high b values resulted in Hopf bifurcations, which delineate regions of parameter space characterized by limit cycles. Since limit cycles so closely approach zero, high bs recovered in this range would likely produce virus-induced epidemic extinctions under experimental conditions. Under more robust representations of immunity, with higher values for either or both induced (r) and constitutive (") rates of antiviral acquisition, Hopf bifurcations occurred at increasingly higher values for b, meaning that persistent infections could establish at higher viral transmission rates ( Figure 2 ). Consistent with our derivation for R 0 , we found that the Branch point threshold for viral invasion was independent of changes to the induced immune parameter (r) but saturated at high values of " that characterize highly constitutive immunity ( Figure 3) . We next fit our theoretical model by least squares to each cell line-virus combination, under absent, induced, and constitutive assumptions of immunity. In general, best fit models recapitulated expected outcomes based on the immune phenotype of the cell line in question, as described in the general literature (Table 1 Ironically, the induced immune model offered a slightly better fit than the constitutive to rVSV-MARV infections on the PaKiT01 cell line (the one cell line-virus combination for which we know a constitutively antiviral cell-receptor incompatibility to be at play). Because constitutive immune assumptions can prohibit pathogen invasion (R 0 <1), model fits to this time series under constitutive assumptions were handicapped by overestimations of ", which prohibited pathogen invasion. Only by incorporating an exceedingly rapid rate of induced antiviral acquisition could the model guarantee that initial infection would be permitted and then rapidly controlled. In all panel (A) plots, the rate of induced immune antiviral acquisition (r) was fixed at 0.01. Panel (B) depicts dynamics under variably induced immunity, ranging from absent (left: r=0) to high (right: r=1). In all panel (B) plots, the rate of constitutive antiviral acquisition (") was fixed at 0.0001 Branch point curves are represented as solid lines and Hopf curves as dashed lines. White space indicates endemic equilibrium (persistence), gray space indicates limit cycles, and black space indicates no infection (extinction). Other parameter values for equilibrium analysis were fixed at: b = .025, m = .001, s = 1/6, c = 0. Special points from bifurcations analyses are listed in Supplementary file 3. In fitting our theoretical model to in vitro data, we estimated the within-host virus transmission rate (b) and the rate(s) of cellular acquisition to antiviral status (r or r + ") ( Table 1 ; Supplementary file 4). Under absent immune assumptions, r and " were fixed at 0 while b was estimated; under induced immune assumptions, " was fixed at 0 while r and b were estimated; and under constitutive immune assumptions, all three parameters (r, ", and b) were simultaneously estimated for each cell-virus combination. Best fit parameter estimates for MOI=0.001 data are visualized in conjunction with br and b -" bifurcations in (r) and (B) the constitutive immunity rate of antiviral acquisition ("). Panels show variation in the extent of immunity, from absent (left) to high (right). Branch point curves are represented as solid lines and Hopf curves as dashed lines. White space indicates endemic equilibrium (persistence), gray space indicates limit cycling, and black space indicates no infection (extinction). Other parameter values for equilibrium analysis were fixed at: b = .025, m = .001, s = 1/6, a = 1/6, c = 0. Special points from bifurcations analyses are listed in Supplementary file 3. space corresponding to theoretical limit cycles, consistent with observed virus-induced epidemic extinctions in stochastic tissue cultures. In contrast to Vero cells, the induced immunity model offered the best fit to all RoNi/7.1 data, consistent with reported patterns in the literature and our own validation by qPCR ( Table 1; Arnold et al., 2018; Kuzmin et al., 2017; Biesold et al., 2011; Pavlovich et al., 2018) . As in Vero cell trials, we estimated highest b values for rVSV-G infections on RoNi/7.1 cell lines but here recovered higher b estimates for rVSV-MARV than for rVSV-EBOV. This reversal was balanced by a higher estimated rate of acquisition to antiviral status (r) for rVSV-EBOV versus rVSV-MARV. In general, we observed that more rapid rates of antiviral acquisition (either induced, r, constitutive, ", or both) correlated with higher transmission rates (b). When offset by r, b values estimated for RoNi/7.1 infections maintained the same amplitude as those estimated for immune-absent Vero cell lines but caused gentler epidemics and reduced cellular mortality (Figure 1) . RoNi/7.1 parameter estimates localized in the region corresponding to endemic equilibrium for the deterministic, theoretical model (Figure 4) , yielding less acute epidemics which nonetheless went extinct in stochastic experiments. Finally, rVSV-G and rVSV-EBOV trials on PaKiT01 cells were best fit by models assuming constitutive immunity, while rVSV-MARV infections on PaKiT01 were matched equivalently by models assuming either induced or constitutive immunity-with induced models favored over constitutive in AIC comparisons because one fewer parameter was estimated (Figure 1-figure supplements 4-5; Supplementary file 4). For all virus infections, PaKiT01 cell lines yielded b estimates a full order of magnitude higher than Vero or RoNi/7.1 cells, with each b balanced by an immune response (either r, or r combined with ") also an order of magnitude higher than that recovered for the other cell lines ( Figure 4 ; Table 1 ). As in RoNi/7.1 cells, PaKiT01 parameter fits localized in the region corresponding to endemic equilibrium for the deterministic theoretical model. Because constitutive immune processes can actually prohibit initial pathogen invasion, constitutive immune fits to rVSV-MARV infections on PaKiT01 cell lines consistently localized at or below the Branch point threshold for virus invasion (R 0 ¼ 1). During model fitting for optimization of ", any parameter tests of " values producing R 0 <1 resulted in no infection and, consequently, produced an exceedingly poor fit to infectious time series data. In all model fits assuming constitutive immunity, across all cell lines, antiviral contributions from " prohibited virus from invading at all. The induced immune model thus produced a more parsimonious recapitulation of these data because virus invasion was always permitted, then rapidly controlled. In order to compare the relative contributions of each cell line's disparate immune processes to epidemic dynamics, we next used our mean field parameter estimates to calculate the initial 'antiviral rate'-the initial accumulation rate of antiviral cells upon virus invasion for each cell-virus-MOI combination-based on the following equation: where P E was calculated from the initial infectious dose (MOI) of each infection experiment and P S was estimated at disease-free equilibrium: Because and " both contribute to this initial antiviral rate, induced and constitutive immune assumptions are capable of yielding equally rapid rates, depending on parameter fits. Indeed, under fully induced immune assumptions, the induced antiviral acquisition rate (r) estimated for rVSV-MARV infection on PaKiT01 cells was so high that the initial antiviral rate exceeded even that estimated under constitutive assumptions for this cell-virus combination (Supplementary file 4) . In reality, we know that NPC1 receptor incompatibilities make PaKiT01 cell lines constitutively refractory to rVSV-MARV infection (Ng and Chandrab, 2018, Unpublished results) and that PaKiT01 cells also constitutively express the antiviral cytokine, IFN-a. Model fitting results suggest that this constitutive expression of IFN-a may act more as a rapidly inducible immune response following virus invasion than as a constitutive secretion of functional IFN-a protein. Nonetheless, as hypothesized, PaKiT01 cell lines were by far the most antiviral of any in our study-with initial antiviral rates estimated several orders of magnitude higher than any others in our study, under either induced or constitutive assumptions ( Table 1 ; Supplementary file 4). RoNi/7.1 cells displayed the second-most-pronounced signature of immunity, followed by Vero cells, for which the initial antiviral rate was essentially zero even under forced assumptions of induced or constitutive immunity ( Table 1 ; Supplementary file 4). Using fitted parameters for b and ", we additionally calculated R 0 , the basic reproduction number for the virus, for each cell line-virus-MOI combination ( Table 1 ; Supplementary file 4). We found that R 0 was essentially unchanged across differing immune assumptions for RoNi/7.1 and Vero cells, for which the initial antiviral rate was low. In the case of PaKiT01 cells, a high initial antiviral rate under either induced or constitutive immunity resulted in a correspondingly high estimation of b (and, consequently, R 0 ) which still produced the same epidemic curve that resulted from the much lower estimates for b and R 0 paired with absent immunity. These findings suggest that antiviral immune responses protect host tissues against virus-induced cell mortality and may facilitate the establishment of more rapid within-host transmission rates. Total monolayer destruction occurred in all cell-virus combinations excepting rVSV-EBOV infections on RoNi/7.1 cells and rVSV-EBOV and rVSV-MARV infections on PaKiT01 cells. Monolayer destruction corresponded to susceptible cell depletion and epidemic turnover where R-effective (the product of R 0 and the proportion susceptible) was reduced below one ( Figure 5) . For rVSV-EBOV infections on RoNi/7.1, induced antiviral cells safeguarded remnant live cells, which birthed new susceptible cells late in the time series. In rVSV-EBOV and rVSV-MARV infections on PaKiT01 cells, this antiviral protection halted the epidemic ( Figure 5 ; R-effective <1) before susceptibles fully declined. In the case of rVSV-EBOV on PaKiT01, the birth of new susceptibles from remnant live cells protected by antiviral status maintained late-stage transmission to facilitate long-term epidemic persistence. Importantly, under fixed parameter values for the infection incubation rate (s) and infectioninduced mortality rate (a), models were unable to reproduce the longer-term infectious time series captured in data from rVSV-EBOV infections on PaKiT01 cell lines without incorporation of cell births, an assumption adopted in previous modeling representations of IFN-mediated viral dynamics in tissue culture (Howat et al., 2006) . In our experiments, we observed that cellular reproduction took place as plaque assays achieved confluency. Finally, because the protective effect of antiviral cells is more clearly observable spatially, we confirmed our results by simulating fitted time series in a spatially-explicit, stochastic reconstruction of our mean field model. In spatial simulations, rates of antiviral acquisition were fixed at fitted values for r and " derived from mean field estimates, while transmission rates (b) were fixed at values ten times greater than those estimated under mean field conditions, accounting for the intensification of parameter thresholds permitting pathogen invasion in local spatial interactions (see Materials and methods; Videos 1-3; Figure 5-figure supplement 3; Supplementary file 5; Webb et al., 2007) . In immune capable time series, spatial antiviral cells acted as 'refugia' which protected live cells from infection as each initial epidemic wave 'washed' across a cell monolayer. Eventual birth of new susceptibles from these living refugia allowed for sustained epidemic transmission in cases where some infectious cells persisted at later timepoints in simulation (Videos 1-3; Figure 5-figure supplement 3 ). Bats are reservoirs for several important emerging zoonoses but appear not to experience disease from otherwise virulent viral pathogens. Though the molecular biological literature has made great progress in elucidating the mechanisms by which bats tolerate viral infections (Zhou et al., 2016; Ahn et al., 2019; Xie et al., 2018; Pavlovich et al., 2018; Zhang et al., 2013) , the impact of unique bat immunity on virus dynamics within-host has not been well-elucidated. We used an innovative combination of in vitro experimentation and within-host modeling to explore the impact of unique bat immunity on virus dynamics. Critically, we found that bat cell lines demonstrated a signature of enhanced interferon-mediated immune response, of either constitutive or induced form, which allowed for establishment of rapid within-host, cell-to-cell virus transmission rates (b). These results were supported by both data-independent bifurcation analysis of our mean field theoretical model, as well as fitting of this model to viral infection time series established in bat cell culture. Additionally, we demonstrated that the antiviral state induced by the interferon pathway protects live cells from mortality in tissue culture, resulting in in vitro epidemics of extended duration that enhance the probability of establishing a long-term persistent infection. Our findings suggest that viruses evolved in bat reservoirs possessing enhanced IFN capabilities could achieve more rapid within-host transmission rates without causing pathology to their hosts. Such rapidly-reproducing viruses would likely generate extreme virulence upon spillover to hosts lacking similar immune capacities to bats. To achieve these results, we first developed a novel, within-host, theoretical model elucidating the effects of unique bat immunity, then undertook bifurcation analysis of the model's equilibrium properties under immune absent, induced, and constitutive assumptions. We considered a cell line to be constitutively immune if possessing any number of antiviral cells at disease-free equilibrium but allowed the extent of constitutive immunity to vary across the parameter range for ", the constitutive rate of antiviral acquisition. In deriving the equation for R 0 , the basic reproduction number, which defines threshold conditions for virus invasion of a tissue (R 0 >1), we demonstrated how the invasion threshold is elevated at high values of constitutive antiviral acquisition, ". Constitutive immune processes can thus prohibit pathogen invasion, while induced responses, by definition, can only control infections post-hoc. Once thresholds for pathogen invasion have been met, assumptions of constitutive immunity will limit the cellular mortality (virulence) incurred at high transmission rates. Regardless of mechanism (induced or constitutive), interferon-stimulated antiviral cells appear to play a key role in maintaining longer term or persistent infections by safeguarding susceptible cells from rapid infection and concomitant cell death. Fitting of our model to in vitro data supported expected immune phenotypes for different bat cell lines as described in the literature. Simple target cell models that ignore the effects of immunity best recapitulated infectious time series derived from IFN-deficient Vero cells, while models assuming induced immune processes most accurately reproduced trials derived from RoNi/7.1 (Rousettus aegyptiacus) cells, which possess a standard virusinduced IFN-response. In most cases, models assuming constitutive immune processes best recreated virus epidemics produced on PaKiT01 (Pteropus alecto) cells, which are known to constitutively express the antiviral cytokine, IFN-a (Zhou et al., 2016) . Model support for induced immune assumptions in fits to rVSV-MARV infections on PaKiT01 cells suggests that the constitutive IFN-a expression characteristic of P. alecto cells may represent more of a constitutive immune priming process than a perpetual, functional, antiviral defense. Results from mean field model fitting were additionally confirmed in spatially explicit stochastic simulations of each time series. As previously demonstrated in within-host models for HIV (Coffin, 1995; Perelson et al., 1996; Nowak et al., 1995; Bonhoeffer et al., 1997; Ho et al., 1995) , assumptions of simple target-cell depletion can often provide satisfactory approximations of viral dynamics, especially those reproduced in simple in vitro systems. Critically, our model fitting emphasizes the need for incorporation of top-down effects of immune control in order to accurately reproduce infectious time series derived from bat cell tissue cultures, especially those resulting from the robustly antiviral PaKiT01 P. alecto cell line. These findings indicate that enhanced IFN-mediated immune pathways in bat reservoirs may promote elevated within-host virus replication rates prior to cross-species emergence. We nonetheless acknowledge the limitations imposed by in vitro experiments in tissue culture, especially involving recombinant viruses and immortalized cell lines. Future work should extend these cell culture studies to include measurements of multiple state variables (i.e. antiviral cells) to enhance epidemiological inference. The continued recurrence of Ebola epidemics across central Africa highlights the importance of understanding bats' roles as reservoirs for virulent zoonotic disease. The past decade has born witness to emerging consensus regarding the unique pathways by which bats resist and tolerate highly virulent infections (Brook and Dobson, 2015; Xie et al., 2018; Zhang et al., 2013; Ahn et al., 2019; Zhou et al., 2016; Ng et al., 2015; Pavlovich et al., 2018) . Nonetheless, an understanding of the mechanisms by which bats support endemic pathogens at the population level, or promote the evolution of virulent pathogens at the individual level, remains elusive. Endemic maintenance of infection is a defining characteristic of a pathogen reservoir (Haydon et al., 2002) , and bats appear to merit such a title, supporting long-term persistence of highly transmissible viral infections in isolated island populations well below expected critical community sizes (Peel et al., 2012) . Researchers debate the relative influence of population-level and within-host mechanisms which might explain these trends (Plowright et al., 2016) , but increasingly, field data are difficult to reconcile without acknowledgement of a role for persistent infections (Peel et al., 2018; Brook et al., 2019) . We present general methods to study cross-scale viral dynamics, which suggest that within-host persistence is supported by robust antiviral responses characteristic of bat immune processes. Viruses which evolve rapid replication rates under these robust antiviral defenses may pose the greatest hazard for cross-species pathogen emergence into spillover hosts with immune systems that differ from those unique to bats. All experiments were carried out on three immortalized mammalian kidney cell lines: Vero (African green monkey), RoNi/7.1 (Rousettus aegyptiacus) (Kühl et al., 2011; Biesold et al., 2011) and PaKiT01 (Pteropus alecto) (Crameri et al., 2009) . The species identifications of all bat cell lines was confirmed morphologically and genetically in the publications in which they were originally described (Kühl et al., 2011; Biesold et al., 2011; Crameri et al., 2009) . Vero cells were obtained from ATCC. Monolayers of each cell line were grown to 90% confluency (~9Â10 5 cells) in 6-well plates. Cells were maintained in a humidified 37˚C, 5% CO 2 incubator and cultured in Dulbecco's modified Eagle medium (DMEM) (Life Technologies, Grand Island, NY), supplemented with 2% fetal bovine serum (FBS) (Gemini Bio Products, West Sacramento, CA), and 1% penicillin-streptomycin (Life Technologies). Cells were tested monthly for mycoplasma contamination while experiments were taking place; all cells assayed negative for contamination at every testing. Previous work has demonstrated that all cell lines used are capable of mounting a type I IFN response upon viral challenge, with the exception of Vero cells, which possess an IFN-b deficiency (Desmyter et al., 1968; Rhim et al., 1969; Emeny and Morgan, 1979) . RoNi/7.1 cells have been shown to mount idiosyncratic induced IFN defenses upon viral infection (Pavlovich et al., 2018; Kuzmin et al., 2017; Arnold et al., 2018; Kühl et al., 2011; Biesold et al., 2011) , while PaKiT01 cells are known to constitutively express the antiviral cytokine, IFN-a (Zhou et al., 2016) . This work is the first documentation of IFN signaling induced upon challenge with the particular recombinant VSVs outlined below. We verified known antiviral immune phenotypes via qPCR. Results were consistent with the literature, indicating a less pronounced role for interferon defense against viral infection in RoNi/7.1 versus PaKiT01 cells. Replication-capable recombinant vesicular stomatitis Indiana viruses, expressing filovirus glycoproteins in place of wild type G (rVSV-G, rVSV-EBOV, and rVSV-MARV) have been previously described (Wong et al., 2010; Miller et al., 2012) . Viruses were selected to represent a broad range of anticipated antiviral responses from host cells, based on a range of past evolutionary histories between the virus glycoprotein mediating cell entry and the host cell's entry receptor. These interactions ranged from the total absence of evolutionary history in the case of rVSV-G infections on all cell lines to a known receptor-level cell entry incompatibility in the case of rVSV-MARV infections on PaKiT01 cell lines. To measure infectivities of rVSVs on each of the cell lines outlined above, so as to calculate the correct viral dose for each MOI, NH 4 Cl (20 mM) was added to infected cell cultures at 1-2 hr postinfection to block viral spread, and individual eGFP-positive cells were manually counted at 12-14 hr post-infection. Previously published work indicates that immortalized kidney cell lines of Rousettus aegyptiacus (RoNi/7.1) and Pteropus alecto (PaKiT01) exhibit different innate antiviral immune phenotypes through, respectively, induced (Biesold et al., 2011; Pavlovich et al., 2018; Kühl et al., 2011; Arnold et al., 2018) and constitutive (Zhou et al., 2016 ) expression of type I interferon genes. We verified these published phenotypes on our own cell lines infected with rVSV-G, rVSV-EBOV, and rVSV-MARV via qPCR of IFN-a and IFN-b genes across a longitudinal time series of infection. Specifically, we carried out multiple time series of infection of each cell line with each of the viruses described above, under mock infection conditions and at MOIs of 0.0001 and 0.001-with the exception of rVSV-MARV on PaKiT01 cell lines, for which infection was only performed at MOI = 0.0001 due to limited viral stocks and the extremely low infectivity of this virus on this cell line (thus requiring high viral loads for initial infection). All experiments were run in duplicate on 6well plates, such that a typical plate for any of the three viruses had two control (mock) wells, two MOI = 0.0001 wells and two MOI = 0.001 wells, excepting PaKiT01 plates, which had two control and four MOI = 0.0001 wells at a given time. We justify this PaKiT01 exemption through the expectation that IFN-a expression is constitutive for these cells, and by the assumption that any expression exhibited at the lower MOI should also be present at the higher MOI. For these gene expression time series, four 6-well plates for each cell line-virus combination were incubated with virus for one hour at 37˚C. Following incubation, virus was aspirated off, and cell monolayers were washed in PBS, then covered with an agar plaque assay overlay to mimic conditions under which infection trials were run. Plates were then harvested sequentially at timepoints of roughly 5, 10, 15, and 20 hr post-infection (exact timing varied as multiple trials were running simultaneously). Upon harvest of each plate, agar overlay was removed, and virus was lysed and RNA extracted from cells using the Zymo Quick RNA Mini Prep kit, according to the manufacturer's instructions and including the step for cellular DNA digestion. Post-extraction, RNA quality was verified via nanodrop, and RNA was converted to cDNA using the Invitrogen Superscript III cDNA synthesis kit, according to the manufacturer's instructions. cDNA was then stored at 4˚C and as a frozen stock at À20˚C to await qPCR. We undertook qPCR of cDNA to assess expression of the type I interferon genes, IFN-a and IFNb, and the housekeeping gene, b-Actin, using primers previously reported in the literature (Supplementary file 6) . For qPCR, 2 ml of each cDNA sample was incubated with 7 ml of deionized water, 1 ml of 5 UM forward/reverse primer mix and 10 ml of iTaq Universal SYBR Green, then cycled on a QuantStudio3 Real-Time PCR machine under the following conditions: initial denaturation at 94 C for 2 min followed by 40 cycles of: denaturation at 95˚C (5 s), annealing at 58˚C (15 s), and extension at 72˚C (10 s). We report simple d-Ct values for each run, with raw Ct of the target gene of interest (IFN-a or IFN-b) subtracted from raw Ct of the b-Actin housekeeping gene in Figure 1 -figure supplement 6. Calculation of fold change upon viral infection in comparison to mock using the d-d-Ct method (Livak and Schmittgen, 2001) was inappropriate in this case, as we wished to demonstrate constitutive expression of IFN-a in PaKiT01 cells, whereby data from mock cells was identical to that produced from infected cells. After being grown to~90% confluency, cells were incubated with pelleted rVSVs expressing eGFP (rVSV-G, rVSV-EBOV, rVSV-MARV). Cell lines were challenged with both a low (0.0001) and high (0.001) multiplicity of infection (MOI) for each virus. In a cell monolayer infected at a given MOI (m), the proportion of cells (P), infected by k viral particles can be described by the Poisson distribution: P k ð Þ ¼ e Àm m k k! , such that the number of initially infected cells in an experiment equals: 1 À e Àm . We assumed that a~90% confluent culture at each trial's origin was comprised of~9x10 5 cells and conducted all experiments at MOIs of 0.0001 and 0.001, meaning that we began each trial by introducing virus to, respectively,~81 or 810 cells, representing the state variable 'E' in our theoretical model. Low MOIs were selected to best approximate the dynamics of mean field infection and limit artifacts of spatial structuring, such as premature epidemic extinction when growing plaques collide with plate walls in cell culture. Six-well plates were prepared with each infection in duplicate or triplicate, such that a control well (no virus) and 2-3 wells each at MOI 0.001 and 0.0001 were incubated simultaneously on the same plate. In total, we ran between 18 and 39 trials at each cell-virus-MOI combination, excepting r-VSV-MARV infections on PaKiT01 cells at MOI = 0.001, for which we ran only eight trials due to the low infectivity of this virus on this cell line, which required high viral loads for initial infection. Cells were incubated with virus for one hour at 37˚C. Following incubation, virus was aspirated off, and cell monolayers were washed in PBS, then covered with a molten viscous overlay (50% 2X MEM/Lglutamine; 5% FBS; 3% HEPES; 42% agarose), cooled for 20 min, and re-incubated in their original humidified 37˚C, 5% CO 2 environment. After application of the overlay, plates were monitored periodically using an inverted fluorescence microscope until the first signs of GFP expression were witnessed (~6-9.5 hr post-infection, depending on the cell line and virus under investigation). From that time forward, a square subset of the center of each well (comprised of either 64-or 36-subframes and corresponding to roughly 60% and 40% of the entire well space) was imaged periodically, using a CellInsight CX5 High Content Screening (HCS) Platform with a 4X air objective (ThermoFisher, Inc, Waltham, MA). Microscope settings were held standard across all trials, with exposure time fixed at 0.0006 s for each image. One color channel was imaged, such that images produced show GFP-expressing cells in white and non-GFP-expressing cells in black (Figure 1-figure supplement 1) . Wells were photographed in rotation, as frequently as possible, from the onset of GFP expression until the time that the majority of cells in the well were surmised to be dead, GFP expression could no longer be detected, or early termination was desired to permit Hoechst staining. In the case of PaKiT01 cells infected with rVSV-EBOV, where an apparently persistent infection established, the assay was terminated after 200+ hours (8+ days) of continuous observation. Upon termination of all trials, cells were fixed in formaldehyde (4% for 15 min), incubated with Hoechst stain (0.0005% for 15 min) (ThermoFisher, Inc, Waltham, MA), then imaged at 4X on the CellInsight CX5 High Content Screening (HCS) Platform. The machine was allowed to find optimal focus for each Hoechst stain image. One color channel was permitted such that images produced showed live nuclei in white and dead cells in black. Hoechst stain colors cellular DNA, and viral infection is thought to interfere with the clarity of the stain (Dembowski and DeLuca, 2015) . As such, infection termination, cell fixation, and Hoechst staining enables generation of a rough time series of uninfectious live cells (i.e. susceptible + antiviral cells) to complement the images which produced time series of proportions infectious. Due to uncertainty over the exact epidemic state of Hoechst-stained cells (i.e. exposed but not yet infectious cells may still stain), we elected to fit our models only to the infectious time series derived from GFPexpressing images and used Hoechst stain images as a post hoc visual check on our fit only ( Figure 5 ; Figure 5 -figure supplements 1-2). Images recovered from the time series above were processed into binary ('infectious' vs. 'non-infectious' or, for Hoechst-stained images, 'live' vs. 'dead') form using the EBImage package (Pau et al., 2010) in R version 3.6 for MacIntosh, after methods further detailed in Supplementary file 7. Binary images were then further processed into time series of infectious or, for Hoechst-stained images, live cells using a series of cell counting scripts. Because of logistical constraints (i.e. many plates of simultaneously running infection trials and only one available imaging microscope), the time course of imaging across the duration of each trial was quite variable. As such, we fitted a series of statistical models to our processed image data to reconstruct reliable values of the infectious proportion of each well per hour for each distinct trial in all cell line-virus-MOI combinations (Figure 1 To derive the expression for R 0 , the basic pathogen reproductive number in vitro, we used Next Generation Matrix (NGM) techniques (Diekmann et al., 1990; Heffernan et al., 2005) , employing Wolfram Mathematica (version 11.2) as an analytical tool. R 0 describes the number of new infections generated by an existing infection in a completely susceptible host population; a pathogen will invade a population when R 0 >1 (Supplementary file 2). We then analyzed stability properties of the system, exploring dynamics across a range of parameter spaces, using MatCont (version 2.2) (Dhooge et al., 2008) for Matlab (version R2018a) (Supplementary file 3). The birth rate, b, and natural mortality rate, m, balance to yield a population-level growth rate, such that it is impossible to estimate both b and m simultaneously from total population size data alone. As such, we fixed b at. 025 and estimated m by fitting an infection-absent version of our mean field model to the susceptible time series derived via Hoechst staining of control wells for each of the three cell lines (Figure 1-figure supplement 7) . This yielded a natural mortality rate, m, corresponding to a lifespan of approximately 121, 191, and 84 hours, respectively, for Vero, RoNi/7.1, and PaKiT01 cell lines (Figure 1-figure supplement 7) . We then fixed the virus incubation rate, s, as the inverse of the shortest observed duration of time from initial infection to the observation of the first infectious cells via fluorescent microscope for all nine cell line -virus combinations (ranging 6 to 9.5 hours). We fixed a, the infection-induced mortality rate, at 1/6, an accepted standard for general viral kinetics (Howat et al., 2006) , and held c, the rate of antiviral cell regression to susceptible status, at 0 for the timespan (<200 hours) of the experimental cell line infection trials. We estimated cell line-virus-MOI-specific values for b, r, and " by fitting the deterministic output of infectious proportions in our mean field model to the full suite of statistical outputs of all trials for each infected cell culture time series (Figure 1-figure supplements 2-3) . Fitting was performed by minimizing the sum of squared differences between the deterministic model output and cell linevirus-MOI-specific infectious proportion of the data at each timestep. We optimized parameters for MOI = 0.001 and 0.0001 simultaneously to leverage statistical power across the two datasets, estimating a different transmission rate, b, for trials run at each infectious dose but, where applicable, estimating the same rates of r and " across the two time series. We used the differential equation solver lsoda() in the R package deSolve (Soetaert et al., 2010) to obtain numerical solutions for the mean field model and carried out minimization using the 'Nelder-Mead' algorithm of the optim() function in base R. All model fits were conducted using consistent starting guesses for the parameters, b (b = 3), and where applicable, r (r = 0.001) and " (" = 0.001). In the case of failed fits or indefinite hessians, we generated a series of random guesses around the starting conditions and continued estimation until successful fits were achieved. All eighteen cell line-virus-MOI combinations of data were fit by an immune absent (" = r = 0) version of the theoretical model and, subsequently, an induced immunity (" = 0; r >0) and constitutive immunity (" >0; r >0) version of the model. Finally, we compared fits across each cell line-virus-MOI combination via AIC. In calculating AIC, the number of fitted parameters in each model (k) varied across the immune phenotypes, with one parameter (b) estimated for absent immune assumptions, two (b and r) for induced immune assumptions, and three (b, r, and ") for constitutive immune assumptions. The sample size (n) corresponded to the number of discrete time steps across all empirical infectious trials to which the model was fitted for each cell-line virus combination. All fitting and model comparison scripts are freely available for download at the following FigShare repository: DOI: 10.6084/m9.figshare.8312807. Finally, we verified all mean field fits in a spatial context, in order to more thoroughly elucidate the role of antiviral cells in each time series. We constructed our spatial model in C++ implemented in R using the packages Rcpp and RcppArmadillo (Eddelbuettel and Francois, 2011; Eddelbuettel and Sanderson, 2017) . Following Nagai and Honda (2001) and Howat et al. (2006) , we modeled this system on a two-dimensional hexagonal lattice, using a ten-minute epidemic timestep for cell state transitions. At the initialization of each simulation, we randomly assigned a duration of natural lifespan, incubation period, infectivity period, and time from antiviral to susceptible status to all cells in a theoretical monolayer. Parameter durations were drawn from a normal distribution centered at the inverse of the respective fixed rates of m, s, a, and c, as reported with our mean field model. Transitions involving the induced (r) and constitutive (") rates of antiviral acquisition were governed probabilistically and adjusted dynamically at each timestep based on the global environment. As such, we fixed these parameters at the same values estimated in the mean field model, and multiplied both r and " by the global proportion of, respectively, exposed and susceptible cells at a given timestep. In contrast to antiviral acquisition rates, transitions involving the birth rate (b) and the transmission rate (b) occurred probabilistically based on each cell's local environment. The birth rate, b, was multiplied by the proportion of susceptible cells within a six-neighbor circumference of a focal dead cell, while b was multiplied by the proportion of infectious cells within a thirty-six neighbor vicinity of a focal susceptible cell, thus allowing viral transmission to extend beyond the immediate nearestneighbor boundaries of an infectious cell. To compensate for higher thresholds to cellular persistence and virus invasion which occur under local spatial conditions (Webb et al., 2007) , we increased the birth rate, b, and the cell-to-cell transmission rate, b, respectively, to six and ten times the values used in the mean field model (Supplementary file 4) . We derived these increases based on the assumption that births took place exclusively based on pairwise nearest-neighbor interactions (the six immediately adjacent cells to a focal dead cell), while viral transmission was locally concentrated but included a small (7.5%) global contribution, representing the thirty-six cell surrounding vicinity of a focal susceptible. We justify these increases and derive their origins further in Supplementary file 5. We simulated ten stochastic spatial time series for all cell-virus combinations under all three immune assumptions at a population size of 10,000 cells and compared model output with data in . Transparent reporting form Data availability All data generated or analysed during this study are included in the manuscript and supporting files. All images and code used in this study have been made available for download at the following Figshare

What is presented in this study?

1918 Influenza: the Mother of All Pandemics Jeffery K. Taubenberger" and David M. Morens1- The “Spanish" influenza pandemic of 1918—1919, which caused :50 million deaths worldwide, remains an ominous warning to public health. Many questions about its origins, its unusual epidemiologic features, and the basis of its pathogenicity remain unanswered. The public health implications of the pandemic therefore remain in doubt even as we now grapple with the feared emergence of a pandemic caused by H5N1 or other virus. However, new information about the 1918 virus is emerging, for example, sequencing of the entire genome from archival autopsy tis- sues. But, the viral genome alone is unlikely to provide answers to some critical questions. Understanding the 1918 pandemic and its implications for future pandemics requires careful experimentation and in-depth historical analysis. ”Curiouser and curiouser/ ” criedAlice Lewis Carroll, Alice’s Adventures in Wonderland, 1865 An estimated one third of the world’s population (or z500 million persons) were infected and had clinical- ly apparent illnesses (1,2) during the 191871919 influenza pandemic. The disease was exceptionally severe. Case- fatality rates were >2.5%, compared to <0.1% in other influenza pandemics (3,4). Total deaths were estimated at z50 million (577) and were arguably as high as 100 mil- lion (7). The impact of this pandemic was not limited to 191871919. All influenza A pandemics since that time, and indeed almost all cases of influenza A worldwide (except- ing human infections from avian Viruses such as H5N1 and H7N7), have been caused by descendants of the 1918 Virus, including “drifted” H1N1 Viruses and reassorted H2N2 and H3N2 Viruses. The latter are composed of key genes from the 1918 Virus, updated by subsequently-incor— porated avian influenza genes that code for novel surface *Armed Forces Institute of Pathology, Rockville, Maryland, USA; and TNational Institutes of Health, Bethesda, Maryland, USA proteins, making the 1918 Virus indeed the “mother” of all pandemics. In 1918, the cause of human influenza and its links to avian and swine influenza were unknown. Despite clinical and epidemiologic similarities to influenza pandemics of 1889, 1847, and even earlier, many questioned whether such an explosively fatal disease could be influenza at all. That question did not begin to be resolved until the 1930s, when closely related influenza Viruses (now known to be H1N1 Viruses) were isolated, first from pigs and shortly thereafter from humans. Seroepidemiologic studies soon linked both of these viruses to the 1918 pandemic (8). Subsequent research indicates that descendants of the 1918 Virus still persists enzootically in pigs. They probably also circulated continuously in humans, undergoing gradual antigenic drift and causing annual epidemics, until the 1950s. With the appearance of a new H2N2 pandemic strain in 1957 (“Asian flu”), the direct H1N1 Viral descen- dants 0f the 1918 pandemic strain disappeared from human circulation entirely, although the related lineage persisted enzootically in pigs. But in 1977, human H1N1 Viruses suddenly “reemerged” from a laboratory freezer (9). They continue to circulate endemically and epidemically. Thus in 2006, 2 major descendant lineages of the 1918 H1N1 Virus, as well as 2 additional reassortant lineages, persist naturally: a human epidemic/endemic H1N1 line- age, a porcine enzootic H1N1 lineage (so-called classic swine flu), and the reassorted human H3N2 Virus lineage, which like the human H1N1 Virus, has led to a porcine H3N2 lineage. None of these Viral descendants, however, approaches the pathogenicity of the 1918 parent Virus. Apparently, the porcine H1N1 and H3N2 lineages uncom- monly infect humans, and the human H1N1 and H3N2 lin- eages have both been associated with substantially lower rates ofillness and death than the virus of 1918. In fact, cur- rent H1N1 death rates are even lower than those for H3N2 lineage strains (prevalent from 1968 until the present). H1N1 Viruses descended from the 1918 strain, as well as H3N2 Viruses, have now been cocirculating worldwide for 29 years and show little evidence of imminent extinction. Trying To Understand What Happened By the early 1990s, 75 years of research had failed to answer a most basic question about the 1918 pandemic: why was it so fatal? No Virus from 1918 had been isolated, but all of its apparent descendants caused substantially milder human disease. Moreover, examination of mortality data from the 1920s suggests that within a few years after 1918, influenza epidemics had settled into a pattern of annual epidemicity associated with strain drifting and sub- stantially lowered death rates. Did some critical Viral genet- ic event produce a 1918 Virus of remarkable pathogenicity and then another critical genetic event occur soon after the 1918 pandemic to produce an attenuated H1N1 Virus? In 1995, a scientific team identified archival influenza autopsy materials collected in the autumn of 1918 and began the slow process of sequencing small Viral RNA fragments to determine the genomic structure of the causative influenza Virus (10). These efforts have now determined the complete genomic sequence of 1 Virus and partial sequences from 4 others. The primary data from the above studies (11717) and a number of reviews covering different aspects of the 1918 pandemic have recently been published ([8720) and confirm that the 1918 Virus is the likely ancestor of all 4 of the human and swine H1N1 and H3N2 lineages, as well as the “extinct” H2N2 lineage. No known mutations correlated with high pathogenicity in other human or animal influenza Viruses have been found in the 1918 genome, but ongoing studies to map Virulence factors are yielding interesting results. The 1918 sequence data, however, leave unanswered questions about the ori- gin of the Virus (19) and about the epidemiology of the pandemic. When and Where Did the 1918 Influenza Pandemic Arise? Before and after 1918, most influenza pandemics developed in Asia and spread from there to the rest of the world. Confounding definite assignment of a geographic point of origin, the 1918 pandemic spread more or less simultaneously in 3 distinct waves during an z12-month period in 191871919, in Europe, Asia, and North America (the first wave was best described in the United States in March 1918). Historical and epidemiologic data are inade- quate to identify the geographic origin of the Virus (21), and recent phylogenetic analysis of the 1918 Viral genome does not place the Virus in any geographic context ([9). Although in 1918 influenza was not a nationally reportable disease and diagnostic criteria for influenza and pneumonia were vague, death rates from influenza and pneumonia in the United States had risen sharply in 1915 and 1916 because of a major respiratory disease epidemic beginning in December 1915 (22). Death rates then dipped slightly in 1917. The first pandemic influenza wave appeared in the spring of 1918, followed in rapid succes- sion by much more fatal second and third waves in the fall and winter of 191871919, respectively (Figure 1). Is it pos- sible that a poorly-adapted H1N1 Virus was already begin- ning to spread in 1915, causing some serious illnesses but not yet sufficiently fit to initiate a pandemic? Data consis- tent with this possibility were reported at the time from European military camps (23), but a counter argument is that if a strain with a new hemagglutinin (HA) was caus- ing enough illness to affect the US national death rates from pneumonia and influenza, it should have caused a pandemic sooner, and when it eventually did, in 1918, many people should have been immune or at least partial- ly immunoprotected. “Herald” events in 1915, 1916, and possibly even in early 1918, if they occurred, would be dif- ficult to identify. The 1918 influenza pandemic had another unique fea- ture, the simultaneous (or nearly simultaneous) infection of humans and swine. The Virus of the 1918 pandemic like- ly expressed an antigenically novel subtype to which most humans and swine were immunologically naive in 1918 (12,20). Recently published sequence and phylogenetic analyses suggest that the genes encoding the HA and neu- raminidase (NA) surface proteins of the 1918 Virus were derived from an avianlike influenza Virus shortly before the start of the pandemic and that the precursor Virus had not circulated widely in humans or swine in the few decades before (12,15, 24). More recent analyses of the other gene segments of the Virus also support this conclu- sion. Regression analyses of human and swine influenza sequences obtained from 1930 to the present place the ini- tial circulation of the 1918 precursor Virus in humans at approximately 191571918 (20). Thus, the precursor was probably not circulating widely in humans until shortly before 1918, nor did it appear to have jumped directly from any species of bird studied to date (19). In summary, its origin remains puzzling. Were the 3 Waves in 1918—1 919 Caused by the Same Virus? If So, How and Why? Historical records since the 16th century suggest that new influenza pandemics may appear at any time of year, not necessarily in the familiar annual winter patterns of interpandemic years, presumably because newly shifted influenza Viruses behave differently when they find a uni- versal or highly susceptible human population. Thereafter, confronted by the selection pressures of population immu- nity, these pandemic Viruses begin to drift genetically and eventually settle into a pattern of annual epidemic recur- rences caused by the drifted Virus variants. Figure 1. Three pandemic waves: weekly combined influenza and pneumonia mortality, United Kingdom, 1918—1919 (21). In the 1918-1919 pandemic, a first or spring wave began in March 1918 and spread unevenly through the United States, Europe, and possibly Asia over the next 6 months (Figure 1). Illness rates were high, but death rates in most locales were not appreciably above normal. A sec- ond or fall wave spread globally from September to November 1918 and was highly fatal. In many nations, a third wave occurred in early 1919 (21). Clinical similari- ties led contemporary observers to conclude initially that they were observing the same disease in the successive waves. The milder forms of illness in all 3 waves were identical and typical of influenza seen in the 1889 pandem- ic and in prior interpandemic years. In retrospect, even the rapid progressions from uncomplicated influenza infec- tions to fatal pneumonia, a hallmark of the 191871919 fall and winter waves, had been noted in the relatively few severe spring wave cases. The differences between the waves thus seemed to be primarily in the much higher fre- quency of complicated, severe, and fatal cases in the last 2 waves. But 3 extensive pandemic waves of influenza within 1 year, occurring in rapid succession, with only the briefest of quiescent intervals between them, was unprecedented. The occurrence, and to some extent the severity, of recur- rent annual outbreaks, are driven by Viral antigenic drift, with an antigenic variant Virus emerging to become domi- nant approximately every 2 to 3 years. Without such drift, circulating human influenza Viruses would presumably disappear once herd immunity had reached a critical threshold at which further Virus spread was sufficiently limited. The timing and spacing of influenza epidemics in interpandemic years have been subjects of speculation for decades. Factors believed to be responsible include partial herd immunity limiting Virus spread in all but the most favorable circumstances, which include lower environ- mental temperatures and human nasal temperatures (bene- ficial to thermolabile Viruses such as influenza), optimal humidity, increased crowding indoors, and imperfect ven- tilation due to closed windows and suboptimal airflow. However, such factors cannot explain the 3 pandemic waves of 1918-1919, which occurred in the spring-sum- mer, summer—fall, and winter (of the Northern Hemisphere), respectively. The first 2 waves occurred at a time of year normally unfavorable to influenza Virus spread. The second wave caused simultaneous outbreaks in the Northern and Southern Hemispheres from September to November. Furthermore, the interwave peri- ods were so brief as to be almost undetectable in some locales. Reconciling epidemiologically the steep drop in cases in the first and second waves with the sharp rises in cases of the second and third waves is difficult. Assuming even transient postinfection immunity, how could suscep- tible persons be too few to sustain transmission at 1 point, and yet enough to start a new explosive pandemic wave a few weeks later? Could the Virus have mutated profoundly and almost simultaneously around the world, in the short periods between the successive waves? Acquiring Viral drift sufficient to produce new influenza strains capable of escaping population immunity is believed to take years of global circulation, not weeks of local circulation. And hav- ing occurred, such mutated Viruses normally take months to spread around the world. At the beginning of other “off season” influenza pan- demics, successive distinct waves within a year have not been reported. The 1889 pandemic, for example, began in the late spring of 1889 and took several months to spread throughout the world, peaking in northern Europe and the United States late in 1889 or early in 1890. The second recurrence peaked in late spring 1891 (more than a year after the first pandemic appearance) and the third in early 1892 (21 ). As was true for the 1918 pandemic, the second 1891 recurrence produced of the most deaths. The 3 recur- rences in 1889-1892, however, were spread over >3 years, in contrast to 191871919, when the sequential waves seen in individual countries were typically compressed into z879 months. What gave the 1918 Virus the unprecedented ability to generate rapidly successive pandemic waves is unclear. Because the only 1918 pandemic Virus samples we have yet identified are from second-wave patients ([6), nothing can yet be said about whether the first (spring) wave, or for that matter, the third wave, represented circulation of the same Virus or variants of it. Data from 1918 suggest that persons infected in the second wave may have been pro- tected from influenza in the third wave. But the few data bearing on protection during the second and third waves after infection in the first wave are inconclusive and do lit- tle to resolve the question of whether the first wave was caused by the same Virus or whether major genetic evolu- tionary events were occurring even as the pandemic exploded and progressed. Only influenza RNAipositive human samples from before 1918, and from all 3 waves, can answer this question. What Was the Animal Host Origin of the Pandemic Virus? Viral sequence data now suggest that the entire 1918 Virus was novel to humans in, or shortly before, 1918, and that it thus was not a reassortant Virus produced from old existing strains that acquired 1 or more new genes, such as those causing the 1957 and 1968 pandemics. On the con- trary, the 1918 Virus appears to be an avianlike influenza Virus derived in toto from an unknown source (17,19), as its 8 genome segments are substantially different from contemporary avian influenza genes. Influenza Virus gene sequences from a number offixed specimens ofwild birds collected circa 1918 show little difference from avian Viruses isolated today, indicating that avian Viruses likely undergo little antigenic change in their natural hosts even over long periods (24,25). For example, the 1918 nucleoprotein (NP) gene sequence is similar to that ofviruses found in wild birds at the amino acid level but very divergent at the nucleotide level, which suggests considerable evolutionary distance between the sources of the 1918 NP and of currently sequenced NP genes in wild bird strains (13,19). One way of looking at the evolutionary distance of genes is to com- pare ratios of synonymous to nonsynonymous nucleotide substitutions. A synonymous substitution represents a silent change, a nucleotide change in a codon that does not result in an amino acid replacement. A nonsynonymous substitution is a nucleotide change in a codon that results in an amino acid replacement. Generally, a Viral gene sub- jected to immunologic drift pressure or adapting to a new host exhibits a greater percentage of nonsynonymous mutations, while a Virus under little selective pressure accumulates mainly synonymous changes. Since little or no selection pressure is exerted on synonymous changes, they are thought to reflect evolutionary distance. Because the 1918 gene segments have more synony- mous changes from known sequences of wild bird strains than expected, they are unlikely to have emerged directly from an avian influenza Virus similar to those that have been sequenced so far. This is especially apparent when one examines the differences at 4-fold degenerate codons, the subset of synonymous changes in which, at the third codon position, any of the 4 possible nucleotides can be substituted without changing the resulting amino acid. At the same time, the 1918 sequences have too few amino acid difierences from those of wild-bird strains to have spent many years adapting only in a human or swine intermedi- ate host. One possible explanation is that these unusual gene segments were acquired from a reservoir of influenza Virus that has not yet been identified or sampled. All of these findings beg the question: where did the 1918 Virus come from? In contrast to the genetic makeup of the 1918 pandem- ic Virus, the novel gene segments of the reassorted 1957 and 1968 pandemic Viruses all originated in Eurasian avian Viruses (26); both human Viruses arose by the same mech- anismireassortment of a Eurasian wild waterfowl strain with the previously circulating human H1N1 strain. Proving the hypothesis that the Virus responsible for the 1918 pandemic had a markedly different origin requires samples of human influenza strains circulating before 1918 and samples of influenza strains in the wild that more closely resemble the 1918 sequences. What Was the Biological Basis for 1918 Pandemic Virus Pathogenicity? Sequence analysis alone does not ofier clues to the pathogenicity of the 1918 Virus. A series of experiments are under way to model Virulence in Vitro and in animal models by using Viral constructs containing 1918 genes produced by reverse genetics. Influenza Virus infection requires binding of the HA protein to sialic acid receptors on host cell surface. The HA receptor-binding site configuration is different for those influenza Viruses adapted to infect birds and those adapted to infect humans. Influenza Virus strains adapted to birds preferentially bind sialic acid receptors with 01 (273) linked sugars (27729). Human-adapted influenza Viruses are thought to preferentially bind receptors with 01 (2%) link- ages. The switch from this avian receptor configuration requires of the Virus only 1 amino acid change (30), and the HAs of all 5 sequenced 1918 Viruses have this change, which suggests that it could be a critical step in human host adaptation. A second change that greatly augments Virus binding to the human receptor may also occur, but only 3 of5 1918 HA sequences have it (16). This means that at least 2 H1N1 receptor-binding vari- ants cocirculated in 1918: 1 with high—affinity binding to the human receptor and 1 with mixed-affinity binding to both avian and human receptors. No geographic or chrono- logic indication eXists to suggest that one of these variants was the precursor of the other, nor are there consistent dif- ferences between the case histories or histopathologic fea- tures of the 5 patients infected with them. Whether the Viruses were equally transmissible in 1918, whether they had identical patterns of replication in the respiratory tree, and whether one or both also circulated in the first and third pandemic waves, are unknown. In a series of in Vivo experiments, recombinant influen- za Viruses containing between 1 and 5 gene segments of the 1918 Virus have been produced. Those constructs bearing the 1918 HA and NA are all highly pathogenic in mice (31). Furthermore, expression microarray analysis performed on whole lung tissue of mice infected with the 1918 HA/NA recombinant showed increased upregulation of genes involved in apoptosis, tissue injury, and oxidative damage (32). These findings are unexpected because the Viruses with the 1918 genes had not been adapted to mice; control experiments in which mice were infected with modern human Viruses showed little disease and limited Viral replication. The lungs of animals infected with the 1918 HA/NA construct showed bronchial and alveolar epithelial necrosis and a marked inflammatory infiltrate, which suggests that the 1918 HA (and possibly the NA) contain Virulence factors for mice. The Viral genotypic basis of this pathogenicity is not yet mapped. Whether pathogenicity in mice effectively models pathogenicity in humans is unclear. The potential role of the other 1918 pro- teins, singularly and in combination, is also unknown. Experiments to map further the genetic basis of Virulence of the 1918 Virus in various animal models are planned. These experiments may help define the Viral component to the unusual pathogenicity of the 1918 Virus but cannot address whether specific host factors in 1918 accounted for unique influenza mortality patterns. Why Did the 1918 Virus Kill So Many Healthy Young Ad ults? The curve of influenza deaths by age at death has histor- ically, for at least 150 years, been U-shaped (Figure 2), exhibiting mortality peaks in the very young and the very old, with a comparatively low frequency of deaths at all ages in between. In contrast, age-specific death rates in the 1918 pandemic exhibited a distinct pattern that has not been documented before or since: a “W—shaped” curve, similar to the familiar U-shaped curve but with the addition of a third (middle) distinct peak of deaths in young adults z20410 years of age. Influenza and pneumonia death rates for those 1534 years of age in 191871919, for example, were 20 times higher than in previous years (35). Overall, near- ly half of the influenza—related deaths in the 1918 pandem- ic were in young adults 20410 years of age, a phenomenon unique to that pandemic year. The 1918 pandemic is also unique among influenza pandemics in that absolute risk of influenza death was higher in those <65 years of age than in those >65; persons <65 years of age accounted for >99% of all excess influenza—related deaths in 191871919. In com- parison, the <65-year age group accounted for 36% of all excess influenza—related deaths in the 1957 H2N2 pandem- ic and 48% in the 1968 H3N2 pandemic (33). A sharper perspective emerges when 1918 age-specific influenza morbidity rates (21) are used to adj ust the W- shaped mortality curve (Figure 3, panels, A, B, and C [35,37]). Persons 65 years of age in 1918 had a dispro- portionately high influenza incidence (Figure 3, panel A). But even after adjusting age-specific deaths by age-specif— ic clinical attack rates (Figure 3, panel B), a W—shaped curve with a case-fatality peak in young adults remains and is significantly different from U-shaped age-specific case- fatality curves typically seen in other influenza years, e.g., 192871929 (Figure 3, panel C). Also, in 1918 those 5 to 14 years of age accounted for a disproportionate number of influenza cases, but had a much lower death rate from influenza and pneumonia than other age groups. To explain this pattern, we must look beyond properties of the Virus to host and environmental factors, possibly including immunopathology (e.g., antibody-dependent infection enhancement associated with prior Virus exposures [38]) and exposure to risk cofactors such as coinfecting agents, medications, and environmental agents. One theory that may partially explain these findings is that the 1918 Virus had an intrinsically high Virulence, tem- pered only in those patients who had been born before 1889, e.g., because of exposure to a then-circulating Virus capable of providing partial immunoprotection against the 1918 Virus strain only in persons old enough (>35 years) to have been infected during that prior era (35). But this the- ory would present an additional paradox: an obscure pre- cursor Virus that left no detectable trace today would have had to have appeared and disappeared before 1889 and then reappeared more than 3 decades later. Epidemiologic data on rates of clinical influenza by age, collected between 1900 and 1918, provide good evi- dence for the emergence of an antigenically novel influen- za Virus in 1918 (21). Jordan showed that from 1900 to 1917, the 5- to 15-year age group accounted for 11% of total influenza cases, while the >65-year age group accounted for 6 % of influenza cases. But in 1918, cases in Figure 2. “U-” and “W—” shaped combined influenza and pneumo- nia mortality, by age at death, per 100,000 persons in each age group, United States, 1911—1918. Influenza- and pneumonia- specific death rates are plotted for the interpandemic years 1911—1917 (dashed line) and for the pandemic year 1918 (solid line) (33,34). Incidence male per 1 .nao persunslage group Mortality per 1.000 persunslige group + Case—fataiity rale 1918—1919 Case fatalily par 100 persons ill wilh P&I pel age group Figure 3. Influenza plus pneumonia (P&l) (combined) age-specific incidence rates per 1,000 persons per age group (panel A), death rates per 1,000 persons, ill and well combined (panel B), and case-fatality rates (panel C, solid line), US Public Health Service house-to-house surveys, 8 states, 1918 (36). A more typical curve of age-specific influenza case-fatality (panel C, dotted line) is taken from US Public Health Service surveys during 1928—1929 (37). the 5 to 15-year-old group jumped to 25% of influenza cases (compatible with exposure to an antigenically novel Virus strain), while the >65-year age group only accounted for 0.6% of the influenza cases, findings consistent with previously acquired protective immunity caused by an identical or closely related Viral protein to which older per- sons had once been exposed. Mortality data are in accord. In 1918, persons >75 years had lower influenza and pneumonia case-fatality rates than they had during the prepandemic period of 191171917. At the other end of the age spectrum (Figure 2), a high proportion of deaths in infancy and early childhood in 1918 mimics the age pat- tern, if not the mortality rate, of other influenza pandemics. Could a 1918-like Pandemic Appear Again? If So, What Could We Do About It? In its disease course and pathologic features, the 1918 pandemic was different in degree, but not in kind, from previous and subsequent pandemics. Despite the extraordi- nary number of global deaths, most influenza cases in 1918 (>95% in most locales in industrialized nations) were mild and essentially indistinguishable from influenza cases today. Furthermore, laboratory experiments with recombi- nant influenza Viruses containing genes from the 1918 Virus suggest that the 1918 and 1918-like Viruses would be as sensitive as other typical Virus strains to the Food and Drug Administrationiapproved antiinfluenza drugs riman- tadine and oseltamivir. However, some characteristics of the 1918 pandemic appear unique: most notably, death rates were 5 7 20 times higher than expected. Clinically and pathologically, these high death rates appear to be the result of several factors, including a higher proportion of severe and complicated infections of the respiratory tract, rather than involvement of organ systems outside the normal range of the influenza Virus. Also, the deaths were concentrated in an unusually young age group. Finally, in 1918, 3 separate recurrences of influenza followed each other with unusual rapidity, resulting in 3 explosive pandemic waves within a year’s time (Figure 1). Each of these unique characteristics may reflect genetic features of the 1918 Virus, but understand- ing them will also require examination of host and envi- ronmental factors. Until we can ascertain which of these factors gave rise to the mortality patterns observed and learn more about the formation of the pandemic, predictions are only educated guesses. We can only conclude that since it happened once, analogous conditions could lead to an equally devastating pandemic. Like the 1918 Virus, H5N1 is an avian Virus (39), though a distantly related one. The evolutionary path that led to pandemic emergence in 1918 is entirely unknown, but it appears to be different in many respects from the cur- rent situation with H5N1. There are no historical data, either in 1918 or in any other pandemic, for establishing that a pandemic “precursor” Virus caused a highly patho- genic outbreak in domestic poultry, and no highly patho- genic avian influenza (HPAI) Virus, including H5N1 and a number of others, has ever been known to cause a major human epidemic, let alone a pandemic. While data bearing on influenza Virus human cell adaptation (e.g., receptor binding) are beginning to be understood at the molecular level, the basis for Viral adaptation to efficient human-to- human spread, the chief prerequisite for pandemic emer- gence, is unknown for any influenza Virus. The 1918 Virus acquired this trait, but we do not know how, and we cur- rently have no way of knowing whether H5N1 Viruses are now in a parallel process of acquiring human-to-human transmissibility. Despite an explosion of data on the 1918 Virus during the past decade, we are not much closer to understanding pandemic emergence in 2006 than we were in understanding the risk of H1N1 “swine flu” emergence in 1976. Even with modern antiviral and antibacterial drugs, vaccines, and prevention knowledge, the return of a pan- demic Virus equivalent in pathogenicity to the Virus of 1918 would likely kill >100 million people worldwide. A pandemic Virus with the (alleged) pathogenic potential of some recent H5N1 outbreaks could cause substantially more deaths. Whether because of Viral, host or environmental fac- tors, the 1918 Virus causing the first or ‘spring’ wave was not associated with the exceptional pathogenicity of the second (fall) and third (winter) waves. Identification of an influenza RNA-positive case from the first wave could point to a genetic basis for Virulence by allowing differ- ences in Viral sequences to be highlighted. Identification of pre-1918 human influenza RNA samples would help us understand the timing of emergence of the 1918 Virus. Surveillance and genomic sequencing of large numbers of animal influenza Viruses will help us understand the genet- ic basis of host adaptation and the extent of the natural reservoir of influenza Viruses. Understanding influenza pandemics in general requires understanding the 1918 pan- demic in all its historical, epidemiologic, and biologic aspects. Dr Taubenberger is chair of the Department of Molecular Pathology at the Armed Forces Institute of Pathology, Rockville, Maryland. His research interests include the molecular patho- physiology and evolution of influenza Viruses. Dr Morens is an epidemiologist with a long-standing inter- est in emerging infectious diseases, Virology, tropical medicine, and medical history. Since 1999, he has worked at the National Institute of Allergy and Infectious Diseases. References 1. Frost WH. Statistics of influenza morbidity. Public Health Rep. 19203558497. 2. Bumet F, Clark E. Influenza: a survey ofthe last 50 years in the light of modern work on the Virus of epidemic influenza. Melbourne: MacMillan; 1942. 3. Marks G, Beatty WK. Epidemics. New York: Scribners, 1976. 4. Rosenau MJ, Last JM. Maxcy-Rosenau preventative medicine and public health. New York: Appleton-Century-Crofts; 1980. 5. Crosby A. America’s forgotten pandemic. Cambridge (UK): Cambridge University Press;1989. 6. Patterson KD, Pyle GF. The geography and mortality of the 1918 influenza pandemic. Bull Hist Med. 1991;65:4–21. 7. Johnson NPAS, Mueller J. Updating the accounts: global mortality of the 1918–1920 “Spanish” influenza pandemic. Bull Hist Med 2002;76:105–15. 8. Shope RE. The incidence of neutralizing antibodies for swine influenza virus in the sera of human beings of different ages. J Exp Med. 1936;63:669–84. 9. Kendal AP, Noble GR, Skehel JJ, Dowdle WR. Antigenic similarity of influenza A (H1N1) viruses from epidemics in 1977–1978 to “Scandinavian” strains isolated in epidemics of 1950–1951. Virology. 1978;89:632–6. 10. Taubenberger JK, Reid AH, Krafft AE, Bijwaard KE, Fanning TG. Initial genetic characterization of the 1918 “Spanish” influenza virus. Science. 1997;275:1793–6. 11. Basler CF, Reid AH, Dybing JK, Janczewski TA, Fanning TG, Zheng H, et al. Sequence of the 1918 pandemic influenza virus nonstructural gene (NS) segment and characterization of recombinant viruses bearing the 1918 NS genes. Proc Natl Acad Sci U S A 2001;98:2746–51. 12. Reid AH, Fanning TG, Hultin JV, Taubenberger JK. Origin and evolution of the 1918 “Spanish” influenza virus hemagglutinin gene. Proc Natl Acad Sci U S A 1999;96:1651–6. 13. Reid AH, Fanning TG, Janczewski TA, Lourens RM, and Taubenberger JK. Novel origin of the 1918 pandemic influenza virus nucleoprotein gene segment. J Virol. 2004;78:12462–70. 14. Reid AH, Fanning TG, Janczewski TA, McCall S, Taubenberger JK. Characterization of the 1918 “Spanish” influenza virus matrix gene segment. J Virol. 2002;76:10717–23. 15. Reid AH, Fanning TG, Janczewski TA, Taubenberger JK. Characterization of the 1918 “Spanish” influenza virus neuraminidase gene. Proc Natl Acad Sci U S A 2000;97:6785–90. 16. Reid AH, Janczewski TA, Lourens RM, Elliot AJ, Daniels RS, Berry CL, et al. 1918 influenza pandemic caused by highly conserved viruses with two receptor-binding variants. Emerg Infect Dis. 2003;9:1249–53. 17. Taubenberger JK, Reid AH, Lourens RM, Wang R, Jin G, Fanning TG. Characterization of the 1918 influenza virus polymerase genes. Nature. 2005;437:889–93. 18. Reid AH, Taubenberger JK. The 1918 flu and other influenza pandemics: “over there” and back again. Lab Invest. 1999;79:95–101. 19. Reid AH, Taubenberger JK, Fanning TG. Evidence of an absence: the genetic origins of the 1918 pandemic influenza virus. Nat Rev Microbiol. 2004;2:909–14. 20. Taubenberger JK, Reid AH, Fanning TG. The 1918 influenza virus: a killer comes into view. Virology. 2000;274:241–5. 21. Jordan E. Epidemic influenza: a survey. Chicago: American Medical Association, 1927. 22. Capps J, Moody A. The recent epidemic of grip. JAMA. 1916;67:1349–50. 33. Oxford JS, Sefton A, Jackson R, Innes W, Daniels RS, Johnson NP. World War I may have allowed the emergence of “Spanish” influenza. Lancet Infect Dis. 2002;2:111–4. 24. Fanning TG, Slemons RD, Reid AH, Janczewski TA, Dean J, Taubenberger JK. 1917 avian influenza virus sequences suggest that the 1918 pandemic virus did not acquire its hemagglutinin directly from birds. J Virol. 2002;76:7860–2. 25. Reid AH, Fanning TG, Slemons RD, Janczewski TA, Dean J, Taubenberger JK. Relationship of pre-1918 avian influenza HA and NP sequences to subsequent avian influenza strains. Avian Dis. 2003;47:921–5. 26. Bean W, Schell M, Katz J, Kawaoka Y, Naeve C, Gorman O, et al. Evolution of the H3 influenza virus hemagglutinin from human and nonhuman hosts. J Virol. 1992;66:1129–38. 27. Weis W, Brown JH, Cusack S, Paulson JC, Skehel JJ, Wiley DC. Structure of the influenza virus haemagglutinin complexed with its receptor, sialic acid. Nature. 1988;333:426–31. 28. Gambaryan AS, Tuzikov AB, Piskarev VE, Yamnikova SS, Lvov DK, Robertson JS, et al. Specification of receptor-binding phenotypes of influenza virus isolates from different hosts using synthetic sialylglycopolymers: non-egg-adapted human H1 and H3 influenza A and influenza B viruses share a common high binding affinity for 6′-sialyl(N-acetyllactosamine). Virology. 1997;232: 345–50. 29. Matrosovich M, Gambaryan A, Teneberg S, Piskarev VE, Yamnikova SS, Lvov DK, et al. Avian influenza A viruses differ from human viruses by recognition of sialyloigosaccharides and gangliosides and by a higher conservation of the HA receptor-binding site. Virology. 1997;233:224–34. 30. Glaser L, Stevens J, Zamarin D, Wilson IA, Garcia-Sastre A, Tumpey TM, et al. A single amino acid substitution in the 1918 influenza virus hemagglutinin changes the receptor binding specificity. J Virol. 2005;79:11533–6. 31. Kobasa D, Takada A, Shinya K, Hatta M, Halfmann P, Theriault S, et al. Enhanced virulence of influenza A viruses with the haemagglutinin of the 1918 pandemic virus. Nature. 2004;431:703–7. 32. Kash JC, Basler CF, Garcia-Sastre A, Carter V, Billharz R, Swayne DE, et al. Global host immune response: pathogenesis and transcriptional profiling of type A influenza viruses expressing the hemagglutinin and neuraminidase genes from the 1918 pandemic virus. J Virol. 2004;78:9499–511. 33. Grove RD, Hetzel AM. Vital statistics rates in the United States: 1940–1960. Washington: US Government Printing Office, 1968. 34. Linder FE, Grove RD. Vital statistics rates in the United States: 1900–1940. Washington: US Government Printing Office, 1943. 35. Simonsen L, Clarke MJ, Schonberger LB, Arden NH, Cox NJ, Fukuda K. Pandemic versus epidemic influenza mortality: a pattern of changing age distribution. J Infect Dis 1998;178:53–60. 36. Frost WH. The epidemiology of influenza. Public Health Rep. 1919;34:1823–61. 37. Collins SD. Age and sex incidence of influenza and pneumonia morbidity and mortality in the epidemic of 1928-1929 with comparative data for the epidemic of 1918–1919. Public Health Rep. 1931;46:1909–37. 38. Majde JA. Influenza: Learn from the past. ASM News. 1996;62:514. 39. Peiris JS, Yu WC, Leung CW, Cheung CY, Ng WF, Nicholls JM, et al. Re-emergence of fatal human influenza A subtype H5N1 disease. Lancet. 2004;363:617–9. Address for correspondence: Jeffery K. Taubenberger, Department of Molecular Pathology, Armed Forces Institute of Pathology, 1413 Research Blvd, Bldg 101, Rm 1057, Rockville, MD 20850-3125, USA; fax. 301-295-9507; email: [email protected] The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the Centers for Disease Control and Prevention or the institutions with which the authors are affiliated.

What was the death toll in the 1918-1919 Spanish Influenza epidemic?

A super-spreading ewe infects hundreds with Q fever at a farmers' market in Germany https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1618839/ SHA: ee1b5a9618dcc4080ed100486cedd0969e80fa4d Authors: Porten, Klaudia; Rissland, Jürgen; Tigges, Almira; Broll, Susanne; Hopp, Wilfried; Lunemann, Mechthild; van Treeck, Ulrich; Kimmig, Peter; Brockmann, Stefan O; Wagner-Wiening, Christiane; Hellenbrand, Wiebke; Buchholz, Udo Date: 2006-10-06 DOI: 10.1186/1471-2334-6-147 License: cc-by Abstract: BACKGROUND: In May 2003 the Soest County Health Department was informed of an unusually large number of patients hospitalized with atypical pneumonia. METHODS: In exploratory interviews patients mentioned having visited a farmers' market where a sheep had lambed. Serologic testing confirmed the diagnosis of Q fever. We asked local health departments in Germany to identiy notified Q fever patients who had visited the farmers market. To investigate risk factors for infection we conducted a case control study (cases were Q fever patients, controls were randomly selected Soest citizens) and a cohort study among vendors at the market. The sheep exhibited at the market, the herd from which it originated as well as sheep from herds held in the vicinity of Soest were tested for Coxiella burnetii (C. burnetii). RESULTS: A total of 299 reported Q fever cases was linked to this outbreak. The mean incubation period was 21 days, with an interquartile range of 16–24 days. The case control study identified close proximity to and stopping for at least a few seconds at the sheep's pen as significant risk factors. Vendors within approximately 6 meters of the sheep's pen were at increased risk for disease compared to those located farther away. Wind played no significant role. The clinical attack rate of adults and children was estimated as 20% and 3%, respectively, 25% of cases were hospitalized. The ewe that had lambed as well as 25% of its herd tested positive for C. burnetii antibodies. CONCLUSION: Due to its size and point source nature this outbreak permitted assessment of fundamental, but seldom studied epidemiological parameters. As a consequence of this outbreak, it was recommended that pregnant sheep not be displayed in public during the 3(rd )trimester and to test animals in petting zoos regularly for C. burnetii. Text: Q fever is a worldwide zoonosis caused by Coxiella burnetii (C. burnetii), a small, gram-negative obligate intracellular bacterium. C. burnetii displays antigenic variation with an infectious phase I and less infectious phase II. The primary reservoir from which human infection occurs consists of sheep, goat and cattle. Although C. burnetii infections in animals are usually asymptomatic, they may cause abortions in sheep and goats [1] . High concentrations of C. burnetii can be found in birth products of infected mammals [2] . Humans frequently acquire infection through inhalation of contaminated aerosols from parturient fluids, placenta or wool [1] . Because the infectious dose is very low [3] and C. burnetii is able to survive in a spore-like state for months to years, outbreaks among humans have also occurred through contaminated dust carried by wind over large distances [4] [5] [6] . C. burnetii infection in humans is asymptomatic in approximately 50% of cases. Approximately 5% of cases are hospitalized, and fatal cases are rare [1] . The clinical presentation of acute Q fever is variable and can resemble many other infectious diseases [2] . However, the most frequent clinical manifestation of acute Q fever is a self-limited febrile illness associated with severe headache. Atypical pneumonia and hepatitis are the major clinical manifestations of more severe disease. Acute Q fever may be complicated by meningoencephalitis or myocarditis. Rarely a chronic form of Q fever develops months after the acute illness, most commonly in the form of endocarditis [1] . Children develop clinical disease less frequently [7, 8] . Because of its non-specific presentation Q fever can only be suspected on clinical grounds and requires serologic confirmation. While the indirect immunofluorescence assay (IFA) is considered to be the reference method, complement fixation (CF), ELISA and microagglutination (MA) can also be used [9] . Acute infections are diagnosed by elevated IgG and/or IgM anti-phase II antibodies, while raised anti-phase I IgG antibodies are characteristic for chronic infections [1] . In Germany, acute Q fever is a notifiable disease. Between 1991 and 2000 the annual number of cases varied from 46 to 273 cases per year [10] . In 2001 and 2002, 293 and 191 cases were notified, respectively [11, 12] . On May 26, 2003 the health department of Soest was informed by a local hospital of an unusually large number of patients with atypical pneumonia. Some patients reported having visited a farmers' market that took place on May 3 and 4, 2003 in a spa town near Soest. Since the etiology was unclear, pathogens such as SARS coronavirus were considered and strict infection control measures implemented until the diagnosis of Q fever was confirmed. An outbreak investigation team was formed and included public health professionals from the local health department, the local veterinary health department, the state health department, the National Consulting Laboratory (NCL) for Coxiellae and the Robert Koch-Institute (RKI), the federal public health institute. Because of the size and point source appearance of the outbreak the objective of the investigation was to identify etiologic factors relevant to the prevention and control of Q fever as well as to assess epidemiological parameters that can be rarely studied otherwise. On May 26 and 27, 2003 we conducted exploratory interviews with patients in Soest hospitalized due to atypical pneumonia. Attending physicians were requested to test serum of patients with atypical pneumonia for Mycoplasma pneumoniae, Chlamydia pneumoniae, Legionella pneumophila, Coxiella burnetii, Influenza A and B, Parainfluenza 1-3, Adenovirus and Enterovirus. Throat swabs were tested for Influenza virus, Adenovirus and SARS-Coronavirus. Laboratory confirmation of an acute Q fever infection was defined as the presence of IgM antibodies against phase II C. burnetii antigens (ELISA or IFA), a 4-fold increase in anti-phase II IgG antibody titer (ELISA or IFA) or in anti phase II antibody titer by CF between acute and convalescent sera. A chronic infection was confirmed when both anti-phase I IgG and anti-phase II IgG antibody titers were raised. Because patients with valvular heart defects and pregnant women are at high risk of developing chronic infection [13, 14] we alerted internists and gynaecologists through the journal of the German Medical Association and asked them to send serum samples to the NCL if they identified patients from these risk groups who had been at the farmers' market during the outbreak. The objective of the first case control study was to establish whether there was a link between the farmers' market and the outbreak and to identify other potential risk factors. We conducted telephone interviews using a standardised questionnaire that asked about attendance at the farmers' market, having been within 1 km distance of one of 6 sheep flocks in the area, tick bites and consumption of unpasteurized milk, sheep or goat cheese. For the purpose of CCS1 we defined a case (CCS1 case) as an adult resident of the town of Soest notified to the statutory sur-veillance system with Q fever, having symptom onset between May 4 and June 3, 2003. Exclusion criterion was a negative IgM-titer against phase II antigens. Two controls per case were recruited from Soest inhabitants by random digit dialing. We calculated the attributable fraction of cases exposed to the farmers' market on May 4 (AFE) as (OR-1)/OR and the attributable fraction for all cases due to this exposure as: The farmers' market was held in a spa town near Soest with many visitors from other areas of the state and even the entire country. To determine the outbreak size we therefore asked local public health departments in Germany to ascertain a possible link to the farmers' market in Soest for all patients notified with Q-fever. A case in this context ("notified case") was defined as any person with a clinical diagnosis compatible with Q fever with or without laboratory confirmation and history of exposure to the farmers' market. Local health departments also reported whether a notified case was hospitalized. To obtain an independent, second estimate of the proportion of hospitalizations among symptomatic patients beyond that reported through the statutory surveillance system we calculated the proportion of hospitalized patients among those persons fulfilling the clinical case definition (as used in the vendors' study (s.b.)) identified through random sampling of the Soest population (within CCS2 (s.b.)) as well as in two cohorts (vendors' study and the 9 sailor friends (see below)). The objective of CCS2 was to identify risk factors associated with attendance of the farmers' market on the second day. We used the same case definition as in CCS1, but included only persons that had visited the farmers' market on May 4, the second day of the market. We selected controls again randomly from the telephone registry of Soest and included only those persons who had visited the farmers' market on May 4 and had not been ill with fever afterwards. Potential controls who became ill were excluded for analysis in CCS2, but were still fully interviewed. This permitted calculation of the attack rate among visitors to the market (see below "Estimation of the overall attack rate") and gave an estimate of the proportion of clinically ill cases that were hospitalized (s.a.). In the vendors' study we investigated whether the distance of the vendor stands from the sheep pen or dispersion of C. burnetii by wind were relevant risk factors for acquiring Q fever. We obtained a list of all vendors including the approximate location of the stands from the organizer. In addition we asked the local weather station for the predominant wind direction on May 4, 2003. Telephone interviews were performed using standardized questionnaires. A case was defined as a person with onset of fever between May 4 and June 3, 2003 and at least three of the following symptoms: headache, cough, dyspnea, joint pain, muscle pain, weight loss of more than 2 kg, fatigue, nausea or vomiting. The relative distance of the stands to the sheep pen was estimated by counting the stands between the sheep pen and the stand in question. Each stand was considered to be one stand unit (approximately 3 meters). Larger stands were counted as 2 units. The direction of the wind in relation to the sheep pen was defined by dividing the wind rose (360°) in 4 equal parts of 90°. The predominant wind direction during the market was south-south-east ( Figure 1 ). For the purpose of the analysis we divided the market area into 4 sections with the sheep pen at its center. In section 1 the wind was blowing towards the sheep pen (plus minus 45°). Section 4 was on the opposite side, i.e. where the wind blew from the sheep pen towards the stands, and sections 2 and 3 were east and west with respect to the wind direction, respectively. Location of the stands in reference to the sheep pen was thus defined in two ways: as the absolute distance to the sheep pen (in stand units or meters) and in reference to the wind direction. We identified a small cohort of 9 sailor friends who visited the farmers' market on May 4, 2003. All of these were serologically tested independently of symptoms. We could therefore calculate the proportion of laboratory confirmed persons who met the clinical case definition (as defined in the cohort study on vendors). The overall attack rate among adults was estimated based on the following sources: (1) Interviews undertaken for recruitment of controls for CCS2 allowed the proportion of adults that acquired symptomatic Q fever among those who visited the farmers' market on the second day; Attributable fraction AFE Number of cases exposed All cases = * (2) Interviews of cases and controls in CCS2 yielded information about accompanying adults and how many of these became later "ill with fever"; (3) Results of the small cohort of 9 sailor friends (s.a.); (4) Results from the cohort study on vendors. Local health departments that identified outbreak cases of Q fever (s.a. "determination of outbreak size and descriptive epidemiology") interviewed patients about the number of persons that had accompanied them to the farmers' market and whether any of these had become ill with fever afterwards. However, as there was no differentiation between adults and children, calculations to estimate the attack rate among adults were performed both with and without this source. To count cases in (1), (3) and (4) we used the clinical case definition as defined in the cohort study on vendors. For the calculation of the attack rate among children elicited in CCS2 was the same for all visitors. The number of children that visited the market could then be estimated from the total number of visitors as estimated by the organizers. We then estimated the number of symptomatic children (numerator). For this we assumed that the proportion of children with Q fever that were seen by physicians and were consequently notified was the same as that of adults. It was calculated as: Thus the true number of children with Q fever was estimated by the number of reported children divided by the estimated proportion reported. Then the attack rate among children could be estimated as follows: Because this calculation was based on several assumptions (number of visitors, proportion of adult visitors and clinical attack rate among adults) we performed a sensitivity analysis where the values of these variables varied. Serum was collected from all sheep and cows displayed in the farmers' market as well as from all sheep of the respective home flocks (70 animals). Samples of 25 sheep from five other flocks in the Soest area were also tested for C. burnetii. Tests were performed by ELISA with a phase I and phase II antigen mixture. We conducted statistical analysis with Epi Info, version 6.04 (CDC, Atlanta, USA). Dichotomous variables in the case control and cohort studies were compared using the Chi-Square test and numerical variables using the Kruskal-Wallis test. P-values smaller than 0.05 were considered statistically significant. The outbreak investigation was conducted within the framework of the Communicable Diseases Law Reform Act of Germany. Mandatory regulations were observed. Patients at the local hospital in Soest reported that a farmers' market had taken place on May 3 and 4, 2003 in a spa town close to the town of Soest. It was located in a park along the main promenade, spanning a distance of approximately 500 meters. The market attracted mainly three groups of people: locals, inhabitants of the greater Soest region, patients from the spa sanatoria and their visiting family or friends. Initial interviewees mentioned also that they had spent time at the sheep pen watching new-born lambs that had been born in the early morning hours of May 4, 2003 . The ewe had eaten the placenta but the parturient fluid on the ground had merely been covered with fresh straw. Overall 171 (65%) of 263 serum samples submitted to the NCL were positive for IgM anti-phase II antibodies by ELISA. Results of throat swabs and serum were negative for other infectious agents. (Figure 2 ). If we assume that symptom onset in cases was normally distributed with a mean of 21 days, 95% of cases (mean +/-2 standard deviations) had their onset between day 10 and 31. The two notified cases with early onset on May 6 and 8, respectively, were laboratory confirmed and additional interviews did not reveal any additional risk factors. Of the 298 cases with known gender, 158 (53%) were male and 140 (47%) were female. Of the notified cases, 189 (63%) were from the county of Soest, 104 (35%) were Porportion reported number of notified adults number of vis = i iting adults attack rate among adults * Attack rate among children estimated true number of childr = e en with Q fever estimated number of children at the market from other counties in the same federal state (Northrhine Westphalia) and 6 (2%) were from five other federal states in Germany (Figure 3 ). Only eight (3%) cases were less than 18 years of age, the mean and median age was 54 and 56 years, respectively ( Figure 4 ). 75 (25%) of 297 notified cases were hospitalized, none died. Calculation of the proportion of cases hospitalized through other information sources revealed that 4 of 19 (21%; 95% CI = 6-46%; (1/5 (CCS2), 2/11 (vendors study) and 1/3 (sailor friends)) clinically ill cases were hospitalized. Laboratory confirmation was reported in 167 (56%) outbreak cases; 66 (22%) were confirmed by an increase in anti-phase II antibody titer (CF), 89 (30%) had IgM antibodies against phase II antigens, 11 (4%) were positive in both tests and one was confirmed by culture. No information was available as to whether the 132 (44%) cases without laboratory confirmation were laboratory tested. 18 patients with valvular heart defects and eleven pregnant women were examined. None of them had clinical signs of Q fever. Two (11%) of 18 cardiological patients and four (36%) of 11 pregnant women had an acute Q fever infection. During childbirth strict hygienic measures were implemented. Lochia and colostrum of all infected women were tested by polymerase chain reaction and were positive in only one woman (case 3; Table 1 ). Serological follow-up of the mothers detected chronic infection in the same woman (case 3) 12 weeks after delivery. One year follow-up of two newborn children (of cases 1 and 3) identified neither acute nor chronic Q fever infections. We recruited 20 cases and 36 controls who visited the farmers' market on May 4 for the second case control study. They did not differ significantly in age and gender (OR for male sex = 1.7; 95%CI = 0.5-5.3; p = 0.26; p-value for age = 0.23). Seventeen (85%) of 20 cases indicated that they had seen the cow (that also was on display at the market next to the sheep) compared to 7 (32%) of Geographical location of Q fever outbreak cases notified to the statutory surveillance system Figure 3 Geographical location of Q fever outbreak cases notified to the statutory surveillance system. or directly at the gate of the sheep pen compared to 8 (32%) of 25 controls (OR = 5.0; 95%CI = 1.2-22.3; p = 0.03). Touching the sheep was also significantly more common among cases (5/20 (25%) CCS2 cases vs. 0/22 (0%) controls; OR undefined; lower 95% CI = 1.1; p = 0.02). 17 (85%) of 20 CCS2 cases, but only 6 (25%) of 24 controls stopped for at least a few seconds at or in the sheep pen, the reference for this variable was "having passed by the pen without stopping" (OR = 17.0; 95%CI = 3.0-112.5; p < 0.01). Among CCS2 cases, self-reported proximity to or time spent with/close to the sheep was not associated with a shorter incubation period. We were able to contact and interview 75 (86%) of 87 vendors, and received second hand information about 7 more (overall response rate: 94%). Fourty-five (56%) were male and 35 (44%) were female. 13 (16%) met the clinical case definition. Of the 11 vendors who worked within two stand units of the sheep pen, 6 (55%) became cases compared to only 7 (10%) of 70 persons who worked in a stand at a greater distance (relative risk (RR) = 5.5 (95%CI = 2.3-13.2; p = 0.002); Figure 1 ). Of these 7 vendors, 4 had spent time within 5 meters of the pen on May 4, one had been near the pen, but at a distance of more than 5 meters, and no information on this variable was available for the remaining 2. In the section of the market facing the wind coming from the pen (section 4, Figure 1 ), 4 (9%) of 44 vendors became cases, compared to 2 (13%) of 15 persons who worked in section 1 (p = 0.6). Among 22 persons who worked in stands that were perpendicular to the wind direction, 7 (32%) became cases. (Table 3 ). In all scenarios the AR among adults was significantly higher than that among children ( Figure 5 ). In total, 5 lambs and 5 ewes were displayed on the market, one of them was pregnant and gave birth to twin lambs at 6:30 a.m. on May 4, 2003 . Of these, 3 ewes including the one that had lambed tested positive for C. burnetii. The animals came from a flock of 67 ewes, of which 66 had given birth between February and June. The majority of the births (57 (86%)) had occurred in February and March, usually inside a stable or on a meadow located away from the town. Six ewes aborted, had stillbirths or abnormally weak lambs. Among all ewes, 17/67 (25%) tested positive for C. burnetii. The percentage of sheep that tested positive in the other 5 sheep flocks in the region ranged from 8% to 24% (8%; 12%; 12%; 16%; 24%). We have described one of the largest Q fever outbreaks in Germany which, due to its point-source nature, provided the opportunity to assess many epidemiological features of the disease that can be rarely studied otherwise. In 1954, more than 500 cases of Q fever were, similar to this outbreak, linked to the abortion of an infected cow at a farmers' market [15] . More recently a large outbreak occurred in Jena (Thuringia) in 2005 with 322 reported cases [16] associated with exposure to a herd of sheep kept on a meadow close to the housing area in which the cases occurred. The first case control study served to confirm the hypothesis of an association between the outbreak and the farmers' market. The fact that only attendance on the second, but not the first day was strongly associated with illness pointed towards the role of the ewe that had given birth Persons accompanying notified cases (source 5) were a mixture of adults and children and are therefore listed separately. in the early morning hours of May 4, 2005 . This strong association and the very high attributable fraction among all cases suggested a point source and justified defining cases notified through the reporting system as outbreak cases if they were clinically compatible with Q fever and gave a history of having visited the farmers' market. The point-source nature of the outbreak permitted calculation of the incubation period of cases which averaged 21 days and ranged from 2 to 48 days with an interquartile range of 16 to 24 days. This is compatible with the literature [1] . An additional interview with the two cases with early onset (2 and 4 days after attending the market on May 4, Attack rates among adults and children in a most likely scenario and 8 other scenarios Figure 5 Attack rates among adults and children in a most likely scenario and 8 other scenarios. Most likely scenario: 3000 visitors, 83% adult visitors and 20% clinical attack rate among adults. Scenarios 1-8 varied in the assumptions made for "number of visitors", "proportion of adult visitors" and "attack rate among adults" (see Table 3 ). Displayed are attack rates and 95% confidence intervals. respectively) could not identify any other source of infection. A short incubation period was recently observed in another Q fever outbreak in which the infectious dose was likely very high [17] . The second case control study among persons who visited the market on May 4 demonstrated that both close proximity to the ewe and duration of exposure were important risk factors. This finding was confirmed by the cohort study on vendors which showed that those who worked in a stand close to (within 6 meters) the sheep pen were at significantly higher risk of acquiring Q fever. The study failed to show a significant role of the location of the stand in reference to the wind direction, although we must take into account that the wind was likely not always and exactly as reported by the weather station. However, if the wind had been important at all more cases might have been expected to have occurred among vendors situated at a greater distance to the sheep. According to statutory surveillance system data, the proportion of clinical cases hospitalized was 25%, similar to the proportion of 21% found in persons pooled from the other studies conducted. Several publications report lower proportions than that found in this investigation: 4% (8/ 191) [7] , 5% [1] and 10% (4/39) [5] ), and there was at least one study with a much higher proportion (63% (10/ 16)) [18] . It is unlikely that hospitals reported cases with Q fever more frequently than private physicians because the proportion hospitalized among Q fever patients identified through random telephone calls in the Soest population or those in the two cohorts was similar to that of notified cases. Thus reporting bias is an unlikely explanation for the relatively high proportion of cases hospitalized. Alternative explanations include overly cautious referral practices on the part of attending physicians or the presumably high infectious dose of the organism in this outbreak, e.g. in those cases that spent time in the sheep pen. The estimated attack rate among adults in the four studies varied between 16% and 33%. The estimate of 23% based on the random sample of persons visiting the market on the second day would seem most immune to recall bias, even if this cannot be entirely ruled out. The estimation based on information about persons accompanying the cases may be subject to an overestimation because these individuals presumably had a higher probability of being close to the sheep pen, similar to the cases. On the other hand the estimate from the cohort study on vendors might be an underestimate, since the vendors obviously had a different purpose for being at the market and may have been less interested in having a look at the sheep. Nevertheless, all estimates were independent from each other and considering the various possible biases, they were remarkably similar. In comparison, in a different outbreak in Germany, in which inhabitants of a village were exposed to a large herd of sheep (n = 1000-2000) [5, 7] the attack rate was estimated as 16%. In a similar outbreak in Switzerland several villages were exposed to approximately 900 sheep [19] . In the most severely affected village, the clinical attack rate was 16% (estimated from the data provided) [19] . It is remarkable that in the outbreak described here, the infectious potential of one pregnant ewe -upon lambing -was comparable to that of entire herds, albeit in different settings. Our estimate of the proportion of serologically confirmed cases that became symptomatic (50% (3/6)) is based on a very small sample, but consistent with the international literature. In the above mentioned Swiss outbreak, 46% of serologically positive patients developed clinical disease [7] . Only approximately half of all symptomatic cases were reported to the statutory surveillance system. Patients who did not seek health care due to mild disease as well as underdiagnosis or underreporting may have contributed to the missing other half. Our estimated 3% attack rate among children is based on a number of successive assumptions and must therefore be interpreted with caution. Nevertheless, sensitivity analysis confirmed that adults had a significantly elevated attack rate compared to children. While it has been suggested that children are at lower risk than adults for developing symptomatic illness [7, 8] few data have been published regarding attack rates of children in comparison to adults. The estimated C. burnetii seroprevalence in the sheep flocks in the area varied from 8% to 24%. The 25% seroprevalence in the flock of the exhibited animals together with a positive polymerase chain reaction in an afterbirth in June 2003 suggested a recent infection of the flock [20] . Seroprevalence among sheep flocks related to human outbreaks tend to be substantially higher than those in flocks not related to human outbreaks. The median seroprevalence in a number of relevant studies performed in the context of human outbreaks [7, 20, 21] , was 40% compared to 1% in sheep flocks not linked to human outbreaks [20] . This outbreak shows the dramatic consequences of putting a large number of susceptible individuals in close contact to a single infected ewe that (in such a setting) can turn into a super-spreader upon lambing. There is always a cultural component in the interaction between people and animals, and these may contribute to outbreaks or changing patterns of incidence. During the past decades urbanization of rural areas and changes in animal husbandry have occurred [20] , with more recent attempts to put a "deprived" urban population "in touch" with farm animals. Petting zoos, family farm vacations or the display of (farm) animals at a market such as this may lead to new avenues for the transmission of zoonotic infectious agents [20, [22] [23] [24] . While not all eventualities can be foreseen, it is important to raise awareness in pet and livestock owners as well as to strengthen recommendations where necessary. This outbreak led to the amendment and extension of existing recommendations [25] which now forbid the display of sheep in the latter third of their pregnancy and require regular testing of animals for C. burnetii in petting zoos, where there is close contact between humans and animals. Due to the size and point source nature this outbreak permitted reassessment of fundamental, but seldom studied epidemiological parameters of Q fever. It also served to revise public health recommendations to account for the changing type and frequency of contact of susceptible humans with potentially infectious animals. Abbreviations AFE = attributable fraction of cases exposed The author(s) declare that they have no competing interests.

What was the interquartile range of the incubation period?

Mucosal immune responses induced by oral administration recombinant Bacillus subtilis expressing the COE antigen of PEDV in newborn piglets https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6418403/ SHA: 5caced13bcb8a42cca41369c5a71ae7df5381ca8 Authors: Wang, Jialu; Huang, Lulu; Mou, Chunxiao; Zhang, En; Wang, Yongheng; Cao, Yanan; Yang, Qian Date: 2019-03-15 DOI: 10.1042/bsr20182028 License: cc-by Abstract: Porcine epidemic diarrhea (PED) is a highly contagious disease in newborn piglets and causes substantial economic losses in the world. PED virus (PEDV) spreads by fecal–oral contact and can be prevented by oral immunization. Therefore, it is necessary to develop an effective oral vaccine against PEDV infection. Currently, Bacillus subtilis as recombinant vaccine carrier has been used for antigen delivery and proved well in immune effect and safety. The present study evaluated the immunogenicity of recombinant Bacillus subtilis (B. subtilis-RC) in piglets via oral administration. After oral immunization in piglets, B. subtilis-RC significantly increased the local mucosal immune responses. Oral administration with B. subtilis-RC significantly improved the level of specific mucosal immunoglobulin A (IgA) antibodies against PEDV infection, through enlarging the area of Peyer’s patches (PPs) and increasing the number of ileum IgA(+) secreting (SIgA) cells. In the meantime, B. subtilis-RC remarkably increased the number of intraepithelial lymphocytes (IELs). We also observed that oral administration of B. subtilis-RC significantly increased CD3(+)T lymphocytes’ numbers and up-regulated the ratio of CD4(+)/CD8(+) T cells. Furthermore, high titers of specific serum immunoglobulin G (IgG) revealed satisfactory systemic immune response against PEDV infection. In summary, our study demonstrated that oral administration of B. subtilis-RC could trigger a high level of local and systemic immune responses and would be a promising candidate vaccine against PEDV infection in piglets. Text: Porcine epidemic diarrhea (PED) characterized by highly fatal acute diarrhea in piglets, results in enormous losses in the worldwide pig industry [1] . The causative agent PED virus (PEDV) belongs to the porcine coronaviruses (CoVs). PEDV infection mainly spreads through the digestive tract [2] , and damages the host intestine mucosal surfaces by infecting the intestine epithelial cells [3] . Therfore enhancing intestinal mucosal immunity can elicit effective mucosal immune responses against PEDV infection [4] . Currently, traditional vaccines (intramuscular route or subcutaneous injection) have been developed and applied widely in the market [5] . These vaccines administered parenterally cannot effectively induce high titers of maternal antibodies and virus-specific IgA antibodies, resulting in inadequate mucosal protection to against PEDV infection [6] . Furthermore, these maternal antibodies in the milk were always degraded by gastric acid and pepsin before entering the intestinal tract. Effective PEDV vaccines must provide adequate mucosal protection in the intestinal tract. However, the effective vaccines are currently lacking [7] . As a superior way of mucosal immunization, oral administration can protect the gut and stimulate the common mucosal immune system [8] . Besides, oral immunization has several attractive features which include safety, and a straightforward, inexpensive, and needle-free approach [9] . Therefore, oral immunization often delivers large amounts of antigens to prevent the diarrheal diseases [10] . Nevertheless, there are several challenges by oral immunization, which consist of physical, chemical, and biological barriers when delivering antigens to the gastrointestinal (GI) tract (such as gastric acids, pepsin, and trypsin in the GI tract) [11] . It is a substantial problem that digestive acids and proteases can degrade antigen proteins for nutrient absorption [12] . Therefore, the vaccine delivery system has been applied to solve the problem. The system can protect antigens from the severe environment of the GI tract and deliver antigens to intestinal mucosa [13] . Currently, Bacillus subtilis (B. subtilis) is widely used as a vaccine delivery system for its unique characteristics. As a nonpathogenic Gram-positive bacterium, B. subtilis has been regarded as a novel probiotic and food additive in humans and animals [14] . The B. subtilis has adjuvant activity and can deliver heterologous antigens to the GI tract, providing additional immunity stimulation [15] . Besides, research had shown that orally administered B. subtilis could also enhance immune regulation and gut health in pigs [16] . Moreover, oral administration of B. subtilis could elicit humoral and cellular immune responses to the maintenance of gut homeostasis by dendritic cells (DCs) [17] . DCs are the most important professional antigen-presenting cells and can effectively regulate antibody titers [18] . DCs naturally exist in the gut-associated lymphoid tissue (GALT), including Peyer's patches (PPs), isolated lymphoid follicles (ILFs), mesenteric lymph nodes (MLNs), and scatter throughout the subepithelial lamina propria (LP) of the small intestine and colon [19] . Furthermore, B. subtilis is convenient for genetic manipulation and has developed a large variety of genetic tools [20] . Therefore, B. subtilis is widely used as an effective vaccine delivery system to induce mucosal immune responses and shows unique effect on the immune system. In the present report, we explored the immune effect of a recombinant B. subtilis (B. subtilis-RC) which had been successfully constructed with expressing PEDV COE protein in piglets. Our research indicated that B. subtilis-RC was beneficial to the mucosal immune system development, and could effectively generate specific antibodies against PEDV infection, suggesting a potential approach for preventing PEDV infection. The B. subtilis WB800 was kindly provided by Dr. Xuewen Gao (from the department of plant pathology, Nanjing Agricultural University) [21] . B. subtilis-RC previously constructed in our laboratory was able to express the gene COE (499-638 amino acids in S protein). Prior to oral administration, the recombinant strain was grown in LB broth at 37 • C for 12 h, and then washed twice with PBS, and suspended in PBS to reach a final concentration of 1 × 10 10 CFU/ml. The PEDV Zhejiang08 strain was provided by the Veterinary Medicine Research Centre of the Beijing Dabeinong Technology Group Co., Ltd. [22] . The virus was cultured in African green monkey kidney cells (Vero cells) and purified by using a discontinuous sucrose density gradient. The virus was UV-inactivated at UV dose of 4 J/cm 2 for 24 h to achieve a complete loss of infectivity [23] . The purified virus concentration was measured using the BCA protein assay kit (Thermo Fisher, MA, U.S.A.). ELISA: Rabbit anti-pig IgG (horseradish peroxidase (HRP)), Goat Anti-Pig IgA (HRP) were purchased from Abcam. Second antibody: DyLight 649-conjugated goat anti-mouse IgG antibody, DyLight 488-conjugated goat anti-rabbit IgG antibody, DyLight 594-conjugated goat anti-rabbit IgG antibody were purchased from Multi-science, Hangzhou, China. ABC-based system (biotinylated goat anti-rabbit IgG antibody) was used as the secondary antibody with DAB as a chromogen was purchased from Boster, Wuhan, China. Specific pathogen-free (SPF) DLY piglets (Duroc and Landrace and Yorkshire) were kindly provided by Jiangsu Academy of Agricultural Sciences (Nanjing, China). The animal experiments had been approved by the Institutional Animal Care and Use Committee of Nanjing Agricultural University and followed the National Institutes of Health's guidelines for the performance of animal experiments. Twelve newborn piglets were randomly divided into three groups (four piglets in each group), and housed under similar conditions in different stables in order to avoid probiotic cross-contamination. The piglets were orally dosed with 100 μl of B. subtilis-RC. The control groups of piglets were orally administered with inactivated PEDV (100 μg/dose) and equal volume of PBS. The immunization protocol was performed on the piglets that were 5 days old ( Figure 1C ), and signed as 0 day. Then booster immunizations were administered on 5 days. Specimen collection was then performed every 7 days post boost immunization ( Figure 1C ). Blood samples were collected weekly from all piglets after the boost immunization and allowed to clot overnight at room temperature to collect serum. Blood samples were separated by centrifugation and stored at −20 • C in order to detect the levels of specific IgG and IgA. Three swabs were collected every week lasting for 1 month, including nasal, oral, and feces swabs for the ELISA. The piglets were sacrificed in 33 days. The same location of the small intestine and ileum tissues from each piglet were fixed with Bonn's liquid and 4% paraformaldehyde. The small intestine tissues in same location were fixed with Bouin Fixative Solution for 24 h, embedded in paraffin, and sectioned at 4-μm thickness. The sections were placed on glass slides. Hematoxylin-eosin staining was applied to the paraffin sections, then observing and taking photographs under optical microscope (OLYMPUS CX23). The number of intraepithelial lymphocytes (IELs) were counted in every 100 epithelial cells under the same multiple light microscope amongst ten pictures from each group [24] . The immunohistochemistry detection was performed with the SABC kit (Boster Bioscience). Hydrogen peroxide was used to deactivate intrinsic peroxidase. Antigen retrieval was performed in a water bath using citrate-EDTA buffer (10 mM citric acid, 2 mM EDTA, 0.05% Tween 20, pH 6.2). Sections were incubated with diluted anti-IgA antibody (1:100; Abcam) overnight at 4 • C. As negative controls, immunostaining performed by incubating samples with control antiserum instead of primary antibody. The addition of biotin-labeled secondary antibody to the slides was followed by adding HRP-labeled streptavidin. After staining with DAB, the slides were recorded using a digital camera (Leica-DM4000B) [25] . The isolated intestines with PPs were transferred to ice-cold PBS. Then, remaining fat and connective tissue was removed and washed thoroughly with ice-cold PBS. Next, the intestine was cut longitudinally into 0.5-cm fragments. The fragments were incubated with 5 ml of 30 mM EDTA and placed in 5 ml digestion solution containing 4% FBS, 0.5 mg/ml each of Collagenase D (Roche) and DNase I (Sigma), and 50 U/ml Dispase (Fisher). The fragments were incubated with Dulbecco's PBS (DPBS) for 20 min at 37 • C by slow rotation (100 rpm). After incubating, the epithelial cells layer which contained the IELs were separated by intensive vortex and passed through a 70-μm cell strainer. Single cell suspension was collected and washed twice by DPBS, the solution was vortexed intensely and passed through a 40-μm cell strainer. Supernatants was washed by precooled RPMI medium 1640 (Thermo Fisher Scientific) and suspended by 10 ml of the 40% fraction of a 40:80 Percoll gradient, overlaid on 5 ml of the 80% fraction in a 15-ml Falcon tube. Percoll gradient separation was performed by centrifuging for 20 min at 2500 rpm. LP lymphocytes (LPLs) were collected at the interphase of the Percoll gradient, then washed and suspended in FACS buffer or T cell medium. In the meantime, flow cytometry analysis was performed on BD Facscalibur (BD Biosciences) instruments and analyzed by FlowJo software. All antibodies were purchased from BD Pharmingen or eBiosciences. Isolated single-cell suspensions were stained with anti-CD3-APC, anti-CD4-FITC, anti-CD8-PE, all at 1:100 dilution for 30 min on ice, and washed with PBS twice, and analyzed by FACS [26] . Cytokines interleukin (IL) 10 (IL-10) and IL-1β (Abcam) were measured by ELISA according to the manufacturer's instructions. Data were acquired on an automated ELISA plate reader at OD 450 nm immediately. PEDV neutralizing antibodies were measured in intestine washing liquid by plaque reduction neutralization test (PRNT). The test was performed as previously described with minor modifications [27] . A total of 450 μl of intestine washing liquid was two-fold serially diluted and mixed with 50 μl viral suspension containing 10 3 TCID 50 PEDV virus for 1 h at 37 • C in 12-well flat bottomed tissue culture plates. The mixture was then inoculated for 1 h at 37 • C and 5% CO 2 . Then, the mixture was inoculated with Vero cells suspension (approximately 1.0 × 10 6 ml −1 ) for another 3-4 days. After staining with Crystal Violet, the plates were observed under a microscope for cytopathic effect. Data were obtained as the means + − S.E.M. of three replicates per test in a single experiment. GraphPad Prism V6.0 (San Diego, CA, U.S.A.) used to perform statistical analyses. Tukey's multiple comparison tests and one-way ANOVA were used to analyze the significance of the difference between means. P-values less than 0.05 (P<0.05) were considered significant and P-values less than 0.01 (P<0.01) as highly significant. PPs are a concentrate of lymphoid tissue and the primary site for immunoglobulin A (IgA) production which is crucial to regulate the homeostatic balance of intestine [28] . The area of PPs is a key immunity indicator. Oral administration with B. subtilis-RC significantly (P<0.01) increased the area of PPs compared with two control groups as shown in Figure 1A . In addition, the villi length of ileum got longer by oral administration with B. subtilis-RC (P<0.01) than the other two groups ( Figure 1B) . These primarily confirmed that B. subtilis-RC was beneficial to maintain the structure of intestine. Intestinal IELs are a large and diverse population of lymphoid cells residing within the intestinal epithelial cells (IECs), and forming the intestinal mucosal barrier [29] . IELs are important part of the gut mucosal immune system. The level of specific anti-PEDV ileum IgA + secreting (SIgA) antibody in piglets was measured by ELISA in the mouth and feces. As shown in Figure 3A ,B, antigen-specific mucosal SIgA in the above sites was clearly higher than inactivated PEDV group (P<0.05 or P<0.01). As expected, the mouth had higher levels of SIgA than other sites. After oral immunization, the level of serum anti-PEDV IgG antibody in piglets immunized with B. subtilis-RC, inactivated PEDV or PBS were determined by ELISA, as shown in Figure 3C . The results indicated that although the titers dropped during sampling period, the IgG level of B. subtilis-RC still significantly increased from 0 to 33 days than inactivated PEDV group (P<0.05 or P<0.01). CD3 + T lymphocytes are the fundamental cell surface markers of T lymphocytes, therefore, the number of CD3 + T lymphocytes could represent the quantity of T lymphocytes. Consequently, we analyzed the number of CD3 + T lymphocytes in ileum. The data indicated that both B. subtilis-RC and inactivated PEDV could dramatically (P<0.05) increase CD3 + T lymphocytes compared with PBS group ( Figure 4A ). These changes showed confident evidence that oral administration with B. subtilis-RC had a good influence on intestinal mucosal immunity in piglets. SIgA is the main immunoglobulin isotype in animals, largely secreted across the intestinal mucosal surface especially in the small intestine [30] . SIgA plays an important role in intestinal mucosal immunity and reflects on the intestinal mucosal immunity. After oral administration with B. subtilis-RC, the number of IgA secreting cells had quickly risen compared with the other two groups (P<0.05) ( Figure 4B) . These results showed that oral administration with B. subtilis-RC was conducive to intestinal mucosal immunity and could increase the number of IgA secreting cells to produce positive effects on against PEDV infection. A great deal of immune cells are scattered in the epithelial cells. IECs indirectly or directly interact with innate and adaptive immune cells by presenting antigens to lymphocytes [31] . Consequently, learning about how the lymphocytes are distributed in the small intestinal mucosa is very meaningful for mucosal immunology. Previous data had shown that CD3 + T lymphocytes significantly (P<0.05) increased ( Figure 4A ), so we further analyzed the immunological classification of CD3 + T lymphocytes. The lymphocyte of the ileum with PPs junction was isolated and the lymphocytes of CD3, CD4, and CD8 were analyzed by three colors flow cytometry ( Figure 5A ). These results showed that CD3 + CD4 + T cells have obviously (P<0.01) increased ( Figure 5B ), nevertheless the CD3 + CD8 + T cells remarkably (P<0.05) declined ( Figure 5C ). After calculation, the ratio of CD4 + /CD8 + T cells increased ( Figure 5D ). This ratio could also further measure the immunity levels of piglets. Cytokine IL-1β and IL-10 levels were determined to evaluate cellular immune responses induced by B. subtilis-RC as shown in Figure 6A ,B. As we can see from the diagram, significantly (P<0.01) higher IL-1β and IL-10 were produced after oral administration with B. subtilis-RC than the other two groups. These all revealed that B. subtilis-RC could stimulate cytokines release to mediate communication with and between cells of the immune system, improving the mucosal immune response to PEDV infection. The PEDV neutralizing antibodies were detected by PRNT assay. Oral administration with B. subtilis-RC could effectively reduce the plaque-forming ability of PEDV (P<0.01) compared with other two groups in Figure 7 . This revealed that B. subtilis-RC could stimulate high level of PEDV neutralizing antibodies against PEDV infection. Amidst the PEDV outbreak, various vaccines have been developed to control diseases and the effects are unsatisfactory. Oral vaccines can induce more robust mucosal immunity than injectable counterparts [32] . Therefore, oral immunization has appeared as an effective strategy for controlling PEDV outbreak [33] . It is now clear that effective mucosal immune response requires serum IgG and mucosal SIgA [34] . SIgA is the basis of the mucosal immune system, playing an important role in maintaining the immune homeostasis, and neutralizing the invasive pathogens. Serum IgG represents systemic immune responses. During PEDV infections, oral immunization elicits not only mucosal but also systemic immune responses very well [35] . Our data showed a strong and long-lasting anti-PEDV IgG response were detected by oral administration with B. subtilis-RC in piglets. Although as time went on, the antibody titers declined a little, it still stayed on overhead compared with control groups and with accordance to the changeable tendency of antibodies. The change of specific IgA showed similar results in mouth and feces mucosa. All these changes had contributed to fight PEDV infection. As the extra immunity boost, B. subtilis-RC reduced the ability of pathogens to cross the intestinal mucosa and the systemic spread of invasive pathogens [36] . The mucosal immune system generates immune responses through immune cells that reside in mucosal compartments. T lymphocytes residing in the mucosa play important roles in mucosal immunity [37] . We further explored the species, amounts, and distribution of T lymphocytes in the intestine mucosa. CD3 is a fundamental cell surface marker of T lymphocytes [38] . The result showed that the number of CD3 + T lymphocytes significantly increased, and these revealed that B. subtilis-RC could stimulate T-cell maturation. According to the molecules expressed on the cell surface, T lymphocytes can further divide into T helper cells (CD4 + T cells) and cytotoxic T cells (CD8 + T cells) [39] . Furthermore, we observed that the ratio of CD4 + /CD8 + T cells increased by oral administration. The CD4/CD8 ratio measures the ratio of T helper cells to cytotoxic T cells. Therefore, we could see that oral administration B. subtilis-RC could strengthen Th1 immune response by raising the ratio of CD4 + /CD8 + T cells. Small intestine morphology can directly reflect the intestinal health and plays an important role in maintaining the intestine immune system [40] . The early stage of PEDV infection is frequently accompanied by necrosis and exfoliation of infected villous epithelial cells, ultimately resulting in acute, severe villous atrophy [41] . Therefore, the effective work of maintaining intestine morphology is a good indicator for assessing the efficacy of vaccines. After oral administration with B. subtilis-RC, we found the area of PPs expanded significantly. PPs are small masses of lymphatic tissue and form an important part of the immune system by recruiting and inducting the T cells to prevent the growth of pathogens in the intestines. Furthermore, an increase in the number of IELs demonstrated the effectiveness of B. subtilis-RC. Moreover, the villi length of ileum showed some encouraging results that a well-formed intestine morphology came into being by B. subtilis-RC. The satisfactory intestine morphology was the first step on the road against PEDV infection. Several morphology results proved that B. subtilis-RC could remarkably maintain the intestine morphology and form comprehensive protection. As previously mentioned, oral administration with B. subtilis-RC could stimulate T-cell proliferation and differentiation and modulate the immune response. Moreover, cytokines are small-molecule proteins with wide biological activity, synthesized and secreted by immune cells and some non-immune cells [42] . As a cell signaling molecule, it mainly acts to regulate immune responses, participating in the differentiation and development of immune cells, mediating inflammatory responses, stimulating hematopoiesis, and participating in tissue repair. Previous studies had demonstrated that PEDV inhibited both NF-κB and pro-inflammatory cytokines [43] . Therefore, cytokines are a key indicator for evaluating the ability of a vaccine to stimulate immune responses. In this study, we had observed that IL-1β and IL-10 increased (P<0.01) remarkably. IL-1β as one of the earliest pro-inflammatory cytokines and is centrally involved in the initiation and regulation of inflammatory and innate immune responses. Research had shown that IL-1β could significantly up-regulate the local and systemic immune tissues post microbial infection [44] . In addition, IL-10 is a potent anti-inflammatory cytokine that plays an essential role in preventing inflammatory and autoimmune pathologies [45] . In summary, both data showed that oral administration with B. subtilis-RC regulated and enhanced immunity by up-regulating cytokines IL-1β and IL-10. In conclusion, the present results demonstrated that oral immunization with B. subtilis-RC could effectively induce local mucosal and systematic immune responses against PEDV infection, while enhancing and regulating the immune function by raising the ratio of CD4 + /CD8 + T cells and cytokines IL-1β and IL-10, thus pointing to a promising oral vaccine candidate for PEDV infection in piglets.

What type of cells form the intestinal mucosal barrier?

Severe Acute Respiratory Syndrome Coronavirus Viroporin 3a Activates the NLRP3 Inflammasome https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6361828/ SHA: f02d0c1e8b0109648e578662dc250abe349a033c Authors: Chen, I-Yin; Moriyama, Miyu; Chang, Ming-Fu; Ichinohe, Takeshi Date: 2019-01-29 DOI: 10.3389/fmicb.2019.00050 License: cc-by Abstract: Nod-like receptor family, pyrin domain-containing 3 (NLRP3) regulates the secretion of proinflammatory cytokines interleukin 1 beta (IL-1β) and IL-18. We previously showed that influenza virus M2 or encephalomyocarditis virus (EMCV) 2B proteins stimulate IL-1β secretion following activation of the NLRP3 inflammasome. However, the mechanism by which severe acute respiratory syndrome coronavirus (SARS-CoV) activates the NLRP3 inflammasome remains unknown. Here, we provide direct evidence that SARS-CoV 3a protein activates the NLRP3 inflammasome in lipopolysaccharide-primed macrophages. SARS-CoV 3a was sufficient to cause the NLRP3 inflammasome activation. The ion channel activity of the 3a protein was essential for 3a-mediated IL-1β secretion. While cells uninfected or infected with a lentivirus expressing a 3a protein defective in ion channel activity expressed NLRP3 uniformly throughout the cytoplasm, NLRP3 was redistributed to the perinuclear space in cells infected with a lentivirus expressing the 3a protein. K(+) efflux and mitochondrial reactive oxygen species were important for SARS-CoV 3a-induced NLRP3 inflammasome activation. These results highlight the importance of viroporins, transmembrane pore-forming viral proteins, in virus-induced NLRP3 inflammasome activation. Text: Severe acute respiratory syndrome coronavirus (SARS-CoV), a member of the genus Betacoronavirus within the family Coronaviridae, is an enveloped virus with a single-stranded positive-sense RNA genome of approximately 30 kb in length. The 5 two-thirds of the genome encodes large polyprotein precursors, open reading frame (ORF) 1 and ORF1b, which are proteolytically cleaved to generate 16 non-structural proteins (Tan et al., 2005) . The 3 one-third of the genome encodes four structural proteins, spike (S), envelope (E), matrix (M) and nucleocapsid (N), and non-structural proteins, along with a set of accessory proteins (3a, 3b, 6, 7a, 7b, 8a, 8b, and 9b) (Perlman and Dandekar, 2005; Tan et al., 2005) . SARS-CoV is the etiological agent of SARS (Drosten et al., 2003; Fouchier et al., 2003; Ksiazek et al., 2003; Kuiken et al., 2003; Peiris et al., 2003) . At least 8,098 laboratory-confirmed cases of human infection, with a fatality rate of 9.6%, were reported to the World Health Organization from November 2002 to July 2003. High levels of proinflammatory cytokines, including tumor necrosis factor (TNF)-α, interleukin (IL)-1β, and IL-6, were detected in autopsy tissues from SARS patients (He et al., 2006) . Although dysregulation of inflammatory cytokines may be involved in lung injury and the pathogenesis of SARS-CoV, the underlying molecular mechanisms are not fully understood. The innate immune systems utilizes pattern recognition receptors (PRRs) to detect pathogen-associated molecular patterns (Medzhitov, 2001; Kawai and Akira, 2010) . Recognition of virus infection plays an important role in limiting virus replication at the early stages of infection. Nod-like receptor family, pyrin domain-containing 3 (NLRP3) is activated by a wide variety of stimuli, including virus infection (Bauernfeind et al., 2011) . Four models describing activation of the NLRP3 inflammasome have been proposed thus far (Hornung and Latz, 2010; Schroder et al., 2010; Tschopp and Schroder, 2010) . First, the disturbances in intracellular ionic concentrations, including K + efflux and Ca 2+ influx, play an important role (Fernandes-Alnemri et al., 2007; Petrilli et al., 2007; Arlehamn et al., 2010; Ichinohe et al., 2010; Ito et al., 2012; Murakami et al., 2012; Munoz-Planillo et al., 2013) . Second, cathepsin B and L, which are specific lysosomal cysteine proteases, are though to play a role after phagocytosis of cholesterol crystals (Duewell et al., 2010) , fibrillar peptide amyloid-beta , silica crystals, and aluminum salts . Third is the release of reactive oxygen species (ROS) or mitochondrial DNA from damaged mitochondria (Zhou et al., , 2011 Nakahira et al., 2011; Shimada et al., 2012) . Finally, viral RNA or RNA cleavage products generated by RNase L activate the NLRP3 inflammasome via the DExD/H-box helicase, DHX33 (Allen et al., 2009; Mitoma et al., 2013; Chen et al., 2014; Chakrabarti et al., 2015) . Upon activation, the NLRP3 is recruited to the mitochondria via association with mitochondrial antiviral signaling (MAVS) or mitofusin 2 expressed on the outer mitochondrial membrane Subramanian et al., 2013) ; these molecules then recruit the apoptosis-associated speck-like protein containing a caspase recruitment domain (ASC) and pro-caspase-1 to form the NLRP3 inflammasome. This event activates the downstream molecule, caspase-1, which catalyzes the proteolytic processing of pro-IL-1β and pro-IL-18 into their active forms and stimulates their secretion (Kayagaki et al., 2015; Shi et al., 2015) . It is increasingly evident that NLRP3 detects RNA viruses by sensing the cellular damage or distress induced by viroporins (Ichinohe et al., 2010; Ito et al., 2012; Triantafilou et al., 2013; Nieto-Torres et al., 2015) , transmembrane pore-forming proteins, encoded by certain RNA viruses; these proteins alter membrane permeability to ions by forming membrane channels (Tan et al., 2005; Chen and Ichinohe, 2015) . A recent study shows that the SARS-CoV E protein, which comprise only 76 amino acids, forms Ca 2+ -permeable ion channels and activates the NLRP3 inflammasome (Nieto-Torres et al., 2015) . Although the E and 3a proteins of SARS-CoV, which comprise 274 amino acids and contain three transmembrane domains (Zeng et al., 2004; Lu et al., 2006) , are thought to act as Na + /K + and K + channels, respectively (Wilson et al., 2004; Lu et al., 2006; Torres et al., 2007; Parthasarathy et al., 2008; Pervushin et al., 2009; Wang et al., 2011) , the role of the 3a protein in activating the NLRP3 inflammasome remains unknown. Here, we examined the role of the 3a protein in activating the NLRP3 inflammasome. Six-week-old female C57BL/6 mice were purchased from The Jackson Laboratory. All animal experiments were approved by the Animal Committees of the Institute of Medical Science (The University of Tokyo). Bone marrow-derived macrophages (BMMs) were prepared as described previously (Ichinohe et al., 2009) . In brief, bone marrow was obtained from the tibia and femur by flushing with Dulbecco's modified Eagle's medium (DMEM; Nacalai Tesque). Bone marrow cells were cultured for 5 days in DMEM supplemented with 30% L929 cell supernatant containing macrophage colony-stimulating factor, 10% heat-inactivated fetal bovine serum (FBS), and L-glutamine (2 mM) at 37 • C/5% CO 2 . HEK293FT cells (a human embryonic kidney cell line) and HeLa cells (a human epithelial carcinoma cell line) were maintained in DMEM supplemented with 10% FBS, penicillin (100 units/ml), and streptomycin (100 µg/ml) (Nacalai Tesque). MDCK cells (Madin-Darby canine kidney cells) and HT-1080 cells (a human fibrosarcoma cell line) were grown in Eagle's minimal essential medium (E-MEM; Nacalai Tesque) supplemented with 10% FBS, penicillin (100 units/ml), and streptomycin (100 µg/ml) (Nacalai Tesque). Influenza A virus strain A/PR8 (H1N1) was grown at 35 • C for 2 days in the allantoic cavities of 10-day-old fertile chicken eggs (Ichinohe et al., 2009) . The viral titer was quantified in a standard plaque assay using MDCK cells (Pang et al., 2013) . Plasmids cDNAs encoding the E and M proteins of SARS-CoV Frankfurt 1 strain (Matsuyama et al., 2005) were obtained by reverse transcription and PCR of total RNA extracted from SARS-CoVinfected Vero cells, followed by PCR amplification using specific primers. pcDNA3.1D-3a-V5His was provided by Ming-Fu Chang (National Taiwan University College of Medicine, Taipei, Taiwan). To generate the plasmids pLenti6-E-V5His, pLenti6-3a-V5His, and pLenti-M-V5His, cDNA fragments of E, 3a, and M were amplified from pcDNA3.1D-E-V5His, pcDNA3.1D-3a-V5His, and pcDNA3.1D-M-V5His using specific primer sets and then ligated into pLenti6-TOPO vectors (Invitrogen). To generate plasmids pCA7-flag-E, pCA7-flag-3a, and pCA7flag-M, pCA7-HA-E, pCA7-HA-3a, and pCA7-HA-M, cDNA fragments of E, 3a, and M were amplified from pcDNA3.1D-E-V5His, pcDNA3.1D-3a-V5His, and pcDNA3.1D-M-V5His using specific primer sets, digested with EcoR I and Not I, and subcloned into the EcoR I-Not I sites of the pCA7-flag-ASC plasmid or pCA7-HA-M2 plasmid, respectively (Ito et al., 2012) . To construct plasmids expressing the E mutant V25F, the mutated E fragments were amplified by inverse PCR with wildtype E-containing plasmids and specific primer sets. The PCR products were cleaved by Dpn I, ligated in a ligase-and T4 kinase-containing reaction and then transformed into DH5α competent cells (TOYOBO). To construct plasmids expressing the 3a mutant 3a-CS, fragments were amplified from wildtype 3a-containing plasmids using 3a-specific primer sets and transformed as described above. HEK293FT cells were seeded in 24-well cluster plates and transfected with 1 µg pLenti6-E/3a/M-V5His, pLenti-GFP (green fluorescent protein), or pLenti-M2 using polyethylenimine (PEI) Max. At 24 h post-transfection, the cells were lysed with RIPA buffer (50 mM Tris-HCl, 1% NP-40, 0.05% sodium dodecyl sulfate (SDS), 150 mM NaCl and 1 mM EDTA). And the lysates were subjected to SDS-polyacrylamide gel electrophoresis (PAGE) followed by electroblotting onto polyvinylidene difluoride (PVDF) membranes. The membranes were incubated over night with mouse anti-V5-tag (R960-25, Invitrogen), mouse anti-influenza A virus M2 (14C2, Abcam), mouse anti-GFP (GF200, Nacalai Tesque), or rabbit antitubulin (DM1A, Santa Cruz) antibodies, followed by horseradish peroxide-conjugated anti-mouse IgG (Jackson Immuno Research Laboratories) or anti-rabbit IgG (Invitrogen). After washing 3 times with washing buffer (0.05% Tween-20/PBS), the membranes were exposed using Chemi-Lumi One Super (Nacalai Tesque), and the chemiluminescent signals were captured by an ImageQuant LAS-4000 mini apparatus (GE Healthcare). To generate lentiviruses expressing V5-tagged SARS-CoV E, 3a, and M proteins, the full-length cDNA encoding each viral protein was cloned into the pLenti6.3/V5-TOPO vector (Invitrogen) using the following primers: SARS-CoV E forward, 5 -caccatgtactcattcgtttcgga-3 , and reverse, 5 -gaccagaagatcaggaactc-3 ; SARS-CoV 3a forward, 5caccatggatttgtttatgagatt-3 , and reverse, 5 -caaaggcacgctagtagtcg-3 ; SARS-CoV M forward, 5 -caccatggcagacaacggtactat-3 , and reverse, 5 -ctgtactagcaaagcaatat-3 . Sub-confluent monolayers of HEK293FT cells seeded in a collagen-coated dish (10 cm in diameter) were transfected with 3 µg of pLenti6.3/V5-TOPO vector expressing each viral protein or EGFP together with ViraPower Packaging Mix (Invitrogen) using Lipofectamine 2000 (Invitrogen). The supernatants containing lentiviruses were harvested and filtered through a 0.45 µm filter (Millipore) at 72-96 h post-transfection (Ito et al., 2012) . The lentiviral titer was then quantified using HT-1080 cells as described previously . Bone marrow-derived macrophages were plated at a density of 8 × 10 5 in 24-well plate and infected with A/PR8 influenza virus or lentivirus at a multiplicity of infection (MOI) of 5 or 0.2 for 1 h, respectively. Then, BMMs were stimulated with 1 µg/ml of LPS and cultured for additional 23 h in complete media. Supernatants were collected at 24 h post-infection and centrifuged to remove cell debris. The amount of IL-1β in the supernatants was measured in an enzyme-linked immunosorbent assay (ELISA) using paired antibodies (eBioscience) (Ichinohe et al., 2010 . To clarify the cellular localization of the wild-type and mutant 3a proteins of SARS-CoV, HeLa cells were cultured on coverslips and transfected with 1 µg of pCA7-flag-3a or pCD7-flag-3a-CS together with 0.5 µg of ER-mCherry or DsRed-Golgi (Ito et al., 2012) . At 24 h post-transfection, cells were fixed with 4% paraformaldehyde and permeabilized with 1% Triton X-100/PBS. After washing with PBS and blocking with 4% BSA/PBS, the cells were incubated with a mouse anti-flag antibody (M2, Sigma) followed by incubation with Alexa Fluor 488-conjugated goat anti-mouse IgG (H+L) (Life Technologies). To observe the cellular distribution of NLRP3 in the E-or 3a-expressing cells, HeLa cells were cultured on coverslips and transfected with 1 µg of pCA7-HA-E, pCA7-HA-EV25F, pCA7-HA-3a, pCA7-HA-3a-CS, or pCA7 control vector together with 0.5 µg of pCA7-NLRP3. At 24 h post-transfection, cells were fixed and permeabilized with 4% paraformaldehyde and 1% Triton X-100/PBS. After washing and blocking, the cells were incubated with rabbit anti-HA (561, MBL) and mouse anti-NLRP3 (Cryo-2; AdipoGen) antibodies, followed by Alexa Fluor 488-conjugated goat anti-rabbit IgG (H+L) and Alexa Fluor 568-conjugated goat anti-mouse IgG (H+L) (Life Technologies). Fluorescent signals were observed by confocal microscopy (A1R + , Nikon). Statistical significance was tested using a two-tailed Student's t-test. P-values < 0.05 were considered statistically significant. We previously demonstrated that the influenza virus M2 protein (a proton-selective ion channel), its H37G mutant (which has lost its proton selectivity and enables the transport of other cations such as Na + and K + ), and the EMCV 2B protein (a Ca 2+ channel) stimulates NLRP3 inflammasome-mediated IL-1β secretion (Ichinohe et al., 2010; Ito et al., 2012) . In addition, the SARS-CoV E protein acts as a Ca 2+ -permeable ion channels that activates the NLRP3 inflammasome (Nieto- Torres et al., 2015) . The fact that 3a protein of SARS-CoV acts as viroporin prompted us to examine whether it also triggers inflammasome activation. Thus, we first generated lentivirus plasmids expressing V5-tagged proteins and confirmed their expression in HEK293FT cells by immunoblot analysis (Figures 1A-C) . We next transduced lipopolysaccharide (LPS)-primed BMMs with the lentiviruses expressing the SARS-CoV E, 3a, M, influenza virus M2, or EMCV 2B proteins. Consistent with previous reports (Ichinohe et al., Figure 1D) . Similarly, the lentiviruses expressing the SARS-CoV E or 3a proteins stimulated IL-1β release from LPS-primed BMMs ( Figure 1D) . Furthermore, IL-1β secretion from LPSprimed BMMs co-infected with E-and 3a-expressing lentiviruses was significantly higher than that from SARS-CoV E-expressing lentivirus-infected cells ( Figure 1E) . These data indicated that the expression of SARS-CoV viroporin 3a is sufficient to stimulate IL-1β secretion by LPS-primed BMMs. Previous studies demonstrated that the N-terminal 40 amino acids of the SARS-CoV E protein are important for ion channel formation, and that mutations N15A and V25F [located in the transmembrane domain (from amino acid residues 7-38)] prevent ion conductivity (Wilson et al., 2004; Torres et al., 2007; Verdia-Baguena et al., 2012) . In addition, the SARS-CoV 3a protein contains a cysteine-rich domain (amino acid residues 127-133) that is involved in the formation of a homodimer to generate the ion channel (Lu et al., 2006; Chan et al., 2009) . Thus, mutation of the cysteine-rich domain blocks the ion conductivity by the 3a protein (Chan et al., 2009) . To this end, we substituted amino acids Cys-127, Cys-130, and Cys-133 within the cysteine-rich domain of the SARS-CoV 3a protein with serine to generate a lentivirus expressing the ion channel activity-loss mutant, 3a-CS (Chan et al., 2009; Figure 2A) . To test whether the ion channel activity of the SARS-CoV 3a protein is required to stimulate secretion of IL-1β, we transduced LPSprimed BMMs with lentiviruses expressing the SARS-CoV E, V25F, 3a, 3a-CS, or M proteins. Consistent with a previous report (Nieto -Torres et al., 2015) , we found that the V25F mutant lentivirus failed to stimulate IL-1β release from BMMs ( Figure 2B) . Notably, the 3a-CS mutant completely abrogated IL-1β secretion (Figure 2B) , suggesting that the ion channel activity of the 3a protein is required for SARS-CoV 3a-induced IL-1β secretion. FIGURE 4 | NLRP3 inflammasome activation by SARS-CoV 3a. HeLa cells were transfected with the expression plasmid encoding NLRP3 and that encoding HA-tagged SARS-CoV 3a, 3a-CS, E, or V25F, and by with a confocal microscope. Scale bars, 10 µm. Data are representative of at least three independent experiments. Next, we determined the subcellular localization of the SARS-CoV 3a protein using confocal microscopy. When the SARS-CoV Cell-free supernatants were collected at 24 h (lentiviruses) or 6 h (ATP) post-infection or stimulation, and analyzed for IL-1β by ELISA. Data are representative of at least three independent experiments, and indicate the mean ± SD; * * P < 0.01 and * * * P < 0.001. 3a protein was expressed in HeLa cells, we observed two main distribution patterns. Consistent with previous reports (Yu et al., 2004; Yuan et al., 2005) , the 3a protein localized to the Golgi apparatus ( Figure 3A ). In addition, the 3a proteins concentrated in spot structures, which mainly localized to the endoplasmic reticulum (ER) (Figure 3B ). By contrast, the 3a-CS mutant was concentrated in the Golgi apparatus rather than in the ER and did not form spot structures (Figures 3A,B) . We next examined the intracellular localization of NLRP3. Activation of the NLRP3 inflammasome led to a redistribution from the cytosol to the perinuclear space, a process considered as a hallmark of NLRP3 activation (Zhou et al., 2011; Ito et al., 2012; Johnson et al., 2013; Moriyama et al., 2016) . Although cells expressing the ion channel activity-loss mutants 3a-CS or V25F uniformly expressed NLRP3 throughout the cytoplasm, it was redistributed to the perinuclear region in SARS-CoV 3a-or E-expressing cells (Figure 4) . Together, these data provide evidence that the ion channel activity of the SARS-CoV 3a protein is essential for triggering the NLRP3 inflammasome. Both K + Efflux and ROS Production Are Involved in the IL-1β Release Induced by the SARS-CoV 3a Protein Finally, we investigated the mechanism by which SARS-CoV 3a triggers NLRP3 inflammasome activation. A previous study showed that the 3a protein of SARS-CoV acts as a K + channel (Lu et al., 2006) . In addition, K + efflux is a well-known activator of the NLRP3 inflammasome (Mariathasan et al., 2006; Petrilli et al., 2007) . These observations prompted us to examine whether K + efflux is required for 3a-mediated IL-1β secretion. To this end, BMMs in K + -rich medium were infected with influenza A virus or lentiviruses expressing the SARS-CoV E or 3a proteins. In agreement with a previous result (Ichinohe et al., 2010) , we found that IL-1β secretion caused by influenza virus was completely blocked when the extracellular K + concentration was increased to 130 mM ( Figure 5A) . The inhibitory effect of the K + -rich medium was also observed when cells were stimulated with lentiviruses expressing the SARS-CoV E or 3a proteins ( Figure 5B ). Since mitochondrial ROS are important for NLRP3 inflammasome activation (Nakahira et al., 2011; Zhou et al., 2011) , we next stimulated BMMs with extracellular ATP or lentiviruses expressing the SARS-CoV E or 3a proteins in the presence or absence of the antioxidant, Mito-TEMPO, a scavenger that is specific for mitochondrial ROS Trnka et al., 2009) . As reported previously (Nakahira et al., 2011; Ito et al., 2012) , treatment of BMMs with Mito-TEMPO completely blocked IL-1β secretion in response to ATP ( Figure 6A) . Similarly, IL-1β release induced by the SARS-CoV E and 3a proteins was significantly inhibited by Mito-TEMPO ( Figure 6B) . These observations indicate that the SARS-CoV 3a protein disrupts intracellular ionic concentrations and causes mitochondrial damages, thereby activating the NLRP3 inflammasome. In summary, we found that the ion channel activity of SARS-CoV 3a protein is essential for activation of the NLRP3 inflammasome. In addition, both K + efflux and mitochondrial ROS production are required for SARS-CoV 3a-mediated IL-1β secretion. Thus far, several models have been proposed to explain NLRP3 inflammasome activation by RNA viruses. First, viral RNA or RNA cleavage products generated by RNase L activate the NLRP3 inflammasome via the DExD/H-box helicase, DHX33 (Allen et al., 2009; Mitoma et al., 2013; Chen et al., 2014; Chakrabarti et al., 2015) . Second, viroporins encoded by RNA viruses activates the NLRP3 inflammasome (Ichinohe et al., 2010; Ito et al., 2012; Triantafilou et al., 2013; Nieto-Torres et al., 2015) . In the case of influenza virus, the proton-selective M2 ion channel in the acidic trans-Golgi network activates the NLRP3 inflammasome (Ichinohe et al., 2010) . Interestingly, an M2 mutant in which histidine was substituted with glycine at position 37 (H37G), causing loss of proton selectivity, enables transport of other cations (i.e., Na + and K + ), thereby leading to enhanced secretion of IL-1β from LPS-primed BMMs and dendritic cells when compared with the wild-type M2 protein. In addition, the 2B proteins of EMCV, poliovirus, enterovirus 71 (EV71), and human rhinovirus (a member of the Picornaviridae family) triggers NLRP3 inflammasome activation by inducing Ca 2+ flux from the ER and Golgi compartments (Ito et al., 2012; Triantafilou et al., 2013) . Furthermore, hepatitis C virus stimulates NLRP3 inflammasome-mediated IL-1β production though its p7 viroporin (Negash et al., 2013; Farag et al., 2017) . Third, a recent study has demonstrated that the 3D protein of EV71 directly interacts with NLRP3 to facilitate the assembly of NLRP3 inflammasome complex (Wang et al., 2017) . In the case of SARS-CoV, the viroporin E forms forms Ca 2+permeable ion channels and activates the NLRP3 inflammasome (Nieto-Torres et al., 2015) . In addition, another viroporin 3a was found to induce NLRP3 inflammasome activation (Yue et al., 2018) . Although alanine substitution at Cys-133, which is required for dimer or tetramer formation (Lu et al., 2006) , still allows activation of the NLRP3 inflammasome by interacting with caspase-1 (Yue et al., 2018) , the ion channel activity-loss mutant 3a-CS (Cys-to-Ser substitution at positions Cys-127, Cys-130, and Cys-133) (Chan et al., 2009 ) completely abrogated IL-1β secretion from LPS-primed BMMs, suggesting that the 3a protein of SARS-CoV has the ability to induce the NLRP3 inflammasome activation by multiple mechanisms. Previous studies show that the 3a protein of SARS-CoV is localized to the plasma membrane (Minakshi and Padhan, 2014) and acts as a K + channel (Lu et al., 2006) , thereby (presumably) stimulating the K + efflux at the plasma membrane. Indeed, we found that IL-1β secretion caused by the 3a protein was significantly inhibited when the extracellular K + concentration increased to 130 mM. Although it remains unclear whether another viroporin 8a of SARS-CoV (Castano-Rodriguez et al., 2018) activates the NLRP3 inflammasome, these data highlights the importance of viroporins in SARS-CoV-induced NLRP3 inflammasome activation. A better understanding of the mechanism that governs the NLRP3 inflammasome will facilitate the development of more effective interventions for the treatment of infectious diseases and increase our understanding of viral pathogenesis.

What is the genus of the SARS coronavirus?

Effectiveness of zinc supplementation on diarrhea and average daily gain in pre-weaned dairy calves: A double-blind, block-randomized, placebo-controlled clinical trial https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6619766/ SHA: ef20a0cd67ce018cf061f154bd8be9d0e58d0f23 Authors: Feldmann, Hillary R.; Williams, Deniece R.; Champagne, John D.; Lehenbauer, Terry W.; Aly, Sharif S. Date: 2019-07-10 DOI: 10.1371/journal.pone.0219321 License: cc-by Abstract: The objective of this clinical trial was to evaluate the effectiveness of zinc supplementation on diarrhea and average daily weight gain (ADG) in pre-weaned dairy calves. A total of 1,482 healthy Holstein heifer and bull calves from a large California dairy were enrolled at 24 to 48 hours of age until hutch exit at approximately 90 days of age. Calves were block-randomized by time to one of three treatments: 1) placebo, 2) zinc methionine (ZM), or 3) zinc sulfate (ZS) administered in milk once daily for 14 days. Serum total protein at enrollment and body weight at birth, treatment end, and hutch exit were measured. Fecal consistency was assessed daily for 28 days post-enrollment. For a random sample of 127 calves, serum zinc concentrations before and after treatment and a fecal antigen ELISA at diarrhea start and resolution for Escherichia coli K99, rotavirus, coronavirus, and Cryptosporidium parvum were performed. Linear regression showed that ZM-treated bull calves had 22 g increased ADG compared to placebo-treated bulls (P = 0.042). ZM-treated heifers had 9 g decreased ADG compared to placebo-treated heifers (P = 0.037), after adjusting for average birth weight. Sex-stratified models showed that high birth weight heifers treated with ZM gained more than placebo-treated heifers of the same birth weight, which suggests a dose-response effect rather than a true sex-specific effect of ZM on ADG. Cox regression showed that ZM and ZS-treated calves had a 14.7% (P = 0.015) and 13.9% (P = 0.022) reduced hazard of diarrhea, respectively, compared to placebo-treated calves. Calves supplemented for at least the first five days of diarrhea with ZM and ZS had a 21.4% (P = 0.027) and 13.0% (P = 0.040) increased hazard of cure from diarrhea, respectively, compared to placebo-treated calves. Logistic regression showed that the odds of microbiological cure at diarrhea resolution for rotavirus, C. parvum, or any single fecal pathogen was not different between treatment groups. Zinc supplementation delayed diarrhea and expedited diarrhea recovery in pre-weaned calves. Additionally, zinc improved weight gain differentially in bulls compared to heifers, indicating a research need for sex-specific dosing. Text: Introduction Diarrhea is the leading cause of morbidity and mortality and the most common reason for antimicrobial drug treatments in pre-weaned dairy heifers [1, 2] . A USDA survey of preweaned dairy heifers reported that 24% experienced diarrhea and 18% received antimicrobial treatment for it [1] . Diarrhea is also a leading cause of morbidity and the second foremost cause of mortality in children with over 1 billion cases and a half a million deaths annually [3, 4] . Zinc supplementation in children decreases the incidence, duration, and severity of diarrhea, increases recovery rates, decreases the use of antibiotics and antidiarrheal medications, and reduces mortality [5] [6] [7] [8] [9] [10] . In a clinical trial that established a non-toxic zinc dose and investigated its therapeutic use for diarrhea in neonatal dairy calves, zinc-treated calves had numerically quicker clinical recovery, increased weight gain, and higher odds of fecal clearance of Cryptosporidium parvum between diarrhea onset and recovery compared with placebotreated calves [11] . As a result, zinc supplementation may be beneficial for prevention of diarrhea in dairy calves and, thus, minimize antimicrobial use. However, studies investigating zinc's potential effectiveness are lacking. In children, both organic (zinc acetate, zinc gluconate, and zinc methionine) and inorganic (zinc sulfate and zinc oxide) zinc formulations are beneficial in the prevention and treatment of childhood diarrhea [12] [13] [14] [15] . However, differing bioavailability was observed in several animal studies [16] [17] [18] [19] . In addition, the underlying mechanism of action of oral zinc is unknown [6] . Hence, contrasting the effect, if any, of organic compared to inorganic zinc formulations in pre-weaned calves may help identify differences in mode of action. The objective of this clinical trial was to compare average daily weight gain (ADG) and the incidence and duration of diarrhea in pre-weaned dairy calves randomly assigned to receive either organic zinc methionine (ZM), inorganic zinc sulfate (ZS), or a placebo in milk once daily for 14 days. By elucidating the potential role of zinc supplementation in prevention of diarrhea in preweaned dairy calves, calf morbidity, mortality, and antimicrobial usage may be mitigated. A double-blind, block randomized, placebo-controlled clinical trial was conducted between December 14, 2015 and June 15, 2016 on a large dairy in California's San Joaquin Valley. The dairy was selected based on the owner and calf manager's willingness to participate in the study. The dairy herd was composed of 5,500 lactating cows, predominantly Holsteins, and housed approximately 1,600 pre-weaned calves. Approximately 75% of the calves were born on the participating dairy and 25% were born on an affiliated dairy located approximately 10 miles away. Calves enrolled in the trial included healthy Holstein heifer or bull calves 24 to 48 hours of age. Calves were determined to be healthy via visual examination by a veterinarian (HF) or a trained researcher. Calves were excluded if they had obvious morbidities or congenital defects, were non-Holstein, born on the affiliated dairy, younger than 24 hours of age or older than 48 hours of age at the time of enrollment. Calves from the affiliated dairy were excluded due to differences in physical location and management practices of pre-partum cows. All procedures were approved by the University of California Davis Institutional Animal Care and Use Committee (protocol number 18067 Approved: March 6, 2014) . 107 g between treatment groups (Stata, College Station, TX). After allowing for 15% attrition and assuming 50% incidence of diarrhea based on study authors expert opinion, and a difference in ADG of 107g [11] , a sample size of 500 calves per treatment group (n = 1500 total) was deemed required. Newborn calves were removed from the dam within an hour of birth and placed in a strawbedded, group calf pen where their navels were dipped in an iodine-based solution. Each calf received 4 liters of colostrum within 1 hour of birth and a second colostrum feeding (2 liters) 6-10 hours after birth. Colostrum was refrigerated for < 48 hours and heated in a hot water bath prior to feeding using an esophageal tube feeder. Within 18 hours of birth, pre-weaned calves were transported to individual metal hutches initially bedded with almond shells. Straw hay was later added to wet and muddy hutches throughout the pre-weaning period. For the first 14 days of life, pre-weaned calves were bottle-fed 1.9 liters of milk twice daily and 1.9 liters of a commercial oral electrolyte solution (Calva Lyte; Calva Products, Inc., Acampo, CA) once daily between milk feedings. Milk consisted of a combination of pasteurized waste milk, rehydrated commercial milk replacer powder (Strauss Feeds LLC, Watertown, WI), tetracycline and neomycin powder, and additional supplements (S1 Table) . The proportion of pasteurized waste milk to milk replacer varied with each feeding, as the volume of waste milk varied with changes in the number and production of cows contributing to the waste milk tank. After 14 days of age, calves were bottle-fed 2.8 liters of milk twice daily. Calves with clinical diarrhea received 1.9 liters of a commercial oral electrolyte solution (NuLife; Genex Cooperative, Inc., Shawano, WI) once daily between milk feedings. A list of ingredients that make up the two oral electrolyte solutions can be found in S2 Table. All pre-weaned calves had free choice access to water and a calf starter grain mix. Calves were gradually weaned over a 10-day period, starting at approximately 60 days of age after which calves received a grower grain mix until 90 days of age when they were moved to group pens. Each calf received 1 mL of a selenium supplement (MU-SE; Merck Animal Health, Boxmeer, Netherlands) intramuscularly within 24 hours of age and an intranasal vaccine (Inforce 3, Zoetis, Inc., Florham Park, NJ) within 48 hours of age and again near the time of weaning. Approximately 8.3% (n = 126) of all enrolled calves were also vaccinated using an autogenous Moraxella bovis/bovoculi bacterin vaccine (Newport Laboratories, Inc., Worthington, MN) for the prevention of pinkeye at 5 and 7 weeks of age. All pre-weaned calves were evaluated daily by dairy personnel for calfhood diseases and treated according to standard on-farm treatment protocols. With regard to diarrhea therapy, calves less than two weeks of age with clinical diarrhea received an oral mixture of 118.5 mL (2.08 g) bismuth subsalicylate (Bismusal Suspension, Durvet, Inc., Blue Springs, MO) and 31.5 mL (1575 mg) spectinomycin (SpectoGard, Bimeda, Inc., Le Sueur, MN) once daily for two days. Calves older than two weeks of age with clinical diarrhea received oral sulfamethoxazole (1600 mg)/trimethoprim (320 mg) (Amneal Pharmaceuticls of NY, Hauppauge, NY) once daily for 2 to 3 days. Repeated treatment was at the discretion of the calf manager. (inductively coupled plasma mass spectrometry). Financial limitations restricted the ability to test more than two samples of each dietary component. Due to variation of zinc content in duplicate samples, the maximum concentration was used to estimate the daily zinc intake during the first 14 days of life (S3 Table) . In blocks of 36, enrolled calves were randomized using a random number generator (Microsoft Corporation, Redmond, WA) to one of three treatment groups: 1) placebo, 2) ZM, or 3) ZS to be administered in the morning milk feeding once daily for 14 days, starting on the day after enrollment. During the 14-day zinc treatment period, study calves that did not drink the entire milk bottle were tube-fed the remaining milk by trained technicians using an esophageal feeder disinfected between uses. The ZM treatment group received 0.45 g zinc methionine complex (equivalent to 80 mg of elemental zinc) as the product Zinpro180 (Zinpro Corporation, Eden Prairie, MN) combined with 0.44 g milk replacer powder. The ZS treatment group received 0.22 g zinc sulfate monohydrate (equivalent to 80 mg of elemental zinc) (Sigma-Aldrich Company, St. Louis, MO) combined with 0.44 g milk replacer powder. The placebo treatment group received approximately 0.44 g fresh milk replacer powder. Zinc supplementation was based on a previously published clinical trial, toxicological studies, and nutritional guidelines [11, [22] [23] [24] [25] . The milk replacer powder used in treatment preparation was the same product used in the pre-weaned calf milk ration. Treatments were weighed (GX-2000 precision scale; A&D Co Ltd., San Jose, CA) at the Dairy Epidemiology Laboratory at the University of California, Davis Veterinary Medicine Teaching and Research Center (VMTRC; Aly Lab) in Tulare, CA and placed in 2.0 mL microcentrifuge tubes with polypropylene snap caps (Fisher Scientific, Pittsburgh, PA). Prior to study commencement, a color was randomly and permanently assigned to each treatment group, after which treatment tubes, calf milk bottles and calf hutches were marked with either pink, orange, or yellow ink. The study investigators and technicians responsible for treatment preparation, allocation, administration and data collection were blinded to the color assignment until completion of the trial. For each calf, the study period started at enrollment (24 to 48 hours of age) and ended when the calf exited the hutch (approximately 90 days of age) and from here onwards will be referred to as the "pre-weaning period." Calf enrollment and study procedures were performed daily at the time of morning feeding. At enrollment, calf characteristics, including sex, birth date, time of first colostrum feeding, and treatment color were recorded. Attitude and feces were assessed daily until 28 days post-enrollment using previously published methods [11] by two study investigators, a veterinarian and a trained researcher. Attitude scoring was based on a threepoint scale. A calf with an attitude score of 1 was bright, alert, and readily stood with stimulation; a calf with a score of 2 was quiet, alert, and stood only with moderate stimulation; a calf with a score of 3 exhibited a dull mentation and remained recumbent in response to stimulation. Fecal scoring was performed only on fresh feces and was also based on a three-point scale, as 1 (solid), 2 (semi-formed/loose), or 3 (watery). If no fresh feces were observed in the hutch, "none seen" (NS) was recorded. Body weight was measured using a digital scale at birth, end of treatment, and hutch exit by farm employees with the exception of end of treatment weights, which were recorded by study investigators. Treatments for farm-diagnosed illnesses were performed and recorded on hutch cards by the calf manager. Study investigators regularly recorded this information from cards in addition to extracting treatment event reports from DairyComp 305 (Valley Agricultural Software, Tulare, CA). Though daily diagnosis and treatment of study calves was performed and recorded by the calf manager, a veterinarian was responsible for examining and determining whether study calves met specific criteria for euthanasia. A calf was euthanized if morbidity was severe enough to significantly depress appetite, hydration status, attitude, mentation, and/or ambulatory capability and the calf showed limited to no immediate response to therapy or supportive care. Study calves were euthanized by the calf manager using an on-farm captive bolt protocol established by the herd veterinarian within 3 hours of the decision to euthanize. Enrolled calves that died prior to hutch exit were necropsied within 24 hours of death by a veterinarian. All calves were monitored throughout the study period for evidence of zinc toxicity. At the end of the study period, calves were cared for at the dairy in accordance with standard commercial operations. Using the same random number generator, a random sample of 127 calves was selected for additional biologic sampling. Approximately 8 to 10% of the study population was selected due to the financial constraints of additional laboratory testing. Serum zinc concentration at baseline and in response to treatment were evaluated for the three treatment groups. Feces collected on the first day of diarrhea and at diarrhea resolution were evaluated for four fecal pathogens (Escherichia. coli K99, bovine rotavirus and coronavirus, and Cryptosporidium parvum oocysts). Serum total protein. At enrollment, blood from each calf was collected from the jugular vein using a 20 gauge 1-inch multi-sample needle (Exelint International Co., Redondo Beach, CA) and placed into a 10 mL red top serum tube (BD Vacutainer, Franklin Lakes, NJ) for determination of total protein. Samples remained at room temperature for up to 12 hours until clotting and were then centrifuged (International Equipment Company, CRU-5000, Needham Heights, MA) for 15 minutes. Total protein (g/dL) was measured by a single investigator (HF) on decanted serum using a handheld refractometer (Sper Scientific, Model 300005, Scottsdale, AZ). Serum zinc. For the 127 randomly-sampled calves, additional blood was collected at enrollment and on the last day of treatment, as described above, and placed into 6.0 mL trace element tubes (BD Vacutainer, Franklin Lakes, NJ). Serum was extracted, as described above, and placed in a 2.0 mL microcentrifuge tube with a polypropylene snap cap (Fisher Scientific, Pittsburgh, PA) and stored at −20˚C until analysis. Using the same random number generator, 36 of the 127 sampled calves were randomly selected for analysis due to limited financial resources. The pre-and post-zinc supplementation serum samples from each calf (n = 72) were analyzed for zinc concentration (ppm) by ICP-OES (inductively coupled plasma-optical emission spectroscopy) at the CAHFS Laboratory. Quality control samples, including method blanks, laboratory control spikes, and reference Sigma serum, were run with each set of study samples. Fecal analysis. For 127 randomly-sampled calves at the first diarrhea episode, fecal samples were collected at two time points, the first day of diarrhea (fecal score > 1) and the day diarrhea resolved (second day of fecal score = 1). Using new gloves and sterile lubricant, fresh feces was collected by digital rectal stimulation into 20 mL polypropylene twist-top jars (The Cary Company, Addison, IL) and stored at -20˚C until analysis. Fecal samples were tested at the Dairy Epidemiology Laboratory, VMTRC by a veterinarian for E. coli K99, bovine rotavirus and coronavirus, and C. parvum oocysts using a commercial kit (Pathasure Enteritis 4; Biovet, Quebec, Canada) that is highly specific (> 90%) and sensitive (E. coli K99, 93%; rotavirus, 100%; coronavirus, 77%) [26, 27] . For calves with a first-day diarrhea sample on or before 7 days of age, both fecal samples were tested for all four pathogens. For calves with a first-day diarrhea sample after 7 days of age, both fecal samples were tested for three pathogens (C. parvum, bovine rotavirus and coronavirus). Samples from calves older than 7 days of age were not tested for E. coli K99 based on calves' susceptibility [28] . Testing was performed according to kit manufacturer guidelines, and a low-temperature incubator (Fisher Scientific, Model 146, Pittsburgh, PA) was used during incubation periods. Test results were recorded as positive or negative using control wells for color comparison. If the color change was darker than the negative control, the sample was considered positive. Milk zinc. For each of the 107 study days (December 15, 2015 to March 31, 2016) of zinc supplementation, approximately 1.5 mL of treated milk from two bottles of each treatment group were randomly collected into 2.0 mL microcentrifuge tubes with polypropylene snap caps (Fisher Scientific, Pittsburgh, PA), and stored at −20˚C until analysis. At the time of analysis, milk samples were thawed at 4˚C, vortexed, pooled by week and treatment group, and analyzed for zinc concentration (ppm) by ICP-MS (inductively coupled plasma mass spectrometry) at the CAHFS Laboratory. Quality control samples, including method blanks, laboratory control spikes, National Institute of Standards and Technology (NIST) reference materials (NIST 1640), and a spiked milk sample, were run with each set of study samples. Data analysis was performed using R Statistic Software version 3.3.1 and Stata IC 14.2 (College Station, TX). Statistical differences were determined at the 5% level of significance using per protocol analysis. An ANOVA was used to compare calves in each treatment group at enrollment with respect to birth weight (kg), serum total protein (g/dL), attitude score, and fecal score. Oral zinc dose at the start and end of treatment was calculated as the zinc supplementation dose (80 mg) divided by calf body weight (kg) at birth and on the last day of treatment, respectively. An ANOVA was used to compare oral zinc dose (mg/kg) at treatment start and end as well as mean body weight (kg) at birth, end of treatment, and hutch exit between treatment groups and between bulls and heifers. A Chi-Square test of Independence was used to compare the proportions of calves by sex as well as mortality between treatment groups. For all analyses, Tukey's Honest Significant Difference method was used to generate pairwise comparisons to further characterize significant differences identified by ANOVA. Residual diagnostics, including Residuals vs. Fitted, Scale-Location, Normal Q-Q, and Cook's distances plots, were used to validate all ANOVA model assumptions. The non-parametric Kruskal-Wallis Rank Sum test was used when assumptions were violated. For the randomly-sampled calves, Fisher exact tests were used to compare fecal pathogen prevalence on the first day of diarrhea and at diarrhea resolution between treatment groups. Pairwise comparisons with Bonferroni adjustment were used to identify specific differences in pathogen prevalence. An ANOVA was used to compare serum zinc concentration before and after treatment between treatment groups and between bulls and heifers. A Kruskal-Wallis Rank Sum test was used to compare rank sums of zinc concentrations in pooled milk samples from different treatment groups. Post-hoc Nemenyi-tests for pairwise multiple comparisons of ranked data were used to identify specific differences in zinc concentrations between groups. For all regression models in this study, univariate regression was first used to evaluate associations between individual predictor variables and outcomes. All variables with statistical and/or biological significance were initially included in multivariate regression models. The final models were built using a manual backwards elimination procedure, with a significance level of P > 0.05 as the removal criterion. Confounding was assessed using the method of change of estimates, where a 10% or greater change in the estimate of the treatment group regression coefficient between the models with and without the confounder variable was used as evidence of confounding [29] [30] [31] [32] . Variables identified as confounders were included in the final model. All possible interactions between treatment group and predictor variables were explored and retained in the final model if statistically significant. Microbiological cure. For the randomly-sampled calves, logistic regression was used to evaluate associations between microbiological cure and treatment group. Other predictor variables of interest included sex, serum total protein, and age on the first day of diarrhea. Microbiological cure was defined as a negative fecal ELISA test at resolution of clinical diarrhea for calves with a positive ELISA test on the first day of diarrhea for at least one of the four fecal pathogens (E. coli K99, bovine rotavirus and coronavirus, and C. parvum). Models were generated for each fecal pathogen individually and an overall model, which evaluated microbiological cure at clinical diarrhea resolution for calves that tested positive for any single pathogen on the first day of diarrhea. Serum total protein and calf age at first diarrhea were included in all final models to control for potential confounding by passive transfer status and age. Mean daily weight change. Linear regression was used to evaluate associations between ADG (kg) and treatment group during the treatment and pre-weaning periods separately. Other predictor variables of interest included sex, birth weight (kg), serum total protein, number of days having diarrhea, age, and volume (L) of milk, Calva, and NuLife electrolytes fed at either end of treatment or hutch exit. For each calf, ADG during the treatment and pre-weaning period was calculated as the difference between birth and end treatment or hutch exit weight, respectively, divided by the number of days between these time points. To explore the possibility of an interaction between treatment group, sex, and birth weight, the final linear regression model for ADG during the pre-weaning period was stratified by sex. Age at the end of treatment or hutch exit and number of days with diarrhea were dropped from all final models in favor of improved Akaike information criterion (AIC). Onset of diarrhea and clinical cure. For all survival analyses, diarrhea was defined as a fecal score greater than 1 while diarrhea cure was defined as the second consecutive day of normal feces (fecal score of 1) following the first diarrhea episode. Subsequent episodes of diarrhea were not included in the analysis. Calves that died or did not experience diarrhea or cure from diarrhea were censored. If fresh feces were not observed on daily calf hutch assessment, a fecal score was not recorded for that day and not included in the analyses. Kaplan-Meier analysis was used to determine median days to first diarrhea event and, for those calves that developed diarrhea during the assessment period, median days to clinical diarrhea cure. A Log Rank test of equality was used to compare survivor functions between treatments. Cox Proportional Hazards regression analysis was used to estimate and compare the hazard of diarrhea and diarrhea cure between treatment groups. Sex, age, serum total protein at enrollment, birth weight (kg), antimicrobial therapy, and application of fresh straw to the hutches were evaluated as predictor variables and potential confounders. When modeling the hazard of diarrhea cure, a binary variable termed therapeutic supplementation indicating whether calves were treated with either ZM, ZS or placebo for all or at least the first 5 days of diarrhea was evaluated as an additional covariate. A five-day period was selected by the authors based on clinical experience, as five days represents a reasonable duration over which most therapeutic treatments for calf diarrhea should be applied and be expected to alleviate disease. The proportional hazards assumption that the hazard of diarrhea is independent of time was assessed using analysis of Schoenfeld residuals and testing whether the log hazard-ratio function is constant over time. Any variable found to violate the proportional hazards assumption was included in the final regression model as a time varying covariate. A total of 1,513 calves were enrolled in the trial. However, due to failure to immediately recognize exclusion criteria, 23 calves were excluded shortly after enrollment. In addition, 8 calves were excluded due to treatment errors. Therefore, a total of 1,482 calves (placebo = 500, ZM = 491, ZS = 491) were included in the final analyses. A total of 242 calves (16.3%) had minimal fecal output at the time of enrollment while 125 calves (8.4%) had abnormal fecal scores of 2 or 3 that were described as meconium. All enrolled calves appeared healthy on visual assessment and hence were assumed to have a normal fecal score at the time of enrollment. The three treatment groups at enrollment did not differ significantly in mean birth weight (kg) (P = 0.244), mean serum total protein (g/dL) (P = 0.541), mean attitude score (P = 0.845), mean fecal score (P = 0.522), as shown in Table 1 , or distribution of calf sex (P = 0.472). Of the 1,482 study calves, 21 (1.4%) died during the trial: 5 calves in the placebo group (1.0%), 11 calves in the ZM group (2.2%), and 5 calves in the ZS group (1.0%). Of these 21 calves that died, 14 (66.7%) were bulls and 7 (33.3%) were heifers. Eighteen of the 21 calves (85.7%) were found dead, rather than euthanized, due to acute and spontaneous death without previous obvious clinical signs of disease. The remaining 3 calves were euthanized prior to death due to severe and/or prolonged morbidity. Characteristics and causes of death based on field necropsy of these calves can be found in S4 Table. There was no significant difference in the proportion of calves that died between treatment groups (P = 0.168), though mortality was significantly higher in bulls compared to heifer calves (P = 0.049). Birth weight data were available for all 1,482 calves. Due to calf mortality between enrollment and completion of treatment (n = 4), end treatment weight data were available for 1,478 calves. Similarly, due to calf mortality (n = 21) or missing data for body weight at hutch exit from the dairy's records (n = 40), hutch exit weight data were available for 1,421 calves. A summary of body weight data stratified by treatment group and sex is presented in Table 2 . Within each treatment group, bull calves showed consistently higher birth weight (P < 0.001), end treatment weight (P < 0.001), and exit hutch weight (P < 0.001) compared to heifer calves. However, at birth, end of treatment, or hutch exit there were no differences in body weight between treatment groups for bulls (P > 0.1) or heifers (P > 0.1). The mean attitude scores during the study period were 1.2 across the three treatment groups (P = 0.208). Of 1,482 calves included in the final analysis, A total of 629 treated milk samples were obtained throughout the 107-day study period and pooled by treatment group and week, yielding a total of 16 pooled samples per treatment group. Zinc concentrations (ppm) were significantly higher in pooled milk samples treated with ZM (P < 0.001) and ZS (P < 0.001) compared to placebo-treated samples, and there were no significant differences between ZM and ZS-treated samples (P = 1.000), as shown in S5 Table. Within zinc treatment groups, oral zinc dose at the start and end of treatment is summarized by sex in S6 Table. For both ZM-and ZS-treated calves, oral zinc dose at the start (P < 0.001) and end (P < 0.001) of treatment was significantly higher in heifer versus bull calves. Serum zinc concentrations before and after treatment were obtained from 36 calves (n = 12 for each treatment group) and are summarized in S7 Table. Overall, there were no significant differences in mean pre-treatment serum zinc concentrations between treatment groups (P = 0.233). Mean post-treatment serum zinc concentrations were significantly higher in calves treated with ZM (P < 0.001) and ZS (P = 0.002) compared to placebo-treated calves, and there were no significant differences among calves treated with ZM and ZS (P = 0.406). Stratification of serum zinc data by treatment group and sex demonstrated that for ZM-treated calves, heifers had a numerically higher post-treatment serum zinc concentration compared to bulls, though the difference was not statistically significant (P = 0.199). In contrast, in ZStreated calves, heifers had a numerically lower post-treatment serum zinc concentration compared to bulls, though the difference was also not statistically significant (P = 0.538). Fecal analysis data were analyzed for 92 of the 127 randomly-selected calves. The remaining 35 calves were not included in the analysis due to not acquiring diarrhea during the assessment period (n = 10), death prior to final sampling (n = 1), exclusion due to improper treatment regimen (n = 1), or incorrect sampling day (n = 23).The 92 calves had a mean age at onset of diarrhea of 13.3, 11.0 and 11.3 days for ZM, ZS and placebo-treated groups, respectively; and a mean age at resolution of diarrhea of 18.8, 16.1 and 15.7 days for ZM, ZS and placebo-treated groups, respectively. There were no significant differences in the prevalence of E. coli K99 (P = 0.694), rotavirus (P = 0.331), coronavirus (P = 0.819), or C. parvum (P = 0.719) fecal shedding on the first day of diarrhea between treatment groups (S8 Table) . There were no significant differences in the prevalence of E. coli K99 (P = 0.256), rotavirus (P = 0.344), or coronavirus (P = 1.000) fecal shedding at resolution of diarrhea between treatment groups, though there was a difference in C. parvum (P = 0.006) fecal shedding between treatment groups (S9 Table) . The prevalence of C. parvum fecal shedding at resolution of diarrhea was significantly higher in calves treated with ZM (P = 0.009) and ZS (P = 0.023) compared to placebo-treated calves, and there were no significant differences among calves treated with ZM and ZS (P = 1.000). Results of logistic regression models for overall and pathogen-specific microbiological cure are presented in Tables 3-5 . For pathogen-specific cure, all calves that tested positive on the first day of diarrhea for coronavirus (n = 4) tested negative at clinical diarrhea resolution. For calves that tested positive on the first day of diarrhea for E. coli K99 (n = 9), all placebo-treated calves (n = 2) tested negative at clinical diarrhea resolution while half (n = 2) of all ZS-treated calves tested either positive or negative at clinical diarrhea resolution, resulting in omission of ZS treatment variable from the model due to collinearity. Hence, logistic regression analyses for microbiological cure in calves that tested positive for coronavirus and E. coli K99 were not possible. For 59 calves that tested positive for rotavirus on the first day of diarrhea (Table 3) , calves treated with ZM had a 50% increased odds of testing negative at diarrhea resolution compared to placebo-treated calves, though this difference was not significant (P = 0.549). Likewise, calves treated with ZS had 100% increased odds (2 times the odds) of testing negative for rotavirus at diarrhea resolution compared to placebo-treated calves, though this difference was also not significant (P = 0.314). However, this model demonstrated a significant main effect of serum total protein, such that for every 1 unit (g/dL) increase in serum total protein at enrollment, the odds of microbiological cure of rotavirus decreased by 79% (P = 0.026). For 40 calves that tested positive for Cryptosporidium parvum on the first day of diarrhea (Table 4) , calves treated with ZM had an 87% reduced odds of testing negative at diarrhea resolution Effect of zinc on diarrhea in calves compared to placebo-treated calves, though this difference was not significant (P = 0.119). Likewise, calves treated with ZS had a 74% reduced odds of testing negative for Cryptosporidium parvum at diarrhea resolution compared to placebo-treated calves, though this difference was also not significant (P = 0.183). For 55 calves that tested positive for any one of the four fecal pathogens (E. coli K99, rotavirus, coronavirus, Cryptosporidium parvum) on the first day of diarrhea (Table 5) , calves treated with ZM had a 25% increased odds of testing negative at diarrhea resolution compared to placebo-treated calves, though this difference was not significant (P = 0.769). Likewise, calves treated with ZS had a 52% increased odds of testing negative for Cryptosporidium parvum at diarrhea resolution compared to placebo-treated calves, though this difference was also not significant (P = 0.633). However, this model demonstrated that heifer calves had 71% lower odds of microbiological cure of any single fecal pathogen compared to bull calves (P = 0.076). A total of 1,482 calves were included in the linear regression model results for ADG during the treatment period, which are presented in Table 6 . There was no significant difference in ADG for ZM-or ZS-treated calves compared to placebo-treated calves, though there were significant main effects of sex, birth weight, and milk volume. Specifically, heifer calves gained 70 g bodyweight per day less compared to bull calves (P < 0.001). For every 1 kg increase in birth weight, calves gained 16 g per day less than their herd mates (P < 0.001). For every 1 L increase in milk volume per day during the treatment period, calves gained an additional 13 g per day (P < 0.001). Table 7 summarizes the linear regression analysis of ADG during the pre-weaning period for 1,421 calves which showed a significant difference in ADG for ZM-treated calves compared to placebo-treated calves and in bull versus heifer calves. Milk volume had a significant effect on ADG, such that for every 1 L increase in milk volume per day during the treatment period, calves gained an additional 2 g bodyweight per day (P = 0.001). Results of the final model showed a significant main effect for ZM-treated bull calves and a significant interaction term for ZM treatment by sex. After controlling for milk volume received during the treatment Table 6 calves (n = 1,482) Effect of zinc on diarrhea in calves period, ZM-treated bulls gained 22 g body weight per day on average more than placebotreated bull calves (P = 0.042) and ZM-treated heifers gained 12 g less body weight per day on average compared to placebo-treated heifers (P = 0.019). When considering the model coefficients for treatment group, sex, and their interaction, bull calves treated with ZM gained 454 g per day (0.432 + 0.022) while female calves treated with ZM gained 0.404 g per day (0.432 + 0.022-0.016-0.034), hence 50 g per day more gain in male calves compared to heifers treated with ZM (P = 0.019). For ZS-treated calves, there was a numerical decrease in weight gain of 5 g per day in bulls and 11 g per day in heifers compared to placebo-treated calves, though the differences were not significant (P = 0.673 bulls; P = 0.681 heifers). Linear regression models of ADG during the pre-weaning period were stratified by sex in order to avoid interpreting a three-way interaction between treatment group, sex, and birth weight. In the heifer model (S10 Table) , the interaction between ZM treatment and birth weight was significant which implied that birth weight modified the effect of ZM treatment on ADG. At a 29 kg birth weight (two standard deviations below the mean), ZM-treated heifers gained 49 g body weight per day on average less than placebo-treated heifers (P = 0.037). However, at a 49 kg birth weight (two standard deviations above the mean), ZM-treated heifers gained 30 g body weight per day on average more than placebo-treated heifers (P = 0.037). In the bull calves model (S11 Table) there was no significant interaction between treatment group and birth weight. A total of 1,482 calves were included in the Kaplan-Meier survival analysis of time to first diarrhea event (Fig 1) . There were no significant differences in median age at onset of diarrhea, specifically, 8, 8 and 7 days for the ZM, ZS and placebo-treated calves, respectively (P = 0.402). Cox proportional hazard regression model for diarrhea hazard are presented in Table 8 . After controlling for age, calves treated with ZM had a 14.7% reduced hazard of diarrhea compared to placebo-treated calves (P = 0.015). Calves treated with ZS had 13.9% reduced hazard of diarrhea compared to placebo-treated calves (P = 0.022). calves (n = 1,421) A total of 1,394 calves were included in the Kaplan-Meier survival analysis of time to clinical diarrhea cure (Fig 2) , as 88 calves failed to acquire diarrhea during the assessment period. There were no significant differences in the median days to diarrhea cure which was 7 days across all 3 treatment groups (P = 0.264). Cox proportional hazard regression model for diarrhea cure hazard are presented in Table 9 . Results of the final model showed a significant interaction term between treatment and therapeutic supplementation as well as the need for age as a time varying covariate. When considering calves that did not receive supplementation, respective to each of the 3 groups, for at least the first five days of diarrhea there was no significant difference between either ZM-and ZS-treated calves compared to placebo-treated calves (P = 0.223 ZM, P = 0.134 ZS). However, when considering calves that were supplemented for at least the first five days of diarrhea, ZM-treated calves experienced a 21.4% higher hazard of cure from diarrhea compared to placebo-treated calves (P = 0.027). Likewise, ZS-treated calves experienced a 13.0% higher hazard of cure from diarrhea compared to placebo-treated calves (P = 0.040). The current trial demonstrated evidence for the beneficial effect of ZM on ADG and neonatal diarrhea as well as an effect of ZS on diarrhea in dairy calves during the pre-weaning period. It is important to consider these results in the context of the entire pre-weaning and hutch period. On average, after 90 days from birth to hutch exit, placebo-treated bull calves gained 38.88 kg body weight while ZM-treated bull calves gained an additional 1.98 kg (40.86 kg). In contrast, the effect of zinc on weight gain in treated heifers depended on birth weight. Low birth weight heifers treated with ZM gained on average less than a placebo-treated heifer of the same birth weight. In contrast, high birth weight heifers treated with ZM gained more than placebo-treated heifers of the same birth weight. The switch in direction of the association between ZM treatment and ADG in heifer calves depending on birth weight suggests a doseresponse effect rather than a true sex-specific effect of ZM on ADG. Hence, low birth weight calves (including heifers) may require a lower dose of ZM to mitigate any negative effect of what is otherwise a suitable dose for higher birth weight calves. These findings are in agreement with a previous randomized clinical trial testing the effect of daily oral zinc in diarrheic neonatal Holstein calves which, showed that ZM-treated calves had a numerically, though not significantly increased ADG compared to calves treated with zinc oxide or placebo due to small sample size [11] . In general, our trial findings are in agreement with the large body of human literature supporting the use of oral zinc for the prevention and treatment of diarrhea and impaired growth in children [5, 10, 33] . Zinc supplementation is widely accepted by global health organizations as a vital component of therapy for childhood diarrhea [3, 4] , however, recent reviews of the literature demonstrated heterogeneity in study results on the basis of age, baseline zinc status, geographic location, and supplementation regimen [10, 34] . Similar to our findings, a sex-specific response to zinc supplementation has been demonstrated in several human studies. Zinc gluconate administered for diarrhea prevention reduced the incidence of dysentery in treated boys but not girls [35] ; when given therapeutically, it reduced diarrhea duration and frequency more dramatically in boys compared to girls [36] . Similarly, zinc sulfate was shown to improve Effect of zinc on diarrhea in calves diarrhea outcomes in boys but improved growth rates in girls [13] . Broadly, these differences between male and female responses to zinc supplementation are not understood, though theories regarding differences in immune function and response [13, 35] , diarrhea etiology [13] , and nutrient requirements [35] have been proposed. In the current study, ZM-treated bulls demonstrated increased ADG compared to placebo-treated bulls while ZM-treated heifers demonstrated decreased ADG compared to placebo-treated heifers. However, due to a significant interaction between ZM treatment and birth weight, this reduction in ADG in ZMtreated heifers was overcome with increasing birth weight, such that ZM-treated heifers with birth weights above 42 kg experienced increased ADG during the pre-weaning period, compared to placebo-treated heifers with birth weights above 42 kg. Differences in the growth response to ZM supplementation between bull and heifer calves may have been related to its effect on feed intake. Previous research on the effects of feeding various doses of oral zinc oxide to pre-ruminant dairy calves demonstrated that high levels of oral zinc supplementation resulted in reduced feed intake [23] . In the current trial, oral ZM dose was estimated to be significantly higher in heifers compared to bulls due to the significantly lower birth weight of heifers. Additionally, serum zinc concentrations in ZM-treated heifers were numerically higher than that of bulls, though this difference was not significant, likely due to the small sample size. Perhaps the higher zinc dose in heifers was associated with reduced feed intake, leading to reduced growth, and that this effect was more pronounced for ZM compared to ZS. The fact that ZM-treated heifers with birth weights approaching those of average bull calves (and, therefore, a similar zinc dose to that in bulls) experienced an increase in ADG over placebo-treated heifers similar to that of bull calves partially supports this theory. Although management practices on the study dairy were designed to be identical for both bulls and heifers, it is possible that subtle, unrecognized differences in nutritional and health management may also have contributed to sex-specific differences in weight gain. Nevertheless, future trials are warranted to investigate the potential differences in the dose-response to zinc supplementation between bulls and heifers. We hypothesized that ADG would be increased in zinc-supplemented calves compared to placebo-supplemented calves due to the potential preventive and therapeutic effects of zinc supplementation on neonatal diarrhea. In other words, calf diarrhea is mitigated by zinc supplementation and, therefore, on the causal pathway between zinc and ADG. However, considering the similarly-reduced hazard of diarrhea and increased hazard of cure from diarrhea in both ZM and ZS treatment groups but a lack of effect of ZS on ADG, it is likely that the effect of ZM on ADG is not solely mediated through its effects on diarrhea. Differences in effectiveness between organic and inorganic formulations also may exist. In fact, the underlying mechanism of action of oral zinc remains unknown [6] . Several theories of the mechanisms of action of zinc in the prevention and treatment of childhood diarrhea exist, including a mucosal-protective role, a diarrhea-induced zinc deficiency, an essential element in cell-mediated immunity, and a modifier of intra-luminal electrolyte secretion and absorption [6, [37] [38] [39] . The clinical and practical implications of effects of ZM supplementation on ADG and diarrhea must be considered. Pre-weaned calf diarrhea remains an ongoing issue for the dairy industry. The deleterious effects on calf health and performance and the resulting economic burden create a strong incentive to treat and prevent diarrhea in pre-weaned calves. On large dairy operations like those in California's Central Valley, small changes in disease incidence and duration as well as animal growth and performance can have profound economic consequences. As a non-antimicrobial product, zinc may become increasingly attractive as antimicrobials in livestock feed are under increased scrutiny and regulation due to concerns about antimicrobial resistance [2, 40] . Prevalence of C. parvum fecal shedding in a random sample of 92 study calves at onset and resolution of diarrhea was significantly higher in calves treated with zinc compared to Placebo-treated calves. In contrast, a previous study where calves that tested positive for C. parvum at the start of diarrhea and were treated with ZM had 16 times higher odds of being fecal ELISA negative at exit compared to the Placebo group (P = 0.08; power = 72.3%) [11] . The difference in findings may be due to the differences in the timing of diarrhea across treatment groups. For the current study's random sample of calves that acquired, survived, and were sampled on the correct days, the mean age of calves on both onset and resolution of diarrhea was higher for ZM and ZS calves compared to placebo-treated calves. Although C. parvum oocyst shedding in infected calves can occur as early as 3 days of age, peak shedding occurs at about 14 days of age [41] . It is possible that the increase in prevalence of C. parvum shedding in ZM and ZS treated groups was due to the increased age of zinc-treated calves compared to placebo-treated calves at resolution of diarrhea. The latter explanation is also supported by our findings that the odds of microbiological cure from C. parvum significantly decreased in older calves, with no significant differences in the odds of cure between treatment groups. In addition, the current testing did not estimate the concentration of C. parvum shedding which may still differ between treatment groups. Despite the large sample size, the current trial was limited to a single California dairy, which may represent other large dairies but does not reflect all the dairy management systems in California or elsewhere. Additionally, our results show that calves respond to zinc supplementation for diarrhea prevention differently depending on chemical formulation and calf sex. The latter could be due to differences in body weight between bulls and heifers and may point towards the need for sex-specific dosing. Furthermore, the current research did not evaluate the potential economic utility of zinc supplementation. Future studies on more accurate dosing of zinc by calf sex, the practical feasibility of weight-based dosing, and the expected cost-effectiveness of zinc administration as part of the management of pre-weaned dairy calves are warranted. Finally, our clinical trial was performed on a single, large, predominately Holstein, California dairy over a six-month period, which precluded our ability to evaluate differences due to season or breed. Hence, future studies to assess any modifying effect of breed and seasonal differences on the effect of zinc on calf health and weight gain are also needed. The current double blind, block-randomized placebo controlled clinical trial tested the effect of a prophylactic daily oral zinc supplementation in neonatal Holstein calves. Bull calves treated with ZM had a significantly increased ADG (22 g per day) during the pre-weaning period compared to placebo-treated bulls. In comparison, ZM-treated heifers had significantly lower average daily gain (9 g per day) compared to placebo-treated heifers, although higher ZM doses in low birthweight heifers may explain the lower ADG. Calves treated with either ZM or ZS had significantly lower risks of diarrhea and significantly higher risk of cure from diarrhea over the first 30 days of life compared to placebo-treated calves and hence the current trial demonstrated that zinc supplementation delayed diarrhea and expedited diarrhea recovery in pre-weaned calves. Additionally, zinc improved weight gain differentially in bulls compared to heifers, indicating the need for further research to investigate zinc dosing in calves. Supporting information S1 (DOCX) S1 Dataset. Raw data collected from trial, organized as separate excel sheets for enrollment, daily assessment, serum total protein, birth weight, exit treatment weight, exit trial weight, serum zinc testing, fecal samples, fecal testing, milk testing, and dead calves. (XLSX)

What preventative measure has been taken to decrease the incidence of diarrhea in children?

Estimating the Unreported Number of Novel Coronavirus (2019-nCoV) Cases in China in the First Half of January 2020: A Data-Driven Modelling Analysis of the Early Outbreak https://doi.org/10.3390/jcm9020388 SHA: bf20dda99538a594eafc258553634fd9195104cb Authors: Zhao, Shi; Musa, Salihu S.; Lin, Qianying; Ran, Jinjun; Yang, Guangpu; Wang, Weiming; Lou, Yijun; Yang, Lin; Gao, Daozhou; He, Daihai; Wang, Maggie H. Date: 2020 DOI: 10.3390/jcm9020388 License: cc-by Abstract: Background: In December 2019, an outbreak of respiratory illness caused by a novel coronavirus (2019-nCoV) emerged in Wuhan, China and has swiftly spread to other parts of China and a number of foreign countries. The 2019-nCoV cases might have been under-reported roughly from 1 to 15 January 2020, and thus we estimated the number of unreported cases and the basic reproduction number, R0, of 2019-nCoV. Methods: We modelled the epidemic curve of 2019-nCoV cases, in mainland China from 1 December 2019 to 24 January 2020 through the exponential growth. The number of unreported cases was determined by the maximum likelihood estimation. We used the serial intervals (SI) of infection caused by two other well-known coronaviruses (CoV), Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) CoVs, as approximations of the unknown SI for 2019-nCoV to estimate R0. Results: We confirmed that the initial growth phase followed an exponential growth pattern. The under-reporting was likely to have resulted in 469 (95% CI: 403&minus;540) unreported cases from 1 to 15 January 2020. The reporting rate after 17 January 2020 was likely to have increased 21-fold (95% CI: 18&minus;25) in comparison to the situation from 1 to 17 January 2020 on average. We estimated the R0 of 2019-nCoV at 2.56 (95% CI: 2.49&minus;2.63). Conclusion: The under-reporting was likely to have occurred during the first half of January 2020 and should be considered in future investigation. Text: A novel coronavirus (2019-nCoV) infected pneumonia infection, which is deadly [1] , was first identified in Wuhan, China in December 2019 [2] . The virus causes a range of symptoms including fever, cough, and shortness of breath [3] . The cumulative number of reported cases slowly increased to cumulative 41 cases by 1 January 2020, and rapidly increased after 16 January 2020. As of 26 January 2020, the still ongoing outbreak had resulted in 2066 (618 of them are in Wuhan) confirmed cases and 56 (45 of them were in Wuhan) deaths in mainland China [4] , and sporadic cases exported from Wuhan were reported in Thailand, Japan, Republic of Korea, Hong Kong, Taiwan, Australia, and the United States, please see the World Health Organization (WHO) news release via https://www.who.int/csr/don/en/ from 14 to 21 January 2020. Using the number of cases exported from Wuhan to other countries, a research group at Imperial College London estimated that there had been 4000 (95%CI: 1000-9700) cases in Wuhan with symptoms onset by 18 January 2020, and the basic reproduction number (R 0 ) was estimated at 2.6 (95%CI: 1.5-3.5) [5] . Leung et al. drew a similar conclusion and estimated the number of cases exported from Wuhan to other major cities in China [6] , and the potentials of travel related risks of disease spreading was also indicated by [7] . Due to an unknown reason, the cumulative number of cases remained at 41 from 1 to 15 January 2020 according to the official report, i.e., no new case was reported during these 15 days, which appears inconsistent with the following rapid growth of the epidemic curve since 16 January 2020. We suspect that the 2019-nCoV cases were under-reported roughly from 1 to 15 January 2020. In this study, we estimated the number of unreported cases and the basic reproduction number, R 0 , of 2019-nCoV in Wuhan from 1 to 15 January 2020 based on the limited data in the early outbreak. The time series data of 2019-nCoV cases in mainland China were initially released by the Wuhan Municipal Health Commission from 10 to 20 January 2020 [8] , and later by the National Health Commission of China after 21 January 2020 [9] . The case time series data in December 2019 were obtained from a published study [3] . All cases were laboratory confirmed following the case definition by the national health commission of China [10] . We chose the data up to 24 January 2020 instead of to the present study completion date. Given the lag between timings of case confirmation and news release of new cases, the data of the most recent few days were most likely to be tentative, and thus they were excluded from the analysis to be consistent. We suspected that there was a number of cases, denoted by ξ, under-reported from 1 to 15 January 2020. The cumulative total number of cases, denoted by C i , of the i-th day since 1 December 2019 is the summation of the cumulative reported, c i , and cumulative unreported cases, Ξ i . We have C i = c i + Ξ i , where c i is observed from the data, and Ξ i is 0 for i before 1 January and ξ for i after 15 January 2020. Following previous studies [11, 12] , we modelled the epidemic curve, i.e., the C i series, as an exponential growing Poisson process. Since the data from 1 to 15 January 2020 appeared constant due to unclear reason(s), we removed these data from the fitting of exponential growth. The ξ and the intrinsic growth rate (γ) of the exponential growth were to be estimated based on the log-likelihood, denoted by , from the Poisson priors. The 95% confidence interval (95% CI) of ξ was estimated by the profile likelihood estimation framework with cutoff threshold determined by a Chi-square quantile [13] , χ 2 pr = 0.95, df = 1 . With γ estimated, the basic reproduction number could be obtained by R 0 = 1/M(−γ) with 100% susceptibility for 2019-nCoV presumed at this early stage. Here, the function M(·) was the Laplace transform, i.e., the moment generating function, of the probability distribution for the serial interval (SI) of the disease [11, 14] , denoted by h(k) and k is the mean SI. Since the transmission chain of 2019-nCoV remained unclear, we adopted the SI information from Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS), which share the similar pathogen as 2019-nCoV [15] [16] [17] . We modelled h(k) as Gamma distributions with mean of 8.0 days and standard deviation (SD) of 3.6 days by averaging the SI mean and SD of SARS, mean of 7.6 days and SD of 3.4 days [18] , and MERS, mean of 8.4 days and SD of 3.8 days [19] . We were also interested in inferring the patterns of the daily number of cases, denoted by ε i for the i-th day, and thus it is obviously that C i = C i−1 + ε i . A simulation framework was developed for the iterative Poisson process such that E[ denoted the expectation. The simulation was implemented starting from 1 January 2020 with a cumulative number of cases seed of 40, the same as reported on 31 December 2019. We conducted 1000 samples and calculated the median and 95% CI. The number of 2019-nCoV unreported cases was estimated at 469 (95% CI: 403-540), see Figure 1a , which was significantly larger than 0. This finding implied the occurrence of under-reporting between 1 and 15 January 2020. After accounting for the effect of under-reporting, the R 0 was estimated at 2.56 (95% CI: 2.49-2.63), see Figure 1b , which is consistent with many existing online preprints with range from 2 to 4 [5, [20] [21] [22] . With the R 0 of 2.56 and ξ of 469, the exponential growing framework fitted the cumulative total number of cases (C i ) remarkably well, see Figure 1c iterative Poisson process such that denoted the expectation. The simulation was implemented starting from 1 January 2020 with a cumulative number of cases seed of 40, the same as reported on 31 December 2019. We conducted 1000 samples and calculated the median and 95% CI. The number of 2019-nCoV unreported cases was estimated at 469 (95% CI: 403−540), see Figure 1a , which was significantly larger than 0. This finding implied the occurrence of under-reporting between 1 and 15 January 2020. After accounting for the effect of under-reporting, the R0 was estimated at 2.56 (95% CI: 2.49−2.63), see Figure 1b , which is consistent with many existing online preprints with range from 2 to 4 [5, [20] [21] [22] . With the R0 of 2.56 and ξ of 469, the exponential growing framework fitted the cumulative total number of cases (Ci) remarkably well, see Figure 1c , referring to McFadden's pseudo-R-squared of 0.99. show the exponential growth fitting results of the cumulative number of cases (Ci) and the daily number of cases (εi) respectively. In panels (c) and (d), the gold squares are the reported cases, the blue bold curve represents the median of the fitting results, the dashed blue curves are the 95% CI of the fitting results, and the purple shading area represents the time window from 1 to 15 January 2020. In panel (c), the blue dots are the cumulative total, i.e., reported and unreported, number of cases. In panel (d), the grey curves are the 1000 simulation samples. Our estimation of R0 rely on the SI of 2019-nCoV, which remains unknown as of 26 January 2020. In this work, we employed the SIs of SARS and MERS as approximations to that of 2019-nCoV. The determination of SI requires the knowledge of the chain of disease transmission that needs a sufficient number of patient samples and periods of time for follow-up [23] , and thus this is unlikely to be achieved shortly. However, using SIs of SARS and MERS as approximation could provide an panels (a,b) , the green shading area represents the 95% CI (on the horizontal axis), and the vertical green line represents the maximum likelihood estimate (MLE) of the number of unreported cases. With the MLE of R 0 at 2.56, panels (c,d) show the exponential growth fitting results of the cumulative number of cases (C i ) and the daily number of cases (ε i ) respectively. In panels (c,d), the gold squares are the reported cases, the blue bold curve represents the median of the fitting results, the dashed blue curves are the 95% CI of the fitting results, and the purple shading area represents the time window from 1 to 15 January 2020. In panel (c), the blue dots are the cumulative total, i.e., reported and unreported, number of cases. In panel (d), the grey curves are the 1000 simulation samples. Our estimation of R 0 rely on the SI of 2019-nCoV, which remains unknown as of 26 January 2020. In this work, we employed the SIs of SARS and MERS as approximations to that of 2019-nCoV. The determination of SI requires the knowledge of the chain of disease transmission that needs a sufficient number of patient samples and periods of time for follow-up [23] , and thus this is unlikely to be achieved shortly. However, using SIs of SARS and MERS as approximation could provide an insight into the transmission potential of 2019-nCoV at the early outbreak. We note that slightly varying the mean and SD of SI would not affect our main conclusions. The R 0 of 2019-nCoV was estimated at 2.56 (95% CI: 2.49-2.63), and it is generally in line with those of SARS, i.e., 2-5 [19, 24, 25] , and MERS, i.e., 2.7-3.9 [26] . For the simulated daily number of cases (ε i ), see Figure 1d , we found that ε i matched the observed daily number after 17 January 2020, but was significantly larger than the observations from 1 to 17 January 2020. This finding implied that under-reporting was likely to have occurred in the first half of January 2020. We estimated that the reporting rate after 17 January 2020 increased 21-fold (95% CI: [18] [19] [20] [21] [22] [23] [24] [25] compared to the situation from 1 to 17 January 2020 on average. One of the possible reasons was that the official diagnostic protocol was released by WHO on 17 January 2020 [27] , and the diagnosis and reporting efforts of 2019-nCoV infections probably increased. Thereafter, the daily number of newly reported cases started increasing rapidly after 17 January 2020, see Figure 1d . We conducted additional sensitivity analysis by varying the starting date of the under-reporting time window, e.g., 1 January 2020 in the main results, from 2 December 2019 to 3 January 2020, and we report our estimates largely hold. The exact value of the reporting rate was difficult to determine due to lack of serological surveillance data. The reporting rate can be determined if serological surveillance data are available for a population; we would know who was infected (seropositive) and who was not (seronegative), with high confidence. The reporting rate is the ratio of reported cases over the number of seropositive individuals. It was statistically evident that increasing in reporting was likely, and thus it should be considered in the future investigation of this outbreak. Previous preprint suggested cumulative cases of 1723 (95% CI: 427-4471) as of 12 January 2020, and 4000 (95% CI: 1000-9700) as of 18 January 2020 based on the aggregated international export cases [5] . Our analysis yielded cumulative cases of 280 (95% CI: 128-613) as of 12 January 2020, and 609 (95% CI: 278-1333) as of 18 January 2020 based on the exponential growing mechanistic in the early outbreak. Although our estimate case number appeared to have a lower mean than those estimated by Imai et al. [5] , they are not statistically different. This study applied a different screening effort to detect the 2019-nCoV cases from that in Imai et al. [5] . Imai et al. assumed the average screening effort at overseas airports that covered travelers arriving from Wuhan. Whereas we assumed a constant screening effort applied in Wuhan at the same point of time, and then a number of cases (i.e., ξ) should have been reported yet failed to be reported in the first half of January 2020 due to all sorts of reasons. It is not surprising that different assumptions yielded different results, and this difference in screening effort also partly explained why the detected cases out of China mainly presented mild symptoms. Thus, it was reasonable that our estimates appeared lower than those estimated by Imai et al. [5] . It must be emphasized that such a gap in the knowledge would be resolved by serological survey study (for a large population to approximate the actual positive rate) or an explicit estimation of the actual reporting rate. Under-reporting was likely to have occurred and resulted in 469 (95% CI: 403-540) unreported cases from 1 to 15 January 2020. The reporting rate after 17 January 2020 was likely to have increased 21-fold (95% CI: 18-25) compared with the situation from 1 to 17 January 2020 on average, and it should be considered in future investigation. We estimated the R 0 at 2019-nCoV to be 2.56 (95% CI: 2.49-2.63). Author Contributions: All authors conceived the study, carried out the analysis, discussed the results, drafted the first manuscript. All authors have read and agreed to the published version of the manuscript.

Who release the time series data from 10th to 20th January 2020?

Screening of FDA-Approved Drugs for Inhibitors of Japanese Encephalitis Virus Infection https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5640845/ SHA: 1bd2f6497996fc0fccd8dffd7f84846d3d36f964 Authors: Wang, Shaobo; Liu, Yang; Guo, Jiao; Wang, Peilin; Zhang, Leike; Xiao, Gengfu; Wang, Wei Date: 2017-10-13 DOI: 10.1128/jvi.01055-17 License: cc-by Abstract: Japanese encephalitis virus (JEV), an arthropod-borne flavivirus, is a major cause of acute viral encephalitis in humans. No approved drug is available for the specific treatment of JEV infections, and the available vaccines are not effective against all clinical JEV isolates. In the study described here, a high-throughput screening of an FDA-approved drug library for inhibitors of JEV was performed. Five hit drugs that inhibited JEV infection with a selective index of >10 were identified. The antiviral activities of these five hit drugs against other flavivirus, including Zika virus, were also validated. As three of the five hit drugs were calcium inhibitors, additional types of calcium inhibitors that confirmed that calcium is essential for JEV infection, most likely during viral replication, were utilized. Adaptive mutant analysis uncovered that replacement of Q130, located in transmembrane domain 3 of the nonstructural NS4B protein, which is relatively conserved in flaviviruses, with R or K conferred JEV resistance to manidipine, a voltage-gated Ca(2+) channel (VGCC) inhibitor, without an apparent loss of the viral growth profile. Furthermore, manidipine was indicated to protect mice against JEV-induced lethality by decreasing the viral load in the brain, while it abrogated the histopathological changes associated with JEV infection. This study provides five antiflavivirus candidates and identifies cytoplasmic calcium to be a novel antiviral target for the treatment of JEV infection. The findings reported here provide therapeutic possibilities for combating infections caused by flaviviruses. IMPORTANCE No approved therapy for the treatment of Japanese encephalitis virus infection is currently available. Repurposing of approved drugs would accelerate the development of a therapeutic stratagem. In this study, we screened a library of FDA-approved drugs and identified five hit drugs, especially calcium inhibitors, exerting antiflavivirus activity that blocked viral replication. The in vivo efficacy and toxicity of manidipine were investigated with a mouse model of JEV infection, and the viral target was identified by generating an adaptive mutant. Text: F laviviruses are taxonomically classified in the genus Flavivirus and family Flaviviridae. These viruses comprise over 70 different pathogens, such as Japanese encephalitis virus (JEV), Zika virus (ZIKV), dengue virus (DENV), West Nile virus (WNV), and yellow fever virus (YFV). Most flaviviruses are arthropod borne and cause public health problems worldwide (1) . The development and usage of vaccines against some flaviviruses, such as JEV, YFV, and tick-borne encephalitis virus (TBEV), have decreased the rates of morbidity and mortality from infections caused by these viruses (2) ; however, flavivirus-induced diseases are still pandemic, and few therapies beyond intensive supportive care are currently available. Flaviviruses have an approximately 11-kb positive-stranded RNA genome containing a single open reading frame (ORF) flanked by untranslated regions (UTRs) at both termini. The ORF encodes three structural proteins, including the capsid (C), membrane (premembrane [prM] and membrane [M] ), and envelope (E), and seven nonstructural proteins (NS1, NS2A, NS2B, NS3, NS4A, NS4B, and NS5) (3) . These seven nonstructural proteins participate in viral replication, virion assembly, and virus escape from immune surveillance. To date, no specific antivirals with activity against flaviviruses are available. To address this, we conducted a screen of a library of 1,018 FDA-approved drugs. Since flaviviruses are similar in structure and pathogenesis, we first utilized JEV as the prototype to screen the drug library and subsequently validated the antiviral activities with ZIKV, WNV, and DENV type 2 (DENV-2). The hit drugs identified in this study offer potential new therapies for the treatment of flavivirus infection and disease. Screening of an FDA-approved drug library for inhibitors of JEV infection. Recombinant viral particles (RVPs) with the luciferase-reporting replicon enveloped by the JEV structural proteins were used to select inhibitors, with a focus on those that inhibit virus entry and replication, by a high-throughput screening (HTS) assay (4, 5) . The number of genomic RNA copies of RVP was determined to be 8.4 ϫ 10 6 copies/ml by using a standard curve generated with plasmids carrying the infectious clone. The HTS assay conditions, including the seeding cell density and RVP dose, were optimized to be 10,000 cells per 96-well plate and 20 l (16 copies/cell) RVP for the infective dose, respectively. Under the optimized conditions, the signal-to-basal (S/B) ratio, coefficient of variation (CV), and Z= factor were 38,374, 2.8%, and 0.89, respectively, which demonstrated that the assay was robust and suitable for the large-scale screening of compounds. A schematic of the HTS assay is depicted in Fig. 1B . After three rounds of screening, five hits with a selective index (SI; which is equal to the 50% cytotoxic concentration [CC 50 [/50% inhibitory concentration [IC 50 ]) of Ͼ10 were selected. The CC 50 values of the hit drugs exhibited in Fig. 1B were similar to those previously published for diverse cell systems but determined using different toxicity assays (6) (7) (8) (9) (10) (11) (12) (13) . Three of the hit drugs, manidipine, cilnidipine, and benidipine hydrochloride, were dihydropyridine (DHP) voltage-gated Ca 2ϩ channel (VGCC) antagonists, while pimecrolimus is an inhibitor of inflammatory cytokine secretion and nelfinavir mesylate is an HIV-1 protease blocker. All five drugs exhibited a dose-dependent inhibition of JEV RVP infection (Fig. 1C) . To validate the antiviral effect, hit drugs were purchased from other commercial sources and tested. In the reconfirmation screen, all hit drugs showed antiviral and cytotoxic effects similar to those found in the primary screen. Validation of hit drugs. To verify the results obtained by the luciferase reporter assays, we also investigated the antiviral effect of the five hit drugs on wild-type JEV strain AT31. As expected from the HTS assay, all five drugs robustly inhibited virus production, with a reduction of approximately 4 to 5 log units at the highest concentration and an approximately 1-log-unit decrease with 2.5 M the drugs (Fig. 2B) . A sharp decrease in JEV RNA levels was also detected (Fig. 2C) . The attenuated RNA levels in the high-dose, middle-dose, and low-dose groups were all above 40%. In particular, in the manidipine-treated group, the inhibitory effect was at least 80% compared to that for the control, which showed a strong inhibition of viral replication. Consistent with the inhibition of virus replication and production, expression of the viral structural protein prM was hardly detectable following treatment with the drugs at the high concentration (Fig. 2D) . Overall, the results in Fig. 2 confirmed that the five hit drugs inhibited JEV infection in a dose-dependent manner in vitro. Drugs inhibit JEV infection during viral RNA synthesis. Because RVPs, which have a natural virus-like envelope on the outside and a replicon on the inside, permitted the quantification of JEV productive entry and replication, a time-of-addition experiment was performed to investigate whether the hit drugs blocked the entry step or the replication step. As shown in Fig. 3B , no suppression of luciferase activity by any of the hit drugs was observed when they were used as treatments before infection or during infection or as a virucide, suggesting that these drugs do not inhibit JEV infection either by inactivating the virus directly or by blocking JEV entry. However, these drugs exerted fully inhibitory effects when they were added at 1 h postinfection, suggesting that viral replication was the stage at which these drugs showed inhibitory activity. To confirm this suggestion, we investigated the inhibitory effects of these drugs on the JEV replicon. The highest concentration of manidipine and nelfinavir mesylate tested in baby hamster kidney (BHK-21) cells was adjusted to 5 M and 10 M, respectively. It was shown that all five drugs inhibited JEV RNA synthesis in a dosedependent manner, while neither drug inhibited the initial translation of replicon RNA (5, 14) (Fig. 3C) , confirming that these drugs inhibited JEV infection at the stage of replication. Hit drugs exhibit broad-spectrum antiflavivirus activity. In order to determine whether the antiviral activity of the five hit drugs extended to other flaviviruses, we explored their antiviral effect against ZIKV. Similar to the findings for JEV, the ZIKV titer was decreased by multiple log units when ZIKV was treated with a high concentration of each of the drugs (Fig. 4A) . Moreover, ZIKV exhibited a higher sensitivity to the two calcium channels inhibitors manidipine and cilnidipine than JEV, with no plaque formation being observed at 10 M. Consistent with this result, sharp decreases in the level of replication of ZIKV RNA and the level of expression of viral protein were also detected (Fig. 4A) . Notably, treatment with 5 M manidipine produced a 95% inhibition of viral replication, translation, and viral yields. Taken together, these results indicate that the hit drugs could effectively inhibit ZIKV infection. Since these drugs exhibited their anti-JEV effects at the stage of viral replication, we further tested the effects against WNV and DENV-2 by using WNV and DENV-2 replicons. Similar to the results for JEV, a dose-dependent reduction in the level of WNV replication was observed with the drug treatments. The same phenotype was observed for DENV-2 for all drugs except nelfinavir mesylate, which showed no effect at the concentrations tested ( Fig. 4B and C). Together, these results indicate that the five hit drugs are excellent candidates for broad-spectrum antiflavivirus treatment. Antiviral effect of calcium inhibitors. Since three hit drugs, manidipine, cilnidipine, and benidipine hydrochloride, were DHP VGCC inhibitors, we asked whether other calcium antagonists could block JEV infection. To address this question, we employed four different classes of inhibitors. Verapamil, a prototype phenylalkylamine (PAA) VGCC inhibitor (15) , exhibited a dose-dependent inhibition of JEV on both African Green monkey kidney (Vero) and human hepatocellular carcinoma (Huh-7) cells (Fig. 5) , which was consistent with the inhibitory effects of the DHP inhibitors, suggesting that calcium channels play an important role in JEV infection. Cyclosporine and 2-aminobiphenyl borate (2-APB), which inhibit the efflux of Ca 2ϩ from the mitochondrial and endoplasmic reticulum (ER) pool, respectively (16) (17) (18) (19) , were also found to block JEV infection effectively. Similarly, treatment with the cell-permeant Ca 2ϩ chelator 1,2-bis-(o-aminophenoxy)-ethane-N,N,N=,N=-tetraacetic acid, tetraacetoxymethyl ester (BAPTA-AM), could also suppress JEV infection. Taken together, we concluded that intracellular Ca 2ϩ is essential for JEV infection and cytoplasmic calcium is a potent target for antiflavivirus treatment. Selection and characterization of manidipine-resistant JEV. To identify the viral target of the calcium channel inhibitor, we selected a manidipine-resistant virus by serially passaging JEV in the presence of manidipine. Viruses from passage 20 (P20) showed robust resistance compared with the wild type (WT) (Fig. 6A ). When JEV from P20 was treated with 5 M or 10 M manidipine, the viral titer was about 10-and 100-fold higher than that of the WT, respectively. Individual virus clones were isolated, and two isolates were randomly selected and amplified. An amino acid substitution was observed in two isolated clones, resulting in a glutamine (Q)-to-arginine (R) switch at amino acid position 130 in transmembrane domain 3 (TMD3) of NS4B, i.e., position 2401 of the translated polyprotein in the JEV infectious cDNA clone (Fig. 6B ). Sequence alignment of NS4B indicated that Q130 was conserved in all flaviviruses except YFV, which possessed a lysine at that position (Fig. 6B) . The conserved Q130 of NS4B may account for the sensitivity of JEV, ZIKV, WNV, and DENV-2 to manidipine, as described above (Fig. 4) , while YFV showed resistance to the drug (data not shown). To confirm that the Q130R mutation did confer manidipine resistance and to investigate the role of Q130 in NS4B function, we produced JEV clones with the Q130R, Q130K, Q130E, or Q130A mutation by introducing the desired mutations into the infectious cDNA clone and rescuing the mutant viruses. To investigate the biological properties of the mutant viruses, we first examined the growth kinetics of the rescued viruses. As shown in Fig. 6C , all mutant viruses had an accumulation of infectious virions and reached the highest titer at 60 h postinfection. Infection of the Q130R and Q130K mutant viruses resulted in growth curves similar to the growth curve for the WT (Fig. 6C) , while the Q130E and Q130A mutants produced smaller amounts of viruses between 24 and 60 h. Analysis of the plaque morphology revealed that the plaques of the Q130R, Q130K, and Q130E mutants were similar to the plaques of the WT, whereas the plaques of the Q130A mutant were smaller than those of the WT. We next investigated the sensitivity of the four mutant viruses to manidipine. As shown in Fig. 6D , the Q130R and Q130K mutant viruses were resistant to manidipine. At a 10 M concentration, manidipine efficiently inhibited WT JEV infection and reduced the viral yields by approximately 4 log units, while the Q130R and Q130K mutant viruses were resistant to manidipine and the viral titer decreased less than 2 log units. The Q130A mutant virus demonstrated moderate resistance and a slightly higher Taken together, it could be concluded that Q130 not only is critical for conferring manidipine sensitivity but also is important for JEV replication. The replacement of glutamine with basic amino acids conferred resistance to manidipine without an apparent loss of growth. In vivo efficacy of manidipine. As manidipine exhibited the strongest inhibitory activities on JEV replication as well as ZIKV infection when its activities were compared with those of the five hit drugs (Fig. 2 and 4A) , we further examined the protective effect of manidipine against JEV-induced lethality in a mouse model. As anticipated, mice in the JEV-infected vehicle-treated group started to show symptoms, including limb paralysis, restriction of movement, piloerection, body stiffening, and whole-body tremor, from day 5 postinfection. Within 21 days postinfection, most mice in the JEV-infected group succumbed to the infection, with the mortality rate being 73% (4 out of 15 animals survived). Manidipine treatment following JEV infection reduced the mortality rate to 20% (12 out of 15 animals survived) (Fig. 7A ). Mice treated with manidipine alone or treated with manidipine and infected with JEV showed little abnormal behavior, similar to the findings for the mice in the vehicle-treated group. These results suggest that manidipine provided effective protection against JEVinduced mortality. To further relate these protective effects to the viral load and histopathological changes in the mouse brains, the viral titer was determined and mouse brain sections were collected and assayed at day 5 and day 21 postinfection, since mice started to show symptoms of JEV infection from day 5 postinfection and most of the surviving mice had recovered at day 21. The results indicated that, during the progression of the disease, manidipine treatment significantly reduced the viral load in infected mice compared to that in infected mice not receiving treatment, while no plaques formed in either the manidipine-or vehicle-treated group, and viral loads were undetectable in each group on day 21 postinfection (Fig. 7B) . As JEV was rapidly cleared from the blood after inoculation and was present in the lymphatic system during the preclinical phase, the effects of manidipine on infection of serum and the spleen were evaluated at earlier time points to detect whether the drug reduced the peripheral viral loads (20, 21) . As shown in Fig. 7C , manidipine had little effect on peripheral JEV infection, which indicated that manidipine protected the mice against JEV-induced lethality by decreasing the viral load in the brain. Similarly, apparent damage in the brain, including meningitis, perivascular cuffing, vacuolar degeneration, and glial nodules, was observed in the JEV-infected and vehicle-treated group on day 5 postinfection, while manidipine treatment remarkably alleviated these phenomena (Fig. 7D) . These results indicate that the alleviation of histopathological changes was accompanied by a reduction in the viral load as well as a reduction in the rate of mortality, further confirming the curative effects of manidipine on viral encephalitis. Among the five hit drugs, manidipine, cilnidipine, and benidipine hydrochloride were VGCC inhibitors. It has been well documented in the literature that Ca 2ϩ inhibitors serve to inhibit virus infection at the stage of either entry (15, 22) or replication (18) and even at the stage of budding (23) . To this end, we first reviewed all 21 calcium inhibitors included in the current library of FDA-approved drugs and found that, in addition to the four DHP VGCC inhibitors listed in Fig. 1B , two other calcium inhibitors, i.e., flunarizine dihydrochloride and lomerizine hydrochloride, were also identified to be primary candidates with levels of inhibition of Ͼ90%. Similarly, three calcium channel antagonists, nisoldipine, felodipine, and nicardipine hydrochloride, showed levels of inhibition of 75%, 72%, and 66%, respectively, in the primary screen. Together, 9 of the 21 calcium inhibitors in the library, accounting for nearly half of the calcium inhibitors, exhibited levels of flavivirus inhibition of greater than 50%, suggesting that calcium, especially the calcium channel, is a potential antiviral target. To address this, another type of VGCC inhibitor, verapamil, an FDA-approved drug not yet included in the drug library used in this study, was investigated. Likewise, a Ca 2ϩ chelator, BAPTA-AM, as well as the Ca 2ϩ inhibitors 2-APB and cyclosporine, targeting ER and the mitochondrial Ca 2ϩ channel, respectively, were employed to investigate the response of JEV infection to the decrease in intracellular Ca 2ϩ levels. In line with the activities of the three hit DHP VGCC inhibitor drugs, the additional Ca 2ϩ inhibitors exerted anti-JEV activity, which indicated that Ca 2ϩ is indispensable for JEV infection. Thus, Ca 2ϩ inhibitors might be utilized as effective treatments for flavivirus infection. As the hit drugs exerted full inhibitory activity when they were added posttreatment, we believe that Ca 2ϩ is important for flavivirus genome replication. Furthermore, selection and genetic analysis of drug-resistant viruses revealed that NS4B is the viral target of manidipine. NS4B is part of the viral replication complex and is supposed to anchor the viral replicase to the ER membrane (24) . Meanwhile, the N-terminal 125amino-acid domain of DENV NS4B was indicated to be responsible for inhibition of the immune response (25) . Notably, several structurally distinct compounds have been identified to inhibit flavivirus replication by intensively targeting the TMD of NS4B (26) (27) (28) (29) (30) (31) (32) . It is thus conceivable that inhibitors targeting TMD of NS4B would perturb its function, leading to the suppression of viral RNA replication. In this study, the replacement of Q130 of NS4B with a basic amino acid conferred the resistance effect without suppressing JEV replication, suggesting that position 130 could tolerate a basic amino acid and that the basic amino acid might be involved in the interplay of NS4B with host proteins rather than viral proteins. Moreover, the efficacy and toxicity of manidipine were monitored in vivo, with manidipine demonstrating effective antiviral activity with favorable biocompatibility. However, the dose used in this study was higher than the dose typically used clinically, representing one of the scenarios most commonly encountered in drug repurposing (33, 34) . As manidipine was approved for use for the long-term treatment of hypertension (35, 36) , pulse-dose treatment with manidipine over the shorter period of time required for the treatment of virus infection might be relatively safe. Moreover, use of a combination of manidipine with other Ca 2ϩ inhibitors might improve its therapeutic efficacy, reduce its toxicity, and reduce the risk of resistance development (37) (38) (39) . Besides the three VGCC inhibitors, two hit drugs, pimecrolimus and nelfinavir mesylate, showed equivalent inhibitory activities on the replication of JEV, ZIKV, WNV, and DENV-2. Although there has been no report on the use of pimecrolimus for the treatment of infectious diseases, we showed that it had a robust effect against JEV with an SI of Ͼ32. The maximum plasma concentration (C max ) of nelfinavir mesylate achieved with an adult dose was 3 to 4 g/ml (40) , which was comparable to the IC 50 reported here. Notably, nelfinavir mesylate was confirmed to inhibit herpes simplex virus 1 (HSV-1) and the replication of several other herpesviruses by interfering directly or indirectly with the later steps of virus formation, such as glycoprotein maturation or virion release, other than functioning in herpesviruses protease (41, 42) . Whether nelfinavir mesylate inhibits flavivirus by interference with the virus protease or by other off-target effects is unknown. Understanding of the mechanism of the antiflavivirus effects of these drugs might uncover novel targets of the drugs, providing further insight into the pathogenesis of flaviviruses. Above all, the findings reported here provide novel insights into the molecular mechanisms underlying flavivirus infection and offer new and promising therapeutic possibilities for combating infections caused by flaviviruses. Cells and viruses. BHK-21, SH-SY5Y (human neuroblastoma), Vero, and Huh-7 cells were cultured in Dulbecco modified Eagle medium (HyClone, Logan, UT, USA) supplemented with 10% fetal bovine serum (Gibco, Grand Island, NY, USA). JEV strain AT31, the WNV replicon, and the DENV-2 replicon expressing Renilla luciferase (Rluc) were kindly provided by Bo Zhang, Wuhan Institute of Virology, Chinese Academy of Sciences (CAS), China. JEV replicon recombinant viral particles (RVPs) were generated as previously described (4, 5) . ZIKV strain H/PF/2013, kindly provided by the European Virus Archive Goes Global, was propagated and titrated in Vero cells. Optimization of HTS assay conditions. The cell density and RVP dose were optimized for the HTS assay. Vero cells at different densities (2,500 to 12,500 cells per well) were infected with from 1.25 to 20 l RVPs (1 to 16 copies per well). The appropriate cell density as well as the RVP dose was selected by comparing the S/B ratio, CV, and Z= values under different conditions as previously described (43) . Methyl-␤-cyclodextrin and dimethyl sulfoxide (DMSO) were used as positive and negative controls, respectively. HTS assay of an FDA-approved compound library. A library of 1,018 FDA-approved drugs was purchased from Selleck Chemicals (Houston, TX, USA). The compounds were stored as 10 mM stock solutions in DMSO at 4°C until use. The first round of the HTS assay was carried out as shown in Fig. 1A . The criteria used to identify the primary candidates were no apparent cytotoxicity and an average level of inhibition of Ͼ90% in duplicate wells. The criteria of dose-dependent inhibition and cell viability of Ͼ80% were applied for the reconfirmation screen. Furthermore, the CC 50 of each compound was calculated, and those compounds displaying SIs over 10 were considered hits in this study. Identification of antiviral effects of five hit drugs. The antiviral effects of the drugs were evaluated by quantitative reverse transcription-PCR (qRT-PCR), immunofluorescence assay (IFA), and plaque assay as previously reported (44) (45) (46) (47) . The experimental timeline is depicted in Fig. 2A . To ensure the effectiveness of the hit drugs in flavivirus replication, BHK-21 cells transfected with the JEV, WNV, or DENV-2 replicon were incubated with each drug at the concentrations indicated above, and the luciferase activities were determined 24 h, 48 h, or 72 h later, respectively. Time-of-addition experiment. To evaluate which stage of the JEV life cycle was inhibited by each hit, a time-of-addition experiment was performed as previously described (43) . Vero cells were infected with 20 l RVPs for 1 h (0 to 1 h). The test compounds were incubated with the cells for 1 h before infection (Ϫ1 to 0 h), during infection (0 to 1 h), and for 23 h postinfection (1 to 24 h) (Fig. 3A) . To exclude a possible direct inactivating effect of the drugs, RVPs were incubated with each drug at 37°C for 1 h, and the mixtures were diluted 25-fold to infect Vero cells. Twenty-four hours later, the luciferase activities were determined as described above (Fig. 3A) . Manidipine-resistant virus. Manidipine-resistant virus was generated by passaging of JEV on Vero cells in the presence of manidipine. Passages 1 to 10 used 5 M manidipine, and passages 11 to 20 used 10 M manidipine. As a control, WT virus was passaged in the presence of 2% DMSO in parallel. Passaging was terminated at passage 20, when no further improvement in resistance was detected. Two manidipine-resistant virus isolates were plaque purified and amplified in the presence of manidipine. Viral RNA was extracted, amplified, and purified for sequencing. An infectious cDNA clone of JEV, strain AT31 (pMWJEAT), kindly provided by T. Wakita, Tokyo Metropolitan Institute for Neuroscience, was used to recover WT and mutant viruses as described previously (4) . Virus titers and manidipine sensitivities were determined by plaque assay in Vero cells. Manidipine administration to JEV-infected mice. Adult female BALB/c mice (age, 4 weeks) were kept in the Laboratory Animal Center of Wuhan Institute of Virology, CAS (Wuhan, China). The mice were randomly divided into four groups (30 mice per group): a JEV-infected and vehicle (2% Tween 80 plus 5% DMSO in phosphate-buffered saline [PBS])-treated group, a manidipine-treated group, a JEV-infected and manidipine-treated group, and a vehicle-treated group. For infection, mice were infected intraperitoneally with 5 ϫ 10 6 PFU of JEV strain AT31. For the manidipine and vehicle treatments, mice were injected intraperitoneally with 25 mg/kg of body weight manidipine or PBS with 2% Tween 80 and 5% DMSO, respectively. Treatments were administered twice a day for the first 2 days and then consecutively administered once a day for up to 21 days. Five mice from each group were sacrificed on days 1, 3, and 5 postinfection. Serum, spleen tissue, and brain tissue samples were collected for viral titer determination and histopathology investigation. Fifteen mice were monitored daily for morbidity and mortality. The mice that showed neurological signs of disease were euthanized according to the Regulations for the Administration of Affairs Concerning Experimental Animals in China. The protocols were reviewed and approved by the Laboratory Animal Care and Use Committee at the Wuhan Institute of Virology, CAS (Wuhan, China).

What is the mechanism of action for manidipine?

On the Coronavirus (COVID-19) Outbreak and the Smart City Network: Universal Data Sharing Standards Coupled with Artificial Intelligence (AI) to Benefit Urban Health Monitoring and Management https://doi.org/10.3390/healthcare8010046 SHA: 90d04764b497a224a1d969f4e317fc19a5feab35 Authors: Allam, Zaheer; Jones, David S. Date: 2020 DOI: 10.3390/healthcare8010046 License: cc-by Abstract: As the Coronavirus (COVID-19) expands its impact from China, expanding its catchment into surrounding regions and other countries, increased national and international measures are being taken to contain the outbreak. The placing of entire cities in ‘lockdown’ directly affects urban economies on a multi-lateral level, including from social and economic standpoints. This is being emphasised as the outbreak gains ground in other countries, leading towards a global health emergency, and as global collaboration is sought in numerous quarters. However, while effective protocols in regard to the sharing of health data is emphasised, urban data, on the other hand, specifically relating to urban health and safe city concepts, is still viewed from a nationalist perspective as solely benefiting a nation’s economy and its economic and political influence. This perspective paper, written one month after detection and during the outbreak, surveys the virus outbreak from an urban standpoint and advances how smart city networks should work towards enhancing standardization protocols for increased data sharing in the event of outbreaks or disasters, leading to better global understanding and management of the same. Text: The novel Coronavirus outbreak, (previously known as the 2019-nCoV and later renamed COVID-19 during the writing of this manuscript) is leading to the closure of entire cities in China, and causing stringent measures to be taken in others. While in distant different continents, far from China where the virus was first reported, places are being placed on high alert. In Wuhan, where the virus broke, schools, roads and markets have been shut down [1] . The same is true in Hong Kong, Beijing and Hubei Province amongst surrounding areas, as precautionary measures are being emphasized to ensure that the spread of the virus is minimized, and complete and accurate information on the virus is being obtained [2] . However, the rate of spread of the virus and the uncertainties surrounding the entire situation has led the World Health Organization (WHO) on 30 January 2019 to declare the Coronavirus outbreak a 'Global Public Health Emergency'. WHO determined, however, not to declare the outbreak a 'Public Health Emergency of International Concern' (PHEIC) which is a higher level of declaration. A PHEIC is defined as "an extraordinary event which is determined to constitute a public health risk to other States through the international spread of disease and to potentially require a coordinated international response" whose scope may include: serious, sudden, unusual or unexpected; carries implications for public health beyond the affected State's national border; and may require immediate international action [3] . With the world having experienced some notable influenza pandemics in the past, a Global Initiative on Sharing All Influenza Data (GISAID) platform [4] was established and was instrumental in the rapid sharing of information by the Chinese scientists regarding the emergence of the COVID-19 virus. Through this platform, scientists from other regions were observed to gain access to information and are, subsequently, able to act in a much faster capacity; like in the case of scientists from the Virus Identification Laboratory based at Doherty Institute, Australia, who managed to grow a similar virus in the laboratory after accessing the data shared by the Chinese scientists [5] . Beyond the aspect of pandemic preparedness and response, the case of COVID-19 virus and its spread provide a fascinating case study for the thematics of urban health. Here, as technological tools and laboratories around the world share data and collectively work to devise tools and cures, similar efforts should be considered between smart city professionals on how collaborative strategies could allow for the maximization of public safety on such and similar scenarios. This is valid as smart cities host a rich array of technological products [6, 7] that can assist in early detection of outbreaks; either through thermal cameras or Internet of Things (IoT) sensors, and early discussions could render efforts towards better management of similar situations in case of future potential outbreaks, and to improve the health fabric of cities generally. While thermal cameras are not sufficient on their own for the detection of pandemics -like the case of the COVID-19, the integration of such products with artificial intelligence (AI) can provide added benefits. The fact that initial screenings of temperature is being pursued for the case of the COVID-19 at airports and in areas of mass convergence is a testament to its potential in an automated fashion. Kamel Boulos et al. [8] supports that data from various technological products can help enrich health databases, provide more accurate, efficient, comprehensive and real-time information on outbreaks and their dispersal, thus aiding in the provision of better urban fabric risk management decisions. The above improvements in the healthcare sector can only be achieved if different smart city products are fashioned to support standardized protocols that would allow for seamless communication between themselves. Weber and Podnar Žarko [9] suggest that IoT devices in use should support open protocols, and at the same time, the device provider should ensure that those fashioned uphold data integrity and safety during communication and transmission. Unfortunately, this has not been the case and, as Vermesan and Friess [10] explain, most smart city products use proprietary solutions that are only understood by the service providers. This situation often creates unnecessary fragmentation of information rendering only a partial integrated view on the dynamics of the urban realm. With restricted knowledge on emergent trends, urban managers cannot effectively take decisions to contain outbreaks and adequately act without compromising the social and economic integrity of their city. This paper, inspired by the case of the COVID-19 virus, explores how urban resilience can be further achieved, and outlines the importance of seeking standardization of communication across and between smart cities. With the advent of the digital age and the plethora of Internet of Things (IoT) devices it brings, there has been a substantial rise in the amount of data gathered by these devices in different sectors like transport, environment, entertainment, sport and health sectors, amongst others [11] . To put this into perspective, it is believed that by the end of 2020, over 2314 exabytes (1 exabyte = 1 billion gigabytes) of data will be generated globally [12] from the health sector. Stanford Medicine [12] acknowledges that this increase, especially in the medical field, is witnessing a proportional increase due to the increase in sources of data that are not limited to hospital records. Rather, the increase is being underpinned by drawing upon a myriad and increasing number of IoT smart devices, that are projected to exponentially increase the global healthcare market to a value of more than USD $543.3 billion by 2025 [13] . However, while the potential for the data market is understood, such issues like privacy of information, data protection and sharing, and obligatory requirements of healthcare management and monitoring, among others, are critical. Moreover, in the present case of the Coronavirus outbreak, this ought to be handled with care to avoid jeopardizing efforts already in place to combat the pandemic. On the foremost, since these cut across different countries, which are part of the global community and have their unique laws and regulations concerning issues mentioned above, it is paramount to observe them as per the dictate of their source country's laws and regulations; hence, underlining the importance of working towards not only the promoting of data through its usage but also the need for standardized and universally agreed protocols. While the significance of such data in advancing efficiency, productivity and processes in different sectors is being lauded, there are criticisms arising as to the nature of data collection, storage, management and accessibility by only a small group of users. The latter particularly includes select ICT corporations that are also located in specific geographies [6, [14] [15] [16] [17] . These criticisms are justified, as in recent years, big data is seen as the new 'gold rush' of the 21st century and limiting its access means higher economic returns and increased influence and control at various scales to those who control data. These associated benefits with big data are clearly influencing geopolitical standings, in both corporate and conventional governance realms, and there is increased competition between powerful economies to ensure that they have the maximum control of big data. As case in point is the amount of 'push and pull' that has arisen from Huawei's 5G internet planned rollout [18] . Though the latter service offers unprecedented opportunities to increase internet speeds, and thereby influence the handling of big data, countries like the U.S. and some European countries that are key proponents and players in global political, economic and health landscapes, are against this rollout, arguing that it is a deceptive way of gathering private data under the guise of espionage. On this, it has been noted that the issue of data control and handling by a few corporations accords with their principles of nationalism, and that these work for their own wellbeing as well as to benefit the territories they are registered in. Therefore, geopolitical issues are expected on the technological front as most large data-rich corporations are located in powerful countries that have influence both economically, health-wise and politically [19] [20] [21] . Such are deemed prized tokens on the international landscape, and it is expected that these economies will continue to work towards their predominant control as much as possible. On the health sector, the same approach is being upheld where critical information and data are not freely shared between economies as that would be seen to be benefiting other in-competition economies, whereas different economies would cherish the maximization of benefits from such data collections. In addition to the obvious deep-rooted social issues related to nationalism, other challenges include the increasing movement of people globally that is being enhanced by reduced costs and higher speed. In particular, these challenges are more pronounced when it comes to public health. This is because most of the health-related data collected not only can compromise local nations, but also captures those of travelers. In such cases, in a bid to improve the health status of a nation, it becomes paramount to factor in data from other regions necessitating unhindered sharing of this data. Such data-sharing truth is emphasized in situations like the recent case of Coronavirus outbreak threatening the global health environment, facilitated by air transportation. The virus was first reported in Wuhan, China, and in a matter of three weeks (by 17th January 2020) over 300 cases were confirmed in that region, and 10 days later (26th January 2020), a total of 2014 cases of Coronavirus have been reported, with 684 of those being confirmed, and with 29 reported outside China. The fatalities from the virus stands at 56 as of 26th January 2020 [22] . The virus had then been confirmed in various countries including Taiwan, South Korea, Japan, Thailand, France, the United States, Singapore and Vietnam [23] . In the above case, though major cities are known to prepare themselves for potential outbreaks, their health policies and protocols are observed to diverge from one another. Thus, without a global collaborative approach, progress towards working for a cure and universally acceptable policy approach can take longer. Such fears, of a lack of international collaboration, were highlighted by the World Health Organization (WHO) during an emergency meeting in Geneva on 22nd January 2020 to determine whether the virus outbreak had reached a level warranting international emergency concern. However, WHO was satisfied that China was being proactive in this case, unlike in 2002, when China withheld information on the outbreak for far too long, causing delays in addressing the epidemic [3] . As in this instance, it is the opinion in this paper that if there was seamless collaboration and seamless sharing of data between different cities, it would not warrant such a high-level meeting to result in action, and instead, a decision could have been made much earlier. On this, the saddest part is that some global cities are less prepared to handle the challenges posed by this type of outbreak for lack of information on issues like symptoms of the virus, the protective measures to be taken, and the treatment procedures that an infected person should be processed through, amongst other issues. The timely response by stakeholders in regard to this new outbreak are commendable compared to previous cases. The latter includes the Severe Acute Respiratory Syndrome (SARS) outbreak in 2002 that took substantial time (from November 2002 to April 2003) to identify and be dealt with [24] ; the Ebola outbreak in West Africa in 2013 that took months to determine; and the Zika Virus that was first reported in 2014 before being successfully identified in 2015. With the Coronavirus (COVID-19) , it took only 17 days (31st December 2019 to 17th January 2020) to be identified. The sharing of data has also been quicker, as immediately after the virus' genetic sequence was discovered, Chinese scientists were able to share the information with the WHO, thus helping in its identification and enabling the auctioning of precautionary measures in other countries. Latest technological tools have also allowed for the receipt of information in realtime, in contrast to traditional epidemiological approaches that would have required months to identify the outbreak type [25] . Similarly, though substantial data and information on the disease has been shared, Wetsman [26] acknowledges that there is a lack of some vital information, like the ease of spread of the virus from person-to-person, and this is a key to containing the disease as interactions between people from different parts of the globe are still active. This hindrance can be made further possible as many cities advance in their smart and safe city model implementation towards constructing sufficient soft and hard urban infrastructures equipped with, for example, thermal imagery sensors to allow for early detections. However, while that is the case, data access to many is a challenge because the information is often seen as being sensitive for national security reasons, whilst at the same time, acknowledging that a virus outbreak is an equal threat to both national security and the economy. The outbreak of any disease has significant impacts on local economies across the globe. For instance, when SARS (Severe Acute Respiratory Syndrome) (SARS-CoV) broke in China in 2002, it was estimated, that the Asian region incurred tremendous negative impacts socially, health-wise and economically, potentially amounting to Asian regional economy losses of between USD $12-18 billion from tourism, travel and retail sales industries alone [27] . The Zika virus outbreak, spread by daytime-active Aedes mosquitoes, is estimated to have cost equator-belt local economies in affected areas between USD $7 and USD $18 billion [28] . The Ebola virus (or Ebola hemorrhagic fever (EHF)) caused an estimated loss of USD $2.2 billion in GDP in three West African economies (Guinea, Liberia and Sierra Leone) in 2015 alone [29] . In regard to the current epidemic of Coronavirus, though it is too early to quantify or project its impacts on the global economy, there are fears that it may take the precedent of other outbreaks where billions of dollars will be lost. The foundations for this escalating loss can be witnessed in the rapid growth of travel bans being enacted by some countries and their international airports, especially specifically restricting people from visiting the affected regions in China and their growth into general non-Chinese travel movements. On this, noting that the outbreak came almost on the eve of the Lunar New Year celebrations, and that it had been estimated that over 400 million people were expected to travel in different parts of the world and China to observe this festivity, the majority have had to reconsider their options as to flights, hotels and entertainment events due to service provider cancellations [30] . Those who had already booked their flights are expected to receive their refunds following the directive by the Civil Aviation Administration of China, however, this move has already affected the share value of Chinese airline companies [30] . The above impacts demonstrate that the issues of virus outbreaks transcend urban safety and impacts upon all other facets of our urban fabric. Therefore, it becomes paramount to ensure that the measures taken to contain a virus transcend nationalist agendas where data and information sharing is normally restricted, to a more global agenda where humanity and global order are encouraged. With such an approach, it would be easier to share urban health data across geographies to better monitor emerging health threats in order to provide more economic stability, thereby ensuring no disruptions on such sectors like tourism and travel industries, amongst others. This is possible by ensuring collaborative, proactive measures to control outbreak spread and thus, human movements. This would remove fears on travelers, and would have positive impacts upon the tourism industry, that has been seen to bear the economic brunt whenever such outbreaks occur. This can be achieved by ensuring that protocols on data sharing are calibrated to remove all hurdles pertaining to sharing of information. On this, Lawpoolsri et al. [31] posits that such issues, like transparency, timelessness of sharing and access and quality of data, should be upheld so that continuous monitoring and assessment can be pursued. Virus outbreaks in recent years have shown that, in the urban realm, data, including health data, can be sourced from diverse places. Presently, in the case of Coronavirus (COVID-19) outbreak, data is being collected from airports through screening and monitoring, through the use of smart sensors installed in airport infrastructures and from personnel working in those air/seaports. For instance, it has been reported that in the U.S.A., screening is being carried out at 20 different airports to ensure that possible affected people are intercepted for quarantine at the point of entry. Beside airports, as reported by Buckley and May [2] , data is also being collected at bus terminals, market places (in Wuhan), subways, and also in health facilities where patients are taken for further medical attention. Such is prevalent especially in China, and other Asian regions where cases of the virus have been recorded and confirmed. In addition to these methods, other smart city data sources include the application of terminal tracking systems that are mostly emphasized in Safe City concepts, where, at the point of entry or departure, relevant data is collected and analyzed. Li et al. [32] highlights that sensors installed in such locations have the potential to receive and distribute data in real-time to digital infrastructures within the network, and their interconnectedness in the network renders them extremely efficient in providing real-time updates on different issues. Urban areas are also known to be amassed with numerous Urban Health sensors, some of which are wearable. Though these are not specifically fashioned to track the present case of virus outbreak, they are able to track other related parameters like heartbeat, blood pressure, body temperature and others variables, that when analyzed can offer valuable insights. Loncar-Turukalo et al. [33] hail these devices for their role in transforming the health care sector especially by allowing for Connected Health (CH) care, where data collected from them can be analyzed and provide insightful information on the health scenario in any given area. Vashist et al. [34] further highlight how emerging features such as spatiotemporal mapping, remote monitoring and management, and enhanced cloud computing capabilities can emanate from such endeavours, leading to better urban management potential. While it is true that the basic source of medical data is generally sourced from general practitioners or medical laboratories-a fact that has also been affirmed in the case of the current epidemic-this paper explores how data sourced from an urban perspective can contribute to the medical narrative. The conviction to dwell on the urban realm in this manuscript is based on the fact that the current epidemic (COVID-19) is transmitted majorly through human-to-human contact, and in most cases, especially where the spread is reported in a different country, the first point of contact is an urban area, where large groups of people convene, like airports or subway stations. In most cases, such facilities, which are mostly based in urban areas, are observed to have installed surveillance technologies to ensure that anyone showing any symptoms of the disease are identified and quarantined. However, even in such cases, as underlined in the present manuscript, the need for anonymizing medical data is emphasized to ensure that the use of current technologies does not breach data privacy and security requirements, across different geographies. In this case, novel technologies like Blockchain technologies and quantum cryptography can aid in the discussion and be made to integrate with data collecting technologies. This would render an increased wealth of data from both the medical field and smart city operators, while ensuring privacy and security; hence, aiding in providing relevant information for better informed decisions. However, despite the indisputable roles that installed devices play in providing relevant health information, their data communication aspect needs to be reviewed. First, communications are seen to be geography-restricted (restricted to a given location), such that they seldom expand or communicate with their like, installed beyond their restricted areas. Secondly, these devices are usually sourced and installed by separate corporations that maintain unique and specific standards for data processing and sharing, and accordingly, tying cities to the sole usage of their product(s). Such strategies are adopted as private corporations try to maximize their economic gains, since the digital solution market is a lucrative one and is expected to continue growing and expanding [6, 7] . For its current application, the standardization of protocols as elaborated in this manuscript need to be pursued to ensure that there is seamless sharing of information and data. By doing this, it is expected that issues like burdens of collecting data, accuracy and other complexity that are experienced (when systems are fragmented) are reduced or eliminated altogether. The standardization can be achieved by, for example, ensuring that all the devices and systems are linked into a single network, like was done in the U.S., where all the surveillance of healthcare were combined into the National Healthcare Safety Network (NHSH) [35] . The fact that cities are increasingly tuning on the concept of Smart Cities and boasting an increased adoption rate of technological and connected products, existing surveillance networks can be re-calibrated to make use of those new sets of databases. Appropriate protocols however have to be drafted to ensure effective actions while ensuring privacy and security of data and people. With scenarios like the present Coronavirus (COVID-19) outbreak, that not only impacts upon the economic status of cities, but also affects their social standing, it becomes imperative to emphasize the adoption of universal standards for data sharing. Such a move could have far reaching impact across cities and territories especially in positively combating outbreaks and disasters in a quicker, safer and standardized way, such that when the cure is discovered, the results can be replicated in various parts of the globe. With a collaborated data sharing protocol, it would be possible to have a larger dataset resulting in increased processing capabilities especially with technologies that are powered by artificial intelligence (AI) tools. Through this way, as noted by Jiang et al. [36] and Allam [37] , it would be possible to facilitate early detection, achieve better diagnosis and provide better urban management decisions for increased efficiency for virus containment. An example of how beneficial collaboration and sharing of data can be occurred during the 2014 Ebola outbreak in West Africa where scientists, health workers and clinicians, amongst other stakeholders from around the world, openly worked together and were able to contain the spread of this pandemic [38] . On this front, Boué et al. [39] highlight that levels of trust and transparency need to be reviewed and enhanced to facilitate unfettered data generation and sharing. Such could lead to an even earlier detection scenario of future virus outbreaks, and in the better curative management of the same, without minimal compromise on urban functions and on an urban economy. Furthermore, in cases of emergencies like the current outbreak of COVID-19 and any other, the need for observance of regulatory practices and international healthcare guidelines are paramount. This would ensure that both healthcare professionals and the general populace are informed, protected and remain within the prescribed rules and regulations. As noted by the WHO [40] , the healthcare guidelines and regulatory practices are advanced to also ensure that the health risk in question is reduced together with its consequences. In the current era of technological advancement, such regulations and guidelines are paramount as they have potential to lead to positive or negative outcomes. The position of this paper is to advance that it now possible to integrate technologies like the use of smart devices through IoT networks and wearable devices, data from mobile apps and others to help users to share information with accredited and certified health professionals, and in this case, improve the outcomes for better cross disciplinary and more resilient protocols and policies.

What platform was instrumental in rapid sharing of COVID-19 information?

Potential Rapid Diagnostics, Vaccine and Therapeutics for 2019 Novel Coronavirus (2019-nCoV): A Systematic Review https://doi.org/10.3390/jcm9030623 SHA: 9b0c87f808b1b66f2937d7a7acb524a756b6113b Authors: Pang, Junxiong; Wang, Min Xian; Ang, Ian Yi Han; Tan, Sharon Hui Xuan; Lewis, Ruth Frances; Chen, Jacinta I. Pei; Gutierrez, Ramona A.; Gwee, Sylvia Xiao Wei; Chua, Pearleen Ee Yong; Yang, Qian; Ng, Xian Yi; Yap, Rowena K. S.; Tan, Hao Yi; Teo, Yik Ying; Tan, Chorh Chuan; Cook, Alex R.; Yap, Jason Chin-Huat; Hsu, Li Yang Date: 2020 DOI: 10.3390/jcm9030623 License: cc-by Abstract: Rapid diagnostics, vaccines and therapeutics are important interventions for the management of the 2019 novel coronavirus (2019-nCoV) outbreak. It is timely to systematically review the potential of these interventions, including those for Middle East respiratory syndrome-Coronavirus (MERS-CoV) and severe acute respiratory syndrome (SARS)-CoV, to guide policymakers globally on their prioritization of resources for research and development. A systematic search was carried out in three major electronic databases (PubMed, Embase and Cochrane Library) to identify published studies in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Supplementary strategies through Google Search and personal communications were used. A total of 27 studies fulfilled the criteria for review. Several laboratory protocols for confirmation of suspected 2019-nCoV cases using real-time reverse transcription polymerase chain reaction (RT-PCR) have been published. A commercial RT-PCR kit developed by the Beijing Genomic Institute is currently widely used in China and likely in Asia. However, serological assays as well as point-of-care testing kits have not been developed but are likely in the near future. Several vaccine candidates are in the pipeline. The likely earliest Phase 1 vaccine trial is a synthetic DNA-based candidate. A number of novel compounds as well as therapeutics licensed for other conditions appear to have in vitro efficacy against the 2019-nCoV. Some are being tested in clinical trials against MERS-CoV and SARS-CoV, while others have been listed for clinical trials against 2019-nCoV. However, there are currently no effective specific antivirals or drug combinations supported by high-level evidence. Text: Since mid-December 2019 and as of early February 2020, the 2019 novel coronavirus (2019-nCoV) originating from Wuhan (Hubei Province, China) has infected over 25,000 laboratory-confirmed cases across 28 countries with about 500 deaths (a case-fatality rate of about 2%). More than 90% of the cases and deaths were in China [1] . Based on the initial reported surge of cases in Wuhan, the majority were males with a median age of 55 years and linked to the Huanan Seafood Wholesale Market [2] . Most of the reported cases had similar symptoms at the onset of illness such as fever, cough, and myalgia or fatigue. Most cases developed pneumonia and some severe and even fatal respiratory diseases such as acute respiratory distress syndrome [3] . The 2019 novel coronavirus (2019-nCoV), a betacoronavirus, forms a clade within the subgenus sarbecovirus of the Orthocoronavirinae subfamily [4] . The severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) are also betacoronaviruses that are zoonotic in origin and have been linked to potential fatal illness during the outbreaks in 2003 and 2012, respectively [5, 6] . Based on current evidence, pathogenicity for 2019-nCoV is about 3%, which is significantly lower than SARS-CoV (10%) and MERS-CoV (40%) [7] . However, 2019-nCoV has potentially higher transmissibility (R0: 1.4-5.5) than both SARS-CoV (R0: [2] [3] [4] [5] and MERS-CoV (R0: <1) [7] . With the possible expansion of 2019-nCoV globally [8] and the declaration of the 2019-nCoV outbreak as a Public Health Emergency of International Concern by the World Health Organization, there is an urgent need for rapid diagnostics, vaccines and therapeutics to detect, prevent and contain 2019-nCoV promptly. There is however currently a lack of understanding of what is available in the early phase of 2019-nCoV outbreak. The systematic review describes and assesses the potential rapid diagnostics, vaccines and therapeutics for 2019-nCoV, based in part on the developments for MERS-CoV and SARS-CoV. A systematic search was carried out in three major electronic databases (PubMed, Embase and Cochrane Library) to identify published studies examining the diagnosis, therapeutic drugs and vaccines for Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS) and the 2019 novel coronavirus (2019-nCoV), in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. There were two independent reviewers each focusing on SARS, MERS, and 2019-nCoV, respectively. A third independent reviewer was engaged to resolve any conflicting article of interest. We used the key words "SARS", "coronavirus", "MERS", "2019 Novel coronavirus", "Wuhan virus" to identify the diseases in the search strategy. The systematic searches for diagnosis, therapeutic drugs and vaccines were carried out independently and the key words "drug", "therapy", "vaccine", "diagnosis", "point of care testing" and "rapid diagnostic test" were used in conjunction with the disease key words for the respective searches. Examples of search strings can be found in Table S1 . We searched for randomized controlled trials (RCTs) and validation trials (for diagnostics test) published in English, that measured (a) the sensitivity and/or specificity of a rapid diagnostic test or a point-of-care testing kit, (b) the impact of drug therapy or (c) vaccine efficacy against either of these diseases with no date restriction applied. For the 2019-nCoV, we searched for all in vitro, animal, or human studies published in English between 1 December 2019 and 6 February 2020, on the same outcomes of interest. In addition, we reviewed the references of retrieved articles in order to identify additional studies or reports not retrieved by the initial searches. Studies that examined the mechanisms of diagnostic tests, drug therapy or vaccine efficacy against SARS, MERS and 2019-nCoV were excluded. A Google search for 2019-nCoV diagnostics (as of 6 February 2020; Table S2 ) yielded five webpage links from government and international bodies with official information and guidelines (WHO, Europe CDC, US CDC, US FDA), three webpage links on diagnostic protocols and scientific commentaries, and five webpage links on market news and press releases. Six protocols for diagnostics using reverse transcriptase polymerase chain reaction (RT-PCR) from six countries were published on WHO's website [9] . Google search for 2019-nCoV vaccines yielded 19 relevant articles. With the emergence of 2019-nCoV, real time RT-PCR remains the primary means for diagnosing the new virus strain among the many diagnostic platforms available ( [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] ; Table S3 ). Among the 16 diagnostics studies selected, one study discussed the use of RT-PCR in diagnosing patients with 2019-nCoV [11] ( Table 1 ). The period and type of specimen collected for RT-PCR play an important role in the diagnosis of 2019-nCoV. It was found that the respiratory specimens were positive for the virus while serum was negative in the early period. It has also suggested that in the early days of illness, patients have high levels of virus despite the mild symptoms. Apart from the commonly used RT-PCR in diagnosing MERS-CoV, four studies identified various diagnostic methods such as reverse transcription loop-mediated isothermal amplification (RT-LAMP), RT-insulated isothermal PCR (RT-iiPCR) and a one-step rRT-PCR assay based on specific TaqMan probes. RT-LAMP has similar sensitivity as real time RT-PCR. It is also highly specific and is used to detect MERS-CoV. It is comparable to the usual diagnostic tests and is rapid, simple and convenient. Likewise, RT-iiPCR and a one-step rRT-PCR assay have also shown similar sensitivity and high specificity for MER-CoV. Lastly, one study focused on the validation of the six commercial real RT-PCR kits, with high accuracy. Although real time RT-PCR is a primary method for diagnosing MERS-CoV, high levels of PCR inhibition may hinder PCR sensitivity (Table 1) . There are eleven studies that focus on SARS-CoV diagnostic testing (Table 1) . These papers described diagnostic methods to detect the virus with the majority of them using molecular testing for diagnosis. Comparison between the molecular test (i.e RT-PCR) and serological test (i.e., ELISA) showed that the molecular test has better sensitivity and specificity. Hence, enhancements to the current molecular test were conducted to improve the diagnosis. Studies looked at using nested PCR to include a pre-amplification step or incorporating N gene as an additional sensitive molecular marker to improve on the sensitivity (Table 1 ). In addition, there are seven potential rapid diagnostic kits (as of 24 January 2020; Table 2 ) available on the market for 2019-nCoV. Six of these are only for research purposes. Only one kit from Beijing Genome Institute (BGI) is approved for use in the clinical setting for rapid diagnosis. Most of the kits are for RT-PCR. There were two kits (BGI, China and Veredus, Singapore) with the capability to detect multiple pathogens using sequencing and microarray technologies, respectively. The limit of detection of the enhanced realtime PCR method was 10 2 -fold higher than the standard real-time PCR assay and 10 7fold higher than conventional PCR methods In the clinical aspect, the enhanced realtime PCR method was able to detect 6 cases of SARS-CoV positive samples that were not confirmed by any other assay [25] • The real time PCR has a threshold sensitivity of 10 genome equivalents per reaction and it has a good reproducibility with the inter-assay coefficients of variation of 1.73 to 2.72%. • 13 specimens from 6 patients were positive with viral load range from 362 to 36,240,000 genome equivalents/mL. The real-time RT-PCR reaction was more sensitive than the nested PCR reaction, as the detection limit for the nested PCR reaction was about 10 3 genome equivalents in the standard cDNA control. [34] Real-time reverse-transcription PCR (rRT-PCR); RNA-dependent RNA polymerase (RdRp); open reading frame 1a (ORF1a); Loop-mediated isothermal amplification (LAMP); enzyme-linked immunosorbent assay (ELISA); immunofluorescent assay (IFA); immunochromatographic test (ICT); nasopharyngeal aspirate (NPA). With the emergence of 2019-nCoV, there are about 15 potential vaccine candidates in the pipeline globally (Table 3 ), in which a wide range of technology (such as messenger RNA, DNA-based, nanoparticle, synthetic and modified virus-like particle) was applied. It will likely take about a year for most candidates to start phase 1 clinical trials except for those funded by Coalition for Epidemic Preparedness Innovations (CEPI). However, the kit developed by the BGI have passed emergency approval procedure of the National Medical Products Administration, and are currently used in clinical and surveillance centers of China [40] . Of the total of 570 unique studies on 2019-nCoV, SARS CoV or MERS-CoV vaccines screened, only four were eventually included in the review. Most studies on SARS and MERS vaccines were excluded as they were performed in cell or animal models ( Figure 1 ). The four studies included in this review were Phase I clinical trials on SARS or MERS vaccines (Table 4 ) [44] [45] [46] [47] . There were no studies of any population type (cell, animal, human) on the 2019-nCoV at the point of screening. The published clinical trials were mostly done in United States except for one on the SARS vaccine done in China [44] . All vaccine candidates for SARS and MERS were reported to be safe, well-tolerated and able to trigger the relevant and appropriate immune responses in the participants. In addition, we highlight six ongoing Phase I clinical trials identified in the ClinicalTrials.gov register ( [48, 49] ); Table S4 ) [50] [51] [52] . These trials are all testing the safety and immunogenicity of their respective MERS-CoV vaccine candidates but were excluded as there are no results published yet. The trials are projected to complete in December 2020 (two studies in Russia [50, 51] ) and December 2021 (in Germany [52] ). Existing literature search did not return any results on completed 2019-nCoV trials at the time of writing. Among 23 trials found from the systematic review (Table 5) , there are nine clinical trials registered under the clinical trials registry (ClinicalTrials.gov) for 2019-nCoV therapeutics [53] [54] [55] [56] [57] [58] [59] [60] [61] . Of which five studies on hydroxychloroquine, lopinavir plus ritonavir and arbidol, mesenchymal stem cells, traditional Chinese medicine and glucocorticoid therapy usage have commenced recruitment. The remaining four studies encompass investigation of antivirals, interferon atomization, darunavir and cobicistat, arbidol, and remdesivir usage for 2019-nCoV patients (Table 5) . Seroconversion measured by S1-ELISA occurred in 86% and 94% participants after 2 and 3 doses, respectively, and was maintained in 79% participants up to study end at week 60. Neutralising antibodies were detected in 50% participants at one or more time points during the study, but only 3% maintained neutralisation activity to end of study. T-cell responses were detected in 71% and 76% participants after 2 and 3 doses, respectively. There were no differences in immune responses between dose groups after 6 weeks and vaccine-induced humoral and cellular responses were respectively detected in 77% and 64% participants at week 60. [47] Molecules developed by the university scientists inhibit two coronavirus enzymes and prevent its replication. The discovered drug targets are said to be more than 95% similar to enzyme targets found on the SARS virus. Researchers note that identified drugs may not be available to address the ongoing outbreak but they hope to make it accessible for future outbreaks. [85] Besides the six completed randomized controlled trials (RCT) selected from the systematic review (Table 6) , there is only one ongoing randomized controlled trial targeted at SARS therapeutics [92] . The studies found from ClinicalTrials.gov have not been updated since 2013. While many prospective and retrospective cohort studies conducted during the epidemic centered on usage of ribavirin with lopinavir/ritonavir or ribavirin only, there has yet to be well-designed clinical trials investigating their usage. Three completed randomized controlled trials were conducted during the SARS epidemic-3 in China, 1 in Taiwan and 2 in Hong Kong [93] [94] [95] [96] [97] . The studies respectively investigated antibiotic usage involving 190 participants, combination of western and Chinese treatment vs. Chinese treatment in 123 participants, integrative Chinese and Western treatment in 49 patients, usage of a specific Chinese medicine in four participants and early use of corticosteroid in 16 participants. Another notable study was an open non-randomized study investigating ribavirin/lopinavir/ritonavir usage in 152 participants [98] . One randomized controlled trial investigating integrative western and Chinese treatment during the SARS epidemic was excluded as it was a Chinese article [94] . There is only one ongoing randomized controlled trial targeted at MERS therapeutics [99] . It investigates the usage of Lopinavir/Ritonavir and Interferon Beta 1B. Likewise, many prospective and retrospective cohort studies conducted during the epidemic centered on usage of ribavirin with lopinavir/ritonavir/ribavirin, interferon, and convalescent plasma usage. To date, only one trial has been completed. One phase 1 clinical trial investigating the safety and tolerability of a fully human polyclonal IgG immunoglobulin (SAB-301) was found in available literature [46] . The trial conducted in the United States in 2017 demonstrated SAB-301 to be safe and well-tolerated at single doses. Another trial on MERS therapeutics was found on ClinicalTrials.gov-a phase 2/3 trial in the United States evaluating the safety, tolerability, pharmacokinetics (PK), and immunogenicity on coadministered MERS-CoV antibodies REGN3048 & REGN3051 [100]. Rapid diagnostics plays an important role in disease and outbreak management. The fast and accurate diagnosis of a specific viral infection enables prompt and accurate public health surveillance, prevention and control measures. Local transmission and clusters can be prevented or delayed by isolation of laboratory-confirmed cases and their close contacts quarantined and monitored at home. Rapid diagnostic also facilitates other specific public health interventions such as closure of high-risk facilities and areas associated with the confirmed cases for prompt infection control and environmental decontamination [11, 101] . Laboratory diagnosis can be performed by: (a) detecting the genetic material of the virus, (b) detecting the antibodies that neutralize the viral particles of interest, (c) detecting the viral epitopes of interest with antibodies (serological testing), or (d) culture and isolation of viable virus particles. The key limitations of genetic material detection are the lack of knowledge of the presence of viable virus, the potential cross-reactivity with non-specific genetic regions and the short timeframe for accurate detection during the acute infection phase. The key limitations of serological testing is the need to collect paired serum samples (in the acute and convalescent phases) from cases under investigation for confirmation to eliminate potential cross-reactivity from non-specific antibodies from past exposure and/or infection by other coronaviruses. The limitation of virus culture and isolation is the long duration and the highly specialized skills required of the technicians to process the samples. All patients recovered. Significantly shorted time from the disease onset to the symptom improvement in treatment (5.10 ± 2.83 days) compared to control group (7.62 ± 2.27 days) (p < 0.05) No significant difference in blood routine improvement, pulmonary chest shadow in chest film improvement and corticosteroid usgae between the 2 groups. However, particularly in the respect of improving clinical symptoms, elevating quality of life, promoting immune function recovery, promoting absorption of pulmonary inflammation, reducing the dosage of cortisteroid and shortening the therapeutic course, treatment with integrative chinese and western medicine treatment had obvious superiority compared with using control treatment alone. Single infusions of SAB-301 up to 50 mg/kg appear to be safe and well-tolerated in healthy participants. [46] Where the biological samples are taken from also play a role in the sensitivity of these tests. For SARS-CoV and MERS-CoV, specimens collected from the lower respiratory tract such as sputum and tracheal aspirates have higher and more prolonged levels of viral RNA because of the tropism of the virus. MERS-CoV viral loads are also higher for severe cases and have longer viral shedding compared to mild cases. Although upper respiratory tract specimens such as nasopharyngeal or oropharyngeal swabs can be used, they have potentially lower viral loads and may have higher risk of false-negatives among the mild MERS and SARS cases [102, 103] , and likely among the 2019-nCoV cases. The existing practices in detecting genetic material of coronaviruses such as SARS-CoV and MERS-CoV include (a) reverse transcription-polymerase chain reaction (RT-PCR), (b) real-time RT-PCR (rRT-PCR), (c) reverse transcription loop-mediated isothermal amplification (RT-LAMP) and (d) real-time RT-LAMP [104] . Nucleic amplification tests (NAAT) are usually preferred as in the case of MERS-CoV diagnosis as it has the highest sensitivity at the earliest time point in the acute phase of infection [102] . Chinese health authorities have recently posted the full genome of 2019-nCoV in the GenBank and in GISAID portal to facilitate in the detection of the virus [11] . Several laboratory assays have been developed to detect the novel coronavirus in Wuhan, as highlighted in WHO's interim guidance on nCoV laboratory testing of suspected cases. These include protocols from other countries such as Thailand, Japan and China [105] . The first validated diagnostic test was designed in Germany. Corman et al. had initially designed a candidate diagnostic RT-PCR assay based on the SARS or SARS-related coronavirus as it was suggested that circulating virus was SARS-like. Upon the release of the sequence, assays were selected based on the match against 2019-nCoV upon inspection of the sequence alignment. Two assays were used for the RNA dependent RNA polymerase (RdRP) gene and E gene where E gene assay acts as the first-line screening tool and RdRp gene assay as the confirmatory testing. All assays were highly sensitive and specific in that they did not cross-react with other coronavirus and also human clinical samples that contained respiratory viruses [11] . The Hong Kong University used two monoplex assays which were reactive with coronaviruses under the subgenus Sarbecovirus (consisting of 2019-nCoV, SARS-CoV and SARS-like coronavirus). Viral RNA extracted from SARS-CoV can be used as the positive control for the suggested protocol assuming that SARS has been eradicated. It is proposed that the N gene RT-PCR can be used as a screening assay while the Orf1b assay acts as a confirmatory test. However, this protocol has only been evaluated with a panel of controls with the only positive control SARS-CoV RNA. Synthetic oligonucleotide positive control or 2019-nCoV have yet to be tested [106] . The US CDC shared the protocol on the real time RT-PCR assay for the detection of the 2019-nCoV with the primers and probes designed for the universal detection of SARS-like coronavirus and the specific detection of 2019-nCoV. However, the protocol has not been validated on other platforms or chemistries apart from the protocol described. There are some limitations for the assay. Analysts engaged have to be trained and familiar with the testing procedure and result interpretation. False negative results may occur due to insufficient organisms in the specimen resulting from improper collection, transportation or handling. Also, RNA viruses may show substantial genetic variability. This could result in mismatch between the primer and probes with the target sequence which can diminish the assay performance or result in false negative results [107] . Point-of-care test kit can potentially minimize these limitations, which should be highly prioritized for research and development in the next few months. Serological testing such as ELISA, IIFT and neutralization tests are effective in determining the extent of infection, including estimating asymptomatic and attack rate. Compared to the detection of viral genome through molecular methods, serological testing detects antibodies and antigens. There would be a lag period as antibodies specifically targeting the virus would normally appear between 14 and 28 days after the illness onset [108] . Furthermore, studies suggest that low antibody titers in the second week or delayed antibody production could be associated with mortality with a high viral load. Hence, serological diagnoses are likely used when nucleic amplification tests (NAAT) are not available or accessible [102] . Vaccines can prevent and protect against infection and disease occurrence when exposed to the specific pathogen of interest, especially in vulnerable populations who are more prone to severe outcomes. In the context of the current 2019-nCoV outbreak, vaccines will help control and reduce disease transmission by creating herd immunity in addition to protecting healthy individuals from infection. This decreases the effective R0 value of the disease. Nonetheless, there are social, clinical and economic hurdles for vaccine and vaccination programmes, including (a) the willingness of the public to undergo vaccination with a novel vaccine, (b) the side effects and severe adverse reactions of vaccination, (c) the potential difference and/or low efficacy of the vaccine in populations different from the clinical trials' populations and (d) the accessibility of the vaccines to a given population (including the cost and availability of the vaccine). Vaccines against the 2019-nCoV are currently in development and none are in testing (at the time of writing). On 23 January 2020, the Coalition for Epidemic Preparedness Innovations (CEPI) announced that they will fund vaccine development programmes with Inovio, The University of Queensland and Moderna, Inc respectively, with the aim to test the experimental vaccines clinically in 16 weeks (By June 2020). The vaccine candidates will be developed by the DNA, recombinant and mRNA vaccine platforms from these organizations [109] . Based on the most recent MERS-CoV outbreak, there are already a number of vaccine candidates being developed but most are still in the preclinical testing stage. The vaccines in development include viral vector-based vaccine, DNA vaccine, subunit vaccine, virus-like particles (VLPs)-based vaccine, inactivated whole-virus (IWV) vaccine and live attenuated vaccine. The latest findings for these vaccines arebased on the review by Yong et al. (2019) in August 2019 [110] . As of the date of reporting, there is only one published clinical study on the MERS-CoV vaccine by GeneOne Life Science & Inovio Pharmaceuticals [47] . There was one SARS vaccine trial conducted by the US National Institute of Allergy and Infectious Diseases. Both Phase I clinical trials reported positive results, but only one has announced plans to proceed to Phase 2 trial [111] . Due to the close genetic relatedness of SARS-CoV (79%) with 2019-nCoV [112] , there may be potential cross-protective effect of using a safe SARS-CoV vaccine while awaiting the 2019-nCoV vaccine. However, this would require small scale phase-by-phase implementation and close monitoring of vaccinees before any large scale implementation. Apart from the timely diagnosis of cases, the achievement of favorable clinical outcomes depends on the timely treatment administered. ACE2 has been reported to be the same cell entry receptor used by 2019-nCoV to infect humans as SARS-CoV [113] . Hence, clinical similarity between the two viruses is expected, particularly in severe cases. In addition, most of those who have died from MERS-CoV, SARS-CoV and 2019-nCoV were advance in age and had underlying health conditions such as hypertension, diabetes or cardiovascular disease that compromised their immune systems [114] . Coronaviruses have error-prone RNA-dependent RNA polymerases (RdRP), which result in frequent mutations and recombination events. This results in quasispecies diversity that is closely associated with adaptive evolution and the capacity to enhance viral-cell entry to cause disease over time in a specific population at-risk [115] . Since ACE2 is abundantly present in humans in the epithelia of the lung and small intestine, coronaviruses are likely to infect the upper respiratory and gastrointestinal tract and this may influence the type of therapeutics against 2019-nCoV, similarly to SAR-CoV. However, in the years following two major coronavirus outbreaks SARS-CoV in 2003 and MERS-CoV in 2012, there remains no consensus on the optimal therapy for either disease [116, 117] . Well-designed clinical trials that provide the gold standard for assessing the therapeutic measures are scarce. No coronavirus protease inhibitors have successfully completed a preclinical development program despite large efforts exploring SARS-CoV inhibitors. The bulk of potential therapeutic strategies remain in the experimental phase, with only a handful crossing the in vitro hurdle. Stronger efforts are required in the research for treatment options for major coronaviruses given their pandemic potential. Effective treatment options are essential to maximize the restoration of affected populations to good health following infections. Clinical trials have commenced in China to identify effective treatments for 2019-nCoV based on the treatment evidence from SARS and MERS. There is currently no effective specific antiviral with high-level evidence; any specific antiviral therapy should be provided in the context of a clinical study/trial. Few treatments have shown real curative action against SARS and MERS and the literature generally describes isolated cases or small case series. Many interferons from the three classes have been tested for their antiviral activities against SARS-CoV both in vitro and in animal models. Interferon β has consistently been shown to be the most active, followed by interferon α. The use of corticosteroids with interferon alfacon-1 (synthetic interferon α) appeared to have improved oxygenation and faster resolution of chest radiograph abnormalities in observational studies with untreated controls. Interferon has been used in multiple observational studies to treat SARS-CoV and MERS-CoV patients [116, 117] . Interferons, with or without ribavirin, and lopinavir/ritonavir are most likely to be beneficial and are being trialed in China for 2019-nCoV. This drug treatment appears to be the most advanced. Timing of treatment is likely an important factor in effectiveness. A combination of ribavirin and lopinavir/ritonavir was used as a post-exposure prophylaxis in health care workers and may have reduced the risk of infection. Ribavirin alone is unlikely to have substantial antiviral activities at clinically used dosages. Hence, ribavirin with or without corticosteroids and with lopinavir and ritonavir are among the combinations employed. This was the most common agent reported in the available literature. Its efficacy has been assessed in observational studies, retrospective case series, retrospective cohort study, a prospective observational study, a prospective cohort study and randomized controlled trial ranging from seven to 229 participants [117] . Lopinavir/ritonavir (Kaletra) was the earliest protease inhibitor combination introduced for the treatment of SARS-CoV. Its efficacy was documented in several studies, causing notably lower incidence of adverse outcomes than with ribavirin alone. Combined usage with ribavirin was also associated with lower incidence of acute respiratory distress syndrome, nosocomial infection and death, amongst other favorable outcomes. Recent in vitro studies have shown another HIV protease inhibitor, nelfinavir, to have antiviral capacity against SARS-CoV, although it has yet to show favorable outcomes in animal studies [118] . Remdesivir (Gilead Sciences, GS-5734) nucleoside analogue in vitro and in vivo data support GS-5734 development as a potential pan-coronavirus antiviral based on results against several coronaviruses (CoVs), including highly pathogenic CoVs and potentially emergent BatCoVs. The use of remdesivir may be a good candidate as an investigational treatment. Improved mortality following receipt of convalescent plasma in various doses was consistently reported in several observational studies involving cases with severe acute respiratory infections (SARIs) of viral etiology. A significant reduction in the pooled odds of mortality following treatment of 0.25 compared to placebo or no therapy was observed [119] . Studies were however at moderate to high risk of bias given their small sample sizes, allocation of treatment based on the physician's discretion, and the availability of plasma. Factors like concomitant treatment may have also confounded the results. Associations between convalescent plasma and hospital length of stay, viral antibody levels, and viral load respectively were similarly inconsistent across available literature. Convalescent plasma, while promising, is likely not yet feasible, given the limited pool of potential donors and issues of scalability. Monoclonal antibody treatment is progressing. SARS-CoV enters host cells through the binding of their spike (S) protein to angiotensin converting enzyme 2 (ACE2) and CD209L [118] . Human monoclonal antibodies to the S protein have been shown to significantly reduce the severity of lung pathology in non-human primates following MERS-CoV infection [120] . Such neutralizing antibodies can be elicited by active or passive immunization using vaccines or convalescent plasma respectively. While such neutralizing antibodies can theoretically be harvested from individuals immunized with vaccines, there is uncertainty over the achievement of therapeutic levels of antibodies. Other therapeutic agents have also been reported. A known antimalarial agent, chloroquine, elicits antiviral effects against multiple viruses including HIV type 1, hepatitis B and HCoV-229E. Chloroquine is also immunomodulatory, capable of suppressing the production and release of factors which mediate the inflammatory complications of viral diseases (tumor necrosis factor and interleukin 6) [121] . It is postulated that chloroquine works by altering ACE2 glycosylation and endosomal pH. Its anti-inflammatory properties may be beneficial for the treatment of SARS. Niclosamide as a known drug used in antihelminthic treatment. The efficacy of niclosamide as an inhibitor of virus replication was proven in several assays. In both immunoblot analysis and immunofluorescence assays, niclosamide treatment was observed to completely inhibit viral antigen synthesis. Reduction of virus yield in infected cells was dose dependent. Niclosamide likely does not interfere in the early stages of virus attachment and entry into cells, nor does it function as a protease inhibitor. Mechanisms of niclosamide activity warrant further investigation [122] . Glycyrrhizin also reportedly inhibits virus adsorption and penetration in the early steps of virus replication. Glycyrrhizin was a significantly potent inhibitor with a low selectivity index when tested against several pathogenic flaviviruses. While preliminary results suggest production of nitrous oxide (which inhibits virus replication) through induction of nitrous oxide synthase, the mechanism of Glycyrrhizin against SARS-CoV remains unclear. The compound also has relatively lower toxicity compared to protease inhibitors like ribavirin [123] . Inhibitory activity was also detected in baicalin [124] , extracted from another herb used in the treatment of SARS in China and Hong Kong. Findings on these compounds are limited to in vitro studies [121] [122] [123] [124] . Due to the rapidly evolving situation of the 2019-nCoV, there will be potential limitations to the systematic review. The systematic review is likely to have publication bias as some developments have yet to be reported while for other developments there is no intention to report publicly (or in scientific platforms) due to confidentiality concerns. However, this may be limited to only a few developments for review as publicity does help in branding to some extent for the company and/or the funder. Furthermore, due to the rapid need to share the status of these developments, there may be reporting bias in some details provided by authors of the scientific articles or commentary articles in traditional media. Lastly, while it is not viable for any form of quality assessment and metaanalysis of the selected articles due to the limited data provided and the heterogeneous style of reporting by different articles, this paper has provided a comprehensive overview of the potential developments of these pharmaceutical interventions during the early phase of the outbreak. This systematic review would be useful for cross-check when the quality assessment and meta-analysis of these developments are performed as a follow-up study. Rapid diagnostics, vaccines and therapeutics are key pharmaceutical interventions to limit transmission of respiratory infectious diseases. Many potential developments on these pharmaceutical interventions for 2019-nCoV are ongoing in the containment phase of this outbreak, potentially due to better pandemic preparedness than before. However, lessons from MERS-CoV and SARS-CoV have shown that the journeys for these developments can still be challenging moving ahead. Supplementary Materials: The following are available online at www.mdpi.com/xxx/s1, Table S1 : Example of full search strategy in Pubmed, Table S2 : Google Search: 2019-nCoV diagnostics, Table S3 : Summary of diagnostic assays developed for 2019-nCoV, Table S4

Who are the majority of cases?

Potential Rapid Diagnostics, Vaccine and Therapeutics for 2019 Novel Coronavirus (2019-nCoV): A Systematic Review https://doi.org/10.3390/jcm9030623 SHA: 9b0c87f808b1b66f2937d7a7acb524a756b6113b Authors: Pang, Junxiong; Wang, Min Xian; Ang, Ian Yi Han; Tan, Sharon Hui Xuan; Lewis, Ruth Frances; Chen, Jacinta I. Pei; Gutierrez, Ramona A.; Gwee, Sylvia Xiao Wei; Chua, Pearleen Ee Yong; Yang, Qian; Ng, Xian Yi; Yap, Rowena K. S.; Tan, Hao Yi; Teo, Yik Ying; Tan, Chorh Chuan; Cook, Alex R.; Yap, Jason Chin-Huat; Hsu, Li Yang Date: 2020 DOI: 10.3390/jcm9030623 License: cc-by Abstract: Rapid diagnostics, vaccines and therapeutics are important interventions for the management of the 2019 novel coronavirus (2019-nCoV) outbreak. It is timely to systematically review the potential of these interventions, including those for Middle East respiratory syndrome-Coronavirus (MERS-CoV) and severe acute respiratory syndrome (SARS)-CoV, to guide policymakers globally on their prioritization of resources for research and development. A systematic search was carried out in three major electronic databases (PubMed, Embase and Cochrane Library) to identify published studies in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Supplementary strategies through Google Search and personal communications were used. A total of 27 studies fulfilled the criteria for review. Several laboratory protocols for confirmation of suspected 2019-nCoV cases using real-time reverse transcription polymerase chain reaction (RT-PCR) have been published. A commercial RT-PCR kit developed by the Beijing Genomic Institute is currently widely used in China and likely in Asia. However, serological assays as well as point-of-care testing kits have not been developed but are likely in the near future. Several vaccine candidates are in the pipeline. The likely earliest Phase 1 vaccine trial is a synthetic DNA-based candidate. A number of novel compounds as well as therapeutics licensed for other conditions appear to have in vitro efficacy against the 2019-nCoV. Some are being tested in clinical trials against MERS-CoV and SARS-CoV, while others have been listed for clinical trials against 2019-nCoV. However, there are currently no effective specific antivirals or drug combinations supported by high-level evidence. Text: Since mid-December 2019 and as of early February 2020, the 2019 novel coronavirus (2019-nCoV) originating from Wuhan (Hubei Province, China) has infected over 25,000 laboratory-confirmed cases across 28 countries with about 500 deaths (a case-fatality rate of about 2%). More than 90% of the cases and deaths were in China [1] . Based on the initial reported surge of cases in Wuhan, the majority were males with a median age of 55 years and linked to the Huanan Seafood Wholesale Market [2] . Most of the reported cases had similar symptoms at the onset of illness such as fever, cough, and myalgia or fatigue. Most cases developed pneumonia and some severe and even fatal respiratory diseases such as acute respiratory distress syndrome [3] . The 2019 novel coronavirus (2019-nCoV), a betacoronavirus, forms a clade within the subgenus sarbecovirus of the Orthocoronavirinae subfamily [4] . The severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) are also betacoronaviruses that are zoonotic in origin and have been linked to potential fatal illness during the outbreaks in 2003 and 2012, respectively [5, 6] . Based on current evidence, pathogenicity for 2019-nCoV is about 3%, which is significantly lower than SARS-CoV (10%) and MERS-CoV (40%) [7] . However, 2019-nCoV has potentially higher transmissibility (R0: 1.4-5.5) than both SARS-CoV (R0: [2] [3] [4] [5] and MERS-CoV (R0: <1) [7] . With the possible expansion of 2019-nCoV globally [8] and the declaration of the 2019-nCoV outbreak as a Public Health Emergency of International Concern by the World Health Organization, there is an urgent need for rapid diagnostics, vaccines and therapeutics to detect, prevent and contain 2019-nCoV promptly. There is however currently a lack of understanding of what is available in the early phase of 2019-nCoV outbreak. The systematic review describes and assesses the potential rapid diagnostics, vaccines and therapeutics for 2019-nCoV, based in part on the developments for MERS-CoV and SARS-CoV. A systematic search was carried out in three major electronic databases (PubMed, Embase and Cochrane Library) to identify published studies examining the diagnosis, therapeutic drugs and vaccines for Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS) and the 2019 novel coronavirus (2019-nCoV), in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. There were two independent reviewers each focusing on SARS, MERS, and 2019-nCoV, respectively. A third independent reviewer was engaged to resolve any conflicting article of interest. We used the key words "SARS", "coronavirus", "MERS", "2019 Novel coronavirus", "Wuhan virus" to identify the diseases in the search strategy. The systematic searches for diagnosis, therapeutic drugs and vaccines were carried out independently and the key words "drug", "therapy", "vaccine", "diagnosis", "point of care testing" and "rapid diagnostic test" were used in conjunction with the disease key words for the respective searches. Examples of search strings can be found in Table S1 . We searched for randomized controlled trials (RCTs) and validation trials (for diagnostics test) published in English, that measured (a) the sensitivity and/or specificity of a rapid diagnostic test or a point-of-care testing kit, (b) the impact of drug therapy or (c) vaccine efficacy against either of these diseases with no date restriction applied. For the 2019-nCoV, we searched for all in vitro, animal, or human studies published in English between 1 December 2019 and 6 February 2020, on the same outcomes of interest. In addition, we reviewed the references of retrieved articles in order to identify additional studies or reports not retrieved by the initial searches. Studies that examined the mechanisms of diagnostic tests, drug therapy or vaccine efficacy against SARS, MERS and 2019-nCoV were excluded. A Google search for 2019-nCoV diagnostics (as of 6 February 2020; Table S2 ) yielded five webpage links from government and international bodies with official information and guidelines (WHO, Europe CDC, US CDC, US FDA), three webpage links on diagnostic protocols and scientific commentaries, and five webpage links on market news and press releases. Six protocols for diagnostics using reverse transcriptase polymerase chain reaction (RT-PCR) from six countries were published on WHO's website [9] . Google search for 2019-nCoV vaccines yielded 19 relevant articles. With the emergence of 2019-nCoV, real time RT-PCR remains the primary means for diagnosing the new virus strain among the many diagnostic platforms available ( [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] ; Table S3 ). Among the 16 diagnostics studies selected, one study discussed the use of RT-PCR in diagnosing patients with 2019-nCoV [11] ( Table 1 ). The period and type of specimen collected for RT-PCR play an important role in the diagnosis of 2019-nCoV. It was found that the respiratory specimens were positive for the virus while serum was negative in the early period. It has also suggested that in the early days of illness, patients have high levels of virus despite the mild symptoms. Apart from the commonly used RT-PCR in diagnosing MERS-CoV, four studies identified various diagnostic methods such as reverse transcription loop-mediated isothermal amplification (RT-LAMP), RT-insulated isothermal PCR (RT-iiPCR) and a one-step rRT-PCR assay based on specific TaqMan probes. RT-LAMP has similar sensitivity as real time RT-PCR. It is also highly specific and is used to detect MERS-CoV. It is comparable to the usual diagnostic tests and is rapid, simple and convenient. Likewise, RT-iiPCR and a one-step rRT-PCR assay have also shown similar sensitivity and high specificity for MER-CoV. Lastly, one study focused on the validation of the six commercial real RT-PCR kits, with high accuracy. Although real time RT-PCR is a primary method for diagnosing MERS-CoV, high levels of PCR inhibition may hinder PCR sensitivity (Table 1) . There are eleven studies that focus on SARS-CoV diagnostic testing (Table 1) . These papers described diagnostic methods to detect the virus with the majority of them using molecular testing for diagnosis. Comparison between the molecular test (i.e RT-PCR) and serological test (i.e., ELISA) showed that the molecular test has better sensitivity and specificity. Hence, enhancements to the current molecular test were conducted to improve the diagnosis. Studies looked at using nested PCR to include a pre-amplification step or incorporating N gene as an additional sensitive molecular marker to improve on the sensitivity (Table 1 ). In addition, there are seven potential rapid diagnostic kits (as of 24 January 2020; Table 2 ) available on the market for 2019-nCoV. Six of these are only for research purposes. Only one kit from Beijing Genome Institute (BGI) is approved for use in the clinical setting for rapid diagnosis. Most of the kits are for RT-PCR. There were two kits (BGI, China and Veredus, Singapore) with the capability to detect multiple pathogens using sequencing and microarray technologies, respectively. The limit of detection of the enhanced realtime PCR method was 10 2 -fold higher than the standard real-time PCR assay and 10 7fold higher than conventional PCR methods In the clinical aspect, the enhanced realtime PCR method was able to detect 6 cases of SARS-CoV positive samples that were not confirmed by any other assay [25] • The real time PCR has a threshold sensitivity of 10 genome equivalents per reaction and it has a good reproducibility with the inter-assay coefficients of variation of 1.73 to 2.72%. • 13 specimens from 6 patients were positive with viral load range from 362 to 36,240,000 genome equivalents/mL. The real-time RT-PCR reaction was more sensitive than the nested PCR reaction, as the detection limit for the nested PCR reaction was about 10 3 genome equivalents in the standard cDNA control. [34] Real-time reverse-transcription PCR (rRT-PCR); RNA-dependent RNA polymerase (RdRp); open reading frame 1a (ORF1a); Loop-mediated isothermal amplification (LAMP); enzyme-linked immunosorbent assay (ELISA); immunofluorescent assay (IFA); immunochromatographic test (ICT); nasopharyngeal aspirate (NPA). With the emergence of 2019-nCoV, there are about 15 potential vaccine candidates in the pipeline globally (Table 3 ), in which a wide range of technology (such as messenger RNA, DNA-based, nanoparticle, synthetic and modified virus-like particle) was applied. It will likely take about a year for most candidates to start phase 1 clinical trials except for those funded by Coalition for Epidemic Preparedness Innovations (CEPI). However, the kit developed by the BGI have passed emergency approval procedure of the National Medical Products Administration, and are currently used in clinical and surveillance centers of China [40] . Of the total of 570 unique studies on 2019-nCoV, SARS CoV or MERS-CoV vaccines screened, only four were eventually included in the review. Most studies on SARS and MERS vaccines were excluded as they were performed in cell or animal models ( Figure 1 ). The four studies included in this review were Phase I clinical trials on SARS or MERS vaccines (Table 4 ) [44] [45] [46] [47] . There were no studies of any population type (cell, animal, human) on the 2019-nCoV at the point of screening. The published clinical trials were mostly done in United States except for one on the SARS vaccine done in China [44] . All vaccine candidates for SARS and MERS were reported to be safe, well-tolerated and able to trigger the relevant and appropriate immune responses in the participants. In addition, we highlight six ongoing Phase I clinical trials identified in the ClinicalTrials.gov register ( [48, 49] ); Table S4 ) [50] [51] [52] . These trials are all testing the safety and immunogenicity of their respective MERS-CoV vaccine candidates but were excluded as there are no results published yet. The trials are projected to complete in December 2020 (two studies in Russia [50, 51] ) and December 2021 (in Germany [52] ). Existing literature search did not return any results on completed 2019-nCoV trials at the time of writing. Among 23 trials found from the systematic review (Table 5) , there are nine clinical trials registered under the clinical trials registry (ClinicalTrials.gov) for 2019-nCoV therapeutics [53] [54] [55] [56] [57] [58] [59] [60] [61] . Of which five studies on hydroxychloroquine, lopinavir plus ritonavir and arbidol, mesenchymal stem cells, traditional Chinese medicine and glucocorticoid therapy usage have commenced recruitment. The remaining four studies encompass investigation of antivirals, interferon atomization, darunavir and cobicistat, arbidol, and remdesivir usage for 2019-nCoV patients (Table 5) . Seroconversion measured by S1-ELISA occurred in 86% and 94% participants after 2 and 3 doses, respectively, and was maintained in 79% participants up to study end at week 60. Neutralising antibodies were detected in 50% participants at one or more time points during the study, but only 3% maintained neutralisation activity to end of study. T-cell responses were detected in 71% and 76% participants after 2 and 3 doses, respectively. There were no differences in immune responses between dose groups after 6 weeks and vaccine-induced humoral and cellular responses were respectively detected in 77% and 64% participants at week 60. [47] Molecules developed by the university scientists inhibit two coronavirus enzymes and prevent its replication. The discovered drug targets are said to be more than 95% similar to enzyme targets found on the SARS virus. Researchers note that identified drugs may not be available to address the ongoing outbreak but they hope to make it accessible for future outbreaks. [85] Besides the six completed randomized controlled trials (RCT) selected from the systematic review (Table 6) , there is only one ongoing randomized controlled trial targeted at SARS therapeutics [92] . The studies found from ClinicalTrials.gov have not been updated since 2013. While many prospective and retrospective cohort studies conducted during the epidemic centered on usage of ribavirin with lopinavir/ritonavir or ribavirin only, there has yet to be well-designed clinical trials investigating their usage. Three completed randomized controlled trials were conducted during the SARS epidemic-3 in China, 1 in Taiwan and 2 in Hong Kong [93] [94] [95] [96] [97] . The studies respectively investigated antibiotic usage involving 190 participants, combination of western and Chinese treatment vs. Chinese treatment in 123 participants, integrative Chinese and Western treatment in 49 patients, usage of a specific Chinese medicine in four participants and early use of corticosteroid in 16 participants. Another notable study was an open non-randomized study investigating ribavirin/lopinavir/ritonavir usage in 152 participants [98] . One randomized controlled trial investigating integrative western and Chinese treatment during the SARS epidemic was excluded as it was a Chinese article [94] . There is only one ongoing randomized controlled trial targeted at MERS therapeutics [99] . It investigates the usage of Lopinavir/Ritonavir and Interferon Beta 1B. Likewise, many prospective and retrospective cohort studies conducted during the epidemic centered on usage of ribavirin with lopinavir/ritonavir/ribavirin, interferon, and convalescent plasma usage. To date, only one trial has been completed. One phase 1 clinical trial investigating the safety and tolerability of a fully human polyclonal IgG immunoglobulin (SAB-301) was found in available literature [46] . The trial conducted in the United States in 2017 demonstrated SAB-301 to be safe and well-tolerated at single doses. Another trial on MERS therapeutics was found on ClinicalTrials.gov-a phase 2/3 trial in the United States evaluating the safety, tolerability, pharmacokinetics (PK), and immunogenicity on coadministered MERS-CoV antibodies REGN3048 & REGN3051 [100]. Rapid diagnostics plays an important role in disease and outbreak management. The fast and accurate diagnosis of a specific viral infection enables prompt and accurate public health surveillance, prevention and control measures. Local transmission and clusters can be prevented or delayed by isolation of laboratory-confirmed cases and their close contacts quarantined and monitored at home. Rapid diagnostic also facilitates other specific public health interventions such as closure of high-risk facilities and areas associated with the confirmed cases for prompt infection control and environmental decontamination [11, 101] . Laboratory diagnosis can be performed by: (a) detecting the genetic material of the virus, (b) detecting the antibodies that neutralize the viral particles of interest, (c) detecting the viral epitopes of interest with antibodies (serological testing), or (d) culture and isolation of viable virus particles. The key limitations of genetic material detection are the lack of knowledge of the presence of viable virus, the potential cross-reactivity with non-specific genetic regions and the short timeframe for accurate detection during the acute infection phase. The key limitations of serological testing is the need to collect paired serum samples (in the acute and convalescent phases) from cases under investigation for confirmation to eliminate potential cross-reactivity from non-specific antibodies from past exposure and/or infection by other coronaviruses. The limitation of virus culture and isolation is the long duration and the highly specialized skills required of the technicians to process the samples. All patients recovered. Significantly shorted time from the disease onset to the symptom improvement in treatment (5.10 ± 2.83 days) compared to control group (7.62 ± 2.27 days) (p < 0.05) No significant difference in blood routine improvement, pulmonary chest shadow in chest film improvement and corticosteroid usgae between the 2 groups. However, particularly in the respect of improving clinical symptoms, elevating quality of life, promoting immune function recovery, promoting absorption of pulmonary inflammation, reducing the dosage of cortisteroid and shortening the therapeutic course, treatment with integrative chinese and western medicine treatment had obvious superiority compared with using control treatment alone. Single infusions of SAB-301 up to 50 mg/kg appear to be safe and well-tolerated in healthy participants. [46] Where the biological samples are taken from also play a role in the sensitivity of these tests. For SARS-CoV and MERS-CoV, specimens collected from the lower respiratory tract such as sputum and tracheal aspirates have higher and more prolonged levels of viral RNA because of the tropism of the virus. MERS-CoV viral loads are also higher for severe cases and have longer viral shedding compared to mild cases. Although upper respiratory tract specimens such as nasopharyngeal or oropharyngeal swabs can be used, they have potentially lower viral loads and may have higher risk of false-negatives among the mild MERS and SARS cases [102, 103] , and likely among the 2019-nCoV cases. The existing practices in detecting genetic material of coronaviruses such as SARS-CoV and MERS-CoV include (a) reverse transcription-polymerase chain reaction (RT-PCR), (b) real-time RT-PCR (rRT-PCR), (c) reverse transcription loop-mediated isothermal amplification (RT-LAMP) and (d) real-time RT-LAMP [104] . Nucleic amplification tests (NAAT) are usually preferred as in the case of MERS-CoV diagnosis as it has the highest sensitivity at the earliest time point in the acute phase of infection [102] . Chinese health authorities have recently posted the full genome of 2019-nCoV in the GenBank and in GISAID portal to facilitate in the detection of the virus [11] . Several laboratory assays have been developed to detect the novel coronavirus in Wuhan, as highlighted in WHO's interim guidance on nCoV laboratory testing of suspected cases. These include protocols from other countries such as Thailand, Japan and China [105] . The first validated diagnostic test was designed in Germany. Corman et al. had initially designed a candidate diagnostic RT-PCR assay based on the SARS or SARS-related coronavirus as it was suggested that circulating virus was SARS-like. Upon the release of the sequence, assays were selected based on the match against 2019-nCoV upon inspection of the sequence alignment. Two assays were used for the RNA dependent RNA polymerase (RdRP) gene and E gene where E gene assay acts as the first-line screening tool and RdRp gene assay as the confirmatory testing. All assays were highly sensitive and specific in that they did not cross-react with other coronavirus and also human clinical samples that contained respiratory viruses [11] . The Hong Kong University used two monoplex assays which were reactive with coronaviruses under the subgenus Sarbecovirus (consisting of 2019-nCoV, SARS-CoV and SARS-like coronavirus). Viral RNA extracted from SARS-CoV can be used as the positive control for the suggested protocol assuming that SARS has been eradicated. It is proposed that the N gene RT-PCR can be used as a screening assay while the Orf1b assay acts as a confirmatory test. However, this protocol has only been evaluated with a panel of controls with the only positive control SARS-CoV RNA. Synthetic oligonucleotide positive control or 2019-nCoV have yet to be tested [106] . The US CDC shared the protocol on the real time RT-PCR assay for the detection of the 2019-nCoV with the primers and probes designed for the universal detection of SARS-like coronavirus and the specific detection of 2019-nCoV. However, the protocol has not been validated on other platforms or chemistries apart from the protocol described. There are some limitations for the assay. Analysts engaged have to be trained and familiar with the testing procedure and result interpretation. False negative results may occur due to insufficient organisms in the specimen resulting from improper collection, transportation or handling. Also, RNA viruses may show substantial genetic variability. This could result in mismatch between the primer and probes with the target sequence which can diminish the assay performance or result in false negative results [107] . Point-of-care test kit can potentially minimize these limitations, which should be highly prioritized for research and development in the next few months. Serological testing such as ELISA, IIFT and neutralization tests are effective in determining the extent of infection, including estimating asymptomatic and attack rate. Compared to the detection of viral genome through molecular methods, serological testing detects antibodies and antigens. There would be a lag period as antibodies specifically targeting the virus would normally appear between 14 and 28 days after the illness onset [108] . Furthermore, studies suggest that low antibody titers in the second week or delayed antibody production could be associated with mortality with a high viral load. Hence, serological diagnoses are likely used when nucleic amplification tests (NAAT) are not available or accessible [102] . Vaccines can prevent and protect against infection and disease occurrence when exposed to the specific pathogen of interest, especially in vulnerable populations who are more prone to severe outcomes. In the context of the current 2019-nCoV outbreak, vaccines will help control and reduce disease transmission by creating herd immunity in addition to protecting healthy individuals from infection. This decreases the effective R0 value of the disease. Nonetheless, there are social, clinical and economic hurdles for vaccine and vaccination programmes, including (a) the willingness of the public to undergo vaccination with a novel vaccine, (b) the side effects and severe adverse reactions of vaccination, (c) the potential difference and/or low efficacy of the vaccine in populations different from the clinical trials' populations and (d) the accessibility of the vaccines to a given population (including the cost and availability of the vaccine). Vaccines against the 2019-nCoV are currently in development and none are in testing (at the time of writing). On 23 January 2020, the Coalition for Epidemic Preparedness Innovations (CEPI) announced that they will fund vaccine development programmes with Inovio, The University of Queensland and Moderna, Inc respectively, with the aim to test the experimental vaccines clinically in 16 weeks (By June 2020). The vaccine candidates will be developed by the DNA, recombinant and mRNA vaccine platforms from these organizations [109] . Based on the most recent MERS-CoV outbreak, there are already a number of vaccine candidates being developed but most are still in the preclinical testing stage. The vaccines in development include viral vector-based vaccine, DNA vaccine, subunit vaccine, virus-like particles (VLPs)-based vaccine, inactivated whole-virus (IWV) vaccine and live attenuated vaccine. The latest findings for these vaccines arebased on the review by Yong et al. (2019) in August 2019 [110] . As of the date of reporting, there is only one published clinical study on the MERS-CoV vaccine by GeneOne Life Science & Inovio Pharmaceuticals [47] . There was one SARS vaccine trial conducted by the US National Institute of Allergy and Infectious Diseases. Both Phase I clinical trials reported positive results, but only one has announced plans to proceed to Phase 2 trial [111] . Due to the close genetic relatedness of SARS-CoV (79%) with 2019-nCoV [112] , there may be potential cross-protective effect of using a safe SARS-CoV vaccine while awaiting the 2019-nCoV vaccine. However, this would require small scale phase-by-phase implementation and close monitoring of vaccinees before any large scale implementation. Apart from the timely diagnosis of cases, the achievement of favorable clinical outcomes depends on the timely treatment administered. ACE2 has been reported to be the same cell entry receptor used by 2019-nCoV to infect humans as SARS-CoV [113] . Hence, clinical similarity between the two viruses is expected, particularly in severe cases. In addition, most of those who have died from MERS-CoV, SARS-CoV and 2019-nCoV were advance in age and had underlying health conditions such as hypertension, diabetes or cardiovascular disease that compromised their immune systems [114] . Coronaviruses have error-prone RNA-dependent RNA polymerases (RdRP), which result in frequent mutations and recombination events. This results in quasispecies diversity that is closely associated with adaptive evolution and the capacity to enhance viral-cell entry to cause disease over time in a specific population at-risk [115] . Since ACE2 is abundantly present in humans in the epithelia of the lung and small intestine, coronaviruses are likely to infect the upper respiratory and gastrointestinal tract and this may influence the type of therapeutics against 2019-nCoV, similarly to SAR-CoV. However, in the years following two major coronavirus outbreaks SARS-CoV in 2003 and MERS-CoV in 2012, there remains no consensus on the optimal therapy for either disease [116, 117] . Well-designed clinical trials that provide the gold standard for assessing the therapeutic measures are scarce. No coronavirus protease inhibitors have successfully completed a preclinical development program despite large efforts exploring SARS-CoV inhibitors. The bulk of potential therapeutic strategies remain in the experimental phase, with only a handful crossing the in vitro hurdle. Stronger efforts are required in the research for treatment options for major coronaviruses given their pandemic potential. Effective treatment options are essential to maximize the restoration of affected populations to good health following infections. Clinical trials have commenced in China to identify effective treatments for 2019-nCoV based on the treatment evidence from SARS and MERS. There is currently no effective specific antiviral with high-level evidence; any specific antiviral therapy should be provided in the context of a clinical study/trial. Few treatments have shown real curative action against SARS and MERS and the literature generally describes isolated cases or small case series. Many interferons from the three classes have been tested for their antiviral activities against SARS-CoV both in vitro and in animal models. Interferon β has consistently been shown to be the most active, followed by interferon α. The use of corticosteroids with interferon alfacon-1 (synthetic interferon α) appeared to have improved oxygenation and faster resolution of chest radiograph abnormalities in observational studies with untreated controls. Interferon has been used in multiple observational studies to treat SARS-CoV and MERS-CoV patients [116, 117] . Interferons, with or without ribavirin, and lopinavir/ritonavir are most likely to be beneficial and are being trialed in China for 2019-nCoV. This drug treatment appears to be the most advanced. Timing of treatment is likely an important factor in effectiveness. A combination of ribavirin and lopinavir/ritonavir was used as a post-exposure prophylaxis in health care workers and may have reduced the risk of infection. Ribavirin alone is unlikely to have substantial antiviral activities at clinically used dosages. Hence, ribavirin with or without corticosteroids and with lopinavir and ritonavir are among the combinations employed. This was the most common agent reported in the available literature. Its efficacy has been assessed in observational studies, retrospective case series, retrospective cohort study, a prospective observational study, a prospective cohort study and randomized controlled trial ranging from seven to 229 participants [117] . Lopinavir/ritonavir (Kaletra) was the earliest protease inhibitor combination introduced for the treatment of SARS-CoV. Its efficacy was documented in several studies, causing notably lower incidence of adverse outcomes than with ribavirin alone. Combined usage with ribavirin was also associated with lower incidence of acute respiratory distress syndrome, nosocomial infection and death, amongst other favorable outcomes. Recent in vitro studies have shown another HIV protease inhibitor, nelfinavir, to have antiviral capacity against SARS-CoV, although it has yet to show favorable outcomes in animal studies [118] . Remdesivir (Gilead Sciences, GS-5734) nucleoside analogue in vitro and in vivo data support GS-5734 development as a potential pan-coronavirus antiviral based on results against several coronaviruses (CoVs), including highly pathogenic CoVs and potentially emergent BatCoVs. The use of remdesivir may be a good candidate as an investigational treatment. Improved mortality following receipt of convalescent plasma in various doses was consistently reported in several observational studies involving cases with severe acute respiratory infections (SARIs) of viral etiology. A significant reduction in the pooled odds of mortality following treatment of 0.25 compared to placebo or no therapy was observed [119] . Studies were however at moderate to high risk of bias given their small sample sizes, allocation of treatment based on the physician's discretion, and the availability of plasma. Factors like concomitant treatment may have also confounded the results. Associations between convalescent plasma and hospital length of stay, viral antibody levels, and viral load respectively were similarly inconsistent across available literature. Convalescent plasma, while promising, is likely not yet feasible, given the limited pool of potential donors and issues of scalability. Monoclonal antibody treatment is progressing. SARS-CoV enters host cells through the binding of their spike (S) protein to angiotensin converting enzyme 2 (ACE2) and CD209L [118] . Human monoclonal antibodies to the S protein have been shown to significantly reduce the severity of lung pathology in non-human primates following MERS-CoV infection [120] . Such neutralizing antibodies can be elicited by active or passive immunization using vaccines or convalescent plasma respectively. While such neutralizing antibodies can theoretically be harvested from individuals immunized with vaccines, there is uncertainty over the achievement of therapeutic levels of antibodies. Other therapeutic agents have also been reported. A known antimalarial agent, chloroquine, elicits antiviral effects against multiple viruses including HIV type 1, hepatitis B and HCoV-229E. Chloroquine is also immunomodulatory, capable of suppressing the production and release of factors which mediate the inflammatory complications of viral diseases (tumor necrosis factor and interleukin 6) [121] . It is postulated that chloroquine works by altering ACE2 glycosylation and endosomal pH. Its anti-inflammatory properties may be beneficial for the treatment of SARS. Niclosamide as a known drug used in antihelminthic treatment. The efficacy of niclosamide as an inhibitor of virus replication was proven in several assays. In both immunoblot analysis and immunofluorescence assays, niclosamide treatment was observed to completely inhibit viral antigen synthesis. Reduction of virus yield in infected cells was dose dependent. Niclosamide likely does not interfere in the early stages of virus attachment and entry into cells, nor does it function as a protease inhibitor. Mechanisms of niclosamide activity warrant further investigation [122] . Glycyrrhizin also reportedly inhibits virus adsorption and penetration in the early steps of virus replication. Glycyrrhizin was a significantly potent inhibitor with a low selectivity index when tested against several pathogenic flaviviruses. While preliminary results suggest production of nitrous oxide (which inhibits virus replication) through induction of nitrous oxide synthase, the mechanism of Glycyrrhizin against SARS-CoV remains unclear. The compound also has relatively lower toxicity compared to protease inhibitors like ribavirin [123] . Inhibitory activity was also detected in baicalin [124] , extracted from another herb used in the treatment of SARS in China and Hong Kong. Findings on these compounds are limited to in vitro studies [121] [122] [123] [124] . Due to the rapidly evolving situation of the 2019-nCoV, there will be potential limitations to the systematic review. The systematic review is likely to have publication bias as some developments have yet to be reported while for other developments there is no intention to report publicly (or in scientific platforms) due to confidentiality concerns. However, this may be limited to only a few developments for review as publicity does help in branding to some extent for the company and/or the funder. Furthermore, due to the rapid need to share the status of these developments, there may be reporting bias in some details provided by authors of the scientific articles or commentary articles in traditional media. Lastly, while it is not viable for any form of quality assessment and metaanalysis of the selected articles due to the limited data provided and the heterogeneous style of reporting by different articles, this paper has provided a comprehensive overview of the potential developments of these pharmaceutical interventions during the early phase of the outbreak. This systematic review would be useful for cross-check when the quality assessment and meta-analysis of these developments are performed as a follow-up study. Rapid diagnostics, vaccines and therapeutics are key pharmaceutical interventions to limit transmission of respiratory infectious diseases. Many potential developments on these pharmaceutical interventions for 2019-nCoV are ongoing in the containment phase of this outbreak, potentially due to better pandemic preparedness than before. However, lessons from MERS-CoV and SARS-CoV have shown that the journeys for these developments can still be challenging moving ahead. Supplementary Materials: The following are available online at www.mdpi.com/xxx/s1, Table S1 : Example of full search strategy in Pubmed, Table S2 : Google Search: 2019-nCoV diagnostics, Table S3 : Summary of diagnostic assays developed for 2019-nCoV, Table S4

What are key limtations of genetic detection?

Accelerated viral dynamics in bat cell lines, with implications for zoonotic emergence https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7064339/ SHA: f2cc0d63ff2c4aaa127c4caae21d8f3a0067e3d5 Authors: Brook, Cara E; Boots, Mike; Chandran, Kartik; Dobson, Andrew P; Drosten, Christian; Graham, Andrea L; Grenfell, Bryan T; Müller, Marcel A; Ng, Melinda; Wang, Lin-Fa; van Leeuwen, Anieke Date: 2020-02-03 DOI: 10.7554/elife.48401 License: cc-by Abstract: Bats host virulent zoonotic viruses without experiencing disease. A mechanistic understanding of the impact of bats’ virus hosting capacities, including uniquely constitutive immune pathways, on cellular-scale viral dynamics is needed to elucidate zoonotic emergence. We carried out virus infectivity assays on bat cell lines expressing induced and constitutive immune phenotypes, then developed a theoretical model of our in vitro system, which we fit to empirical data. Best fit models recapitulated expected immune phenotypes for representative cell lines, supporting robust antiviral defenses in bat cells that correlated with higher estimates for within-host viral propagation rates. In general, heightened immune responses limit pathogen-induced cellular morbidity, which can facilitate the establishment of rapidly-propagating persistent infections within-host. Rapidly-transmitting viruses that have evolved with bat immune systems will likely cause enhanced virulence following emergence into secondary hosts with immune systems that diverge from those unique to bats. Text: Bats have received much attention in recent years for their role as reservoir hosts for emerging viral zoonoses, including rabies and related lyssaviruses, Hendra and Nipah henipaviruses, Ebola and Marburg filoviruses, and SARS coronavirus (Calisher et al., 2006; Wang and Anderson, 2019) . In most non-Chiropteran mammals, henipaviruses, filoviruses, and coronaviruses induce substantial morbidity and mortality, display short durations of infection, and elicit robust, long-term immunity in hosts surviving infection (Nicholls et al., 2003; Hooper et al., 2001; Mahanty and Bray, 2004) . Bats, by contrast, demonstrate no obvious disease symptoms upon infection with pathogens that are highly virulent in non-volant mammals (Schountz et al., 2017) but may, instead, support viruses as longterm persistent infections, rather than transient, immunizing pathologies (Plowright et al., 2016) . Recent research advances are beginning to shed light on the molecular mechanisms by which bats avoid pathology from these otherwise virulent pathogens (Brook and Dobson, 2015) . Bats leverage a suite of species-specific mechanisms to limit viral load, which include host receptor sequence incompatibilities for some bat-virus combinations (Ng et al., 2015; Takadate et al., 2020) and constitutive expression of the antiviral cytokine, IFN-a, for others (Zhou et al., 2016) . Typically, the presence of viral RNA or DNA in the cytoplasm of mammalian cells will induce secretion of type I interferon proteins (IFN-a and IFN-b), which promote expression and translation of interferon-stimulated genes (ISGs) in neighboring cells and render them effectively antiviral (Stetson and Medzhitov, 2006) . In some bat cells, the transcriptomic blueprints for this IFN response are expressed constitutively, even in the absence of stimulation by viral RNA or DNA (Zhou et al., 2016) . In non-flying mammals, constitutive IFN expression would likely elicit widespread inflammation and concomitant immunopathology upon viral infection, but bats support unique adaptations to combat inflammation (Zhang et al., 2013; Ahn et al., 2019; Xie et al., 2018; Pavlovich et al., 2018) that may have evolved to mitigate metabolic damage induced during flight (Kacprzyk et al., 2017) . The extent to which constitutive IFN-a expression signifies constitutive antiviral defense in the form of functional IFN-a protein remains unresolved. In bat cells constitutively expressing IFN-a, some protein-stimulated, downstream ISGs appear to be also constitutively expressed, but additional ISG induction is nonetheless possible following viral challenge and stimulation of IFN-b (Zhou et al., 2016; Xie et al., 2018) . Despite recent advances in molecular understanding of bat viral tolerance, the consequences of this unique bat immunity on within-host virus dynamics-and its implications for understanding zoonotic emergence-have yet to be elucidated. The field of 'virus dynamics' was first developed to describe the mechanistic underpinnings of long-term patterns of steady-state viral load exhibited by patients in chronic phase infections with HIV, who appeared to produce and clear virus at equivalent rates (Nowak and May, 2000; Ho et al., 1995) . Models of simple target cell depletion, in which viral load is dictated by a bottom-eLife digest Bats can carry viruses that are deadly to other mammals without themselves showing serious symptoms. In fact, bats are natural reservoirs for viruses that have some of the highest fatality rates of any viruses that people acquire from wild animals -including rabies, Ebola and the SARS coronavirus. Bats have a suite of antiviral defenses that keep the amount of virus in check. For example, some bats have an antiviral immune response called the interferon pathway perpetually switched on. In most other mammals, having such a hyper-vigilant immune response would cause harmful inflammation. Bats, however, have adapted anti-inflammatory traits that protect them from such harm, include the loss of certain genes that normally promote inflammation. However, no one has previously explored how these unique antiviral defenses of bats impact the viruses themselves. Now, Brook et al. have studied this exact question using bat cells grown in the laboratory. The experiments made use of cells from one bat species -the black flying fox -in which the interferon pathway is always on, and another -the Egyptian fruit bat -in which this pathway is only activated during an infection. The bat cells were infected with three different viruses, and then Brook et al. observed how the interferon pathway helped keep the infections in check, before creating a computer model of this response. The experiments and model helped reveal that the bats' defenses may have a potential downside for other animals, including humans. In both bat species, the strongest antiviral responses were countered by the virus spreading more quickly from cell to cell. This suggests that bat immune defenses may drive the evolution of faster transmitting viruses, and while bats are well protected from the harmful effects of their own prolific viruses, other creatures like humans are not. The findings may help to explain why bats are often the source for viruses that are deadly in humans. Learning more about bats' antiviral defenses and how they drive virus evolution may help scientists develop better ways to predict, prevent or limit the spread of viruses from bats to humans. More studies are needed in bats to help these efforts. In the meantime, the experiments highlight the importance of warning people to avoid direct contact with wild bats. up resource supply of infection-susceptible host cells, were first developed for HIV (Perelson, 2002) but have since been applied to other chronic infections, including hepatitis-C virus (Neumann et al., 1998) , hepatitis-B virus (Nowak et al., 1996) and cytomegalovirus (Emery et al., 1999) . Recent work has adopted similar techniques to model the within-host dynamics of acute infections, such as influenza A and measles, inspiring debate over the extent to which explicit modeling of top-down immune control can improve inference beyond the basic resource limitation assumptions of the target cell model (Baccam et al., 2006; Pawelek et al., 2012; Saenz et al., 2010; Morris et al., 2018) . To investigate the impact of unique bat immune processes on in vitro viral kinetics, we first undertook a series of virus infection experiments on bat cell lines expressing divergent interferon phenotypes, then developed a theoretical model elucidating the dynamics of within-host viral spread. We evaluated our theoretical model analytically independent of the data, then fit the model to data recovered from in vitro experimental trials in order to estimate rates of within-host virus transmission and cellular progression to antiviral status under diverse assumptions of absent, induced, and constitutive immunity. Finally, we confirmed our findings in spatially-explicit stochastic simulations of fitted time series from our mean field model. We hypothesized that top-down immune processes would overrule classical resource-limitation in bat cell lines described as constitutively antiviral in the literature, offering a testable prediction for models fit to empirical data. We further predicted that the most robust antiviral responses would be associated with the most rapid within-host virus propagation rates but also protect cells against virus-induced mortality to support the longest enduring infections in tissue culture. We first explored the influence of innate immune phenotype on within-host viral propagation in a series of infection experiments in cell culture. We conducted plaque assays on six-well plate monolayers of three immortalized mammalian kidney cell lines: [1] Vero (African green monkey) cells, which are IFN-defective and thus limited in antiviral capacity (Desmyter et al., 1968) ; [2] RoNi/7.1 (Rousettus aegyptiacus) cells which demonstrate idiosyncratic induced interferon responses upon viral challenge (Kuzmin et al., 2017; Arnold et al., 2018; Biesold et al., 2011; Pavlovich et al., 2018) ; and [3] PaKiT01 (Pteropus alecto) cells which constitutively express IFN-a (Zhou et al., 2016; Crameri et al., 2009) . To intensify cell line-specific differences in constitutive immunity, we carried out infectivity assays with GFP-tagged, replication-competent vesicular stomatitis Indiana viruses: rVSV-G, rVSV-EBOV, and rVSV-MARV, which have been previously described (Miller et al., 2012; Wong et al., 2010) . Two of these viruses, rVSV-EBOV and rVSV-MARV, are recombinants for which cell entry is mediated by the glycoprotein of the bat-evolved filoviruses, Ebola (EBOV) and Marburg (MARV), thus allowing us to modulate the extent of structural, as well as immunological, antiviral defense at play in each infection. Previous work in this lab has demonstrated incompatibilities in the NPC1 filovirus receptor which render PaKiT01 cells refractory to infection with rVSV-MARV (Ng and Chandrab, 2018, Unpublished results) , making them structurally antiviral, over and above their constitutive expression of IFN-a. All three cell lines were challenged with all three viruses at two multiplicities of infection (MOI): 0.001 and 0.0001. Between 18 and 39 trials were run at each cell-virus-MOI combination, excepting rVSV-MARV infections on PaKiT01 cells at MOI = 0.001, for which only eight trials were run (see Materials and methods; Figure 1 -figure supplements 1-3, Supplementary file 1). Because plaque assays restrict viral transmission neighbor-to-neighbor in two-dimensional cellular space (Howat et al., 2006) , we were able to track the spread of GFP-expressing virus-infected cells across tissue monolayers via inverted fluorescence microscopy. For each infection trial, we monitored and re-imaged plates for up to 200 hr of observations or until total monolayer destruction, processed resulting images, and generated a time series of the proportion of infectious-cell occupied plate space across the duration of each trial (see Materials and methods). We used generalized additive models to infer the time course of all cell culture replicates and construct the multi-trial dataset to which we eventually fit our mechanistic transmission model for each cell line-virus-specific combination ( Figure 1; Figure 1 -figure supplements 1-5). All three recombinant vesicular stomatitis viruses (rVSV-G, rVSV-EBOV, and rVSV-MARV) infected Vero, RoNi/7.1, and PaKiT01 tissue cultures at both focal MOIs. Post-invasion, virus spread rapidly across most cell monolayers, resulting in virus-induced epidemic extinction. Epidemics were less severe in bat cell cultures, especially when infected with the recombinant filoviruses, rVSV-EBOV and rVSV-MARV. Monolayer destruction was avoided in the case of rVSV-EBOV and rVSV-MARV infections on PaKiT01 cells: in the former, persistent viral infection was maintained throughout the 200 hr duration of each experiment, while, in the latter, infection was eliminated early in the time series, preserving a large proportion of live, uninfectious cells across the duration of the experiment. We assumed this pattern to be the result of immune-mediated epidemic extinction (Figure 1) . Patterns from MOI = 0.001 were largely recapitulated at MOI = 0.0001, though at somewhat reduced total proportions (Figure 1-figure supplement 5 ). A theoretical model fit to in vitro data recapitulates expected immune phenotypes for bat cells We next developed a within-host model to fit to these data to elucidate the effects of induced and constitutive immunity on the dynamics of viral spread in host tissue ( Figure 1 ). The compartmental within-host system mimicked our two-dimensional cell culture monolayer, with cells occupying five distinct infection states: susceptible (S), antiviral (A), exposed (E), infectious (I), and dead (D). We modeled exposed cells as infected but not yet infectious, capturing the 'eclipse phase' of viral integration into a host cell which precedes viral replication. Antiviral cells were immune to viral infection, in accordance with the 'antiviral state' induced from interferon stimulation of ISGs in tissues adjacent to infection (Stetson and Medzhitov, 2006) . Because we aimed to translate available data into modeled processes, we did not explicitly model interferon dynamics but instead scaled the rate of cell progression from susceptible to antiviral (r) by the proportion of exposed cells (globally) in the system. In systems permitting constitutive immunity, a second rate of cellular acquisition of antiviral status (") additionally scaled with the global proportion of susceptible cells in the model. Compared with virus, IFN particles are small and highly diffusive, justifying this global signaling assumption at the limited spatial extent of a six-well plate and maintaining consistency with previous modeling approximations of IFN signaling in plaque assay (Howat et al., 2006) . To best represent our empirical monolayer system, we expressed our state variables as proportions (P S , P A , P E , P I , and P D ), under assumptions of frequency-dependent transmission in a wellmixed population (Keeling and Rohani, 2008) , though note that the inclusion of P D (representing the proportion of dead space in the modeled tissue) had the functional effect of varying transmission with infectious cell density. This resulted in the following system of ordinary differential equations: We defined 'induced immunity' as complete, modeling all cells as susceptible to viral invasion at disease-free equilibrium, with defenses induced subsequent to viral exposure through the term r. By contrast, we allowed the extent of constitutive immunity to vary across the parameter range of " > 0, defining a 'constitutive' system as one containing any antiviral cells at disease-free equilibrium. In fitting this model to tissue culture data, we independently estimated both r and "; as well as the cell-to-cell transmission rate, b, for each cell-virus combination. Since the extent to which constitutively-expressed IFN-a is constitutively translated into functional protein is not yet known for bat hosts (Zhou et al., 2016) , this approach permitted our tissue culture data to drive modeling inference: even in PaKiT01 cell lines known to constitutively express IFN-a, the true constitutive extent of the system (i.e. the quantity of antiviral cells present at disease-free equilibrium) was allowed to vary through estimation of ": For the purposes of model-fitting, we fixed the value of c, the return rate of antiviral cells to susceptible status, at 0. The small spatial scale and short time course (max 200 hours) of our experiments likely prohibited any return of antiviral cells to susceptible status in our empirical system; nonetheless, we retained the term c in analytical evaluations of our model because regression from antiviral to susceptible status is possible over long time periods in vitro and at the scale of a complete organism (Radke et al., 1974; Rasmussen and Farley, 1975; Samuel and Knutson, 1982) . Before fitting to empirical time series, we undertook bifurcation analysis of our theoretical model and generated testable hypotheses on the basis of model outcomes. From our within-host model system (Equation 1-5), we derived the following expression for R 0 , the pathogen basic reproduction number (Supplementary file 2): Pathogens can invade a host tissue culture when R 0 >1. Rapid rates of constitutive antiviral acquisition (") will drive R 0 <1: tissue cultures with highly constitutive antiviral immunity will be therefore resistant to virus invasion from the outset. Since, by definition, induced immunity is stimulated following initial virus invasion, the rate of induced antiviral acquisition (r) is not incorporated into the equation for R 0 ; while induced immune processes can control virus after initial invasion, they cannot prevent it from occurring to begin with. In cases of fully induced or absent immunity (" ¼ 0), the R 0 equation thus reduces to a form typical of the classic SEIR model: At equilibrium, the theoretical, mean field model demonstrates one of three infection states: endemic equilibrium, stable limit cycles, or no infection ( Figure 2) . Respectively, these states approximate the persistent infection, virus-induced epidemic extinction, and immune-mediated epidemic extinction phenotypes previously witnessed in tissue culture experiments ( Figure 1 ). Theoretically, endemic equilibrium is maintained when new infections are generated at the same rate at which infections are lost, while limit cycles represent parameter space under which infectious and susceptible populations are locked in predictable oscillations. Endemic equilibria resulting from cellular regeneration (i.e. births) have been described in vivo for HIV (Coffin, 1995) and in vitro for herpesvirus plaque assays (Howat et al., 2006) , but, because they so closely approach zero, true limit cycles likely only occur theoretically, instead yielding stochastic extinctions in empirical time series. Bifurcation analysis of our mean field model revealed that regions of no infection (pathogen extinction) were bounded at lower threshold (Branch point) values for b, below which the pathogen was unable to invade. We found no upper threshold to invasion for b under any circumstances (i.e. b high enough to drive pathogen-induced extinction), but high b values resulted in Hopf bifurcations, which delineate regions of parameter space characterized by limit cycles. Since limit cycles so closely approach zero, high bs recovered in this range would likely produce virus-induced epidemic extinctions under experimental conditions. Under more robust representations of immunity, with higher values for either or both induced (r) and constitutive (") rates of antiviral acquisition, Hopf bifurcations occurred at increasingly higher values for b, meaning that persistent infections could establish at higher viral transmission rates ( Figure 2 ). Consistent with our derivation for R 0 , we found that the Branch point threshold for viral invasion was independent of changes to the induced immune parameter (r) but saturated at high values of " that characterize highly constitutive immunity ( Figure 3) . We next fit our theoretical model by least squares to each cell line-virus combination, under absent, induced, and constitutive assumptions of immunity. In general, best fit models recapitulated expected outcomes based on the immune phenotype of the cell line in question, as described in the general literature (Table 1 Ironically, the induced immune model offered a slightly better fit than the constitutive to rVSV-MARV infections on the PaKiT01 cell line (the one cell line-virus combination for which we know a constitutively antiviral cell-receptor incompatibility to be at play). Because constitutive immune assumptions can prohibit pathogen invasion (R 0 <1), model fits to this time series under constitutive assumptions were handicapped by overestimations of ", which prohibited pathogen invasion. Only by incorporating an exceedingly rapid rate of induced antiviral acquisition could the model guarantee that initial infection would be permitted and then rapidly controlled. In all panel (A) plots, the rate of induced immune antiviral acquisition (r) was fixed at 0.01. Panel (B) depicts dynamics under variably induced immunity, ranging from absent (left: r=0) to high (right: r=1). In all panel (B) plots, the rate of constitutive antiviral acquisition (") was fixed at 0.0001 Branch point curves are represented as solid lines and Hopf curves as dashed lines. White space indicates endemic equilibrium (persistence), gray space indicates limit cycles, and black space indicates no infection (extinction). Other parameter values for equilibrium analysis were fixed at: b = .025, m = .001, s = 1/6, c = 0. Special points from bifurcations analyses are listed in Supplementary file 3. In fitting our theoretical model to in vitro data, we estimated the within-host virus transmission rate (b) and the rate(s) of cellular acquisition to antiviral status (r or r + ") ( Table 1 ; Supplementary file 4). Under absent immune assumptions, r and " were fixed at 0 while b was estimated; under induced immune assumptions, " was fixed at 0 while r and b were estimated; and under constitutive immune assumptions, all three parameters (r, ", and b) were simultaneously estimated for each cell-virus combination. Best fit parameter estimates for MOI=0.001 data are visualized in conjunction with br and b -" bifurcations in (r) and (B) the constitutive immunity rate of antiviral acquisition ("). Panels show variation in the extent of immunity, from absent (left) to high (right). Branch point curves are represented as solid lines and Hopf curves as dashed lines. White space indicates endemic equilibrium (persistence), gray space indicates limit cycling, and black space indicates no infection (extinction). Other parameter values for equilibrium analysis were fixed at: b = .025, m = .001, s = 1/6, a = 1/6, c = 0. Special points from bifurcations analyses are listed in Supplementary file 3. space corresponding to theoretical limit cycles, consistent with observed virus-induced epidemic extinctions in stochastic tissue cultures. In contrast to Vero cells, the induced immunity model offered the best fit to all RoNi/7.1 data, consistent with reported patterns in the literature and our own validation by qPCR ( Table 1; Arnold et al., 2018; Kuzmin et al., 2017; Biesold et al., 2011; Pavlovich et al., 2018) . As in Vero cell trials, we estimated highest b values for rVSV-G infections on RoNi/7.1 cell lines but here recovered higher b estimates for rVSV-MARV than for rVSV-EBOV. This reversal was balanced by a higher estimated rate of acquisition to antiviral status (r) for rVSV-EBOV versus rVSV-MARV. In general, we observed that more rapid rates of antiviral acquisition (either induced, r, constitutive, ", or both) correlated with higher transmission rates (b). When offset by r, b values estimated for RoNi/7.1 infections maintained the same amplitude as those estimated for immune-absent Vero cell lines but caused gentler epidemics and reduced cellular mortality (Figure 1) . RoNi/7.1 parameter estimates localized in the region corresponding to endemic equilibrium for the deterministic, theoretical model (Figure 4) , yielding less acute epidemics which nonetheless went extinct in stochastic experiments. Finally, rVSV-G and rVSV-EBOV trials on PaKiT01 cells were best fit by models assuming constitutive immunity, while rVSV-MARV infections on PaKiT01 were matched equivalently by models assuming either induced or constitutive immunity-with induced models favored over constitutive in AIC comparisons because one fewer parameter was estimated (Figure 1-figure supplements 4-5; Supplementary file 4). For all virus infections, PaKiT01 cell lines yielded b estimates a full order of magnitude higher than Vero or RoNi/7.1 cells, with each b balanced by an immune response (either r, or r combined with ") also an order of magnitude higher than that recovered for the other cell lines ( Figure 4 ; Table 1 ). As in RoNi/7.1 cells, PaKiT01 parameter fits localized in the region corresponding to endemic equilibrium for the deterministic theoretical model. Because constitutive immune processes can actually prohibit initial pathogen invasion, constitutive immune fits to rVSV-MARV infections on PaKiT01 cell lines consistently localized at or below the Branch point threshold for virus invasion (R 0 ¼ 1). During model fitting for optimization of ", any parameter tests of " values producing R 0 <1 resulted in no infection and, consequently, produced an exceedingly poor fit to infectious time series data. In all model fits assuming constitutive immunity, across all cell lines, antiviral contributions from " prohibited virus from invading at all. The induced immune model thus produced a more parsimonious recapitulation of these data because virus invasion was always permitted, then rapidly controlled. In order to compare the relative contributions of each cell line's disparate immune processes to epidemic dynamics, we next used our mean field parameter estimates to calculate the initial 'antiviral rate'-the initial accumulation rate of antiviral cells upon virus invasion for each cell-virus-MOI combination-based on the following equation: where P E was calculated from the initial infectious dose (MOI) of each infection experiment and P S was estimated at disease-free equilibrium: Because and " both contribute to this initial antiviral rate, induced and constitutive immune assumptions are capable of yielding equally rapid rates, depending on parameter fits. Indeed, under fully induced immune assumptions, the induced antiviral acquisition rate (r) estimated for rVSV-MARV infection on PaKiT01 cells was so high that the initial antiviral rate exceeded even that estimated under constitutive assumptions for this cell-virus combination (Supplementary file 4) . In reality, we know that NPC1 receptor incompatibilities make PaKiT01 cell lines constitutively refractory to rVSV-MARV infection (Ng and Chandrab, 2018, Unpublished results) and that PaKiT01 cells also constitutively express the antiviral cytokine, IFN-a. Model fitting results suggest that this constitutive expression of IFN-a may act more as a rapidly inducible immune response following virus invasion than as a constitutive secretion of functional IFN-a protein. Nonetheless, as hypothesized, PaKiT01 cell lines were by far the most antiviral of any in our study-with initial antiviral rates estimated several orders of magnitude higher than any others in our study, under either induced or constitutive assumptions ( Table 1 ; Supplementary file 4). RoNi/7.1 cells displayed the second-most-pronounced signature of immunity, followed by Vero cells, for which the initial antiviral rate was essentially zero even under forced assumptions of induced or constitutive immunity ( Table 1 ; Supplementary file 4). Using fitted parameters for b and ", we additionally calculated R 0 , the basic reproduction number for the virus, for each cell line-virus-MOI combination ( Table 1 ; Supplementary file 4). We found that R 0 was essentially unchanged across differing immune assumptions for RoNi/7.1 and Vero cells, for which the initial antiviral rate was low. In the case of PaKiT01 cells, a high initial antiviral rate under either induced or constitutive immunity resulted in a correspondingly high estimation of b (and, consequently, R 0 ) which still produced the same epidemic curve that resulted from the much lower estimates for b and R 0 paired with absent immunity. These findings suggest that antiviral immune responses protect host tissues against virus-induced cell mortality and may facilitate the establishment of more rapid within-host transmission rates. Total monolayer destruction occurred in all cell-virus combinations excepting rVSV-EBOV infections on RoNi/7.1 cells and rVSV-EBOV and rVSV-MARV infections on PaKiT01 cells. Monolayer destruction corresponded to susceptible cell depletion and epidemic turnover where R-effective (the product of R 0 and the proportion susceptible) was reduced below one ( Figure 5) . For rVSV-EBOV infections on RoNi/7.1, induced antiviral cells safeguarded remnant live cells, which birthed new susceptible cells late in the time series. In rVSV-EBOV and rVSV-MARV infections on PaKiT01 cells, this antiviral protection halted the epidemic ( Figure 5 ; R-effective <1) before susceptibles fully declined. In the case of rVSV-EBOV on PaKiT01, the birth of new susceptibles from remnant live cells protected by antiviral status maintained late-stage transmission to facilitate long-term epidemic persistence. Importantly, under fixed parameter values for the infection incubation rate (s) and infectioninduced mortality rate (a), models were unable to reproduce the longer-term infectious time series captured in data from rVSV-EBOV infections on PaKiT01 cell lines without incorporation of cell births, an assumption adopted in previous modeling representations of IFN-mediated viral dynamics in tissue culture (Howat et al., 2006) . In our experiments, we observed that cellular reproduction took place as plaque assays achieved confluency. Finally, because the protective effect of antiviral cells is more clearly observable spatially, we confirmed our results by simulating fitted time series in a spatially-explicit, stochastic reconstruction of our mean field model. In spatial simulations, rates of antiviral acquisition were fixed at fitted values for r and " derived from mean field estimates, while transmission rates (b) were fixed at values ten times greater than those estimated under mean field conditions, accounting for the intensification of parameter thresholds permitting pathogen invasion in local spatial interactions (see Materials and methods; Videos 1-3; Figure 5-figure supplement 3; Supplementary file 5; Webb et al., 2007) . In immune capable time series, spatial antiviral cells acted as 'refugia' which protected live cells from infection as each initial epidemic wave 'washed' across a cell monolayer. Eventual birth of new susceptibles from these living refugia allowed for sustained epidemic transmission in cases where some infectious cells persisted at later timepoints in simulation (Videos 1-3; Figure 5-figure supplement 3 ). Bats are reservoirs for several important emerging zoonoses but appear not to experience disease from otherwise virulent viral pathogens. Though the molecular biological literature has made great progress in elucidating the mechanisms by which bats tolerate viral infections (Zhou et al., 2016; Ahn et al., 2019; Xie et al., 2018; Pavlovich et al., 2018; Zhang et al., 2013) , the impact of unique bat immunity on virus dynamics within-host has not been well-elucidated. We used an innovative combination of in vitro experimentation and within-host modeling to explore the impact of unique bat immunity on virus dynamics. Critically, we found that bat cell lines demonstrated a signature of enhanced interferon-mediated immune response, of either constitutive or induced form, which allowed for establishment of rapid within-host, cell-to-cell virus transmission rates (b). These results were supported by both data-independent bifurcation analysis of our mean field theoretical model, as well as fitting of this model to viral infection time series established in bat cell culture. Additionally, we demonstrated that the antiviral state induced by the interferon pathway protects live cells from mortality in tissue culture, resulting in in vitro epidemics of extended duration that enhance the probability of establishing a long-term persistent infection. Our findings suggest that viruses evolved in bat reservoirs possessing enhanced IFN capabilities could achieve more rapid within-host transmission rates without causing pathology to their hosts. Such rapidly-reproducing viruses would likely generate extreme virulence upon spillover to hosts lacking similar immune capacities to bats. To achieve these results, we first developed a novel, within-host, theoretical model elucidating the effects of unique bat immunity, then undertook bifurcation analysis of the model's equilibrium properties under immune absent, induced, and constitutive assumptions. We considered a cell line to be constitutively immune if possessing any number of antiviral cells at disease-free equilibrium but allowed the extent of constitutive immunity to vary across the parameter range for ", the constitutive rate of antiviral acquisition. In deriving the equation for R 0 , the basic reproduction number, which defines threshold conditions for virus invasion of a tissue (R 0 >1), we demonstrated how the invasion threshold is elevated at high values of constitutive antiviral acquisition, ". Constitutive immune processes can thus prohibit pathogen invasion, while induced responses, by definition, can only control infections post-hoc. Once thresholds for pathogen invasion have been met, assumptions of constitutive immunity will limit the cellular mortality (virulence) incurred at high transmission rates. Regardless of mechanism (induced or constitutive), interferon-stimulated antiviral cells appear to play a key role in maintaining longer term or persistent infections by safeguarding susceptible cells from rapid infection and concomitant cell death. Fitting of our model to in vitro data supported expected immune phenotypes for different bat cell lines as described in the literature. Simple target cell models that ignore the effects of immunity best recapitulated infectious time series derived from IFN-deficient Vero cells, while models assuming induced immune processes most accurately reproduced trials derived from RoNi/7.1 (Rousettus aegyptiacus) cells, which possess a standard virusinduced IFN-response. In most cases, models assuming constitutive immune processes best recreated virus epidemics produced on PaKiT01 (Pteropus alecto) cells, which are known to constitutively express the antiviral cytokine, IFN-a (Zhou et al., 2016) . Model support for induced immune assumptions in fits to rVSV-MARV infections on PaKiT01 cells suggests that the constitutive IFN-a expression characteristic of P. alecto cells may represent more of a constitutive immune priming process than a perpetual, functional, antiviral defense. Results from mean field model fitting were additionally confirmed in spatially explicit stochastic simulations of each time series. As previously demonstrated in within-host models for HIV (Coffin, 1995; Perelson et al., 1996; Nowak et al., 1995; Bonhoeffer et al., 1997; Ho et al., 1995) , assumptions of simple target-cell depletion can often provide satisfactory approximations of viral dynamics, especially those reproduced in simple in vitro systems. Critically, our model fitting emphasizes the need for incorporation of top-down effects of immune control in order to accurately reproduce infectious time series derived from bat cell tissue cultures, especially those resulting from the robustly antiviral PaKiT01 P. alecto cell line. These findings indicate that enhanced IFN-mediated immune pathways in bat reservoirs may promote elevated within-host virus replication rates prior to cross-species emergence. We nonetheless acknowledge the limitations imposed by in vitro experiments in tissue culture, especially involving recombinant viruses and immortalized cell lines. Future work should extend these cell culture studies to include measurements of multiple state variables (i.e. antiviral cells) to enhance epidemiological inference. The continued recurrence of Ebola epidemics across central Africa highlights the importance of understanding bats' roles as reservoirs for virulent zoonotic disease. The past decade has born witness to emerging consensus regarding the unique pathways by which bats resist and tolerate highly virulent infections (Brook and Dobson, 2015; Xie et al., 2018; Zhang et al., 2013; Ahn et al., 2019; Zhou et al., 2016; Ng et al., 2015; Pavlovich et al., 2018) . Nonetheless, an understanding of the mechanisms by which bats support endemic pathogens at the population level, or promote the evolution of virulent pathogens at the individual level, remains elusive. Endemic maintenance of infection is a defining characteristic of a pathogen reservoir (Haydon et al., 2002) , and bats appear to merit such a title, supporting long-term persistence of highly transmissible viral infections in isolated island populations well below expected critical community sizes (Peel et al., 2012) . Researchers debate the relative influence of population-level and within-host mechanisms which might explain these trends (Plowright et al., 2016) , but increasingly, field data are difficult to reconcile without acknowledgement of a role for persistent infections (Peel et al., 2018; Brook et al., 2019) . We present general methods to study cross-scale viral dynamics, which suggest that within-host persistence is supported by robust antiviral responses characteristic of bat immune processes. Viruses which evolve rapid replication rates under these robust antiviral defenses may pose the greatest hazard for cross-species pathogen emergence into spillover hosts with immune systems that differ from those unique to bats. All experiments were carried out on three immortalized mammalian kidney cell lines: Vero (African green monkey), RoNi/7.1 (Rousettus aegyptiacus) (Kühl et al., 2011; Biesold et al., 2011) and PaKiT01 (Pteropus alecto) (Crameri et al., 2009) . The species identifications of all bat cell lines was confirmed morphologically and genetically in the publications in which they were originally described (Kühl et al., 2011; Biesold et al., 2011; Crameri et al., 2009) . Vero cells were obtained from ATCC. Monolayers of each cell line were grown to 90% confluency (~9Â10 5 cells) in 6-well plates. Cells were maintained in a humidified 37˚C, 5% CO 2 incubator and cultured in Dulbecco's modified Eagle medium (DMEM) (Life Technologies, Grand Island, NY), supplemented with 2% fetal bovine serum (FBS) (Gemini Bio Products, West Sacramento, CA), and 1% penicillin-streptomycin (Life Technologies). Cells were tested monthly for mycoplasma contamination while experiments were taking place; all cells assayed negative for contamination at every testing. Previous work has demonstrated that all cell lines used are capable of mounting a type I IFN response upon viral challenge, with the exception of Vero cells, which possess an IFN-b deficiency (Desmyter et al., 1968; Rhim et al., 1969; Emeny and Morgan, 1979) . RoNi/7.1 cells have been shown to mount idiosyncratic induced IFN defenses upon viral infection (Pavlovich et al., 2018; Kuzmin et al., 2017; Arnold et al., 2018; Kühl et al., 2011; Biesold et al., 2011) , while PaKiT01 cells are known to constitutively express the antiviral cytokine, IFN-a (Zhou et al., 2016) . This work is the first documentation of IFN signaling induced upon challenge with the particular recombinant VSVs outlined below. We verified known antiviral immune phenotypes via qPCR. Results were consistent with the literature, indicating a less pronounced role for interferon defense against viral infection in RoNi/7.1 versus PaKiT01 cells. Replication-capable recombinant vesicular stomatitis Indiana viruses, expressing filovirus glycoproteins in place of wild type G (rVSV-G, rVSV-EBOV, and rVSV-MARV) have been previously described (Wong et al., 2010; Miller et al., 2012) . Viruses were selected to represent a broad range of anticipated antiviral responses from host cells, based on a range of past evolutionary histories between the virus glycoprotein mediating cell entry and the host cell's entry receptor. These interactions ranged from the total absence of evolutionary history in the case of rVSV-G infections on all cell lines to a known receptor-level cell entry incompatibility in the case of rVSV-MARV infections on PaKiT01 cell lines. To measure infectivities of rVSVs on each of the cell lines outlined above, so as to calculate the correct viral dose for each MOI, NH 4 Cl (20 mM) was added to infected cell cultures at 1-2 hr postinfection to block viral spread, and individual eGFP-positive cells were manually counted at 12-14 hr post-infection. Previously published work indicates that immortalized kidney cell lines of Rousettus aegyptiacus (RoNi/7.1) and Pteropus alecto (PaKiT01) exhibit different innate antiviral immune phenotypes through, respectively, induced (Biesold et al., 2011; Pavlovich et al., 2018; Kühl et al., 2011; Arnold et al., 2018) and constitutive (Zhou et al., 2016 ) expression of type I interferon genes. We verified these published phenotypes on our own cell lines infected with rVSV-G, rVSV-EBOV, and rVSV-MARV via qPCR of IFN-a and IFN-b genes across a longitudinal time series of infection. Specifically, we carried out multiple time series of infection of each cell line with each of the viruses described above, under mock infection conditions and at MOIs of 0.0001 and 0.001-with the exception of rVSV-MARV on PaKiT01 cell lines, for which infection was only performed at MOI = 0.0001 due to limited viral stocks and the extremely low infectivity of this virus on this cell line (thus requiring high viral loads for initial infection). All experiments were run in duplicate on 6well plates, such that a typical plate for any of the three viruses had two control (mock) wells, two MOI = 0.0001 wells and two MOI = 0.001 wells, excepting PaKiT01 plates, which had two control and four MOI = 0.0001 wells at a given time. We justify this PaKiT01 exemption through the expectation that IFN-a expression is constitutive for these cells, and by the assumption that any expression exhibited at the lower MOI should also be present at the higher MOI. For these gene expression time series, four 6-well plates for each cell line-virus combination were incubated with virus for one hour at 37˚C. Following incubation, virus was aspirated off, and cell monolayers were washed in PBS, then covered with an agar plaque assay overlay to mimic conditions under which infection trials were run. Plates were then harvested sequentially at timepoints of roughly 5, 10, 15, and 20 hr post-infection (exact timing varied as multiple trials were running simultaneously). Upon harvest of each plate, agar overlay was removed, and virus was lysed and RNA extracted from cells using the Zymo Quick RNA Mini Prep kit, according to the manufacturer's instructions and including the step for cellular DNA digestion. Post-extraction, RNA quality was verified via nanodrop, and RNA was converted to cDNA using the Invitrogen Superscript III cDNA synthesis kit, according to the manufacturer's instructions. cDNA was then stored at 4˚C and as a frozen stock at À20˚C to await qPCR. We undertook qPCR of cDNA to assess expression of the type I interferon genes, IFN-a and IFNb, and the housekeeping gene, b-Actin, using primers previously reported in the literature (Supplementary file 6) . For qPCR, 2 ml of each cDNA sample was incubated with 7 ml of deionized water, 1 ml of 5 UM forward/reverse primer mix and 10 ml of iTaq Universal SYBR Green, then cycled on a QuantStudio3 Real-Time PCR machine under the following conditions: initial denaturation at 94 C for 2 min followed by 40 cycles of: denaturation at 95˚C (5 s), annealing at 58˚C (15 s), and extension at 72˚C (10 s). We report simple d-Ct values for each run, with raw Ct of the target gene of interest (IFN-a or IFN-b) subtracted from raw Ct of the b-Actin housekeeping gene in Figure 1 -figure supplement 6. Calculation of fold change upon viral infection in comparison to mock using the d-d-Ct method (Livak and Schmittgen, 2001) was inappropriate in this case, as we wished to demonstrate constitutive expression of IFN-a in PaKiT01 cells, whereby data from mock cells was identical to that produced from infected cells. After being grown to~90% confluency, cells were incubated with pelleted rVSVs expressing eGFP (rVSV-G, rVSV-EBOV, rVSV-MARV). Cell lines were challenged with both a low (0.0001) and high (0.001) multiplicity of infection (MOI) for each virus. In a cell monolayer infected at a given MOI (m), the proportion of cells (P), infected by k viral particles can be described by the Poisson distribution: P k ð Þ ¼ e Àm m k k! , such that the number of initially infected cells in an experiment equals: 1 À e Àm . We assumed that a~90% confluent culture at each trial's origin was comprised of~9x10 5 cells and conducted all experiments at MOIs of 0.0001 and 0.001, meaning that we began each trial by introducing virus to, respectively,~81 or 810 cells, representing the state variable 'E' in our theoretical model. Low MOIs were selected to best approximate the dynamics of mean field infection and limit artifacts of spatial structuring, such as premature epidemic extinction when growing plaques collide with plate walls in cell culture. Six-well plates were prepared with each infection in duplicate or triplicate, such that a control well (no virus) and 2-3 wells each at MOI 0.001 and 0.0001 were incubated simultaneously on the same plate. In total, we ran between 18 and 39 trials at each cell-virus-MOI combination, excepting r-VSV-MARV infections on PaKiT01 cells at MOI = 0.001, for which we ran only eight trials due to the low infectivity of this virus on this cell line, which required high viral loads for initial infection. Cells were incubated with virus for one hour at 37˚C. Following incubation, virus was aspirated off, and cell monolayers were washed in PBS, then covered with a molten viscous overlay (50% 2X MEM/Lglutamine; 5% FBS; 3% HEPES; 42% agarose), cooled for 20 min, and re-incubated in their original humidified 37˚C, 5% CO 2 environment. After application of the overlay, plates were monitored periodically using an inverted fluorescence microscope until the first signs of GFP expression were witnessed (~6-9.5 hr post-infection, depending on the cell line and virus under investigation). From that time forward, a square subset of the center of each well (comprised of either 64-or 36-subframes and corresponding to roughly 60% and 40% of the entire well space) was imaged periodically, using a CellInsight CX5 High Content Screening (HCS) Platform with a 4X air objective (ThermoFisher, Inc, Waltham, MA). Microscope settings were held standard across all trials, with exposure time fixed at 0.0006 s for each image. One color channel was imaged, such that images produced show GFP-expressing cells in white and non-GFP-expressing cells in black (Figure 1-figure supplement 1) . Wells were photographed in rotation, as frequently as possible, from the onset of GFP expression until the time that the majority of cells in the well were surmised to be dead, GFP expression could no longer be detected, or early termination was desired to permit Hoechst staining. In the case of PaKiT01 cells infected with rVSV-EBOV, where an apparently persistent infection established, the assay was terminated after 200+ hours (8+ days) of continuous observation. Upon termination of all trials, cells were fixed in formaldehyde (4% for 15 min), incubated with Hoechst stain (0.0005% for 15 min) (ThermoFisher, Inc, Waltham, MA), then imaged at 4X on the CellInsight CX5 High Content Screening (HCS) Platform. The machine was allowed to find optimal focus for each Hoechst stain image. One color channel was permitted such that images produced showed live nuclei in white and dead cells in black. Hoechst stain colors cellular DNA, and viral infection is thought to interfere with the clarity of the stain (Dembowski and DeLuca, 2015) . As such, infection termination, cell fixation, and Hoechst staining enables generation of a rough time series of uninfectious live cells (i.e. susceptible + antiviral cells) to complement the images which produced time series of proportions infectious. Due to uncertainty over the exact epidemic state of Hoechst-stained cells (i.e. exposed but not yet infectious cells may still stain), we elected to fit our models only to the infectious time series derived from GFPexpressing images and used Hoechst stain images as a post hoc visual check on our fit only ( Figure 5 ; Figure 5 -figure supplements 1-2). Images recovered from the time series above were processed into binary ('infectious' vs. 'non-infectious' or, for Hoechst-stained images, 'live' vs. 'dead') form using the EBImage package (Pau et al., 2010) in R version 3.6 for MacIntosh, after methods further detailed in Supplementary file 7. Binary images were then further processed into time series of infectious or, for Hoechst-stained images, live cells using a series of cell counting scripts. Because of logistical constraints (i.e. many plates of simultaneously running infection trials and only one available imaging microscope), the time course of imaging across the duration of each trial was quite variable. As such, we fitted a series of statistical models to our processed image data to reconstruct reliable values of the infectious proportion of each well per hour for each distinct trial in all cell line-virus-MOI combinations (Figure 1 To derive the expression for R 0 , the basic pathogen reproductive number in vitro, we used Next Generation Matrix (NGM) techniques (Diekmann et al., 1990; Heffernan et al., 2005) , employing Wolfram Mathematica (version 11.2) as an analytical tool. R 0 describes the number of new infections generated by an existing infection in a completely susceptible host population; a pathogen will invade a population when R 0 >1 (Supplementary file 2). We then analyzed stability properties of the system, exploring dynamics across a range of parameter spaces, using MatCont (version 2.2) (Dhooge et al., 2008) for Matlab (version R2018a) (Supplementary file 3). The birth rate, b, and natural mortality rate, m, balance to yield a population-level growth rate, such that it is impossible to estimate both b and m simultaneously from total population size data alone. As such, we fixed b at. 025 and estimated m by fitting an infection-absent version of our mean field model to the susceptible time series derived via Hoechst staining of control wells for each of the three cell lines (Figure 1-figure supplement 7) . This yielded a natural mortality rate, m, corresponding to a lifespan of approximately 121, 191, and 84 hours, respectively, for Vero, RoNi/7.1, and PaKiT01 cell lines (Figure 1-figure supplement 7) . We then fixed the virus incubation rate, s, as the inverse of the shortest observed duration of time from initial infection to the observation of the first infectious cells via fluorescent microscope for all nine cell line -virus combinations (ranging 6 to 9.5 hours). We fixed a, the infection-induced mortality rate, at 1/6, an accepted standard for general viral kinetics (Howat et al., 2006) , and held c, the rate of antiviral cell regression to susceptible status, at 0 for the timespan (<200 hours) of the experimental cell line infection trials. We estimated cell line-virus-MOI-specific values for b, r, and " by fitting the deterministic output of infectious proportions in our mean field model to the full suite of statistical outputs of all trials for each infected cell culture time series (Figure 1-figure supplements 2-3) . Fitting was performed by minimizing the sum of squared differences between the deterministic model output and cell linevirus-MOI-specific infectious proportion of the data at each timestep. We optimized parameters for MOI = 0.001 and 0.0001 simultaneously to leverage statistical power across the two datasets, estimating a different transmission rate, b, for trials run at each infectious dose but, where applicable, estimating the same rates of r and " across the two time series. We used the differential equation solver lsoda() in the R package deSolve (Soetaert et al., 2010) to obtain numerical solutions for the mean field model and carried out minimization using the 'Nelder-Mead' algorithm of the optim() function in base R. All model fits were conducted using consistent starting guesses for the parameters, b (b = 3), and where applicable, r (r = 0.001) and " (" = 0.001). In the case of failed fits or indefinite hessians, we generated a series of random guesses around the starting conditions and continued estimation until successful fits were achieved. All eighteen cell line-virus-MOI combinations of data were fit by an immune absent (" = r = 0) version of the theoretical model and, subsequently, an induced immunity (" = 0; r >0) and constitutive immunity (" >0; r >0) version of the model. Finally, we compared fits across each cell line-virus-MOI combination via AIC. In calculating AIC, the number of fitted parameters in each model (k) varied across the immune phenotypes, with one parameter (b) estimated for absent immune assumptions, two (b and r) for induced immune assumptions, and three (b, r, and ") for constitutive immune assumptions. The sample size (n) corresponded to the number of discrete time steps across all empirical infectious trials to which the model was fitted for each cell-line virus combination. All fitting and model comparison scripts are freely available for download at the following FigShare repository: DOI: 10.6084/m9.figshare.8312807. Finally, we verified all mean field fits in a spatial context, in order to more thoroughly elucidate the role of antiviral cells in each time series. We constructed our spatial model in C++ implemented in R using the packages Rcpp and RcppArmadillo (Eddelbuettel and Francois, 2011; Eddelbuettel and Sanderson, 2017) . Following Nagai and Honda (2001) and Howat et al. (2006) , we modeled this system on a two-dimensional hexagonal lattice, using a ten-minute epidemic timestep for cell state transitions. At the initialization of each simulation, we randomly assigned a duration of natural lifespan, incubation period, infectivity period, and time from antiviral to susceptible status to all cells in a theoretical monolayer. Parameter durations were drawn from a normal distribution centered at the inverse of the respective fixed rates of m, s, a, and c, as reported with our mean field model. Transitions involving the induced (r) and constitutive (") rates of antiviral acquisition were governed probabilistically and adjusted dynamically at each timestep based on the global environment. As such, we fixed these parameters at the same values estimated in the mean field model, and multiplied both r and " by the global proportion of, respectively, exposed and susceptible cells at a given timestep. In contrast to antiviral acquisition rates, transitions involving the birth rate (b) and the transmission rate (b) occurred probabilistically based on each cell's local environment. The birth rate, b, was multiplied by the proportion of susceptible cells within a six-neighbor circumference of a focal dead cell, while b was multiplied by the proportion of infectious cells within a thirty-six neighbor vicinity of a focal susceptible cell, thus allowing viral transmission to extend beyond the immediate nearestneighbor boundaries of an infectious cell. To compensate for higher thresholds to cellular persistence and virus invasion which occur under local spatial conditions (Webb et al., 2007) , we increased the birth rate, b, and the cell-to-cell transmission rate, b, respectively, to six and ten times the values used in the mean field model (Supplementary file 4) . We derived these increases based on the assumption that births took place exclusively based on pairwise nearest-neighbor interactions (the six immediately adjacent cells to a focal dead cell), while viral transmission was locally concentrated but included a small (7.5%) global contribution, representing the thirty-six cell surrounding vicinity of a focal susceptible. We justify these increases and derive their origins further in Supplementary file 5. We simulated ten stochastic spatial time series for all cell-virus combinations under all three immune assumptions at a population size of 10,000 cells and compared model output with data in . Transparent reporting form Data availability All data generated or analysed during this study are included in the manuscript and supporting files. All images and code used in this study have been made available for download at the following Figshare

What difference bats demonstrate compared to most non-Chiropteran mammals?

Improved Pharmacological and Structural Properties of HIV Fusion Inhibitor AP3 over Enfuvirtide: Highlighting Advantages of Artificial Peptide Strategy https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4541410/ SHA: f2fcc16391f946c99717b63ec9a24e5384aac381 Authors: Zhu, Xiaojie; Zhu, Yun; Ye, Sheng; Wang, Qian; Xu, Wei; Su, Shan; Sun, Zhiwu; Yu, Fei; Liu, Qi; Wang, Chao; Zhang, Tianhong; Zhang, Zhenqing; Zhang, Xiaoyan; Xu, Jianqing; Du, Lanying; Liu, Keliang; Lu, Lu; Zhang, Rongguang; Jiang, Shibo Date: 2015-08-19 DOI: 10.1038/srep13028 License: cc-by Abstract: Enfuvirtide (T20), is the first HIV fusion inhibitor approved for treatment of HIV/AIDS patients who fail to respond to the current antiretroviral drugs. However, its clinical application is limited because of short half-life, drug resistance and cross-reactivity with the preexisting antibodies in HIV-infected patients. Using an artificial peptide strategy, we designed a peptide with non-native protein sequence, AP3, which exhibited potent antiviral activity against a broad spectrum of HIV-1 strains, including those resistant to T20, and had remarkably longer in vivo half-life than T20. While the preexisting antibodies in HIV-infected patients significantly suppressed T20’s antiviral activity, these antibodies neither recognized AP3, nor attenuated its anti-HIV-1 activity. Structurally different from T20, AP3 could fold into single-helix and interact with gp41 NHR. The two residues, Met and Thr, at the N-terminus of AP3 form a hook-like structure to stabilize interaction between AP3 and NHR helices. Therefore, AP3 has potential for further development as a new HIV fusion inhibitor with improved antiviral efficacy, resistance profile and pharmacological properties over enfuvirtide. Meanwhile, this study highlighted the advantages of artificially designed peptides, and confirmed that this strategy could be used in developing artificial peptide-based viral fusion inhibitors against HIV and other enveloped viruses. Text: The sequences of gp41 NHR-or CHR-derived peptides. The residues corresponding to the NHR pocket region are marked in red. The residues for the PBD are marked in blue, and the MT-hook residues adjacent to the N terminus of PBD are marked in green. 5HRu peptide consists of 5 copies of artificial sequence template (AEELAKK) underlined. The mutant residues in PBD of AP2 and AP3 were highlighted in pink. (b) The inhibitory activity of AP1, AP2, AP3 and T20 on infection by HIV-1 IIIB (subtype B, X4) in MT-2 cells (left panel) by HIV-1 Bal (subtype B, R5) in M7 cells (right panel). Each sample was tested in triplicate and the experiment was repeated twice. The data are presented as means ± SD. Scientific RepoRts | 5:13028 | DOi: 10 .1038/srep13028 To address these obstacles, many efforts have been made to optimize T20 and gp41 CHR-derived peptides. Some of these peptides have better inhibitory activities against T20-resistant strains and/or longer half-life than T20. However, they still have the problem to cross-react with the preexisting antibodies in the sera of HIV-infected patients because they contain some native CHR sequences. Based on the universal artificial peptide template of 5HRu, we previously designed the artificial peptides of AP1 (PBD-m4HR) and AP2 (PBDtrp-m4HR), and have made preliminary research on their inhibitory activity against HIV-1 Env-mediated cell-cell fusion 16 . In the present study, we designed a new artificial peptide, AP3 (Fig. 1a) , aiming to apply the "M-T hook" structure to stabilize the interaction of the artificial peptide with the hydrophobic pocket on the gp41 NHR trimer 17, 18 . After comprehensively studying its antiviral activity, biochemical property, crystal structure, functional mechanism, in vivo half-life and, for the first time, the effect of preexisting antibodies in the sera of HIV-infected patients, we found that the newly designed artificial peptide, AP3, exhibited improved antiviral activity, drug resistance profile and pharmacological properties over T20. Particularly, the preexisting antibodies in the sera of HIV-infected patients did not suppress, but enhanced the anti-HIV-1 activity of AP3. These results suggest that AP3 has potential for development as a new anti-HIV drug and confirm that this strategy can be used for designing artificial antiviral peptides against other enveloped viruses, such as SARS-CoV 19 , MERS-CoV 20 , and paramyxovirus 21 . AP3 inhibited HIV-1 infection with higher potency than T20. Our previously designed artificial peptides AP1 and AP2 could inhibit HIV-1 Env-mediated cell-cell membrane fusion 16 . He and colleagues reported that adding two amino acids of Met and Thr to the N-terminus of a CHR-peptide could enhance their anti-HIV-1 activity 17, 18 . Here we designed a new artificial peptide, AP3, by adding Met and Thr to the N-terminus of AP2 (Fig. 1a) . We then compared AP3 with AP1, AP2 and T20 for their anti-HIV-1 activity against divergent HIV-1 strains, including the laboratory-adapted viruses, IIIB (subtype B, X4) and Bal (subtype B, R5), and a series of primary HIV-1 isolates, as well as the T20-resistant strains. As shown in Fig. 1b , AP3 exhibited higher inhibitory activities on infection by HIV-1 IIIB and HIV-1 Bal strains (IC 50 : 3.06 and 15.09 nM, respectively) than AP1 (IC 50 : 86.25 and 396.14 nM, respectively), AP2 (IC 50 : 23.05 and 49.95 nM, respectively), and T20 (IC 50 : 13.63 and 30.21 nM, respectively). The inhibitory activity of AP3 on infection by divergent primary HIV-1 isolates with distinct genotypes (subtypes A -E and group O) and phenotypes (R5 and X4) was also higher than that of AP2 and T20 (Table 1) . While T20 was not effective against T20-resistant HIV-1 strains at the concentration as high as 2,000 nM, AP3 could effectively inhibit infection of these strains with IC 50 in the range of 13 ~ 90 nM, which was about 2-to 4-fold more effective than AP2 (Table 1 ). These results indicate that the artificial peptide AP3 has remarkably improved anti-HIV-1 activity against a broad spectrum of HIV-1 strains, including T20resistant variants, over T20 and the artificial peptides AP1 and AP2. The preexisting antibodies in HIV-1-infected patients neither recognized AP3, nor attenuated its anti-HIV-1 activity. Previous studies have shown that the preexisting antibodies in HIV-1-infected patients, including those cross-reacting with T20 and those specific for the binding sites of T20 in gp120 (e.g., the C1 and V3 loop regions) and gp41 (e.g., the NHR domain), could significantly block the fusion inhibitory activity of T20 14, 15 . Here we investigated the influence of preexisting antibodies against AP3 peptide. As shown in Fig. 2a , both T20 and C46 reacted with the antibodies in sera from five HIV-1-infected patients; however, none of the three artificial peptides AP1, AP2 and AP3 was recognized by the preexisting antibodies. The inhibitory activity of T20 on HIV-1 IIIB infection was reduced about 1.9-fold to > 3.6-fold in the presence of the sera from HIV-1-infected patients ( Fig. 2b and Supplementary Table S1), confirming that the preexisting antibodies in sera of HIV/AIDS patients can attenuate the anti-HIV-1 activity of T20 14, 15 . However, none of the artificial peptides in the present study showed significant decrease of anti-HIV-1 activity in the presence of patients' sera. Instead, the antiviral activity of AP3 increased in the presence of antisera from HIV-1-infected patients ( Fig. 2b and Supplementary Table S1), suggesting that anti-HIV-1 antibodies actually enhanced the anti-HIV-1 activity of AP3, possibly because the binding of the antibodies to some sites in gp120 or gp41 promote the interaction of AP3 with viral gp41 NHR region. AP3 had longer half-life than T20. Although T20 has shown efficacy in inhibiting HIV-1 infection, its major weakness lies in its short half-life in plasma (about 2 h) [22] [23] [24] . As a result, T20 has to be administered subcutaneously twice daily at 90 mg per dose, often causing serious injection-site reactions 25, 26 . Here, we performed pharmacokinetic studies by intravenous administration of AP3, AP2, and T20, respectively, to SD rat at a dose of 1 mg/kg, in order to compare their in vivo circulation time. As expected, T20 exhibited a shorter half-life and lower AUC (0-t) from systemic circulation, while AP3 and AP2 demonstrated much higher concentration and longer circulation time ( Table 2 ). The pharmacokinetic profiles of AP3 and AP2 fit a non-compartment model. The pharmacokinetic parameters were calculated with PK Solver. The in vivo elimination half-life of AP3 (t 1/2 = 6.02 h) was about 2.8-fold longer than that of T20 (t 1/2 = 1.57 h). This result provided the theoretical basis for reducing the injection frequency and dose of the fusion inhibitor, in conjugation with the improved antiviral potency of AP3. Therefore, replacement of T20 with AP3 may significantly reduce injection-site reactions and the drug cost, which would promote the clinical applications of the HIV fusion inhibitor in resource-poor regions or countries. AP3 was much more resistant than T20 to proteolytic degradation by proteinase K and rat liver homogenate. We compared the stability of T20 and AP3 in the presence of proteinase K (a broad-spectrum serine proteinase) and rat liver homogenate. After treatment with 20 ng/mL of proteinase K for 2 h at 37 °C, only 29% of the parental T20 peptide remained, as detected by LC-MS analysis. Under the same condition, AP3 retained 100% of its prototype (Fig. 3a ). In addition, AP3 showed a significantly enhanced in vitro metabolic stability over T20 in the presence of liver homogenate (Fig. 3b) . These results indicate that the artificial peptide AP3 is much more resistant to proteolytic degradation than the natural peptide T20, which may contribute to its significant longer in vivo half-life than T20 as described above. AP3 formed stable α-helical complex and block gp41 6-HB formation. To investigate the antiviral mechanism of AP3, the thermal stability of AP3/N36 complex was compared with that of AP1/N36, AP2/N36, T20/N36, and C34/N36 complexes by circular-dichroism (CD) spectroscopy 27 . Because T20 lacks the pocket-binding domain (PBD), the T20/N36 complex did not show a typical α -helical conformation, in consistence with our previous studies 8, 9 . Similar to the α -helicity of C34/N36 complex 3 , the AP1/N36, AP2/N36 and AP3/N36 complexes all formed a saddle-shaped negative peak at 208 nm and 222 nm, indicating their α -helical structures (Fig. 4a) Fig. 4b) , indicating that the α -helical complex formed by AP3 and N36 is the most stable among the four complexes. Then we compared the inhibitory activity of AP3 with that of AP1 and AP2 on 6-HB formation between C34 and N36. Since T20 cannot block 6-HB formation 8, 9 , we used a small-molecule HIV-1 fusion inhibitor, ADS-J1 28, 29 , to replace T20 as a control of 6-HB inhibition. As expected, ADS-J1 could effectively inhibit 6-HB formation with IC 50 of 2.75 μ M 8, 9, [27] [28] [29] . AP3 was highly effective against 6-HB formation in a dose-dependent manner with an IC 50 value of 0.24 μ M, about 30-and 15-fold more potent than AP1 and AP2, respectively (Fig. 4c) , confirming that AP3 can potently block gp41 6-HB fusion core formation, thus inhibiting HIV-1 fusion with the target cell membrane. Structural basis for the potent fusion inhibitory activity of the artificial peptide AP3. To elucidate the molecular determinants of these artificial peptides, we successfully solved all three complex structures of AP1/AP2/AP3 peptides binding with gp41 NHR. For AP1 and AP2, an optimized linker Each sample was tested in triplicate and the experiment was repeated twice. The data are presented as means ± SD. *P < 0.05, **P < 0.01, ***P < 0.001. "SGGRGG" was used to assemble the NHR and the artificial peptide into a single recombinant protein (N36-L6-AP1 or N36-L6-AP2). However, a similar strategy failed on the crystallization of AP3; therefore, we decided to cocrystallize the synthetic peptide N45 and AP3 peptide, and eventually the complex crystals were obtained. Interestingly, the crystals of three different inhibitors belong to three distinctive space groups: P2 1 for N36-L6-AP1, R32 for N36-L6-AP2, and P6 3 for N45/AP3. As expected, the NHR portions in three structures all form a trimeric core, while the AP1, AP2 or AP3 portion folds into a single-helix conformation and binds to NHR-trimer to form a typical 6-HB, similar to that of the HIV-1 gp41 core structure formed by the native CHR peptide C34 and N36 (Fig. 5a) . Also, the conserved hydrophobic residues, such as W43, W46 and I50, in the artificial peptides were deeply buried into the hydrophobic Table 2 . Pharmacokinetic parameters of AP2, AP3 and T20 following intravenous administration at 1 mg/kg in male SD rats (n = 2). Figure 3 . Sensitivity of AP3 and T20 to proteolytic degradation by proteinase K and rat liver homogenate. (a) After digestion by proteinase K at pH 7.2 and (b) rat liver homogenate, the residual amount of AP3 and T20 was detected by LC-MS analysis. The experiment was performed in triplicate and the data are presented as means ± SD. The inhibition of AP1, AP2, AP3, T20 and ADS-J1 against 6-HB formation between N36 and C34 was detected by ELISA using the 6-HB-specific mAb NC-1. Each sample was tested in triplicate, and the data are presented as means ± SD. grooves formed between each pair of NHR helices, similar to the corresponding residues of W628, W631 and I635 in the native gp41 CHR (Fig. 5b) . AP peptides exhibited better affinity against gp41 natural CHR. In C34, which contains the natural CHR sequence from W628 to L661, no strong interaction between I642 and Q565 in the viral gp41 NHR-CHR complex was found (Fig. 5c) . However, in the corresponding sequence (from W43 to K76) of AP1 and AP2, a hydrogen bond was established between S57 (corresponding to I642 in CHR) and Q18 (corresponding to Q565 in NHR) in N36-L6-AP1, N36-L6-AP2 and N45/AP3. Thus, S57 in AP1/AP2/AP3 plays a role in stabilizing the interactions between the artificial peptide inhibitor and its NHR target, resulting in their stronger binding affinity. Moreover, in NHR-CHR, L567 and L568 on two adjacent NHRs form a hydrophobic groove, in which T639 is buried (Supplementary Fig. S1a ). However, in N36-L6-AP1, N36-L6-AP2 and N45/AP3, I54 (corresponding to T639 in CHR) can strongly bind to L20 and L21 through fully hydrophobic side chain interactions. Similarly, the interaction of I64 (corresponding to S659 in CHR) with L10 and L11 (corresponding to L567 and L568 in NHR, respectively) in N36-L6-AP1, N36-L6-AP2 and N45/AP3 has been significantly enhanced ( Supplementary Fig. S1b ). Like the gp41 CHR helix, the helices of AP1, AP2 and AP3 also have two different sides, a hydrophobic side facing toward the NHR and a hydrophilic one facing outward. It is expected that the enhancement of the hydrophilicity of the exposed side of the inhibitors can increase their antiviral activity and solubility. To achieve this goal, the amino acid residues with hydrophobicity, or low hydrophilicity, like N637, S640, L641 and S644 in CHR, were changed to the amino acid residues with high hydrophilicity, like E52, K55, K56 and E59 in AP1, AP2 and AP3, respectively. Moreover, the hydrophobic residue M629 in CHR was replaced with a hydrophilic residue E44 in AP2 and AP3 ( Supplementary Fig. S2 ). These hydrophilic residues, such as glutamic acid and lysine, can increase the solubility of whole peptide and, hence, stabilize the complex formed by the inhibitor and its target. It has been proved that the EE-KK double salt bridge can stabilize helix conformation 30 . We have identified this kind of interaction between i and i + 3 or i + 4 positions on the three complex structures. In N36-L6-AP1, R48 interacts with E45 and E52 to form a salt bridge network. In N36-L6-AP2, E45 interacts with K48, and E52 binds to K56, while in N45/AP3, K69 binds to E66 ( Supplementary Fig. S2 ). These strong salt bridges formed by the oppositely charged residues stabilize AP peptide conformation, bringing its inhibitory effect into full play. As previously reported, addition of the "M-T hook" to the CHR peptides C34 and sifuvertide could dramatically improve the anti-HIV-1 activity 17, 18 . As expected, the N-terminal Met and Thr of AP3 forms a hook-like structure (Fig. 5d) . The hydrophobic methionine side chain of M41 accommodates the groove between AP3 and NHR helices, capping the hydrophobic pocket. This interaction leads to a series of conformational changes. The main chain of AP3 at W43 moves 1.91 Å closer to NHR compared to AP2 (Supplementary Fig. S3 ). The side chain of W43 in AP3 flips around 90 degrees and is buried deeper than that of AP2. The side chain of E44 turns back to interact to D47, but the E45 side chain turns back from K48 and interacts with T42. Therefore, this M-T hook structure could further stabilize the binding between AP3 and NHR target. Enfuvirtide, also known as T20, was approved by the U.S. FDA as the first HIV entry inhibitor-based antiviral drug for use with other anti-HIV medicines to treat HIV-1 infected adults and children at ages 6-16 years 23,31,32 (http://www.fuzeon.com). Although T20 is an indispensable anti-HIV drug for HIV/ AIDS patients who have failed to respond to the current antiretroviral therapeutics, its shortcomings have limited its clinical application. T20 has lower anti-HIV activity and shorter half-life than other CHR peptides containing PBD, such as C34 and C38 8, 9, 33 . In addition, T20-resistant HIV-1 variants emerged shortly (e.g., 14 days) after its use in patients 34 . Most of the T20-resistant viruses carried mutations in the GIV motif (residues 36-45: GIVQQQNNLL) in the gp41 NHR domain 10, [34] [35] [36] [37] [38] . The lack of PBD contributes to the major weaknesses of T20 described above. Since the conserved hydrophobic pocket in the gp41 NHR-trimer plays a critical role in stabilizing the interaction between the gp41 NHR and CHR and formation of the fusogenic 6-HB core 1, 39, 40 , the PBD-containing CHR-peptide, like C34, can bind to viral gp41 trimer more strongly and stably, thus possessing more potent anti-HIV activity than T20, a CHR peptide without PBD 8, 9 . In the absence of PBD, T20 mainly interacts with the middle region of the NHR domain containing the GIV motif. Therefore, a virus with mutations in this motif is generally resistant to T20 10, [34] [35] [36] [37] [38] . Compared with other anti-HIV drugs, another weakness of T20 is its cross-reactivity with the preexisting antibodies in HIV-1-infected patients. Besides gp41, T20 could also bind to some regions in gp120. The preexisting antibodies specific for the T20's binding sites in gp120 and gp41 may indirectly suppress the anti-HIV activity of T20 14, 15 . Addition of PBD to the N-terminus of T20, such as T-1249, could significantly improve the anti-HIV-1 potency, half-life and drug-resistance profile 33, [41] [42] [43] . Addition of M-T hook structure to the N-terminus of a PBD-containing CHR-peptides, such as MT-C34 or MT-SFT, could further increase the anti-HIV-1 activity of the corresponding CHR-peptides 17, 18 . Deletion of the GIV-motif-binding domain from a CHR-peptide, such as CP621-652 and CP32M, is another effective approach to increase the genetic barrier to drug resistance 44, 45 . However, none of the above approaches is effective in preventing the cross-reaction of T20 with the preexisting anti-gp41 antibodies in HIV/AIDS patients, since the above-modified peptides mainly contain the native sequences of the HIV-1 gp41 CHR domain. Our previous studies have shown that AP1 and AP2, artificial peptides with non-native protein sequences, could form coiled-coil structure to interact with gp41 NHR and inhibit HIV-1 Env-mediated cell-cell fusion 16 . In the present study, we designed a new artificial peptide, AP3, by adding M-T hook structure to the N-terminus of AP2 (Fig. 1a) , followed by investigating the influence of preexisting anti-gp41 antibodies in HIV-infected patients on AP3, using AP1, AP2 and T20 as controls. We demonstrated that sera of HIV-infected patients could bind to T20 and significantly reduce its potency against HIV-1. However, these same serum samples did not interact with the three artificial peptides and hardly impaired their antiviral activity. Surprisingly, the antibodies in the sera could even enhance AP3's anti-HIV-1 activity (Fig. 2a,b and Supplementary Table S1 ). These results confirmed, for the first time, that replacement of the native viral sequence in T20 with an artificial sequence is an effective approach to overcome a key shortcoming of T20 whereby its anti-HIV activity could be attenuated by preexisting anti-gp41 antibodies in HIV/AIDS patients. It is worthwhile to explore why the antibodies in the sera is able to enhance the anti-HIV-1 activity of AP3. Our recent study has demonstrated that T20's anti-HIV-1 activity is enhanced by a non-neutralizing antibody directed against the NHR domain of the HIV-1 gp41 46 . We thus hypothesize that some of the anti-gp41 antibodies in HIV/AIDS patients may bind to a site in NHR domain adjacent to the AP3's binding region, resulting in increased interaction between AP3 and NHR-trimer and enhanced antiviral activity of AP3. We then compared the inhibitory activity of AP3 with M-T hook and T20/AP2 without M-T hook on infection by divergent HIV-1 strains. AP3 was more effective than either AP2 or T20 in inhibiting infection by the laboratory-adapted strains and the primary isolates of HIV-1, including those resistant to T20 (Fig. 1b, Table 1 ). One may question whether AP3 can also induce drug-resistant viruses in patients if it is used in clinics to treat HIV-infected patients. We believe that AP3 is expected to have much higher genetic barrier to resistance than T20 because AP3 contains PBD, while T20 lacks PBD. Dwyer et al. 33 used T2544, a PBD-containing CHR-peptide, to carry out a passaging experiment, using T20 as a control. They demonstrated that T20 could induce a mutant virus with high resistance (81-fold) to T20 in about 1 month, while T2544 failed to induce a resistant strain in more than 2 months in culture. After extending the passaging experiment for almost 8 months, they identified one strain with a weak resistance (8.3-fold) to T-2544, and the related mutation sites were not in the gp41 pocket region, suggesting that the PBD-containing CHR-peptides, including AP3, may have difficulty to induce drug-resistance. AP3 also had longer half-life than T20 (Table 2) , possibly because the artificial peptide AP3 is less sensitive to the proteolytic enzymes than T20 with native viral protein sequence. Removal of the proteolytic enzymes' cleavage sites in AP3 peptide is expected to further extend its half-life. These results confirmed that replacement of native protein sequence with artificial sequence and addition of the M-T hook to the PBD-containing peptide is a sound strategy for designing HIV fusion inhibitory peptides with improved antiviral activity and pharmacological properties when compared to T20. Since the three-dimensional structures of AP peptides had not been investigated before the present study, the optimization of these artificial peptide inhibitors could not be performed rationally. Our structural studies of the artificial peptides AP1/AP2/AP3 in complex with NHR showed that AP peptides, just like the CHR peptide C34, could bind to gp41 NHR to form a canonical 6-HB structure (Fig. 5a) . It is well known that a deep hydrophobic pocket exists in each groove on the surface of the viral gp41 NHR trimer. The hydrophobic residues I635, W631 and W628 in the gp41 CHR bind with the hydrophobic residues in the wall of this pocket, resulting in the formation of stable 6-HB by the strong interaction between CHR and NHR. This important feature has been well preserved in the AP1/AP2/AP3 6-HB structures (Fig. 5b) , which may account for the potent HIV-1 fusion inhibitory activities of these artificial peptides. A new hydrogen bond, which was established between S57 and Q18 in AP1/AP2/AP3 complexes, does not exist in the viral gp41 CHR-NHR complex, suggesting that S57 may play an important role in stabilizing the interactions between the peptide and NHR, resulting in binding affinities of AP1/AP2/AP3 that are stronger than those of HIV-1 gp41 CHR to NHR. Furthermore, the EE-KK double salt bridge formed between the i and i + 4 positions in the AP1/AP2/AP3 structures could stabilize helix conformation and increase the inhibitory effect of these peptides. Compared with AP1, triple-site mutations were introduced in AP2 and AP3, i.e. M44E, R48K and E49K. Those substitutions not only increase solubility of the peptide, but also trigger a series of rearrangements of certain intrahelical salt bridges to improve the stability of CHR helix structure and HIV-1 fusion inhibitory activity. M-T hook was previously demonstrated to be an effective step toward increasing the stable interaction between a CHR-peptide and the HIV-1 gp41 pocket 17, 18 . Therefore, AP2 was further optimized by incorporating Met and Thr at its N-terminus. CD spectroscopy and thermal denaturation results both indicate that the incorporation of M-T hook contribute to the formation of a more stable 6-HB core structure between AP3 (M-T hook-optimized AP2) and N36. In addition, the EE-KK double salt bridge formed between i and i + 4 positions in the N36-L6-AP3 structure contributed to increased CHR helix and 6-HB stability, resulting in improved potency of AP3, as has been noted in studies of CHR-peptides with EE-KK double mutations 30, 33, 47, 48 . Also, the HIV-1 fusion activity and half-life of AP2 may have been strengthened and extended, respectively, by the addition of M-T hook in the design of AP3. In conclusion, AP3, an artificial peptide with both PBD and M-T hook structures, exhibited improved anti-HIV-1 activity and drug-resistance profile, as well as prolonged half-life. Moreover, it did not react with the preexisting antibodies in the sera of HIV/AIDS patients. Consequently, its antiviral activity Scientific RepoRts | 5:13028 | DOi: 10.1038/srep13028 was not significantly affected by these antibodies. Therefore, AP3 shows promise as a candidate for further development as a new HIV fusion inhibitor for clinical use. This study also provides important structure and activity information for the rational design of novel artificially peptide inhibitors. Besides, our results highlighted the advantages of artificially designed peptides and confirmed that this strategy could be widely used in development of artificial peptide-based virus fusion inhibitors against HIV-1 and other enveloped viruses with class I membrane fusion proteins, such as SARS-CoV 19 , MERS-CoV 20 , and paramyxovirus 49 . Ethics statement. This study did not involve human experimentation; the only human materials used were serum samples obtained from HIV-1-infected individuals with the approval by the Ethics Committee of the Shanghai Public Health Clinical Center, Fudan University (Protocol No. SPHCC-125-2). The methods were carried out in accordance with the approved guidelines. All of these sera samples came from adults; no minor was involved in this study. Written informed consent for the use of the clinical specimens was obtained from all patients involved in this study. Peptide synthesis. A panel of peptides (Fig. 1a) , including T20, C34, C46, AP1, AP2, AP3, as well as NHR-derived N-peptides, N36 and N45, were synthesized with a standard solid-phase FMOC method, as described previously 8, 50 . All peptides were acetylated at the N terminus and amidated at the C terminus. The peptides were found to be about 95% pure by HPLC and were identified by mass spectrometry (Perseptive Biosystems, Framingham, MA, USA). Concentrations of the peptides were determined by UV absorbance and a theoretically calculated molar-extinction coefficient based on tryptophan and tyrosine residues. Qualification assay. Chromatographic analyses were performed using an ODS-C8 column (5 μ m, 100 mm × 2.0 mm ID) kept at ambient temperature. The mobile phase was composed of acetonitrile-water-formic acid in the ratio of 50:50:0.1 (v/v/v) at a flow rate of 0.3 mL/min. The sample injection volume was 10 μ L. Acetonitrile was HPLC grade, and other chemical reagents and solvents were analytical grade. A Thermo TSQ Quantum Discovery MAX triple-quadruple tandem mass spectrometer equipped with ESI source (San Jose, CA) and Surveyor LC pump were used for LC-MS analysis. Data acquisition and data processing were performed by using Xcalibur software and LCQuan 2.0 data analysis program (Thermo Finnigan), respectively. Optimized MS parameters were as below: 4800 V spray voltage, 40.0 psi sheath gas pressure, 1.0 psi auxiliary valve flow, and 300 °C of capillary temperature. When running collision-induced dissociation (CID), the pressure was set to 1.5 mTorr. The selected reaction monitoring (SRM) mode was used for AP3 while the selected ion monitoring (SIM) mode was preformed for T20. The following transitions were recorded: m/z 670.5 for AP3, m/z 1498.6 for T20. The masses of synthetic peptides T20, AP1, AP2 and AP3 were determined by MALDI-TOF-MS (Supplementary Fig. S4 and S5 ). Expression and purification of fusion protein N36-L6-AP1 and N36-L6-AP2. Using overlapping PCR, the DNA fragment encoding AP1 or AP2 peptide was attached to the 3′-end of the cDNA of gp41 NHR ("N36", 546-581), with a short linker ("L6", SGGRGG) between them. Then, the whole sequence was subcloned into the pET-28a vector (Novagen, USA) with an artificial SUMO-tag between the N-terminal His-tag and the target protein. The pET-28a-SUMO-N36-L6-AP1-or pET-28a-SUMO-N36-L6-AP2-transformed E. coli cells were induced by adding 1 mM IPTG and incubating overnight at 16 °C. Fusion protein was purified by Ni-NTA affinity resin (Qiagen, Valencia, CA, USA), and the His-SUMO-tag was cleaved off by Ulp1 enzyme treatment at 4 °C for 2 h. The purified N36-L6-AP1 or N36-L6-AP2 was applied onto a Superdex-75 gel filtration column (GE Healthcare, Piscataway, NJ, USA). Fractions containing N36-L6-AP1 or N36-L6-AP2 trimer were collected and concentrated to different concentrations by ultrafiltration. Crystallization, data collection, and structure determination. The fusion protein N36-L6-AP1 was crystallized at 16 °C using the hanging drop, vapor-diffusion method. The drops were set on a siliconized cover clip by equilibrating a mixture containing 1 μ l protein solution (25 mg/ml N36-L6-AP1 trimer in 20 mM Tris-HCl pH 8.0 and 150 mM NaCl) and 1 μ l reservoir solution (0.1 M Tris-HCl pH 8.5, 32% (w/v) PEG3350, and 0.2 M MgCl 2 ) against a 400 μ l reservoir solution. After one week, single crystals formed and were flash frozen by liquid nitrogen for future data collection. Fusion protein N36-L6-AP2 was crystallized in a similar way with a different reservoir solution (0.1 M Tris-HCl pH 8.0, 34% (w/v) PEG3350, and 0.2 M MgCl 2 ). To obtain the complex crystal of AP3 and NHR, synthesized AP3 was first mixed with peptide N45 at 1:1 molar ratio and then applied onto a Superdex-75 gel filtration column (GE Healthcare, Piscataway, NJ, USA) to isolate the formed 6-HB. Fractions containing N45/AP3 trimer were collected and concentrated to 30 mg/ml, then crystallized at 16 °C using the hanging drop, vapor-diffusion method.The drops were set on a siliconized cover clip by equilibrating a mixture containing 1 μ l protein solution (20 mM Tris-HCl pH 8.0 and 150 mM NaCl) and 1 μ l reservoir solution (0.2 M Ammonium Sulfate, 0.1 M Bis-Tris pH 6.5, and 25% w/v PEG 3350) against a 400 μ l reservoir solution. After 3 days, single crystals formed and were flash frozen by liquid nitrogen for future data collection. The datasets of N36-L6-AP1 were collected at 100 K at beamline 19-ID of the Advanced Photon Source (Argonne National Laboratory, USA). The datasets of N36-L6-AP2 were collected on an in-house x-ray source (MicroMax 007 x-ray generator, Rigaku, Japan) at the Institute of Biophysics, ChineseAcademy of Sciences. The datasets of AP3/N45 complex crystals were collected at beamline BL-19U1 of the Shanghai Synchrotron Radiation Facility, China. X-ray diffraction data were integrated and scaled using the HKL2000 program 51 . The phasing problem of all three structures was solved by the molecular replacement method using PHENIX.phaser 52 with a crystal structure of HIV gp41 NHR-CHR (PDB entry: 1SZT) as a search model. The final models were manually adjusted in COOT 53 and refined with PHENIX.refine 54 . All coordinates were deposited in the Protein Data Bank (N36-L6-AP1: 5CMU; N36-L6-AP2: 5CN0; and N45/AP3: 5CMZ). The statistics of data collection and structure refinement are given in Supplementary Table S2 . Determination of the cross-reactivity of the native and artificial peptides with the preexisting antibodies in HIV-1-infected patients by sandwich ELISA. A sandwich ELISA was conducted to determine the cross-reactivity of the peptides with the preexisting antibodies in HIV-1-infected patients. T20, C46, AP1, AP2 and AP3 were coated onto the wells of 96-well polystyrene plates (Costar, Corning Inc., Corning, NY) at 10 μ g/ml. The wells were then blocked with 1% gelatin, followed by addition of 50 μ l of serially diluted sera from HIV-1-infected patients and incubation at 37 °C for 1 h. Then, HRP-labeled goat-anti-human IgG (Abcam, UK) and TMB were added sequentially. A450 was determined with an ELISA reader (Ultra 384, Tecan). patients. Inhibition of peptides on HIV-1 IIIB (subtype B, X4)infection in the presence of HIV-1-infected patients' sera was determined as previously described 55 . Briefly, each peptide was mixed with serially diluted serum from an HIV-1-infected patient at room temperature for 30 min. Next, the mixture of peptide/serum and HIV-1 (100 TCID 50 ) were added to MT-2 cells (1 × 10 5 /ml) in RPMI 1640 medium containing 10% FBS. After incubation at 37 °C overnight, the culture supernatants were replaced with fresh culture medium. On the fourth day post-infection, culture supernatants were collected for detection of p24 antigen by ELISA. CD Spectroscopy and Thermal Midpoint Analysis. The secondary structure of AP1, AP2 or AP3 peptides mixed with N36 was analyzed by CD spectroscopy as previously described 56 . Briefly, each peptide or peptide mixture was dissolved in phosphate-buffered saline (PBS: 50 mM sodium phosphate and 150 mM NaCl, pH 7.2) at the final concentration of 10 μ M and incubated at 37 °C for 30 min before cooling down to 4 °C. The CD spectra of each sample were acquired on a Jasco spectropolarimeter (Model J-815, Jasco Inc., Japan) at 4 °C using a 5 nm bandwidth, 0.1 nm resolution, 0.1 cm path length, and an average time of 5.0 sec. Spectra were corrected by the subtraction of a blank corresponding to the solvent composition of each sample. Thermal midpoint analysis was used to determine the temperature at which 50% of the 6-HB formed by the CHR and NHR would decompose. It was monitored at 222 nm from 4 °C to 98 °C by applying a thermal gradient of 5 °C/min. The melting curve was smoothed, and the midpoint of the thermal unfolding transition (Tm) values was calculated using Jasco software utilities as described above. Inhibition of gp41 six-helix bundle formation by sandwich ELISA. Inhibition of gp41 six-helix bundle formation by a testing peptide was determined with a sandwich ELISA described previously 57 . Briefly, a testing peptide (ADS-J1 as a control) at graded concentrations was preincubated with peptide N36 (1 μ M) at 37 °C for 30 min, followed by the addition of peptide C34 (1 μ M) and incubation at 37 °C for another 30 min. The mixture was added to a 96-well polystyrene plate (Costar, Corning Inc., Corning, NY) precoated with anti-N36/C34 antibodies (2 μ g/ml) purified from mouse antisera specifically against the gp41 six-helix bundle 58 . Then, mAb NC-1, HRP-labeled rabbit-anti-mouse IgG (Sigma), and TMB were added in order. A450 was determined by an ELISA reader (Ultra 384, Tecan). Inhibition activities of AP1, AP2, and AP3 on HIV-1 infection were determined as previously described 57 . For inhibition of HIV-1 IIIB (subtype B, X4) infection,100 TCID 50 of the virus was added to 1 × 10 5 /ml MT-2 cells in RPMI 1640 medium containing 10% FBS in the presence or absence of the test peptide overnight. Then, the culture supernatants were changed to fresh media. On the fourth day post-infection, culture supernatants were collected for detection of p24 antigen by ELISA. For inhibition of infection by the HIV-1 strain Bal (subtype B, R5), M7 cells (1 × 10 5 /ml) were precultured overnight and infected with Bal at 100 TCID 50 in the presence or absence of the test peptide or protein overnight. Then, the culture supernatants were changed to fresh media. On the fourth day post-infection, the culture supernatants were discarded, and fresh media were complemented again. The supernatants were collected on the seventh day post-infection and tested for p24 antigen by ELISA as previously described 55 . The percent inhibition of p24 production was calculated. Analysis of the half-life of peptide inhibitors. Four male SD rats weighing approximately 200 g each were obtained from the Shanghai Medical School Animal Center and were used for the half-life assay. Animals were treated in accordance with the Animal Welfare Act and the "Guide for the Care and Use of Laboratory Animals" (NIH Publication 86-23, revised 1985). Either AP2 or AP3 was intravenously injected at the concentration of 1 mg/ml. After injection, blood samples were acquired from rat orbit at several time points (8 and 30 min and 1.5, 3, 6, 9, 12, and 24 h after peptide injection) and placed in clean tubes. To study the pharmacokinetics of AP2 and AP3 in rats and provide experimental evidence for the possible pharmacokinetics in human, a double-antibody sandwich ELISA method was established for rapid determination of AP2 and AP3 in rat plasma. Briefly, 96-well polystyrene plates (Costar, Corning Inc., Corning, NY) were precoated with antibody against AP2 or AP3 (5 μ g/ml) purified from rabbit anti-sera 59 . They were then preincubated with serum samples diluted 20 times at 37 °C for 1 h, followed by the addition of anti-AP2 or anti-AP3 antibody (1:1000) purified from mouse antisera specifically against AP2 or AP3 59 at 37 °C for another 1 h. Then, HRP-labeled rabbit-anti-mouse IgG (Sigma, USA) and TMB were added in order. Absorbance at 450 nm was determined by an ELISA reader (Ultra 384, Tecan). The standard peptide parameters were obtained first. Then, the plasma peptide concentrations were determined as a function of time, and the half-life was calculated by using PK Solver for Microsoft Excel to obtain pharmacokinetic parameters. Assessment of sensitivity of peptides to proteolytic digestion by proteinase K and proteolytic enzymes in liver homogenate. The peptides (10 μ g/mL) were prepared in PBS pH 7.2 containing 20 ng/ml proteinase K. The resulting mixture were incubated at 37 °C in a water bath and taken out at different time intervals (0, 5, 15, 30, 60, 120 minutes), followed by quenching the samples with ethyl alcohol and quantitating the peptides by LC-MS analysis as described above. To test the sensitivity of peptides to the proteolytic enzymes in liver homogenate, 3 male SD rats (250 ± 20 g) were sacrificed under anesthesia. The whole liver was quickly removed from each rat, washed in ice-cold PBS (50 mM, pH 7.2), weighed and cut into small pieces, which were resuspended in PBS to 100 mg wet liver tissue/2.5 ml PBS. The samples were pooled and homogenized, followed by centrifugation at 9,000 g for 20 min at 4 °C. The supernatants were collected. The test peptides were added to the liver homogenate at a final concentration of 10 μ g/ml. The resulting mixture was incubated 37 °C in a water bath, and the residue peptides in the mixture were quantitated as described above.

What do the results suggest?

Nucleolar Protein Trafficking in Response to HIV-1 Tat: Rewiring the Nucleolus https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3499507/ SHA: efa871aeaf22cbd0ce30e8bd1cb3d1afff2a98f9 Authors: Jarboui, Mohamed Ali; Bidoia, Carlo; Woods, Elena; Roe, Barbara; Wynne, Kieran; Elia, Giuliano; Hall, William W.; Gautier, Virginie W. Date: 2012-11-15 DOI: 10.1371/journal.pone.0048702 License: cc-by Abstract: The trans-activator Tat protein is a viral regulatory protein essential for HIV-1 replication. Tat trafficks to the nucleoplasm and the nucleolus. The nucleolus, a highly dynamic and structured membrane-less sub-nuclear compartment, is the site of rRNA and ribosome biogenesis and is involved in numerous cellular functions including transcriptional regulation, cell cycle control and viral infection. Importantly, transient nucleolar trafficking of both Tat and HIV-1 viral transcripts are critical in HIV-1 replication, however, the role(s) of the nucleolus in HIV-1 replication remains unclear. To better understand how the interaction of Tat with the nucleolar machinery contributes to HIV-1 pathogenesis, we investigated the quantitative changes in the composition of the nucleolar proteome of Jurkat T-cells stably expressing HIV-1 Tat fused to a TAP tag. Using an organellar proteomic approach based on mass spectrometry, coupled with Stable Isotope Labelling in Cell culture (SILAC), we quantified 520 proteins, including 49 proteins showing significant changes in abundance in Jurkat T-cell nucleolus upon Tat expression. Numerous proteins exhibiting a fold change were well characterised Tat interactors and/or known to be critical for HIV-1 replication. This suggests that the spatial control and subcellular compartimentaliation of these cellular cofactors by Tat provide an additional layer of control for regulating cellular machinery involved in HIV-1 pathogenesis. Pathway analysis and network reconstruction revealed that Tat expression specifically resulted in the nucleolar enrichment of proteins collectively participating in ribosomal biogenesis, protein homeostasis, metabolic pathways including glycolytic, pentose phosphate, nucleotides and amino acids biosynthetic pathways, stress response, T-cell signaling pathways and genome integrity. We present here the first differential profiling of the nucleolar proteome of T-cells expressing HIV-1 Tat. We discuss how these proteins collectively participate in interconnected networks converging to adapt the nucleolus dynamic activities, which favor host biosynthetic activities and may contribute to create a cellular environment supporting robust HIV-1 production. Text: The nucleolus is a highly ordered subnuclear compartment organised around genetic loci called nucleolar-organising regions (NORs) formed by clusters of hundreds of rDNA gene repeats organised in tandem head-to-tail repeat [1, 2] . A membrane-less organelle originally described as the ''Ribosome Factory'', the nucleolus is dedicated to RNA-polymerase-I-directed rDNA transcription, rRNA processing mediated by small nucleolar ribonucleoproteins (soRNPs) and ribosome assembly. Ribosome biogenesis is essential for protein synthesis and cell viability [2] and ultimately results in the separate large (60S) and small (40S) ribosomal subunits, which are subsequently exported to the cytoplasm. This fundamental cellular process, to which the cell dedicates most of its energy resources, is tightly regulated to match dynamic changes in cell proliferation, growth rate and metabolic activities [3] . The nucleolus is the site of additional RNA processing, including mRNA export and degradation, the maturation of uridine-rich small nuclear RNPs (U snRNPs), which form the core of the spliceosome, biogenesis of t-RNA and microRNAs (miRNAs) [4] . The nucleolus is also involved in other cellular processes including cell cycle control, oncogenic processes, cellular stress responses and translation [4] . The concept of a multifunctional and highly dynamic nucleolus has been substantiated by several studies combining organellar proteomic approaches and quantitative mass spectrometry, and describing thousands of proteins transiting through the nucleolus in response to various metabolic conditions, stress and cellular environments [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16] . Collectively, the aforementioned studies represent landmarks in understanding the functional complexity of the nucleolus, and demonstrated that nucleolar proteins are in continuous exchange with other nuclear and cellular compartments in response to specific cellular conditions. Of importance, the nucleolus is also the target of viruses including HIV-1, hCMV, HSV and KSHV, as part of their replication strategy [2, 17] . Proteomics studies analysing the nucleoli of cells infected with Human respiratory syncytial virus (HRSV), influenza A virus, avian coronavirus infectious bronchitis virus (IBV) or adenovirus highlighted how viruses can distinctively disrupt the distribution of nucleolar proteins [2, 17, 18, 19, 20, 21, 22, 23, 24] . Interestingly, both HIV-1 regulatory proteins Tat and Rev localise to the nucleoplasm and nucleolus. Both their sequences encompass a nucleolar localisation signal (NoLS) overlapping with their nuclear localisation signal (NLS), which governs their nucleolar localisation [25, 26, 27, 28, 29, 30, 31] . Furthermore, Tat and Rev interact with the nucleolar antigen B23, which is essential for their nucleolar localisation [25, 26, 27, 28, 29, 30] . Nevertheless, a recent study described that in contrast to Jurkat T-cells and other transformed cell lines where Tat is associated with the nucleus and nucleolus, in primary T-cells Tat primarily accumulates at the plasma membrane, while trafficking via the nucleus where it functions [32] . While the regulation of their active nuclear import and/or export, as mediated by the karyopherin/importin family have been well described, the mechanisms distributing Tat and Rev between the cytoplasm, nucleoplasm and the nucleolus remains elusive [33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48] . Importantly, two major studies by Machienzi et al. have revealed important functional links between HIV-1 replication and the nucleolus [49, 50] . First, they could inhibit HIV-1 replication and Tat transactivation function employing a TAR decoy specifically directed to the nucleolus. Furthermore, using a similar approach, with an anti-HIV-1 hammerhead ribozyme fused to the U16 small nucleolar RNA and therefore targeted to the nucleolus, they could dramatically suppress HIV-1 replication. Collectively, these findings strongly suggest that HIV-1 transcripts and Tat nucleolar trafficking are critical for HIV-1 replication. However the nature of these contributions remains to be elucidated. In this report, we systematically analysed the nucleolar proteome perturbations occurring in Jurkat T-cells constitutively expressing HIV-1 Tat, using a quantitative mass spectrometry approach. Following the detailed annotation of the quantitative abundance changes in the nucleolar protein composition upon Tat expression, we focussed on the Tat-affected cellular complexes and signalling pathways associated with ribosome biogenesis, spliceosome, molecular chaperones, DNA replication and repair and metabolism and discuss their potential involvement in HIV-1 pathogenesis. In this study, we investigated the quantitative changes in the nucleolar proteome of Jurkat T cells constitutively expressing HIV-1 Tat (86aa) versus their Tat-negative counterpart, using stable isotope labelling with amino acids in cell culture (SILAC) technology, followed by ESI tandem mass spectrometry and implemented the experimental approach described in Figure 1A . First, using retroviral gene delivery, we transduced HIV-1 Tat fused to a tandem affinity purification (TAP) tag (consisting of two protein G and a streptavidin binding peptide) or TAP tag alone (control vector) in Jurkat leukemia T cell clone E6-1 and sorted the transduced cells (GFP positive) by FACS. This resulted in a highly enriched population of polyclonal transduced cells presenting different expression levels of the transgene ( Figure 1B) . The functionality of TAP-Tat was confirmed by transfecting Jurkat TAP-Tat and TAP cells with a luciferase reporter gene vector under the control of the HIV-1 LTR (pGL3-LTR) [36] . TAP-Tat up regulated gene expression from the HIV-1 LTR by up to 28 fold compared to control ( Figure 1C ). To further address the functionality of Tat fused to TAP, we compared Jurkat TAP-Tat with Jurkat-tat, a cell line stably expressing untagged Tat [51] . Both cell line exhibited comparable HIV-1 LTR activity following transfection with pGL3-LTR ( Figure S1 ). Next, Tat expression and subcellular localization was verified by subcellular fractionation followed by WB analysis ( Figure 1E ). TAP-Tat displayed a prominent nuclear/nucleolar localization but could also be detected in the cytoplasm. These observations were further validated by immunofluorescence microscopy ( Figure 1E ). Of note, Jurkat-tat presented similar patterns for Tat subcellular distribution as shown by immunofluorescence microscopy and subcellular fractionation followed by WB analysis (Figure S2 and S3). We next compared the growth rate and proliferation of the Jurkat TAP and TAP-Tat cell lines (Materials and Methods S1), which were equivalent ( Figure S4A ). Similarly, FACS analysis confirmed that the relative populations in G1, S, and G2/M were similar for Jurkat TAP-Tat and TAP cells ( Figure S4B ). We labeled Jurkat TAP-Tat and Jurkat TAP cells with light (R0K0) and heavy (R6K6) isotope containing arginine and lysine, respectively. Following five passages in their respective SILAC medium, 85 million cells from each culture were harvested, pooled and their nucleoli were isolated as previously described ( Figure 1A ) [52] . Each step of the procedure was closely monitored by microscopic examination. To assess the quality of our fractionation procedure, specific enrichment of known nucleolar antigens was investigated by Western Blot analysis ( Figure 1D ). Nucleolin (110 kDa) and Fibrillarin (FBL) (34 kDa), two major nucleolar proteins known to localise to the granular component of the nucleolus, were found to be highly enriched in the mixed nucleolar fraction. Of note, nucleolin was equally distributed between the nuclear and cytoplasmic fractions. This distribution pattern for nucleolin appears to be specific for Jurkat T-cells as show previously [52, 53] . The nuclear protein PARP-1 (Poly ADPribose polymerase 1) (113 kDa) was present in the nuclear and nucleoplasmic fraction but was depleted in the nucleolar fraction. Alpha-tubulin (50 kDa) was highly abundant in the cytoplasmic fraction and weakly detected in the nuclear fractions. Collectively, these results confirmed that our methods produced a highly enriched nucleolar fraction without significant cross contamination. Subsequently, the nucleolar protein mixture was trypsindigested and the resulting peptides were analysed by mass spectrometry. Comparative quantitative proteomic analysis was performed using MaxQuant to analyse the ratios in isotopes for each peptide identified. A total of 2427 peptides were quantified, representing 520 quantified nucleolar proteins. The fully annotated list of the quantified nucleolar proteins is available in Table S1 and the raw data from the mass spectrometry analysis was deposited in the Tranche repository database (https:// proteomecommons.org/tranche/), which can be accessed using the hash keys described in materials and methods. We annotated the quantified proteins using the ToppGene Suite tools [54] and extracted Gene Ontology (GO) and InterPro annotations [55] . The analysis of GO biological processes ( Figure 1F ) revealed that the best-represented biological processes included transcription (24%), RNA processing (23%), cell cycle process (13%) and chromosome organisation (15%), which reflects nucleolar associated functions and is comparable to our previous characterisation of Jurkat T-cell nucleolar proteome [52] . Subcellular distribution analysis ( Figure 1F ) revealed that our dataset contained proteins known to localise in the nucleolus (49%), in the nucleus (24%) while 15% of proteins were previously described to reside exclusively in the cytoplasm. The subcellular distribution was similar to our previous analysis of the Jurkat T-cell nucleolar proteome [52] . Table S1 . The distribution of protein ratios are represented in Figure 1G as log 2 (abundance change). The SILAC ratios indicate changes in protein abundance in the nucleolar fraction of Jurkat TAP-Tat cells in comparison with Jurkat TAP cells. The distribution of the quantified proteins followed a Gaussian distribution ( Figure 1G ). A total of 49 nucleolar proteins exhibited a 1.5 fold or greater significant change (p,0.05) upon Tat expression (Table 1) . Of these, 30 proteins were enriched, whereas 19 proteins were depleted. Cells displayed no changes in the steady state content of some of the major and abundant constituents of the nucleolus, including nucleophosmin (NPM1/ B23), C23, FBL, nucleolar protein P120 (NOL1), and nucleolar protein 5A (NOL5A). The distinct ratios of protein changes upon Tat expression could reflect specific nucleolar reorganization and altered activities of the nucleolus. We performed WB analysis to validate the SILAC-based results obtained by our quantitative proteomic approach ( Figure 2 ). 15 selected proteins displayed differential intensity in the nucleolar fractions upon Tat expression, including 9 enriched (HSP90b, STAT3, pRb, CK2a, CK2a', HSP90a, Transportin, ZAP70, DDX3), and 3 depleted (ILF3, BOP1, and SSRP1) proteins. In addition, we also tested by WB analysis, protein abundance not affected by Tat expression (Importin beta, FBL, B23, C23). These results highlight the concordance in the trend of the corresponding SILAC ratios, despite some differences in the quantitative ranges. Of note, using WB, we could observe a change of intensity for protein with a SILAC fold change as low as 1.25-fold. Of note, the question remains as to which fold change magnitude might constitute a biologically relevant consequence. On the one hand, the threshold of protein abundance changes can be determined statistically and would then highlight the larger abundance changes as illustrated in Table 1 . Alternatively, the coordinated enrichment or depletion of a majority of proteins belonging to a distinct cellular complex or pathway would allow the definition of a group of proteins of interest and potential significance. Therefore, we next focused on both enriched or depleted individual proteins with activities associated with HIV-1 or Tat molecular pathogenesis, and on clustered modifications affecting entire cellular signaling pathways and macromolecular complexes. We initially focused on signaling proteins interacting with Tat and/or associated HIV-1 molecular pathogenesis and whose abundance in the nucleolus was modulated by Tat expression. Phospho-protein phosphatases. Phospho-protein phosphatase PP1 and PP2A are essential serine/threonine phosphatases [56, 57] . Importantly, PP1 accounts for 80% of the Ser/Thr phosphatase activity within the nucleolus. In our study, PP1 was found to be potentially enriched by 1.52-fold in the nucleolus of Jurkat cells expressing Tat, which supports previous studies describing the nuclear and nucleolar targeting of PP1a by HIV-1 Tat and how PP1 upregulates HIV-1 transcription [58, 59, 60, 61, 62] . PP1 c was also identified as part of the in vitro nuclear interactome [63] . Similarly, PPP2CA, the PP2A catalytic subunit (1.29-fold) and its regulatory subunit PP2R1A (1.27-fold) were similarly enriched upon Tat expression. Interestingly, Tat association with the PP2A subunit promoters results in the overexpression and up regulation of PP2A activity in lymphocytes [64, 65] . Furthermore, PP2A contributes to the regulation of HIV-1 transcription and replication [61, 66] . Retinoblastoma Protein. The tumour suppressor gene pRb protein displayed a 1.4-fold change in the nucleolus upon Tat expression [67] . Furthermore, WB analysis confirmed the distinct translocation of pRb from the nucleoplasm to the nucleolus by Tat ( Figure 2 ). Depending on the cell type, pRb can be hyperphosphorylated or hypophosphorylated upon Tat expression and can negatively or positively regulate Tat-mediated transcription respectively [68, 69, 70] . Interestingly, the hyperphosphorylation of pRB triggers in its translocation into the nucleolus [71] . Phosphorylation of pRB is also associated with an increase in ribosomal biogenesis and cell growth [72] . STAT3. The transcription factor signal transducer and activator of transcription 3 (STAT3) was significantly enriched (1.86-fold) in the nucleolar fraction by Tat constitutive expression. Furthermore, WB analysis indicated that Tat expression could promote the relocalisation of STAT3 from the cytoplasm to the nucleus, with a distinct enrichment in the nucleolus ( Figure 2) . Interestingly, previous studies have demonstrated Tat-mediated activation of STAT3 signaling, as shown by its phosphorylation status [73] . Interestingly, STAT3 phosphorylation induced dimerisation of the protein followed its translocation to the nucleus [74] . YBX1. YBX1, the DNA/RNA binding multifunctional protein was enriched by 1.38-fold in the nucleolus of Jurkat cells upon Tat expression. Interestingly, YBX1 interacts with Tat and TAR and modulates HIV-1 gene expression [63, 75] . ZAP70. The protein tyrosine kinase ZAP70 (Zeta-chainassociated protein kinase 70) was enriched by 1.24-fold in the nucleolus of Jurkat cells expressing Tat [76] . Furthermore, WB analysis revealed that Tat expression could promote the relocalisation of ZAP70 from the cytoplasm to the nucleus, with a distinct enrichment in the nucleolus ( Figure 2 ). Of note, ZAP70 is part of the in vitro nuclear Tat interactome [63] . Matrin 3. The inner nuclear matrix protein, Matrin 3 (MATR3), presented a 1.39-fold change in the nucleolus of Jurkat cells expressing Tat. It localizes in the nucleolasm with a diffuse pattern excluded from the nucleoli [77] . Matrin 3 has been identified as part of the in vitro HIV-1 Tat nuclear interactome [63] . Two recent studies have described Matrin 3 as part of ribonucleoprotein complexes also including HIV-1 Rev and (Rev Response Element) RRE-containing HIV-1 RNA, and promoting HIV-1 post-transcriptional regulation [78, 79, 80] . CASP10. The pro-apototic signaling molecule, Caspase 10 (CASP10), was significantly depleted from the nucleolus of Jurkat-Tat cells (0.82-fold) [81] . Importantly, Tat expression downregulates CASP10 expression and activity in Jurkat cells [82] . ADAR1. Adenosine deaminase acting on RNA (ADAR1), which converts adenosines to inosines in double-stranded RNA, was significantly depleted from the nucleolus of Jurkat-Tat cells (0.78-fold). Interestingly, ADAR1 over-expression up-regulates HIV-1 replication via an RNA editing mechanism [83, 84, 85, 86, 87, 88] . Furthermore, ADAR1 belongs to the in vitro HIV-1 Tat nuclear interactome [63] . To underline the structural and functional relationships of the nucleolar proteins affected by HIV-1 Tat, we constructed a network representation of our dataset. We employed Cytoscape version 2.6.3 [89] and using the MiMI plugin [90] to map previously characterised interactions, extracted from protein interaction databases (BIND, DIP, HPRD, CCSB, Reactome, IntAct and MINT). This resulted in a highly dense and connected network comprising 416 proteins (nodes) out of the 536 proteins, linked by 5060 undirected interactions (edges) ( Figure 3A ). Centrality analysis revealed a threshold of 23.7 interactions per protein. Topology analysis using the CentiScaPe plugin [91] showed that the node degree distribution follows a power law ( Figure S5 ), characteristic of a scale-free network. Importantly, when we analysed the clustering coefficient distribution ( Figure S6 ) we found that the network is organised in a hierarchical architecture [92] , where connected nodes are part of highly clustered areas maintained by few hubs organised around HIV-1 Tat. Furthermore, node degree connection analysis of our network identified HIV-1 Tat as the most connected protein ( Figure S6 ). Specifically, the topology analysis indicated that the values for Tat centralities were the highest (Node degree, stress, radiality, closeness, betweeness and centroid), characterising Tat as the main hub protein of the nucleolar network. Indeed, a total of 146 proteins have been previously described to interact with Tat ( Figure 3B , Table S2 ). These proteins are involved in a wide range of cellular processes including chromosomal organization, DNA and RNA processing and cell cycle control. Importantly, aver the third of these proteins exhibit an increase in fold ratio change (59 proteins with a ratio .1.2 fold). In parallel, we characterised the magnitude of the related protein abundance changes observed in distinct cellular pathways ( Figure 4) . Ribosomal biogenesis. We initially focused on ribosome biogenesis, the primary function of the nucleolus. We could observe a general and coordinated increase in the abundance of ribosomal proteins in the nucleolus by Tat expression (Figure 4 ). While some ribosomal proteins remained unaffected, Tat caused the nucleolar accumulation of several distinct large and small ribosomal proteins, except RPL35A, for which Tat expression caused a marked decrease at the nucleolar level (0.29-fold). Similarly, several proteins involved in rRNA processing exhibited an overall increase in nucleolar accumulation upon Tat expression. These include human canonical members of the L7ae family together with members participating in Box C/D, H/ACA and U3 snoRNPs ( Figure 4) . Conversely, BOP1, a component of the PeBoW (Pescadillo Bop1 WDR12) complex essential for maturation of the large ribosomal subunit, was significantly depleted from the nucleolus of Jurkat TAP-Tat cells (0.81-fold) and this was confirmed by WB analysis (Figure 2 ) [93] . Nevertheless, the other PeBoW complex components, Pes1 (0.94-fold) and WDR12 (1.1fold), were not affected by Tat expression. Of note, we did not detect change in the abundance of protein participating in rDNA transcription such as RNAPOLI, UBF. Spliceosome. We identified and quantified in our dataset 55 proteins out of the 108 known spliceosomal proteins [94] . These proteins include the small nuclear ribonucleoproteins U1, U2 and U5, Sm D1, D2, D3, F and B, and the heterogeneous nuclear ribonucleoproteins. Our data suggested a distinct increase in the abundance of specific spliceosome complex proteins upon expression of HIV-1 Tat in Jurkat T-cells (Figure 3 and 4) . The only three proteins that were significantly depleted from the nucleolus upon expression of HIV-1 Tat were RBMX (0.89-fold), HNRNPA2B1 (0.84-fold) and SNRPA (0.81-fold). Several investigations showed expression alteration in cellular splicing factors in HIV-1 infected cells [95, 96] . Molecular chaperones. We have identified several molecular chaperones, co-chaperones and other factors involved into proteostasis to be highly enriched in the nucleolus of T-cells upon Tat expression (Figure 3 and 4) , many of which were previously characterised as part of the Tat nuclear interactome [63] . Several heat-shock proteins including DNAJs, specific HSP90, HSP70 and HSP40 isoforms and their co-factors were distinctively enriched in the nucleolar fraction of Jurkat cells expressing Tat ( Figure 4 ). As shown by WB, while HSP90a and b are mostly cytoplasmic, Tat expression triggers their relocalisation to the nucleus and nucleolus, corroborating our proteomic quantitative approach (Figure 2) . Similarly, heat-shock can cause the HSP90 and HSP70 to relocalise to the nucleolus [97, 98, 99, 100, 101] . In a recent study, Fassati's group has shown that HSP90 is present at the HIV-1 promoter and may directly regulate viral gene expression [102] . We also observed the coordinated increased abundance of class I (GroEL and GroES) and class II (chaperonin containing TCP-1 (CTT)) chaperonin molecules (Figure 3 and 4) upon Tat expression. Ubiquitin-proteasome pathway. The ubiquitin-proteasome pathway is the major proteolytic system of eukaryotic cells [103] . Importantly, the nuclear ubiquitin-proteasome pathway controls the supply of ribosomal proteins and is important to ribosome biogenesis [104, 105] . The 26S proteasome is composed of the 20S core particle (CP) and the 19S regulatory particle (RP). Alternatively, CP can associate with the 11S RP to form the immunoproteasome. All the quantified proteins in our study are part of the 19S regulatory complex and include PSMD2 (1.5-fold), PSMD3 (1.32-fold), PSMD11 (1.25-fold) and PSMD13 (0.72-fold), the only proteasome component significantly depleted from the nucleolus in the presence of Tat (Figure 4) . Interestingly, Tat interacts with distinct subunits of the proteasome system, including the 19S, 20S and 11S subunits. The consequences of these interactions include the competition of Tat with 11S RP or 19S RP for binding to the 20S CP, which resulted in the inhibition of the 20S peptidase activity [106, 107, 108, 109, 110, 111] . Furthermore, Tat was shown to modify the proteasome composition and activity, which affects the generation of peptide antigens recognized by cytotoxic T-lymphocytes [112] . Importantly, a recent study demonstrated that in the absence of Tat, proteasome components are associated to the HIV-1 promoter and proteasome activity limits transcription [113] . Addition of Tat promoted the dissociation of the 19S subunit from the 20S proteasome, followed by the distinct enrichment of the 19S-like complex in nuclear extracts together with the Tat-mediated recruitment of the 19S subunits to the HIV-1 promoter, which facilitated its transcriptional elongation [113] . We also quantified UBA1 (1.36-fold), the E3 ubiquitin-protein ligase UHRF1 (1.13-fold), UBC (1-fold) and two Ubiquitinspecific-peptidases, USP30 (1.28-fold) and USP20 (0.06-fold) (Figure 4) . DNA replication and repair. Upon HIV-1 Tat expression, we observed the coordinated nucleolar enrichment of several cellular factors associated with DNA replication and repairs pathways (Figure 4) . Tat induced the coordinated enrichment of the miniature chromosome maintenance MCM2-7 complex (from 1.23-to 3.30fold, respectively) [114] . MCM7, 6 and 3 were identified as part of the in vitro nuclear interactome of HIV-1 Tat [63] . The structural maintenance of chromosomes 2, SMC2, was enriched (1.35-fold) in the nucleolar fraction by Tat expression. SMC2 was identified as part of the in vitro nuclear interactome of HIV-1 Tat [63] . While replication factor C1 (RFC1) and RFC2 (1.31-and 1.28-fold respectively) displayed an increased fold change and RFC5/3 were not affected, RFC4 was severely depleted (0.69-fold) from the nucleolar fraction upon Tat expression [115] . RFC1 and RFC2 were identified as part of the in vitro nuclear interactome of HIV-1 Tat [63] . Tat induced the enrichment of XRCC6 (1.27-fold) and XRCC5 (1.36-fold) in the nucleolus, which are involved in the repair of non-homologous DNA end joining (NHEJ) [116] . XRCC6 associates with viral preintegration complexes containing HIV-1 Integrase and also interact with Tat and TAR [117, 118, 119] . Furthermore, in a ribozyme-based screen, XRCC5 (Ku80) knockdown decreased both retroviral integration and Tatmediated transcription [120] . As part of the base excision repair (BER), we have identified a major apurinic/apyrimidinic endonuclease 1 (APEX1) (1.29-fold) . Importantly, in a siRNA screen targeting DNA repair factors, APEX1 knockdown was found to inhibit HIV-1 infection by more 60% [121] . The high mobility group (HMG) protein, HMGA1 (1.30-fold), was enriched in the nucleolus following Tat expression [122] . HMGA1 interact with HIV-1 Integrase and is part of the HIV-1 pre-integration complex [123, 124] . Importantly, HMGA1 has been identified in a proteomic screen, as a cellular cofactor interacting with the HIV-1 59leader [125] . Metabolism. Our proteomic data suggest that Tat induces perturbations in glycolysis, the pentose phosphate pathway, and nucleotide and amino acid biosynthesis (Figure 4 and Figure S7 ). Notably, in T cells expressing Tat, we detected co-ordinated changes in the abundance of proteins not previously known to be associated with Tat pathogenesis, which revealed unexpected connections with with glycolysis and the pentose phosphate pathway, including the following glycolitic enzymes, lactate dehydrogenase B (LDHB) (1.37-fold), glyceraldehyde 3-phosphate dehydrogenase (GAPDH) (1.17-fold) and phosphoglyceric acid mutase (PGAM1) (0.89-fold) ( Figure 4 and Figure S7 ). Briefly, GPI catalyzes the reversible isomerization of glucose-6-phosphate in fructose-6-phosphate. Subsequently, PFKP catalyzes the irreversible conversion of fructose-6-phosphate to fructose-1,6-bisphosphate and is a key regulatory enzyme in glycolysis. At the end of the glycolytic pathway, PKM2, in its tetrameric form, is known to generate ATP and pyruvate, while LDHB diverts the majority of the pyruvate to lactate production and regeneration of NAD+ in support to continued glycolysis, a phenomenon described for proliferative Tcells [126] . Of note, in highly proliferating cells, PKM2 can be found in its dimeric form and its activity is altered. This upregulates the availibility of glucose intermediates, which are rerouted to the pentose phosphate and serine biosynthesis pathways for the production of biosynthetic precursors of nucleotides, phospholipids and amino acids. As part of the pentose phosphate pathway, we have characterised the significant enrichment of glucose-6-phosphate dehydrogenase (G6PD) (2.11-fold), which branches of the glycolysis pathway to generate NADPH, ribose-5phosphate an important precursor for the synthesis of nucleotides. Consistent with this, we detected the coordinated increase in the abundance of enzymes which plays a central role in the synthesis of purines and pyrimidines. More specifically, IMPDH2 (1.66fold), a rate-limiting enzyme at the branch point of purine nucleotide biosynthesis, leading to the generation of guanine nucleotides, phosphoribosyl pyrophosphate synthetase 2 (PRPS2) (1.41-fold), cytidine-5-prime-triphosphate synthetase (CTPS) (1.74-fold) which catalyses the conversion of UTP to CTP and the ribonucleotide reductase large subunit (RRM1) (1.56-fold). In parralel, we noted the increased abundance of the phosphoserine aminotransferase PSAT1 (1.90-fold), an enzyme implicated in serine biosynthesis, which has been linked with cell proliferation in vitro. The host-virus interface is a fundamental aspect in defining the molecular pathogenesis of HIV-1 [127, 128, 129, 130, 131, 132, 133] . Indeed, with its limited repertoire of viral proteins, HIV-1 relies extensively on the host cell machinery for its replication. Several recent studies have capitalized on the recent advances in the ''OMICS'' technologies, and have revealed important insights into this finely tuned molecular dialogue [132, 134] . HIV-1 Tat is essential for viral replication and orchestrates HIV-1 gene expression. The viral regulatory protein is known to interact with an extensive array of cellular proteins and to modulate cellular gene expression and signaling pathway [135, 136] . We and others have employed system-level approaches to investigate Tat interplay with the host cell machinery, which have characterised HIV-1 Tat as a critical mediator of the host-viral interface [137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149] . Here, we have investigated the nucleolar proteins trafficking in response to HIV-1 Tat expression in T-cells, with the view to provide unique and novel insights on the role of proteins compartimentalisation by Tat in the fine-tuning of protein availability and function. We have developed for this study, a cellular model using Jurkat T-cells stably expressing Tat fused in its N-ternminal to TAP-tag. Jurkat T-cells are robust and present the advantage to grow without stimulations and are easely transduced using retroviral gene delivery. Importantly, they have been widely employed to evaluate Tat-mediated pathogenesis using system-wide approaches and to analyse T-cell key cellular signaling pathways and functions [144, 150, 151, 152] . Indeed, we have found them particularly suited for prolongued in vitro culture in SILAC medium and subsequent isolation of their nucleolus followed by MS analysis, which requires up to 85 millions of cells. We fused Tat to the TAP tag to enable future downstream applications such as Tandem affinity purification or Chromatin IP analysis. Importantly, we have confirm that N-terminal TAP-tag did not interfere with Tat function nor its localisation in Jurkat cells, when compared to untagged-Tat. Of note, Tat subcellular distribution can vary according to the cell type employed. While Tat is known to accumulate in the nucleus and nucleolus in Jurkat cells and other transformed cell lines, in primary T-cells, Tat was described to primarily accumulate at the plasma membrane, while trafficking via the nucleus where it functions [32] . These differences remain to be characterised but could be related to different expression levels of transport factors in transformed cell lines versus primary cells, as recently described by Kuusisto et al. [39] . Furthermore, Stauber and Pavlakis have suggested that Tat nucleolar localisation could be the results of Tat overexpression [31] . Here, we have selected and employed a polyclonal population of Jurkat T-cells expressing Tat at different levels. We propose that this heterogeneity in Tat expression levels might reflect Tat stochastic expression described during viral replication [153] . Using a quantitative proteomic strategy based on an organellar approach, we quantified over 520 nucleolar proteins, including 49 proteins exhibiting a significant fold change. The extent to which the induced variations in the abundance of nucleolar proteins are biologically relevant and can affect cellular and/or viral processes remains to be determined. Nevertheless, the biological nature of the pathways and macromolecular complexes affected enable us to discuss their potential associations with HIV-1 pathogenesis. HIV-1 Tat is expressed early following HIV-1 genome integration and mediates the shift to the viral production phase, associated with robust proviral gene expression, viral proteins assembly and ultimately, virions budding and release. In this context and based on our results, we propose that Tat could participate in shaping the intracellular environment and metabolic profile of T cells to favor host biosynthetic activities supporting robust virions production. Indeed, we observed the distinct nucleolar enrichment of ribosomal proteins and enzymes associated with ribosomal biogenesis, which could be indicative of an increase in protein synthesis. With the notable exeption of RPL35A nucleolar depletion, ribosomal proteins and enzymes associated with ribosomal biogenesis were in the top 20 most enriched nucleolar proteins (NHP2L1, RLP14, RPL17, RPL27, RPS2, RPL13). Furthermore, this effect appears to be specific to HIV-1 Tat since transcription inhibition by Actinomycin D resulted in the overall depletion of ribosomal proteins in the nucleolus [9] . Moreover, quantitative proteomics analysis of the nucleous in adenovirus-infected cells showed a mild decrease in ribosomal proteins [24] . Whether this reflect a shift in ribosome biogenesis and/or a change in the composition of the ribosomal subunits remains to be determined. Nevertheless, the adapted need for elevated ribosome production is intuitive for a system that needs to support the increased demand for new viral proteins synthesis. In parralel, we observed the concordant modulation of pathways regulating protein homeostasis. We noted the significant nucleolar accumulation of multiple molecular chaperones including the HSPs, the TCP-1 complex, and CANX/CALR molecules and the disrupted nucleolar abundance of proteins belonging to the ubiquitin-proteasome pathway, which controls the supply of ribosomal proteins [104, 105] . These observations further support previous studies describibing the modulation of the proteasomal activity by Tat, which affect the expression, assembly, and localization of specific subunits of the proteasomal complexes [106, 107, 108, 109, 110, 111, 113] . We also observed the concomitant depletion of CASP10 in the nucleolus of Jurkat TAP-Tat. It has been suggested that CASP10 could be targeted to the nucleolus to inhibit protein synthesis [154] . Interestingly, the presence and potential roles of molecular chaperones in the nucleolus have been highlighted by Banski et al, who elaborate on how the chaperone network could regulate ribosome biogenesis, cell signaling, and stress response [97, 155] . As viral production progresses into the late phase and cellular stress increases, nucleolar enrichment of molecular chaperones by Tat could not only enable adequat folding of newly synthetised viral proteins but could also promote tolerance of infected cells to stress and maintain cell viability. Coincidentally, we observed the marked nucleolar enrichment of enzymes belonging to metabolic pathways including glycolysis, pentose phosphate, nucleotide and amino acid biosynthetic pathways. Similarly, these pathways are elevated in proliferative T-cells or in cancer cells following a metabolic shift to aerobic glycolysis, also known as the Warburg effect [156, 157, 158, 159] . There, glucose intermediates from the glycolysis pathway are not only commited to energy production and broke-down into pyruvate for the TCA cycle, but are redirected to alternative pathways, including the pentose phosphate pathway, and used as metabolic precursors to produce nucleotides, amino acids, acetyl CoA and NADPH for redox homeostasis. Consistently, we also noted the concomittant nucleolar enrichment of enzymes belonging to the nucleotide synthesis pathway, including IMPH2, a rate limiting enzyme known to control the pool of GTP. Similarly, we noted the nucleolar enrichment of PSAT1, an enzyme involved in serine and threonin metabolism, which is associated with cellular proliferation [160] . Collectively, we propose that by controlling protein homeostasis and metabolic pathways, Tat could meet both the energetic and biosynthetic demand of HIV-1 productive infection. Of note, while nucleotide metabolism enzymes are associated with the nucleus, glycolysis takes place in the cytoplasm. Nevertheless, glycolytic enzymes have been detected in both the nuclear and nucleolar fractions by proteomic analyses [8, 161] . Furthermore glycolytic enzymes, such as PKM2, LDH, phosphoglycerate kinase, GAPDH, and aldolase, also have been reported to display nuclear localization and bind to DNA [162] . More specifically, PKM2 is known to associate with promoter and participate in the regulation of gene expression as a transcriptional coactivator [163] . HIV-1 Tat has previously been described as an immunoregulator and more specifically, has been reported both to inhibit or to promote TCR signaling [164] . We have observed the nucleolar enrichment by Tat of key proximal or downstream components of T-cell signaling pathways, including ZAP70, ILF3 and STAT3, which play crucial roles in T-cell development and activation. We had previously identified them as T-cell specific components of the nucleolus, and IF studies suggested that their association with the nucleolus could be regulated by specific conditions [165] . Our results further support that Tat could contribute to the dysregulation of TCR-derived signals and that the nucleolus could represent an important spatial link for TCR signaling molecules. We observed the coordinated nucleolar enrichment of key components of the DNA replication, recombination and repair pathways by Tat. These include XRCC5 and XRCC6, HMGA1, APEX1, MCM2-7, SMC2, RFC1 and RFC2, while RFC4 was found to be significantly depleted. Interestingly, these cofactors have been associated with the efficiency of retroviral DNA integration into the host DNA or the integrity of integrated provirus [166] . Whether the increased abundance of these factors within the nucleolus could be associated with their potential participation in the integration and maintenance of provirus gene integrity, remains to be determined. The mechanisms of Tat-mediated segregation and compartimentalisation of proteins in or out of the nucleolus may depend on factor(s) inherent for each protein and the nature of their relationship with Tat, since subcellular fractionation combined with WB analysis showed that the pattern and extent of subcellular redistribution between proteins varied. We could observe cases where Tat upregulated the expression of proteins which resulted in a general increase of theses proteins throughout the cellular compartments including the nucleolus (DDX3, TNPO1). Alternatively, Tat could trigger the nucleolar translocation of proteins directly from the cytoplasm or the nucleoplasm (pRb). Additionally, we observed cytoplasmic proteins redistributed to both the nucleoplasm and nucleolus upon Tat expression (STAT3, ZAP70 and HSP90). Finally, we also noted protein depletion in the nucleolar fraction accompanied by an increase in the nucleoplasm (SSRP1). It remains difficult at this stage, to appreciate whether the accumulation of specific proteins would result in their activation or inhibition by sequestering them away from their site of action. Conversely, the depletion of a protein from the nucleolus could either result in the down-regulation of its activity in this location or could be the result of its mobilization from its storage site, the nucleolus, to the nucleoplasm or cytoplasm where it can perform its function. Remarkably, we identified several known HIV-1 Tat partners involved in HIV-1 pathogenesis, which suggests that Tat could physically modulate their nucleolar targeting or their recruitment to specific site in the nucleoplasm or cytoplasm. Tat could also promote post-translational modifications, which could mediate the targeting of specific proteins to the nucleolus. This is exemplified by the following enriched proteins, pRb, PP1 and STAT3, for which phosphorylation is induced by Tat. Importantly, their phosphorylation status determines their subcellular distribution, thus providing a potential mechanism for their redistribution by Tat. Moreover, our data indicates that serine/threonine kinases (CK2 a') and phosphatases (PP1) were significantly enriched in the nucleolar fractions of Jurkat TAP-Tat. These enzymes account for the majority of the phosphorylation/ dephosphorylation activity in the nucleolus and can act as regulators of nucleolar protein trafficking. In addition, Tat significantly decreased the levels of SUMO-2 in the nucleolus. Similarly, SUMO-mediated post-translational modifications are known to modulate nucleolar protein localization [104] . Given the potential importance of post-translational modifications, including phosphorylation in the Tat-mediated change of abundance of nucleolar proteins, a more targeted proteomic approach such as the enrichment for phosphopetides, would extend the resolution of our screening approach. The control of protein turnover is also an important mean to modulate the abundance of nucleolar proteins. Ribosomal proteins are degraded by the Ubiquitin-Proteasome pathway to ensure their abundance matches up with rRNA transcription levels. Conversely, heat shock proteins HSP90s protect them from degradation. Interestingly, our data showing that Tat modulation the abundance proteins associated with the Ubiquitin-proteasome and heat-shock pathway. This could contribute to the observed enrichment of ribosomal proteins by Tat. Nevertheless, we cannot exclude that the increased abundance of ribosomal proteins in the nucleolus could be the result of Tat-mediated prevention of their export to the cytoplasm. Interestingly, using a different cellular system, a drosophila melanogaster Tat transgenic strain, Ponti et al, analysed the effects of Tat on ribosome biogenesis, following 3 days heat shock treatment to induce Tat expression under the control of the hsp70 promoter [167] . Following Tat expression, they observed a defect in pre-rRNA processing associated with a decrease in the level of 80S ribosomes [167] . Nevertheless, the different cellular system employed combined with the 3 days heatshock induction make their results difficult to compare with ours. While previous system-level studies have monitored the effects of HIV-1 Tat expression on T cells, to our knowledge, we have presented here the first proteomic analysis of dynamic composition of the nucleolus in response to HIV-1 Tat expression. Using quantitative proteomics, we have underlined the changes in abundance of specific nucleolar proteins and have highlighted the extensive and coordinated nucleolar reorganization in response to Tat constitutive expression. Our findings underscore that Tat expressing T-cells exhibit a unique nucleolar proteomic profile, which may reflect a viral strategy to facilitate the progression to robust viral production. Importantly, we noted the functional relationship of nucleolar proteins of our dataset with HIV-1 pathogenesis and HIV-1 Tat in particular. This further increases our confidence in our experimental strategy and suggests a role for Tat in the spatial control and subcellular compartimentaliation of these cellular cofactors. Ultimatly, our study provides new insights on the importance of Tat in the cross talk between nucleolar functions and viral pathogenesis. Importantly, we have also identified changes in nucleolar protein abundance that were not previously associated with HIV-1 pathogenesis, including proteins associated with metabolic pathways, which provide new potential targets and cellular pathways for therapeutic intervention. Jurkat T-cells, clone E6.1 (ATCC), Jurkat NTAP-Tat and Jurkat NTAP were maintained in RPMI-1640 medium supplemented with 10% (v/v) foetal bovine serum (Gibco, EU approved), and antibiotics. Phoenix-GP cells (G.P. Nolan; www.stanford.edu/ group/nolan/), were maintained in DMEM medium supplemented with 10% (v/v) foetal bovine serum (GIBCO, EU approved). Cells were counted using Scepter TM 2.0 Cell Counter (Millipore). The sequence of HIV-1 Tat (HIV-1 HXB2, 86 amino acids) was sub-cloned into pENTR 2B vector (Invitrogen, A10463). Using the Gateway technology (Invitrogen), we introduced the HIV-1 Tat sequence into the plasmid pCeMM-NTAP(GS)-Gw [168] . Phoenix cells (G.P. Nolan; www.stanford.edu/group/ nolan/), were transfected using Fugene 6 (Roche) with 5 mg of the plasmid NTAP-Tat or NTAP and 3 mg of the pMDG-VSVG. Viral supernatants were collected after 48 h, filtered and used to transduce the Jurkat cell lines. The construct is termed NTAP-Tat, the empty vector was termed NTAP. Using retroviral gene delivery, we stably transduced Jurkat cells (clone E6.1 (ATCC)). The positive clones named Jurkat NTAP-Tat and Jurkat NTAP were sorted to enrich the population of cells expressing GFP using the BC MoFlo XDP cell sorter (Beckman Coulter). Sub-cellular fractions (10 mg) were resolved by SDS-PAGE and transferred onto BioTrace PVDF membranes (Pall corporation). The following primary antibodies were used: a-Tubulin (Sc 5286), C23 (Sc 6013), and Fibrillarin (Sc 25397) were from Santa Cruz Biotechnology, and PARP (AM30) from Calbiochem, mouse anti-ZAP 70 (05-253, Millipore), rabbit anti-STAT3 (06-596, Millipore), rabbit anti-ILF3 (ab92355, Abcam), rabbit anti-HSP90 beta (ab32568, Abcam), mouse anti-ADAR1 (ab88574, Abcam), rabbit anti-HDAC1 (ab19845, Abcam), rabbit anti-SSRP1 (ab21584, Abcam) rabbit anti-BOP1 (ab86982, Abcam), mouse anti-KpNB1 (ab10303, Abcam), rabbit anti-HIV-1 Tat (ab43014, Abcam), rabbit anti-CK2A (ab10466, Abcam), rabbit anti-DDX3X (ab37160, Abcam), mouse anti-TNPO1 (ab2811, Abcam), mouse anti-HSP90A (CA1023, MERCK), and rabbit-anti RB1 (sc-102, Santa Cruz).The following secondary antibodies were used ECL: Anti-mouse IgG and ECL Anti-rabbit IgG (GE Healthcare), and Donkey anti-goat IgG (Sc 2020) (Santa Cruz Biotechnology). For SILAC analysis SILAC-RPMI R0K0 and SILAC-RPMI R6K6 (Dundee cells) media supplemented with 10% dialyzed FBS (GIBCO, 26400-036) were used. The Jurkat cells expressing NTAP-Tat and NTAP were serially passaged and grown for five doublings to ensure full incorporation of the labelled amino acids. Cells viability was checked with Trypan Blue (0.4% solution, SIGMA) and further confirmed using PI staining and FACS analysis. Cells were mixed to the ratio 1:1 to obtain 140610 6 cells. Nucleoli were isolated from the mixed cell population as previously described in Jarboui et al., [165] . Nucleolar extracts (100 mg) were resuspended in 50 mM ammonium bicarbonate and in solution trypsin digested as previously described in Jarboui et al. [165] . Sample was run on a Thermo Scientific LTQ ORBITRAP XL mass spectrometer connected to an Eksigent NANO LC.1DPLUS chromatography system incorporating an auto-sampler. Sample was loaded onto a Biobasic C18 PicofritTM column (100 mm length, 75 mm ID) and was separated by an increasing acetonitrile gradient, using a 142 min reverse phase gradient (0-40% acetonitrile for 110 min) at a flow rate of 300 nL min-1. The mass spectrometer was operated in positive ion mode with a capillary temperature of 200uC, a capillary voltage of 46V, a tube lens voltage of 140V and with a potential of 1800 V applied to the frit. All data was acquired with the mass spectrometer operating in automatic data dependent switching mode. A high resolution MS scan was performed using the Orbitrap to select the 5 most intense ions prior to MS/MS analysis using the Ion trap. The incorporation efficiency of labelled amino-acids was determined by analysing the peptides identified in isolated nucleoli from cell population maintained in ''Heavy'' medium as described in [169] . Our analysis showed that we had an incorporation efficiency .95% (data not shown). The MS/MS spectra were searched for peptides identification and quantification using the MaxQuant software [170] (version 1.1.1.36), the Human IPI Database (version 3.83) and the Andromeda search engine associated to MaxQuant [171] . Standard settings were used for MaxQuant with the Acetyl (Protein N-term) as variable modification and Carbamidomethyl (Cys) as fixed modification, 2 missed cleavage were allowed, except that the filtering of labelled amino acids was prohibited. Initial mass deviation of precursor ion and fragment ions were 7 ppm and 0.5 Da, respectively. Each protein ratio was calculated as the intensity-weighted average of the individual peptides ratios. Proteins were identified with the minimum of one peptide with a false discovery rate less than 1%. Gene ontology, KEGG pathway and Pfam terms were extracted from UNIPROT entries using Perseus, a software from the MaxQuant Data analysis package (http://www.maxquant.org ), and the ToppGene suite tools [54] . The Jurkat NTAP-Tat and Jurkat NTAP were transfected using the Amaxa electroporation system (Amaxa biosystem) with the pGL3 (pGL3-LTR) (Promega) as recommended by Amaxa Biosystem. Dual-luciferase assays (Promega) were performed according to the manufacturer's instructions. Luciferase activity was measured and normalized against the total amount of proteins as quantified by the BCA protein quantification kit (Pierce, Thermo Scientific). To preserve their original shape, we performed immunostaining of Jurkat cells in suspension. Cells were fixed in 2% PFA for 10 min at RT, permeabilised in 0.5% Triton X-100 for 15 min at RT and blocked with 5% FCS. Cells were incubated with the rabbit HIV-1 Tat antibody (ab43014, Abcam) followed by the secondary antibody anti-Rabbit alexa fluor 647 (A-21246, Invitrogen). Cells were allowed to attach to Cell-Tak (BD) coated Silanised Slides (DaoCytomation), and stained with DAPI. Images were captured with a Carl Zeiss Confocal Microscope equipped with a Plan-Apochromat 63X/1.4 oil DIC objective. The proteomics RAW Data file from the mass spectrometry analysis was deposited to the Tranche repository(https:// proteomecommons.org/tranche/) [172] . The file can be accessed and downloaded using the following hash key: (R3O5SV5Z6HvWqrBNDhp21tXFetluDWYxvwMIfU-h6e1kMgarauCSq4dlNcxeUvFOHDEzLeDcg4X5Y8reSb6-MUA6wM1kIAAAAAAAAB/w = = ). Materials and Methods S1 Description of the methods employed to examine cell cycle, cell viability and cell proliferation analysis. (DOCX)

How many proteins were shown to change the amount of Jurkat T-cell nucleolus significantly?

Genomic characterization of the 2019 novel human-pathogenic coronavirus isolated from a patient with atypical pneumonia after visiting Wuhan https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7067204/ SHA: c097a8a9a543d69c34f10e5c3fd78019e560026a Authors: Chan, Jasper Fuk-Woo; Kok, Kin-Hang; Zhu, Zheng; Chu, Hin; To, Kelvin Kai-Wang; Yuan, Shuofeng; Yuen, Kwok-Yung Date: 2020-01-28 DOI: 10.1080/22221751.2020.1719902 License: cc-by Abstract: A mysterious outbreak of atypical pneumonia in late 2019 was traced to a seafood wholesale market in Wuhan of China. Within a few weeks, a novel coronavirus tentatively named as 2019 novel coronavirus (2019-nCoV) was announced by the World Health Organization. We performed bioinformatics analysis on a virus genome from a patient with 2019-nCoV infection and compared it with other related coronavirus genomes. Overall, the genome of 2019-nCoV has 89% nucleotide identity with bat SARS-like-CoVZXC21 and 82% with that of human SARS-CoV. The phylogenetic trees of their orf1a/b, Spike, Envelope, Membrane and Nucleoprotein also clustered closely with those of the bat, civet and human SARS coronaviruses. However, the external subdomain of Spike’s receptor binding domain of 2019-nCoV shares only 40% amino acid identity with other SARS-related coronaviruses. Remarkably, its orf3b encodes a completely novel short protein. Furthermore, its new orf8 likely encodes a secreted protein with an alpha-helix, following with a beta-sheet(s) containing six strands. Learning from the roles of civet in SARS and camel in MERS, hunting for the animal source of 2019-nCoV and its more ancestral virus would be important for understanding the origin and evolution of this novel lineage B betacoronavirus. These findings provide the basis for starting further studies on the pathogenesis, and optimizing the design of diagnostic, antiviral and vaccination strategies for this emerging infection. Text: Coronaviruses (CoVs) are enveloped, positive-sense, single-stranded RNA viruses that belong to the subfamily Coronavirinae, family Coronavirdiae, order Nidovirales. There are four genera of CoVs, namely, Alphacoronavirus (αCoV), Betacoronavirus (βCoV), Deltacoronavirus (δCoV), and Gammacoronavirus (γCoV) [1] . Evolutionary analyses have shown that bats and rodents are the gene sources of most αCoVs and βCoVs, while avian species are the gene sources of most δCoVs and γCoVs. CoVs have repeatedly crossed species barriers and some have emerged as important human pathogens. The best-known examples include severe acute respiratory syndrome CoV (SARS-CoV) which emerged in China in 2002-2003 to cause a large-scale epidemic with about 8000 infections and 800 deaths, and Middle East respiratory syndrome CoV (MERS-CoV) which has caused a persistent epidemic in the Arabian Peninsula since 2012 [2, 3] . In both of these epidemics, these viruses have likely originated from bats and then jumped into another amplification mammalian host [the Himalayan palm civet (Paguma larvata) for SARS-CoV and the dromedary camel (Camelus dromedarius) for MERS-CoV] before crossing species barriers to infect humans. Prior to December 2019, 6 CoVs were known to infect human, including 2 αCoV (HCoV-229E and HKU-NL63) and 4 βCoV (HCoV-OC43 [ HCoV-OC43 and HCoV-HKU1 usually cause self-limiting upper respiratory infections in immunocompetent hosts and occasionally lower respiratory tract infections in immunocompromised hosts and elderly [4] . In contrast, SARS-CoV (lineage B βCoV) and MERS-CoV (lineage C βCoV) may cause severe lower respiratory tract infection with acute respiratory distress syndrome and extrapulmonary manifestations, such as diarrhea, lymphopenia, deranged liver and renal function tests, and multiorgan dysfunction syndrome, among both immunocompetent and immunocompromised hosts with mortality rates of ∼10% and ∼35%, respectively [5, 6] . On 31 December 2019, the World Health Organization (WHO) was informed of cases of pneumonia of unknown cause in Wuhan City, Hubei Province, China [7] . Subsequent virological testing showed that a novel CoV was detected in these patients. As of 16 January 2020, 43 patients have been diagnosed to have infection with this novel CoV, including two exported cases of mild pneumonia in Thailand and Japan [8, 9] . The earliest date of symptom onset was 1 December 2019 [10] . The symptomatology of these patients included fever, malaise, dry cough, and dyspnea. Among 41 patients admitted to a designated hospital in Wuhan, 13 (32%) required intensive care and 6 (15%) died. All 41 patients had pneumonia with abnormal findings on chest computerized tomography scans [10] . We recently reported a familial cluster of 2019-nCoV infection in a Shenzhen family with travel history to Wuhan [11] . In the present study, we analyzed a 2019-nCoV complete genome from a patient in this familial cluster and compared it with the genomes of related βCoVs to provide insights into the potential source and control strategies. The complete genome sequence of 2019-nCoV HKU-SZ-005b was available at GenBank (accession no. MN975262) ( Table 1 ). The representative complete genomes of other related βCoVs strains collected from human or mammals were included for comparative analysis. These included strains collected from human, bats, and Himalayan palm civet between 2003 and 2018, with one 229E coronavirus strain as the outgroup. Phylogenetic tree construction by the neighbour joining method was performed using MEGA X software, with bootstrap values being calculated from 1000 trees [12] . The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (1000 replicates) was shown next to the branches [13] . The tree was drawn to scale, with branch lengths in the same units as those of the evolutionary distances used to infer the phylogenetic tree. The evolutionary distances were computed using the Poisson correction method and were in the units of the number of amino acid substitutions per site [14] . All ambiguous positions were removed for each sequence pair (pairwise deletion option). Evolutionary analyses were conducted in MEGA X [15] . Multiple alignment was performed using CLUSTAL 2.1 and further visualized using BOX-SHADE 3.21. Structural analysis of orf8 was performed using PSI-blast-based secondary structure PREDiction (PSIPRED) [16] . For the prediction of protein secondary structure including beta sheet, alpha helix, and coil, initial amino acid sequences were input and analysed using neural networking and its own algorithm. Predicted structures were visualized and highlighted on the BOX-SHADE alignment. Prediction of transmembrane domains was performed using the TMHMM 2.0 server (http://www.cbs.dtu.dk/services/TMHMM/). Secondary structure prediction in the 5 ′ -untranslated region (UTR) and 3 ′ -UTR was performed using the RNAfold WebServer (http://rna.tbi.univie.ac.at/cgi-bin/ RNAWebSuite/RNAfold.cgi) with minimum free energy (MFE) and partition function in Fold algorithms and Table 2 . Putative functions and proteolytic cleavage sites of 16 nonstructural proteins in orf1a/b as predicted by bioinformatics. Putative function/domain Amino acid position Putative cleave site complex with nsp3 and 6: DMV formation complex with nsp3 and 4: DMV formation short peptide at the end of orf1a basic options. The human SARS-CoV 5 ′ -and 3 ′ -UTR were used as references to adjust the prediction results. The single-stranded RNA genome of the 2019-nCoV was 29891 nucleotides in size, encoding 9860 amino acids. The G + C content was 38%. Similar to other (Table 2 ). There are no remarkable differences between the orfs and nsps of 2019-nCoV with those of SARS-CoV (Table 3) . The major distinction between SARSr-CoV and SARS-CoV is in orf3b, Spike and orf8 but especially variable in Spike S1 and orf8 which were previously shown to be recombination hot spots. Spike glycoprotein comprised of S1 and S2 subunits. The S1 subunit contains a signal peptide, followed by an N-terminal domain (NTD) and receptor-binding domain (RBD), while the S2 subunit contains conserved fusion peptide (FP), heptad repeat (HR) 1 and 2, transmembrane domain (TM), and cytoplasmic domain (CP). We found that the S2 subunit of 2019-nCoV is highly conserved and shares 99% identity with those of the two bat SARS-like CoVs (SL-CoV ZXC21 and ZC45) and human SARS-CoV (Figure 2 ). Thus the broad spectrum antiviral peptides against S2 would be an important preventive and treatment modality for testing in animal models before clinical trials [18] . Though the S1 subunit of 2019-nCoV shares around 70% identity to that of the two bat SARS-like CoVs and human SARS-CoV (Figure 3(A) ), the core domain of RBD (excluding the external subdomain) are highly conserved (Figure 3(B) ). Most of the amino acid differences of RBD are located in the external subdomain, which is responsible for the direct interaction with the host receptor. Further investigation of this soluble variable external subdomain region will reveal its receptor usage, interspecies transmission and pathogenesis. Unlike 2019-nCoV and human SARS-CoV, most known bat SARSr-CoVs have two stretches of deletions in the spike receptor binding domain (RBD) when compared with that of human SARS-CoV. But some Yunnan strains such as the WIV1 had no such deletions and can use human ACE2 as a cellular entry receptor. It is interesting to note that the two bat SARS-related coronavirus ZXC21 and ZC45, being closest to 2019-nCoV, can infect suckling rats and cause inflammation in the brain tissue, and pathological changes in lung & intestine. However, these two viruses could not be isolated in Vero E6 cells and were not investigated further. The two retained deletion sites in the Spike genes of ZXC21 and ZC45 may lessen their likelihood of jumping species barriers imposed by receptor specificity. A novel short putative protein with 4 helices and no homology to existing SARS-CoV or SARS-r-CoV protein was found within Orf3b ( Figure 4 ). It is notable that SARS-CoV deletion mutants lacking orf3b replicate to levels similar to those of wildtype virus in several cell types [19] , suggesting that orf3b is dispensable for viral replication in vitro. But orf3b may have a role in viral pathogenicity as Vero E6 but not 293T cells transfected with a construct expressing Orf3b underwent necrosis as early as 6 h after transfection and underwent simultaneous necrosis and apoptosis at later time points [20] . Orf3b was also shown to inhibit expression of IFN-β at synthesis and signalling [21] . Subsequently, orf3b homologues identified from three bat SARSrelated-CoV strains were C-terminally truncated and lacked the C-terminal nucleus localization signal of SARS-CoV [22] . IFN antagonist activity analysis demonstrated that one SARS-related-CoV orf3b still possessed IFN antagonist and IRF3-modulating activities. These results indicated that different orf3b proteins display different IFN antagonist activities and this function is independent of the protein's nuclear localization, suggesting a potential link between bat SARS-related-CoV orf3b function and pathogenesis. The importance of this new protein in 2019-nCoV will require further validation and study. Orf8 orf8 is an accessory protein found in the Betacoronavirus lineage B coronaviruses. Human SARS-CoVs isolated from early-phase patients, all civet SARS-CoVs, and other bat SARS-related CoVs contain fulllength orf8 [23] . However, a 29-nucleotide deletion, Bat SL-CoV ZXC21 2018 Bat which causes the split of full length of orf8 into putative orf8a and orf8b, has been found in all SARS-CoV isolated from mid-and late-phase human patients [24] . In addition, we have previously identified two bat SARS-related-CoV (Bat-CoV YNLF_31C and YNLF_34C) and proposed that the original SARS-CoV full-length orf8 is acquired from these two bat SARS-related-CoV [25] . Since the SARS-CoV is the closest human pathogenic virus to the 2019-nCoV, we performed phylogenetic analysis and multiple alignments to investigate the orf8 amino acid sequences. The orf8 protein sequences used in the analysis derived from early phase SARS-CoV that includes full-length orf8 (human SARS-CoV GZ02), the mid-and late-phase SARS-CoV that includes the split orf8b (human SARS-CoV Tor2), civet SARS-CoV (paguma SARS-CoV), two bat SARS-related-CoV containing full-length orf8 (bat-CoV YNLF_31C and YNLF_34C), 2019-nCoV, the other two closest bat SARS-related-CoV to 2019-nCoV SL-CoV ZXC21 and ZC45), and bat SARS-related-CoV HKU3-1 ( Figure 5(A) ). As expected, orf8 derived from 2019-nCoV belongs to the group that includes the closest genome sequences of bat SARS-related-CoV ZXC21 and ZC45. Interestingly, the new 2019-nCoV orf8 is distant from the conserved orf8 or Figure 5(B) ) which was shown to trigger intracellular stress pathways and activates NLRP3 inflammasomes [26] , but this is absent in this novel orf8 of 2019-nCoV. Based on a secondary structure prediction, this novel orf8 has a high possibility to form a protein with an alpha-helix, following with a betasheet(s) containing six strands ( Figure 5(C) ). The genome of 2019-nCoV has overall 89% nucleotide identity with bat SARS-related-CoV SL-CoVZXC21 (MG772934.1), and 82% with human SARS-CoV BJ01 2003 (AY278488) and human SARS-CoV Tor2 (AY274119). The phylogenetic trees constructed using the amino acid sequences of orf1a/b and the 4 structural genes (S, E, M, and N) were shown (Figure 6(A-E) ). For all these 5 genes, the 2019-nCoV was clustered with lineage B βCoVs. It was most closely related to the bat SARS-related CoVs ZXC21 and ZC45 found in Chinese horseshoe As shown in Figure 7 (A-C), the SARS-CoV 5 ′ -UTR contains SL1, SL2, SL3, SL4, S5, SL5A, SL5B, SL5C, SL6, SL7, and SL8. The SL3 contains trans-cis motif [27] . The SL1, SL2, SL3, SL4, S5, SL5A, SL5B, and SL5C structures were similar among the 2019-nCoV, human SARS-CoV and the bat SARS-related ZC45. In the 2019-nCoV, part of the S5 found was inside Figure 7 Continued the orf1a/b (marked in red), which was similar to SARS-CoV. In bat SARS-related CoV ZC45, the S5 was not found inside orf1a/b. The 2019-nCoV had the same SL6, SL7, and SL8 as SARS-CoV, and an additional stem loop. Bat SARS-related CoV ZC45 did not have the SARS-COV SL6-like stem loop. Instead, it possessed two other stem loops in this region. All three strains had similar SL7 and SL8. The bat SARS-like CoV ZC45 also had an additional stem loop between SL7 and SL8. Overall, the 5 ′ -UTR of 2019-nCoV was more similar to that of SARS-CoV than the bat SARS-related CoV ZC 45. The biological relevance and effects of virulence of the 5 ′ -UTR structures should be investigated further. The 2019-nCoV had various 3 ′ -UTR structures, including BSL, S1, S2, S3, S4, L1, L2, L3, and HVR (Figure 7(D-F) ). The 3 ′ -UTR was conserved among 2019-nCoV, human SARS-CoV and SARS-related CoVs [27] . In summary, 2019-nCoV is a novel lineage B Betacoronavirus closely related to bat SARS-related coronaviruses. It also has unique genomic features which deserves further investigation to ascertain their roles in viral replication cycle and pathogenesis. More animal sampling to determine its natural animal reservoir and intermediate animal host in the market is important. This will shed light on the evolutionary history of this emerging coronavirus which has jumped into human after the other two zoonotic Betacoroanviruses, SARS-CoV and MERS-CoV.

What do the results indicate?

SARS to novel coronavirus – old lessons and new lessons https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7026896/ SHA: 5d254ed178c092d3639ce70ae9653593acc471f9 Authors: McCloskey, Brian; Heymann, David L. Date: 2020-02-05 DOI: 10.1017/s0950268820000254 License: cc-by Abstract: The response to the novel coronavirus outbreak in China suggests that many of the lessons from the 2003 SARS epidemic have been implemented and the response improved as a consequence. Nevertheless some questions remain and not all lessons have been successful. The national and international response demonstrates the complex link between public health, science and politics when an outbreak threatens to impact on global economies and reputations. The unprecedented measures implemented in China are a bold attempt to control the outbreak – we need to understand their effectiveness to balance costs and benefits for similar events in the future. Text: On 29 December 2019 clinicians in a hospital in Wuhan City, China noticed a clustering of cases of unusual pneumonia (with the first case identified at that time on 12 December) with an apparent link to a market that sells live fish, poultry and animals to the public. This event was reported to the World Health Organisation (WHO) on 31 December [1]. Within 4 weeks, by 26 January 2020, the causative organism had been identified as a novel coronavirus, the genome of the virus had been sequenced and published, reverse transcription polymerase chain reaction tests had been developed, the WHO R&D Blueprint had been activated to accelerate diagnostics, therapeutics and vaccine development and a candidate vaccine was ready for initial laboratory testing. Currently Chinese health authorities are building a 1000 bed hospital in Wuhan in 10 days. By 26 January also, almost 50 million people in Wuhan and neighbouring cities had effectively been placed in quarantine while the WHO had determined that the event should not yet be declared as a Public Health Emergency of International Concern (PHEIC) [2] and had recommended no specific travel restrictions. The WHO have emphasised the importance of exit screening at ports in countries showing transmission of the novel coronavirus and have provided guidance for countries implementing entry screening at airports while acknowledging that evidence for the effectiveness of entry screening is equivocal. This response is one of the swiftest, coordinated global responses to an emerging infectious disease the world has seen in modern times, but is it the appropriate response, will it be effective and is it sustainable? According to the situation report published by the WHO on 28 January 2020 [3], a total of 2798 confirmed 2019-nCoV cases have been reported globally; of these, 2761 cases were from China, including Hong Kong (8 cases), Macau (5) and Taipei (4). Thirty-seven confirmed cases have been reported outside of China in eleven countries in Europe, North America, Australia and Asia; of these 37 exported cases, 36 had a travel history from China or an epidemiological link to a case from China. Of the confirmed cases in China, 461 have been reported as severely ill, with 80 deaths to date. This outbreak and the response to it illustrate some key issues about how global preparedness and response capacity for outbreaks have evolved over almost two decades since the severe acute respiratory syndrome (SARS) epidemic of 2002/3 and what lessons have, or have not, been learned. It also raises questions about the impact these lessons have had on the way agencies and governments respond to these events and about the role of the WHO and the International Health Regulations (IHR). One of the critical lessons from the SARS experience was the absolute necessity to be able to coordinate the international resources that are available in an outbreak and to get them focussed on identifying priorities and solving problems. The WHO established the means to do this for SARS and it has since been further developed and integrated into global preparedness, especially after the West Africa Ebola epidemic. Organisations such as the Global Outbreak Alert and Response Network (GOARN), the Coalition for Epidemic Preparedness Innovations (CEPI), the Global Research Collaboration For Infectious Disease Preparedness (GloPID-R) and the Global Initiative on Sharing All Influenza Data (GISAID) have been supported by the WHO Research Blueprint and its Global Coordinating Mechanism to provide a forum where those with the expertise and capacity to contribute to managing new threats can come together both between and during outbreaks to develop innovative solutions to emerging problems. This global coordination has been active in the novel coronavirus outbreak. WHO's response system includes three virtual groups based on those developed for SARS to collate real time information to inform real time guidelines, and a first candidate vaccine is ready for laboratory testing within 4 weeks of the virus being identified. Another key factor in successfully preventing and managing emerging threats is the rapid and transparent sharing of information between countries and agencies. There was extensive criticism of China for its perceived failure to share information about the emerging SARS infection early enough in the outbreak to allow countries to prepare and respond. There were similar concerns about information sharing as Middle East Respiratory Syndrome (MERS) emerged and evolved in the Middle East in 2012, particularly in Saudi Arabia, and about the emergence of Ebola in West Africa in 2014. On this occasion information sharing seems to have been rapid and effective (while recognising that the information available in the early stages of an outbreak is always less than the global community would like). The WHO was notified of the original clustering within days and the full genomic sequence of the new virus was published less than 2 weeks after the cluster was first detected. The WHO has expressed its satisfaction with the actions of the Chinese authorities in sharing information with the WHO. Working with journalists and the media to help them understand the science and epidemiology, particularly in a fast moving event, will improve risk communication to the public and reduce inappropriate concerns and panic. While reporting of this outbreak shows signs of the efforts of epidemiologists, infectious disease experts, national and international public health agencies and others engaging with journalists, there are also signs that this is not yet achieving it's goal. For example, the public perception is that the increase in case numbers reported daily by the Chinese authorities represents a daily escalation in the epidemic while the reality is that these numbers are also the result of active, aggressive, case finding in China and some of these cases are 'old' cases newly recognised as being due to the novel coronavirus. Similarly the virus is usually described by the media as 'deadly' and although this is true in the sense that it has caused deaths, the nuances of uncertain case fatality rates in the early stages of an outbreak are not being communicated. The current estimated case fatality rate seems to be around 3% which is significant but not comparable to the 10% rate for SARS or 34% reported for MERS. These misperceptions are still driving public anxiety. To supplement formal reporting mechanisms between countries and with WHO (including the IHR), the use of informal mechanisms such as media and social media reports was advocated in the light of the SARS experience. There are now globally several systems that provide collated information from informal reporting including networks of experts and scanning of media and social media. These contribute to, and amplify, epidemic intelligence and are being integrated with national and international surveillance systems. The value, and the challenges, of this additional source of information has been evident in the current outbreak. The value comes from ensuring that early indications of cases beyond the initial outbreak city have been detected and can supplement the global risk assessment and monitoring of the evolution of the outbreak. The challenges lie in the volume and diversity of the information available and the relative lack of verification mechanisms, such that one of these systems (ProMed) has commented that it was becoming increasingly difficult to assimilate the information being supplied [4] and to make meaningful interpretations. Early in the outbreak it was reported that health workers had not been infected. This was reassuring because it is health workers who many times, and inadvertently, amplify transmission. Failure to wash hands between patients, for example, can result not only in autoinfection, but also in infection of patients hospitalised for other causes when they provide care. Autoinfection is not only a risk for the health worker, but also for their families and the communities in which they live, depending on the transmissibility and means of transmission. More recently infection, and at least one death, in health workers has been confirmed. Although not unexpected this does add to the epidemiological risk. A characteristic of the SARS outbreak was the variability of transmissibility between cases and the occurrence of 'superspreading events' where a case infected significantly more contacts than the average. This was also seen with MERS in the outbreak in the Republic of Korea (RoK). In this current novel coronavirus outbreak, such superspreading events have not been documented but the epidemiology is still not clear. Confirming whether or not this is happening must be an urgent task for the Chinese investigation. Modellers have suggested reproductive rates (R 0 ) of 3.8 (95% confidence interval, 3.6-4.0) [5] and 2.6 (1.5-3.5) [6] ; R 0 for SARS was estimated at around 3 in the absence of control measures [7] . The economic impact of major outbreaks can be substantial for the affected country. This was seen clearly in SARS, MERS in RoK and Ebola in West Africa. One analyst estimates that the current coronavirus outbreak's likely impact will range from a 0.8% cut to real GDP if the epidemic is controlled within 3 months, to a 1.9% cost to GDP if the epidemic lasts 9 months [8] . This may increase substantially in the light of the extended restrictions on movement, and therefore trade and commerce, within China. The emergence of a significant respiratory illness linked to a novel coronavirus represents a test of the global capacity to detect and mange emerging disease threats. Its emergence in China adds an additional dimension in the light of previous experience with SARS. The timing of the outbreak immediately before the Chinese Lunar New Year with its attendant population movements adds extra risk and urgency to the response. The rapid sharing of information in this outbreak and the speed of the coordinated response both in the country and internationally suggest that lessons have been learned from SARS that improve global capacity. The international networks and forums that now exist have facilitated the bringing together of expertise from around the world to focus research and development efforts and maximise the impact. At this early stage in the outbreak information remains incomplete and key clinical and epidemiological questions have not yet been answered, but the deficit seems to be due more to the constraints of investigating an emerging disease than to any unwillingness to engage and share information with partners. There are some indications of areas where further improvement is necessary. The global media response to the unfolding events has been relatively balanced and informed but the nuances of the evolving situation have not been critically examined in partnership with the media and as a result the public perception of the risk may be exaggeratedalthough it of course remains possible that the outbreak will develop in a way that matches up to the perceived risk. The lack of appreciation of the uncertainties in determining a meaningful case fatality rate and the significance of ascertainment bias at the beginning of an outbreak, along with the impact of aggressive case finding on case numbers, are examples of where understanding could be improved. This is always a challenging process when balancing the resources focussed on analysing the situation on the ground with resources directed at interpreting the information for journalists but in SARS, the R 0 was seen to decrease in response to information reaching the public and the public then adopting risk reduction actions [6] ; so accurate public risk communication is critical to success. It would be helpful to find a forum where this can be explored with the media community after the event. The increase in access to early information from diverse sources including media and social media adds an important dimension to identifying and tracking new events globally and is a key part of the overall epidemic intelligence system. However, it is also a potential source of disinformation. When, as has been seen in this outbreak, the volume of information coming in exceeds any capacity to collate and analyse it and to attempt to cross-reference and verify separate items, there is a risk that the information fuels speculation and media and public concern. Again there is a fine balance between information that encourages appropriate risk avoidance actions and information that encourages inappropriate actions; however the public health is usually better served by more information rather than less. The role of a declaration of a PHEIC in managing a serious outbreak has been questioned in the light of Ebola in West Africa and in the Democratic Republic of Congo [9] and has been challenged again with this outbreak. The binary nature of a PHEIC declaration (either an event is a PHEIC or it isn'tthere are no intermediate options) and the specificity of the three defined criteria for a PHEIC have caused difficulty for Emergency Committees in considering whether a given event should be a PHEIC. The lack of a clear understanding of what a PHEIC declaration is meant to achieve adds to the Emergency Committee's difficulties, as does the relative paucity of clinical and epidemiological answers at this stage of the investigation. In this instance the Emergency Committee were divided in coming to a conclusion but decided on balance that the current situation, although an emergency, should not as yet be declared a PHEIC [2]. As with Ebola in the DRC, there has been criticism of the WHO for this decision but, as with Ebola, it is not immediately clear what would be different in the response if a PHEIC was declared. The WHO is working on improving the way in which Emergency Committees develop their advice for the Director General but, as recommended by this Emergency Committee and the post-Ebola IHR Review Committee in 2015, the development of an intermediate alert alongside WHO's risk assessment process may be helpful. A key function of a PHEIC declaration is that it is the (only) gateway to the WHO Temporary Recommendations on possible travel and trade restrictions to limit international spread of a disease. In this case several countries globally had already implemented entry screening at airports and China had begun closing down international travel from Wuhan before the Emergency Committee had finished their deliberations. While the WHO would not, and could not, interfere with the sovereign decisions of member states, the lack of influence on travel and trade decisions could prove problematic. Alongside the speed of the response in this outbreak, we have seen dramatic changes in the scale of the response. The imposition of very extensive quarantine measures on millions of people as an attempt to break the transmission of the virus is unprecedented. We do not know whether they will be effective; indeed we do not know how we will determine if they have been effectivewhat end point can we measure that will provide an answer to that question? If recent suggestions that people infected with this coronavirus may be infectious while incubating or asymptomatic, and the reports that up to 5 m people left Wuhan before the travel restrictions were imposed, are confirmed, the efficacy of these control measures will be more challenged. Given the likely impact on at least the Chinese economy and probably the global economy, it will be important to understand the role and the effectiveness of public health measures on this scale for the future. However, the imposition of these dramatic measures does also raise a wider question: if there is an impact from these measures, what other countries would (or could) implement such measures? Would other countries accept the self-imposed economic damage that China has accepted to try and contain this outbreak? Is it reasonable to consider that national governments would close down public transport into and out of London, New York or Paris in the week before Christmas even if it were shown to be an effective control measure? These decisions and questions cross the interface between public health, science and politics. The response to this outbreak in China was inevitably influenced by the historical reaction to the country's response to SARS and the world's suspicion of China's lack of cooperation at that time. The current response is therefore framed within a context of not wanting to be seen to be behaving in the same way with this event. This may indicate another impact of the SARS (and MERS and Ebola) experience on the response to subsequent outbreaksa tendency to look at worst case scenarios and respond accordingly and a fear of 'getting it wrong'. This can deter leaders at all levels, from outbreak teams to national governments, from making judgements when all the information they would like is not available in case those judgments turn out to be wrong when the full information becomes available. In emergency response it is generally better to over-react and then scale back if necessary rather than under-react and then act too late. Response should be on a 'no regrets' basismake the best decisions possible on the basis of the best information and science available at the time but do not judge or criticise if later information suggests a different course of action. The early response must recognise what is known and what is not known and look at what of the unknowns can reasonably be estimated by reference to previous outbreaks, similar pathogens, early reporting and modelling, etc. The risk assessment and response can then be modified and refined as information on the unknowns evolves. Key to that approach, however, is confidence that decisions will not be criticised based on information that was not available at the time. It is also important to be ready to change decisions when the available information changessomething that both scientists and politicians can find difficult. In that context, China should not be judged for implementing what might appear to be extreme measures but China should also be prepared to discontinue the measures quickly if evidence suggests they are not the best way to solve the problem. By closing airports the international spread from Wuhan may be decreased, but success will depend on how effective the measures really are at stopping people moving out of the affected area as well as on the behaviour of the virus. As always, only time will tellbut time is scarce.

How long did it take to publish the full genomic sequence of SARS-CoV-2 after it was identified?

Mucosal immune responses induced by oral administration recombinant Bacillus subtilis expressing the COE antigen of PEDV in newborn piglets https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6418403/ SHA: 5caced13bcb8a42cca41369c5a71ae7df5381ca8 Authors: Wang, Jialu; Huang, Lulu; Mou, Chunxiao; Zhang, En; Wang, Yongheng; Cao, Yanan; Yang, Qian Date: 2019-03-15 DOI: 10.1042/bsr20182028 License: cc-by Abstract: Porcine epidemic diarrhea (PED) is a highly contagious disease in newborn piglets and causes substantial economic losses in the world. PED virus (PEDV) spreads by fecal–oral contact and can be prevented by oral immunization. Therefore, it is necessary to develop an effective oral vaccine against PEDV infection. Currently, Bacillus subtilis as recombinant vaccine carrier has been used for antigen delivery and proved well in immune effect and safety. The present study evaluated the immunogenicity of recombinant Bacillus subtilis (B. subtilis-RC) in piglets via oral administration. After oral immunization in piglets, B. subtilis-RC significantly increased the local mucosal immune responses. Oral administration with B. subtilis-RC significantly improved the level of specific mucosal immunoglobulin A (IgA) antibodies against PEDV infection, through enlarging the area of Peyer’s patches (PPs) and increasing the number of ileum IgA(+) secreting (SIgA) cells. In the meantime, B. subtilis-RC remarkably increased the number of intraepithelial lymphocytes (IELs). We also observed that oral administration of B. subtilis-RC significantly increased CD3(+)T lymphocytes’ numbers and up-regulated the ratio of CD4(+)/CD8(+) T cells. Furthermore, high titers of specific serum immunoglobulin G (IgG) revealed satisfactory systemic immune response against PEDV infection. In summary, our study demonstrated that oral administration of B. subtilis-RC could trigger a high level of local and systemic immune responses and would be a promising candidate vaccine against PEDV infection in piglets. Text: Porcine epidemic diarrhea (PED) characterized by highly fatal acute diarrhea in piglets, results in enormous losses in the worldwide pig industry [1] . The causative agent PED virus (PEDV) belongs to the porcine coronaviruses (CoVs). PEDV infection mainly spreads through the digestive tract [2] , and damages the host intestine mucosal surfaces by infecting the intestine epithelial cells [3] . Therfore enhancing intestinal mucosal immunity can elicit effective mucosal immune responses against PEDV infection [4] . Currently, traditional vaccines (intramuscular route or subcutaneous injection) have been developed and applied widely in the market [5] . These vaccines administered parenterally cannot effectively induce high titers of maternal antibodies and virus-specific IgA antibodies, resulting in inadequate mucosal protection to against PEDV infection [6] . Furthermore, these maternal antibodies in the milk were always degraded by gastric acid and pepsin before entering the intestinal tract. Effective PEDV vaccines must provide adequate mucosal protection in the intestinal tract. However, the effective vaccines are currently lacking [7] . As a superior way of mucosal immunization, oral administration can protect the gut and stimulate the common mucosal immune system [8] . Besides, oral immunization has several attractive features which include safety, and a straightforward, inexpensive, and needle-free approach [9] . Therefore, oral immunization often delivers large amounts of antigens to prevent the diarrheal diseases [10] . Nevertheless, there are several challenges by oral immunization, which consist of physical, chemical, and biological barriers when delivering antigens to the gastrointestinal (GI) tract (such as gastric acids, pepsin, and trypsin in the GI tract) [11] . It is a substantial problem that digestive acids and proteases can degrade antigen proteins for nutrient absorption [12] . Therefore, the vaccine delivery system has been applied to solve the problem. The system can protect antigens from the severe environment of the GI tract and deliver antigens to intestinal mucosa [13] . Currently, Bacillus subtilis (B. subtilis) is widely used as a vaccine delivery system for its unique characteristics. As a nonpathogenic Gram-positive bacterium, B. subtilis has been regarded as a novel probiotic and food additive in humans and animals [14] . The B. subtilis has adjuvant activity and can deliver heterologous antigens to the GI tract, providing additional immunity stimulation [15] . Besides, research had shown that orally administered B. subtilis could also enhance immune regulation and gut health in pigs [16] . Moreover, oral administration of B. subtilis could elicit humoral and cellular immune responses to the maintenance of gut homeostasis by dendritic cells (DCs) [17] . DCs are the most important professional antigen-presenting cells and can effectively regulate antibody titers [18] . DCs naturally exist in the gut-associated lymphoid tissue (GALT), including Peyer's patches (PPs), isolated lymphoid follicles (ILFs), mesenteric lymph nodes (MLNs), and scatter throughout the subepithelial lamina propria (LP) of the small intestine and colon [19] . Furthermore, B. subtilis is convenient for genetic manipulation and has developed a large variety of genetic tools [20] . Therefore, B. subtilis is widely used as an effective vaccine delivery system to induce mucosal immune responses and shows unique effect on the immune system. In the present report, we explored the immune effect of a recombinant B. subtilis (B. subtilis-RC) which had been successfully constructed with expressing PEDV COE protein in piglets. Our research indicated that B. subtilis-RC was beneficial to the mucosal immune system development, and could effectively generate specific antibodies against PEDV infection, suggesting a potential approach for preventing PEDV infection. The B. subtilis WB800 was kindly provided by Dr. Xuewen Gao (from the department of plant pathology, Nanjing Agricultural University) [21] . B. subtilis-RC previously constructed in our laboratory was able to express the gene COE (499-638 amino acids in S protein). Prior to oral administration, the recombinant strain was grown in LB broth at 37 • C for 12 h, and then washed twice with PBS, and suspended in PBS to reach a final concentration of 1 × 10 10 CFU/ml. The PEDV Zhejiang08 strain was provided by the Veterinary Medicine Research Centre of the Beijing Dabeinong Technology Group Co., Ltd. [22] . The virus was cultured in African green monkey kidney cells (Vero cells) and purified by using a discontinuous sucrose density gradient. The virus was UV-inactivated at UV dose of 4 J/cm 2 for 24 h to achieve a complete loss of infectivity [23] . The purified virus concentration was measured using the BCA protein assay kit (Thermo Fisher, MA, U.S.A.). ELISA: Rabbit anti-pig IgG (horseradish peroxidase (HRP)), Goat Anti-Pig IgA (HRP) were purchased from Abcam. Second antibody: DyLight 649-conjugated goat anti-mouse IgG antibody, DyLight 488-conjugated goat anti-rabbit IgG antibody, DyLight 594-conjugated goat anti-rabbit IgG antibody were purchased from Multi-science, Hangzhou, China. ABC-based system (biotinylated goat anti-rabbit IgG antibody) was used as the secondary antibody with DAB as a chromogen was purchased from Boster, Wuhan, China. Specific pathogen-free (SPF) DLY piglets (Duroc and Landrace and Yorkshire) were kindly provided by Jiangsu Academy of Agricultural Sciences (Nanjing, China). The animal experiments had been approved by the Institutional Animal Care and Use Committee of Nanjing Agricultural University and followed the National Institutes of Health's guidelines for the performance of animal experiments. Twelve newborn piglets were randomly divided into three groups (four piglets in each group), and housed under similar conditions in different stables in order to avoid probiotic cross-contamination. The piglets were orally dosed with 100 μl of B. subtilis-RC. The control groups of piglets were orally administered with inactivated PEDV (100 μg/dose) and equal volume of PBS. The immunization protocol was performed on the piglets that were 5 days old ( Figure 1C ), and signed as 0 day. Then booster immunizations were administered on 5 days. Specimen collection was then performed every 7 days post boost immunization ( Figure 1C ). Blood samples were collected weekly from all piglets after the boost immunization and allowed to clot overnight at room temperature to collect serum. Blood samples were separated by centrifugation and stored at −20 • C in order to detect the levels of specific IgG and IgA. Three swabs were collected every week lasting for 1 month, including nasal, oral, and feces swabs for the ELISA. The piglets were sacrificed in 33 days. The same location of the small intestine and ileum tissues from each piglet were fixed with Bonn's liquid and 4% paraformaldehyde. The small intestine tissues in same location were fixed with Bouin Fixative Solution for 24 h, embedded in paraffin, and sectioned at 4-μm thickness. The sections were placed on glass slides. Hematoxylin-eosin staining was applied to the paraffin sections, then observing and taking photographs under optical microscope (OLYMPUS CX23). The number of intraepithelial lymphocytes (IELs) were counted in every 100 epithelial cells under the same multiple light microscope amongst ten pictures from each group [24] . The immunohistochemistry detection was performed with the SABC kit (Boster Bioscience). Hydrogen peroxide was used to deactivate intrinsic peroxidase. Antigen retrieval was performed in a water bath using citrate-EDTA buffer (10 mM citric acid, 2 mM EDTA, 0.05% Tween 20, pH 6.2). Sections were incubated with diluted anti-IgA antibody (1:100; Abcam) overnight at 4 • C. As negative controls, immunostaining performed by incubating samples with control antiserum instead of primary antibody. The addition of biotin-labeled secondary antibody to the slides was followed by adding HRP-labeled streptavidin. After staining with DAB, the slides were recorded using a digital camera (Leica-DM4000B) [25] . The isolated intestines with PPs were transferred to ice-cold PBS. Then, remaining fat and connective tissue was removed and washed thoroughly with ice-cold PBS. Next, the intestine was cut longitudinally into 0.5-cm fragments. The fragments were incubated with 5 ml of 30 mM EDTA and placed in 5 ml digestion solution containing 4% FBS, 0.5 mg/ml each of Collagenase D (Roche) and DNase I (Sigma), and 50 U/ml Dispase (Fisher). The fragments were incubated with Dulbecco's PBS (DPBS) for 20 min at 37 • C by slow rotation (100 rpm). After incubating, the epithelial cells layer which contained the IELs were separated by intensive vortex and passed through a 70-μm cell strainer. Single cell suspension was collected and washed twice by DPBS, the solution was vortexed intensely and passed through a 40-μm cell strainer. Supernatants was washed by precooled RPMI medium 1640 (Thermo Fisher Scientific) and suspended by 10 ml of the 40% fraction of a 40:80 Percoll gradient, overlaid on 5 ml of the 80% fraction in a 15-ml Falcon tube. Percoll gradient separation was performed by centrifuging for 20 min at 2500 rpm. LP lymphocytes (LPLs) were collected at the interphase of the Percoll gradient, then washed and suspended in FACS buffer or T cell medium. In the meantime, flow cytometry analysis was performed on BD Facscalibur (BD Biosciences) instruments and analyzed by FlowJo software. All antibodies were purchased from BD Pharmingen or eBiosciences. Isolated single-cell suspensions were stained with anti-CD3-APC, anti-CD4-FITC, anti-CD8-PE, all at 1:100 dilution for 30 min on ice, and washed with PBS twice, and analyzed by FACS [26] . Cytokines interleukin (IL) 10 (IL-10) and IL-1β (Abcam) were measured by ELISA according to the manufacturer's instructions. Data were acquired on an automated ELISA plate reader at OD 450 nm immediately. PEDV neutralizing antibodies were measured in intestine washing liquid by plaque reduction neutralization test (PRNT). The test was performed as previously described with minor modifications [27] . A total of 450 μl of intestine washing liquid was two-fold serially diluted and mixed with 50 μl viral suspension containing 10 3 TCID 50 PEDV virus for 1 h at 37 • C in 12-well flat bottomed tissue culture plates. The mixture was then inoculated for 1 h at 37 • C and 5% CO 2 . Then, the mixture was inoculated with Vero cells suspension (approximately 1.0 × 10 6 ml −1 ) for another 3-4 days. After staining with Crystal Violet, the plates were observed under a microscope for cytopathic effect. Data were obtained as the means + − S.E.M. of three replicates per test in a single experiment. GraphPad Prism V6.0 (San Diego, CA, U.S.A.) used to perform statistical analyses. Tukey's multiple comparison tests and one-way ANOVA were used to analyze the significance of the difference between means. P-values less than 0.05 (P<0.05) were considered significant and P-values less than 0.01 (P<0.01) as highly significant. PPs are a concentrate of lymphoid tissue and the primary site for immunoglobulin A (IgA) production which is crucial to regulate the homeostatic balance of intestine [28] . The area of PPs is a key immunity indicator. Oral administration with B. subtilis-RC significantly (P<0.01) increased the area of PPs compared with two control groups as shown in Figure 1A . In addition, the villi length of ileum got longer by oral administration with B. subtilis-RC (P<0.01) than the other two groups ( Figure 1B) . These primarily confirmed that B. subtilis-RC was beneficial to maintain the structure of intestine. Intestinal IELs are a large and diverse population of lymphoid cells residing within the intestinal epithelial cells (IECs), and forming the intestinal mucosal barrier [29] . IELs are important part of the gut mucosal immune system. The level of specific anti-PEDV ileum IgA + secreting (SIgA) antibody in piglets was measured by ELISA in the mouth and feces. As shown in Figure 3A ,B, antigen-specific mucosal SIgA in the above sites was clearly higher than inactivated PEDV group (P<0.05 or P<0.01). As expected, the mouth had higher levels of SIgA than other sites. After oral immunization, the level of serum anti-PEDV IgG antibody in piglets immunized with B. subtilis-RC, inactivated PEDV or PBS were determined by ELISA, as shown in Figure 3C . The results indicated that although the titers dropped during sampling period, the IgG level of B. subtilis-RC still significantly increased from 0 to 33 days than inactivated PEDV group (P<0.05 or P<0.01). CD3 + T lymphocytes are the fundamental cell surface markers of T lymphocytes, therefore, the number of CD3 + T lymphocytes could represent the quantity of T lymphocytes. Consequently, we analyzed the number of CD3 + T lymphocytes in ileum. The data indicated that both B. subtilis-RC and inactivated PEDV could dramatically (P<0.05) increase CD3 + T lymphocytes compared with PBS group ( Figure 4A ). These changes showed confident evidence that oral administration with B. subtilis-RC had a good influence on intestinal mucosal immunity in piglets. SIgA is the main immunoglobulin isotype in animals, largely secreted across the intestinal mucosal surface especially in the small intestine [30] . SIgA plays an important role in intestinal mucosal immunity and reflects on the intestinal mucosal immunity. After oral administration with B. subtilis-RC, the number of IgA secreting cells had quickly risen compared with the other two groups (P<0.05) ( Figure 4B) . These results showed that oral administration with B. subtilis-RC was conducive to intestinal mucosal immunity and could increase the number of IgA secreting cells to produce positive effects on against PEDV infection. A great deal of immune cells are scattered in the epithelial cells. IECs indirectly or directly interact with innate and adaptive immune cells by presenting antigens to lymphocytes [31] . Consequently, learning about how the lymphocytes are distributed in the small intestinal mucosa is very meaningful for mucosal immunology. Previous data had shown that CD3 + T lymphocytes significantly (P<0.05) increased ( Figure 4A ), so we further analyzed the immunological classification of CD3 + T lymphocytes. The lymphocyte of the ileum with PPs junction was isolated and the lymphocytes of CD3, CD4, and CD8 were analyzed by three colors flow cytometry ( Figure 5A ). These results showed that CD3 + CD4 + T cells have obviously (P<0.01) increased ( Figure 5B ), nevertheless the CD3 + CD8 + T cells remarkably (P<0.05) declined ( Figure 5C ). After calculation, the ratio of CD4 + /CD8 + T cells increased ( Figure 5D ). This ratio could also further measure the immunity levels of piglets. Cytokine IL-1β and IL-10 levels were determined to evaluate cellular immune responses induced by B. subtilis-RC as shown in Figure 6A ,B. As we can see from the diagram, significantly (P<0.01) higher IL-1β and IL-10 were produced after oral administration with B. subtilis-RC than the other two groups. These all revealed that B. subtilis-RC could stimulate cytokines release to mediate communication with and between cells of the immune system, improving the mucosal immune response to PEDV infection. The PEDV neutralizing antibodies were detected by PRNT assay. Oral administration with B. subtilis-RC could effectively reduce the plaque-forming ability of PEDV (P<0.01) compared with other two groups in Figure 7 . This revealed that B. subtilis-RC could stimulate high level of PEDV neutralizing antibodies against PEDV infection. Amidst the PEDV outbreak, various vaccines have been developed to control diseases and the effects are unsatisfactory. Oral vaccines can induce more robust mucosal immunity than injectable counterparts [32] . Therefore, oral immunization has appeared as an effective strategy for controlling PEDV outbreak [33] . It is now clear that effective mucosal immune response requires serum IgG and mucosal SIgA [34] . SIgA is the basis of the mucosal immune system, playing an important role in maintaining the immune homeostasis, and neutralizing the invasive pathogens. Serum IgG represents systemic immune responses. During PEDV infections, oral immunization elicits not only mucosal but also systemic immune responses very well [35] . Our data showed a strong and long-lasting anti-PEDV IgG response were detected by oral administration with B. subtilis-RC in piglets. Although as time went on, the antibody titers declined a little, it still stayed on overhead compared with control groups and with accordance to the changeable tendency of antibodies. The change of specific IgA showed similar results in mouth and feces mucosa. All these changes had contributed to fight PEDV infection. As the extra immunity boost, B. subtilis-RC reduced the ability of pathogens to cross the intestinal mucosa and the systemic spread of invasive pathogens [36] . The mucosal immune system generates immune responses through immune cells that reside in mucosal compartments. T lymphocytes residing in the mucosa play important roles in mucosal immunity [37] . We further explored the species, amounts, and distribution of T lymphocytes in the intestine mucosa. CD3 is a fundamental cell surface marker of T lymphocytes [38] . The result showed that the number of CD3 + T lymphocytes significantly increased, and these revealed that B. subtilis-RC could stimulate T-cell maturation. According to the molecules expressed on the cell surface, T lymphocytes can further divide into T helper cells (CD4 + T cells) and cytotoxic T cells (CD8 + T cells) [39] . Furthermore, we observed that the ratio of CD4 + /CD8 + T cells increased by oral administration. The CD4/CD8 ratio measures the ratio of T helper cells to cytotoxic T cells. Therefore, we could see that oral administration B. subtilis-RC could strengthen Th1 immune response by raising the ratio of CD4 + /CD8 + T cells. Small intestine morphology can directly reflect the intestinal health and plays an important role in maintaining the intestine immune system [40] . The early stage of PEDV infection is frequently accompanied by necrosis and exfoliation of infected villous epithelial cells, ultimately resulting in acute, severe villous atrophy [41] . Therefore, the effective work of maintaining intestine morphology is a good indicator for assessing the efficacy of vaccines. After oral administration with B. subtilis-RC, we found the area of PPs expanded significantly. PPs are small masses of lymphatic tissue and form an important part of the immune system by recruiting and inducting the T cells to prevent the growth of pathogens in the intestines. Furthermore, an increase in the number of IELs demonstrated the effectiveness of B. subtilis-RC. Moreover, the villi length of ileum showed some encouraging results that a well-formed intestine morphology came into being by B. subtilis-RC. The satisfactory intestine morphology was the first step on the road against PEDV infection. Several morphology results proved that B. subtilis-RC could remarkably maintain the intestine morphology and form comprehensive protection. As previously mentioned, oral administration with B. subtilis-RC could stimulate T-cell proliferation and differentiation and modulate the immune response. Moreover, cytokines are small-molecule proteins with wide biological activity, synthesized and secreted by immune cells and some non-immune cells [42] . As a cell signaling molecule, it mainly acts to regulate immune responses, participating in the differentiation and development of immune cells, mediating inflammatory responses, stimulating hematopoiesis, and participating in tissue repair. Previous studies had demonstrated that PEDV inhibited both NF-κB and pro-inflammatory cytokines [43] . Therefore, cytokines are a key indicator for evaluating the ability of a vaccine to stimulate immune responses. In this study, we had observed that IL-1β and IL-10 increased (P<0.01) remarkably. IL-1β as one of the earliest pro-inflammatory cytokines and is centrally involved in the initiation and regulation of inflammatory and innate immune responses. Research had shown that IL-1β could significantly up-regulate the local and systemic immune tissues post microbial infection [44] . In addition, IL-10 is a potent anti-inflammatory cytokine that plays an essential role in preventing inflammatory and autoimmune pathologies [45] . In summary, both data showed that oral administration with B. subtilis-RC regulated and enhanced immunity by up-regulating cytokines IL-1β and IL-10. In conclusion, the present results demonstrated that oral immunization with B. subtilis-RC could effectively induce local mucosal and systematic immune responses against PEDV infection, while enhancing and regulating the immune function by raising the ratio of CD4 + /CD8 + T cells and cytokines IL-1β and IL-10, thus pointing to a promising oral vaccine candidate for PEDV infection in piglets.

What is the role of dendritic cells in the immune response?

iNR-Drug: Predicting the Interaction of Drugs with Nuclear Receptors in Cellular Networking https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3975431/ SHA: ee55aea26f816403476a7cb71816b8ecb1110329 Authors: Fan, Yue-Nong; Xiao, Xuan; Min, Jian-Liang; Chou, Kuo-Chen Date: 2014-03-19 DOI: 10.3390/ijms15034915 License: cc-by Abstract: Nuclear receptors (NRs) are closely associated with various major diseases such as cancer, diabetes, inflammatory disease, and osteoporosis. Therefore, NRs have become a frequent target for drug development. During the process of developing drugs against these diseases by targeting NRs, we are often facing a problem: Given a NR and chemical compound, can we identify whether they are really in interaction with each other in a cell? To address this problem, a predictor called “iNR-Drug” was developed. In the predictor, the drug compound concerned was formulated by a 256-D (dimensional) vector derived from its molecular fingerprint, and the NR by a 500-D vector formed by incorporating its sequential evolution information and physicochemical features into the general form of pseudo amino acid composition, and the prediction engine was operated by the SVM (support vector machine) algorithm. Compared with the existing prediction methods in this area, iNR-Drug not only can yield a higher success rate, but is also featured by a user-friendly web-server established at http://www.jci-bioinfo.cn/iNR-Drug/, which is particularly useful for most experimental scientists to obtain their desired data in a timely manner. It is anticipated that the iNR-Drug server may become a useful high throughput tool for both basic research and drug development, and that the current approach may be easily extended to study the interactions of drug with other targets as well. Text: With the ability to directly bind to DNA ( Figure 1 ) and regulate the expression of adjacent genes, nuclear receptors (NRs) are a class of ligand-inducible transcription factors. They regulate various biological processes, such as homeostasis, differentiation, embryonic development, and organ physiology [1] [2] [3] . The NR superfamily has been classified into seven families: NR0 (knirps or DAX like) [4, 5] ; NR1 (thyroid hormone like), NR2 (HNF4-like), NR3 (estrogen like), NR4 (nerve growth factor IB-like), NR5 (fushi tarazu-F1 like), and NR6 (germ cell nuclear factor like). Since they are involved in almost all aspects of human physiology and are implicated in many major diseases such as cancer, diabetes and osteoporosis, nuclear receptors have become major drug targets [6, 7] , along with G protein-coupled receptors (GPCRs) [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] , ion channels [18] [19] [20] , and kinase proteins [21] [22] [23] [24] . Identification of drug-target interactions is one of the most important steps for the new medicine development [25, 26] . The method usually adopted in this step is molecular docking simulation [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] . However, to make molecular docking study feasible, a reliable 3D (three dimensional) structure of the target protein is the prerequisite condition. Although X-ray crystallography is a powerful tool in determining protein 3D structures, it is time-consuming and expensive. Particularly, not all proteins can be successfully crystallized. For example, membrane proteins are very difficult to crystallize and most of them will not dissolve in normal solvents. Therefore, so far very few membrane protein 3D structures have been determined. Although NMR (Nuclear Magnetic Resonance) is indeed a very powerful tool in determining the 3D structures of membrane proteins as indicated by a series of recent publications (see, e.g., [44] [45] [46] [47] [48] [49] [50] [51] and a review article [20] ), it is also time-consuming and costly. To acquire the 3D structural information in a timely manner, one has to resort to various structural bioinformatics tools (see, e.g., [37] ), particularly the homologous modeling approach as utilized for a series of protein receptors urgently needed during the process of drug development [19, [52] [53] [54] [55] [56] [57] . Unfortunately, the number of dependable templates for developing high quality 3D structures by means of homology modeling is very limited [37] . To overcome the aforementioned problems, it would be of help to develop a computational method for predicting the interactions of drugs with nuclear receptors in cellular networking based on the sequences information of the latter. The results thus obtained can be used to pre-exclude the compounds identified not in interaction with the nuclear receptors, so as to timely stop wasting time and money on those unpromising compounds [58] . Actually, based on the functional groups and biological features, a powerful method was developed recently [59] for this purpose. However, further development in this regard is definitely needed due to the following reasons. (a) He et al. [59] did not provide a publicly accessible web-server for their method, and hence its practical application value is quite limited, particularly for the broad experimental scientists; (b) The prediction quality can be further enhanced by incorporating some key features into the formulation of NR-drug (nuclear receptor and drug) samples via the general form of pseudo amino acid composition [60] . The present study was initiated with an attempt to develop a new method for predicting the interaction of drugs with nuclear receptors by addressing the two points. As demonstrated by a series of recent publications [10, 18, [61] [62] [63] [64] [65] [66] [67] [68] [69] [70] and summarized in a comprehensive review [60] , to establish a really effective statistical predictor for a biomedical system, we need to consider the following steps: (a) select or construct a valid benchmark dataset to train and test the predictor; (b) represent the statistical samples with an effective formulation that can truly reflect their intrinsic correlation with the object to be predicted; (c) introduce or develop a powerful algorithm or engine to operate the prediction; (d) properly perform cross-validation tests to objectively evaluate the anticipated accuracy of the predictor; (e) establish a user-friendly web-server for the predictor that is accessible to the public. Below, let us elaborate how to deal with these steps. The data used in the current study were collected from KEGG (Kyoto Encyclopedia of Genes and Genomes) [71] at http://www.kegg.jp/kegg/. KEGG is a database resource for understanding high-level functions and utilities of the biological system, such as the cell, the organism and the ecosystem, from molecular-level information, especially large-scale molecular datasets generated by genome sequencing and other high-throughput experimental technologies. Here, the benchmark dataset can be formulated as where is the positive subset that consists of the interactive drug-NR pairs only, while the negative subset that contains of the non-interactive drug-NR pairs only, and the symbol represents the union in the set theory. The so-called "interactive" pair here means the pair whose two counterparts are interacting with each other in the drug-target networks as defined in the KEGG database [71] ; while the "non-interactive" pair means that its two counterparts are not interacting with each other in the drug-target networks. The positive dataset contains 86 drug-NR pairs, which were taken from He et al. [59] . The negative dataset contains 172 non-interactive drug-NR pairs, which were derived according to the following procedures: (a) separating each of the pairs in into single drug and NR; (b) re-coupling each of the single drugs with each of the single NRs into pairs in a way that none of them occurred in ; (c) randomly picking the pairs thus formed until reaching the number two times as many as the pairs in . The 86 interactive drug-NR pairs and 172 non-interactive drug-NR pairs are given in Supplementary Information S1, from which we can see that the 86 + 172 = 258 pairs in the current benchmark dataset are actually formed by 25 different NRs and 53 different compounds. Since each of the samples in the current network system contains a drug (compound) and a NR (protein), the following procedures were taken to represent the drug-NR pair sample. First, for the drug part in the current benchmark dataset, we can use a 256-D vector to formulate it as given by where D represents the vector for a drug compound, and d i its i-th (i = 1,2, ,256) component that can be derived by following the "2D molecular fingerprint procedure" as elaborated in [10] . The 53 molecular fingerprint vectors thus obtained for the 53 drugs in are, respectively, given in Supplementary Information S2. The protein sequences of the 25 different NRs in are listed in Supplementary Information S3. Suppose the sequence of a nuclear receptor protein P with L residues is generally expressed by where 1 R represents the 1st residue of the protein sequence P , 2 R the 2nd residue, and so forth. Now the problem is how to effectively represent the sequence of Equation (3) with a non-sequential or discrete model [72] . This is because all the existing operation engines, such as covariance discriminant (CD) [17, 65, [73] [74] [75] [76] [77] [78] [79] , neural network [80] [81] [82] , support vector machine (SVM) [62] [63] [64] 83] , random forest [84, 85] , conditional random field [66] , nearest neighbor (NN) [86, 87] ; K-nearest neighbor (KNN) [88] [89] [90] , OET-KNN [91] [92] [93] [94] , and Fuzzy K-nearest neighbor [10, 12, 18, 69, 95] , can only handle vector but not sequence samples. However, a vector defined in a discrete model may completely lose all the sequence-order information and hence limit the quality of prediction. Facing such a dilemma, can we find an approach to partially incorporate the sequence-order effects? Actually, one of the most challenging problems in computational biology is how to formulate a biological sequence with a discrete model or a vector, yet still keep considerable sequence order information. To avoid completely losing the sequence-order information for proteins, the pseudo amino acid composition [96, 97] or Chou's PseAAC [98] was proposed. Ever since the concept of PseAAC was proposed in 2001 [96] , it has penetrated into almost all the areas of computational proteomics, such as predicting anticancer peptides [99] , predicting protein subcellular location [100] [101] [102] [103] [104] [105] [106] , predicting membrane protein types [107, 108] , predicting protein submitochondria locations [109] [110] [111] [112] , predicting GABA(A) receptor proteins [113] , predicting enzyme subfamily classes [114] , predicting antibacterial peptides [115] , predicting supersecondary structure [116] , predicting bacterial virulent proteins [117] , predicting protein structural class [118] , predicting the cofactors of oxidoreductases [119] , predicting metalloproteinase family [120] , identifying cysteine S-nitrosylation sites in proteins [66] , identifying bacterial secreted proteins [121] , identifying antibacterial peptides [115] , identifying allergenic proteins [122] , identifying protein quaternary structural attributes [123, 124] , identifying risk type of human papillomaviruses [125] , identifying cyclin proteins [126] , identifying GPCRs and their types [15, 16] , discriminating outer membrane proteins [127] , classifying amino acids [128] , detecting remote homologous proteins [129] , among many others (see a long list of papers cited in the References section of [60] ). Moreover, the concept of PseAAC was further extended to represent the feature vectors of nucleotides [65] , as well as other biological samples (see, e.g., [130] [131] [132] ). Because it has been widely and increasingly used, recently two powerful soft-wares, called "PseAAC-Builder" [133] and "propy" [134] , were established for generating various special Chou's pseudo-amino acid compositions, in addition to the web-server "PseAAC" [135] built in 2008. According to a comprehensive review [60] , the general form of PseAAC for a protein sequence P is formulated by where the subscript  is an integer, and its value as well as the components ( 1, 2, , ) u u   will depend on how to extract the desired information from the amino acid sequence of P (cf. Equation (3)). Below, let us describe how to extract useful information to define the components of PseAAC for the NR samples concerned. First, many earlier studies (see, e.g., [136] [137] [138] [139] [140] [141] ) have indicated that the amino acid composition (AAC) of a protein plays an important role in determining its attributes. The AAC contains 20 components with each representing the occurrence frequency of one of the 20 native amino acids in the protein concerned. Thus, such 20 AAC components were used here to define the first 20 elements in Equation (4); i.e., (1) ( 1, 2, , 20) ii fi   (5) where f i (1) is the normalized occurrence frequency of the i-th type native amino acid in the nuclear receptor concerned. Since AAC did not contain any sequence order information, the following steps were taken to make up this shortcoming. To avoid completely losing the local or short-range sequence order information, we considered the approach of dipeptide composition. It contained 20 × 20 = 400 components [142] . Such 400 components were used to define the next 400 elements in Equation (4); i.e., (2) 20 ( 1, 2, , 400) jj fj where (2) j f is the normalized occurrence frequency of the j-th dipeptides in the nuclear receptor concerned. To incorporate the global or long-range sequence order information, let us consider the following approach. According to molecular evolution, all biological sequences have developed starting out from a very limited number of ancestral samples. Driven by various evolutionary forces such as mutation, recombination, gene conversion, genetic drift, and selection, they have undergone many changes including changes of single residues, insertions and deletions of several residues [143] , gene doubling, and gene fusion. With the accumulation of these changes over a long period of time, many original similarities between initial and resultant amino acid sequences are gradually faded out, but the corresponding proteins may still share many common attributes [37] , such as having basically the same biological function and residing at a same subcellular location [144, 145] . To extract the sequential evolution information and use it to define the components of Equation (4), the PSSM (Position Specific Scoring Matrix) was used as described below. According to Schaffer [146] , the sequence evolution information of a nuclear receptor protein P with L amino acid residues can be expressed by a 20 L matrix, as given by where (7) were generated by using PSI-BLAST [147] to search the UniProtKB/Swiss-Prot database (The Universal Protein Resource (UniProt); http://www.uniprot.org/) through three iterations with 0.001 as the E-value cutoff for multiple sequence alignment against the sequence of the nuclear receptor concerned. In order to make every element in Equation (7) be scaled from their original score ranges into the region of [0, 1], we performed a conversion through the standard sigmoid function to make it become Now we extract the useful information from Equation (8) Moreover, we used the grey system model approach as elaborated in [68] to further define the next 60 components of Equation (4) ( 1, 2, , 20) In the above equation, w 1 , w 2 , and w 3 are weight factors, which were all set to 1 in the current study; f j (1) has the same meaning as in Equation (5) where   and Combining Equations (5), (6), (10) and (12), we found that the total number of the components obtained via the current approach for the PseAAC of Equation (4) and each of the 500 components is given by (1) ( Since the elements in Equations (2) and (4) are well defined, we can now formulate the drug-NR pair by combining the two equations as given by   (19) where G represents the drug-NR pair, Å the orthogonal sum, and the 256 + 500 = 756 components are defined by Equations (2) and (18) . For the sake of convenience, let us use x i (i = 1, 2, , 756) to represent the 756 components in Equation (19); i.e., (20) To optimize the prediction quality with a time-saving approach, similar to the treatment [148] [149] [150] , let us convert Equation (20) to where the symbol means taking the average of the quantity therein, and SD means the corresponding standard derivation. In this study, the SVM (support vector machine) was used as the operation engine. SVM has been widely used in the realm of bioinformatics (see, e.g., [62] [63] [64] [151] [152] [153] [154] ). The basic idea of SVM is to transform the data into a high dimensional feature space, and then determine the optimal separating hyperplane using a kernel function. For a brief formulation of SVM and how it works, see the papers [155, 156] ; for more details about SVM, see a monograph [157] . In this study, the LIBSVM package [158] was used as an implementation of SVM, which can be downloaded from http://www.csie.ntu.edu.tw/~cjlin/libsvm/, the popular radial basis function (RBF) was taken as the kernel function. For the current SVM classifier, there were two uncertain parameters: penalty parameter C and kernel parameter  . The method of how to determine the two parameters will be given later. The predictor obtained via the aforementioned procedure is called iNR-Drug, where "i" means identify, and "NR-Drug" means the interaction between nuclear receptor and drug compound. To provide an intuitive overall picture, a flowchart is provided in Figure 2 to show the process of how the predictor works in identifying the interactions between nuclear receptors and drug compounds. To provide a more intuitive and easier-to-understand method to measure the prediction quality, the following set of metrics based on the formulation used by Chou [159] [160] [161] in predicting signal peptides was adopted. According to Chou's formulation, the sensitivity, specificity, overall accuracy, and Matthew's correlation coefficient can be respectively expressed as [62, [65] [66] [67] Sn 1 where N  is the total number of the interactive NR-drug pairs investigated while N   the number of the interactive NR-drug pairs incorrectly predicted as the non-interactive NR-drug pairs; N  the total number of the non-interactive NR-drug pairs investigated while N   the number of the non-interactive NR-drug pairs incorrectly predicted as the interactive NR-drug pairs. According to Equation (23) we can easily see the following. When 0 N    meaning none of the interactive NR-drug pairs was mispredicted to be a non-interactive NR-drug pair, we have the sensitivity Sn = 1; while NN    meaning that all the interactive NR-drug pairs were mispredicted to be the non-interactive NR-drug pairs, we have the sensitivity Sn = 0 . Likewise, when 0 N    meaning none of the non-interactive NR-drug pairs was mispredicted, we have the specificity Sp we have MCC = 0 meaning total disagreement between prediction and observation. As we can see from the above discussion, it is much more intuitive and easier to understand when using Equation (23) to examine a predictor for its four metrics, particularly for its Mathew's correlation coefficient. It is instructive to point out that the metrics as defined in Equation (23) are valid for single label systems; for multi-label systems, a set of more complicated metrics should be used as given in [162] . How to properly test a predictor for its anticipated success rates is very important for its development as well as its potential application value. Generally speaking, the following three cross-validation methods are often used to examine the quality of a predictor and its effectiveness in practical application: independent dataset test, subsampling or K-fold (such as five-fold, seven-fold, or 10-fold) crossover test and jackknife test [163] . However, as elaborated by a penetrating analysis in [164] , considerable arbitrariness exists in the independent dataset test. Also, as demonstrated in [165] , the subsampling (or K-fold crossover validation) test cannot avoid arbitrariness either. Only the jackknife test is the least arbitrary that can always yield a unique result for a given benchmark dataset [73, 74, 156, [166] [167] [168] . Therefore, the jackknife test has been widely recognized and increasingly utilized by investigators to examine the quality of various predictors (see, e.g., [14, 15, 68, 99, 106, 107, 124, 169, 170] ). Accordingly, in this study the jackknife test was also adopted to evaluate the accuracy of the current predictor. As mentioned above, the SVM operation engine contains two uncertain parameters C and  . To find their optimal values, a 2-D grid search was conducted by the jackknife test on the benchmark dataset . The results thus obtained are shown in Figure 3 , from which it can be seen that the iNR-Drug predictor reaches its optimal status when C = 2 3 and 9 2    . The corresponding rates for the four metrics (cf. Equation (23)) are given in Table 1 , where for facilitating comparison, the overall accuracy Acc reported by He et al. [59] on the same benchmark dataset is also given although no results were reported by them for Sn, Sp and MCC. It can be observed from the table that the overall accuracy obtained by iNR-Drug is remarkably higher that of He et al. [59] , and that the rates achieved by iNR-Drug for the other three metrics are also quite higher. These facts indicate that the current predictor not only can yield higher overall prediction accuracy but also is quite stable with low false prediction rates. As mentioned above (Section 3.2), the jackknife test is the most objective method for examining the quality of a predictor. However, as a demonstration to show how to practically use the current predictor, we took 41 NR-drug pairs from the study by Yamanishi et al. [171] that had been confirmed by experiments as interactive pairs. For such an independent dataset, 34 were correctly identified by iNR-Drug as interactive pairs, i.e., Sn = 34 / 41 = 82.92%, which is quite consistent with the rate of 79.07% achieved by the predictor on the benchmark dataset via the jackknife test as reported in Table 1 . It is anticipated that the iNR-Drug predictor developed in this paper may become a useful high throughput tool for both basic research and drug development, and that the current approach may be easily extended to study the interactions of drug with other targets as well. Since user-friendly and publicly accessible web-servers represent the future direction for developing practically more useful predictors [98, 172] , a publicly accessible web-server for iNR-Drug was established. For the convenience of the vast majority of biologists and pharmaceutical scientists, here let us provide a step-by-step guide to show how the users can easily get the desired result by using iNR-Drug web-server without the need to follow the complicated mathematical equations presented in this paper for the process of developing the predictor and its integrity. Step 1. Open the web server at the site http://www.jci-bioinfo.cn/iNR-Drug/ and you will see the top page of the predictor on your computer screen, as shown in Figure 4 . Click on the Read Me button to see a brief introduction about iNR-Drug predictor and the caveat when using it. Step 2. Either type or copy/paste the query NR-drug pairs into the input box at the center of Figure 4 . Each query pair consists of two parts: one is for the nuclear receptor sequence, and the other for the drug. The NR sequence should be in FASTA format, while the drug in the KEGG code beginning with the symbol #. Examples for the query pairs input and the corresponding output can be seen by clicking on the Example button right above the input box. Step 3. Click on the Submit button to see the predicted result. For example, if you use the three query pairs in the Example window as the input, after clicking the Submit button, you will see on your screen that the "hsa:2099" NR and the "D00066" drug are an interactive pair, and that the "hsa:2908" NR and the "D00088" drug are also an interactive pair, but that the "hsa:5468" NR and the "D00279" drug are not an interactive pair. All these results are fully consistent with the experimental observations. It takes about 3 minutes before each of these results is shown on the screen; of course, the more query pairs there is, the more time that is usually needed. Step 4. Click on the Citation button to find the relevant paper that documents the detailed development and algorithm of iNR-Durg. Step 5. Click on the Data button to download the benchmark dataset used to train and test the iNR-Durg predictor. Step 6. The program code is also available by clicking the button download on the lower panel of Figure 4 .

What are the shortcomings of X-ray crystallography?

Respiratory Viral Infections in Exacerbation of Chronic Airway Inflammatory Diseases: Novel Mechanisms and Insights From the Upper Airway Epithelium https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7052386/ SHA: 45a566c71056ba4faab425b4f7e9edee6320e4a4 Authors: Tan, Kai Sen; Lim, Rachel Liyu; Liu, Jing; Ong, Hsiao Hui; Tan, Vivian Jiayi; Lim, Hui Fang; Chung, Kian Fan; Adcock, Ian M.; Chow, Vincent T.; Wang, De Yun Date: 2020-02-25 DOI: 10.3389/fcell.2020.00099 License: cc-by Abstract: Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations (GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018) . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway (such as chronic rhinosinusitis, CRS) and lower airway (such as asthma and chronic obstructive pulmonary disease, COPD) which greatly affect the patients' quality of life (Calus et al., 2012; Bao et al., 2015) . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness (Xepapadaki and Papadopoulos, 2010) . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease (Hashimoto et al., 2008; Viniol and Vogelmeier, 2018) . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway (Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018) . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively (Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019) . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations (Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019) . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases (Rowan et al., 2015; Tan et al., 2017) . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection (Kutter et al., 2018) . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity (Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017) . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the "United Airway" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s (Pattemore et al., 1992) ; with bacterial infections previously considered as the likely culprit for acute exacerbation (Stevens, 1953; Message and Johnston, 2002) . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s (Message and Johnston, 2002) . Rhinovirus (RV) and respiratory syncytial virus (RSV) are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases (Jartti and Gern, 2017) . Other viruses such as parainfluenza virus (PIV), influenza virus (IFV) and adenovirus (AdV) have also been implicated in acute exacerbations but to a much lesser extent (Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019) . More recently, other viruses including bocavirus (BoV), human metapneumovirus (HMPV), certain coronavirus (CoV) strains, a specific enterovirus (EV) strain EV-D68, human cytomegalovirus (hCMV) and herpes simplex virus (HSV) have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway (Mallia and Johnston, 2006; Britto et al., 2017) . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche (Busse et al., 2010) . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection (Vareille et al., 2011; Braciale et al., 2012) . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms (Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019) . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole (Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019) . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I (IFNα/β) and type III (IFNλ) interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α (MIP-1α) and monocyte chemotactic protein 1 (MCP-1) (Wark and Gibson, 2006; Matsukura et al., 2013) . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells (APCs) that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon (IFNγ), IL-2, IL-4, IL-5, IL-9, and IL-12 (Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012) . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells (Wark and Gibson, 2006; Braciale et al., 2012) . The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp (CRSwNP), viral infections such as RV and RSV promote a Type 2-biased immune response (Becker, 2006; Jackson et al., 2014; Jurak et al., 2018) . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP (Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015) . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp (CRSsNP) are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 (Cukic et al., 2012; Brightling and Greening, 2019) . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases (MMPs) released from infiltrating neutrophils (Linden et al., 2019) . Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases (Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019) . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin (TSLP), and their interaction with group 2 innate lymphoid cells (ILC2) has also recently been identified (Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019) . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier (Gabryelska et al., 2019; Roan et al., 2019) . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated (Scanlon and McKenzie, 2012; Li and Hendriks, 2013) . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway (Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019) . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation (Camelo et al., 2017) . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation (Silver et al., 2016) . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals (Yan et al., 2016; Tan et al., 2018a) ; despite augmenting a type 2 exacerbation in chronically inflamed airways (Jurak et al., 2018) . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | www.frontiersin.org mechanisms have been implicated in acute exacerbations during and after viral infection (Murray et al., 2006) . Murray et al. (2006) has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance (Kim et al., 2008; Stolz et al., 2019) in particular the late onset of a bacterial infection (Singanayagam et al., 2018 (Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the "united airway" hypothesis (Kurai et al., 2013) . On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease (Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019) . Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance (Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b) . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells (Becker, 2006; McKendry et al., 2016) . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV (Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019) . Additionally, there are also evidence of reduced type I (IFNβ) and III (IFNλ) interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium (Contoli et al., 2006; Hwang et al., 2019; Wark, 2019) . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation (Wood et al., 2011; Essaidi-Laziosi et al., 2018) . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer (Imperiale and Jiang, 2015) , further contributing to chronic activation of inflammation when they infect the airway (Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017) . With that note, human papilloma virus (HPV), a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies (de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015) . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection (Chi et al., 2011; Ford et al., 2013; Papi et al., 2013) . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance (Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b) . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV (Yan et al., 2016; Guibas et al., 2018) and certain CoV (including the recently emerged COVID-19 virus) (Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020) , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection (Yan et al., 2016; Tan et al., 2019) . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium (Ampomah et al., 2018; Tan et al., 2019) . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway (Guibas et al., 2018) . Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections (Vasileiou et al., 2017; Zheng et al., 2018) ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M (OSM) which causes tight junction opening (Pothoven et al., 2015; Tian et al., 2018) . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases (Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018) . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 (ANGPTL4) and bactericidal/permeabilityincreasing fold-containing family member A1 (BPIFA1) are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018) . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment (Diver et al., 2019) . In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present (Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019) . In addition, viral infection may disrupt biofilm colonies in the upper airway (e.g., Streptococcus pneumoniae) microbiome to be release into the lower airway and worsening the inflammation (Marks et al., 2013; Chao et al., 2014) . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth (Siegel et al., 2014; Mallia et al., 2018) . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms (Singanayagam et al., 2018 (Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019) . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles (Teo et al., 2018) . These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases (Wark et al., 2013; Singanayagam et al., 2018) . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome (Bakken et al., 2011) . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism (with literature evidence) Oxidative stress ROS production (RV, RSV, IFV, HSV) As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation (see text for abbreviations). that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection (Griggs et al., 2017) . Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown (Peng et al., , 2019 Qiu et al., 2018) . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 (DNAH5) and multiciliate differentiation And DNA synthesis associated cell cycle protein (MCIDAS) (Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model (Zhu et al., 2020) . Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells (Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a) . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation (Zhu et al., 2009) . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs (miRNAs) are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases (Tan et al., 2014) . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation (Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018) . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases (Wardzynska et al., 2020) . Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids (Kim et al., 2017) . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections (Hsu et al., 2016 (Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations (Moheimani et al., 2018) . Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections (Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018) . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes (Cardenas et al., 2019; Gomez, 2019) . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations (McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019) . In addition, Spalluto et al. (2017) also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases (Spalluto et al., 2017) . Finally, viral infection can result in enhanced production of reactive oxygen species (ROS), oxidative stress and mitochondrial dysfunction in the airway epithelium (Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018) . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway (Barnes, 2017; van der Vliet et al., 2018) . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018) . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway (Tiwari et al., 2002) . A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing (RNA-seq) or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway (Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a) . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation (Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019) . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition (Ito et al., 2019; Li and Di Santo, 2019) . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment.

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Emergent severe acute respiratory distress syndrome caused by adenovirus type 55 in immunocompetent adults in 2013: a prospective observational study https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4243941/ SHA: f5b706d0529bfcf7e2d1dfc037df5b6f95fc5ec0 Authors: Sun, Bing; He, Hangyong; Wang, Zheng; Qu, Jiuxin; Li, Xuyan; Ban, Chengjun; Wan, Jun; Cao, Bin; Tong, Zhaohui; Wang, Chen Date: 2014-08-12 DOI: 10.1186/s13054-014-0456-6 License: cc-by Abstract: INTRODUCTION: Since 2008, severe cases of emerging human adenovirus type 55 (HAdV-55) in immunocompetent adults have been reported sporadically in China. The clinical features and outcomes of the most critically ill patients with severe acute respiratory distress syndrome (ARDS) caused by HAdV-55 requiring invasive mechanical ventilation (IMV) and/or extracorporeal membrane oxygenation (ECMO) are lacking. METHODS: We conducted a prospective, single-center observational study of pneumonia with ARDS in immunocompetent adults admitted to our respiratory ICU. We prospectively collected and analyzed clinical, laboratory, radiological characteristics, sequential tests of viral load in respiratory tract and blood, treatments and outcomes. RESULTS: The results for a total of five consecutive patients with severe ARDS with confirmed HAdV-55 infection were included. All five patients were immunocompetent young men with a median age of 32 years. The mean time from onset to dyspnea was 5 days. Arterial blood gas analysis at ICU admission revealed profound hypoxia. Mean partial oxygen pressure/fraction of inspired oxygen was 58.1. Mean durations from onset to a single-lobe consolidation shown on chest X-rays (CXRs) and, from the first positive CXR to bilateral multilobar lung infiltrates, were 2 days and 4.8 days, respectively. The viral load was higher than 1 × 10(8) copies in three patients and was 1 × 10(4) in one patient. It was negative in the only patient who survived. The mean duration for noninvasive positive pressure ventilation (NPPV) failure and IMV failure were 30.8 hours and 6.2 days, respectively. Four patients received venovenous ECMO. Four (80%) of the five patients died despite receiving appropriate respiratory support. CONCLUSIONS: HAdV-55 may cause severe ARDS in immunocompetent young men. Persistent high fever, dyspnea and rapid progression to respiratory failure within 2 weeks, together with bilateral consolidations and infiltrates, are the most frequent clinical manifestations of HAdV-55-induced severe ARDS. Viral load monitoring may help predict disease severity and outcome. The NPPV and IMV failure rates were very high, but ECMO may still be the respiratory support therapy of choice. TRIAL REGISTRATION: Clinicaltrials.gov NCT01585922. Registered 20 April 2012 Text: Human adenoviruses (HAdVs) are notorious pathogens in people with compromised immune function and a frequent cause of outbreaks of acute respiratory disease among young children. Life-threatening adenoviral pneumonia has previously been documented among military trainees, patients with AIDS and transplant recipients [1] [2] [3] [4] [5] . Human adenovirus type 55 (HAdV-55), which is emerging as a highly virulent pathogen for acute fatal adenoviral pneumonia among immunocompetent adults in China, has gained increasing attention [6] . HAdV-55 is a newly identified, emergent acute respiratory disease pathogen causing two recent outbreaks in China in 2006 [7] and in Singapore in 2005 [8] . In 2011, this pathogen apparently re-emerged in Beijing, China, causing several cases of severe community-acquired pneumonia [9] . This pathogen was fully characterized by whole-genome sequencing [10] . Comparative studies showed that the ability of HAdV to cause severe disease may relate to the serotypes of HAdVs. Severe adenoviral pneumonia induced by HAdV-55 has been reported to be more closely related to severe cases compared to other serotypes (HAdV-3, HAdV-7 and HAdV-14) [6] . Current knowledge of HAdV-55-induced severe acute respiratory distress syndrome (ARDS) requiring invasive mechanical ventilation and/or extracorporeal membrane oxygenation (ECMO) support in immunocompetent adults is derived from single case reports or relatively small, single-center series. As a result, little information is available on HAdV-55 pneumonia complicated with severe ARDS, the frequency of which is expected to increase in the coming years. Here we describe the clinical features and outcomes of five prospective cases of HAdV-55 pneumonia complicated with severe ARDS in immunocompetent adults in our ICU. Beginning in May 2012, a randomized trial of noninvasive positive pressure ventilation (NPPV) in ARDS patients was carried out in our center (ClinicalTrials.gov ID: NCT01585922). From May 2012 to April 2014, all adult patients with ARDS caused by pneumonia who were admitted to the respiratory ICU of Beijing Chao-Yang Hospital were prospectively enrolled. Severe ARDS was diagnosed according to the Berlin definition: (1) developing within 1 week of a known clinical insult or new or worsening respiratory symptoms; (2) bilateral opacities not fully explained by effusions, lobar and/or lung collapse, or nodules; (3) respiratory failure not fully explained by cardiac failure or fluid overload; (4) partial oxygen pressure/ fraction of inspired oxygen (PaO 2 /FiO 2 ) ≤100 mmHg with positive end-expiratory pressure (PEEP) ≥5 cmH 2 O; and (5) a chest radiograph with three or four quadrants with opacities. Patients with HAdV-55 infection and severe ARDS who failed conventional NPPV and invasive mechanical ventilation (IMV) were included in the analysis. This study was approved by the Institutional Review Board of Beijing Chao-Yang Hospital (LLKYPJ2012031). Data were analyzed anonymously. Each patient gave written informed consent for their data to be used for research and publication. Clinical information collected by investigators with a standardized data form included the following: demographic characteristics (age and sex), comorbidities, clinical symptoms (fever, cough, sputum, dyspnea, chest pain, rash, nausea, vomiting, abdominal pain, diarrhea and headache), signs (body temperature, heart rate, respiratory frequency, blood pressure and crackles in the lungs), laboratory tests (whole-blood cell count and blood chemistry) and microbiological findings and images of the lung (chest X-ray (CXR) and computed tomography). Concomitant medications, respiratory support, complications and outcomes were also recorded. Patients' specimens, including sputum, whole blood and serum samples, were collected upon admission and during hospitalization. Microbiological tests were performed at the Department of Infectious Disease and Clinical Microbiology in our center, and the detection methods used were described in our previous report [6] . Common viruses causing respiratory illness were screened using a kit with 15 different viral assays. Serum samples were used for Mycoplasma pneumoniae, Chlamydia pneumoniae and Legionella pneumophila antibodies. All patients had their HAdV-55 infection confirmed by RT-PCR assay. Partial sequences of the hexon gene were analyzed to type the phylogeny of HAdV-55 strains. The adenoviral load was also performed on both respiratory specimens and blood by multiplex RT-PCR assay. Viral pneumonia was diagnosed based on the presence of HAdV detected in sputum or throat swab samples by molecular methods. Continuous variables were summarized as mean ± standard deviation (SD) or median (interquartile range). During the study period, a total of eight patients diagnosed with HAdV infection and respiratory failure were admitted to our ICU, and seven of them received a diagnosis of ARDS. Five consecutive patients with severe ARDS with confirmed HAdV-55 infection were admitted to our ICU between April and July 2013. They were included in the analysis. The other two patients had mild ARDS and were infected with other types of HAdVs. All five patients were immunocompetent young men with a median age of 32 years (range, 28 to 40 years). All of the patients shared a B blood type and came from the same city: Baoding city, Hebei province, northern China. All patients had no exposure to farm animals, corn or hay. Patient 3 had tuberculosis pleuritis and received antituberculosis therapy at ICU admission. His blood tests, including the T-SPOT tuberculosis assay (Oxford Immunotec, Marlborough, MA, USA) and antibody of Mycobacterium tuberculosis, were negative. Flulike symptoms, such as fever, cough and little sputum, were commonly observed at the onset of illness. All patients presented with a high fever, with a mean body temperature of 39.5°C (range, 39.0°C to 40.0°C), which persisted for 8 days (range, 6 to 11 days). Productive cough was observed in two patients. Dull substernal chest pain and rash were also observed in two patients. All patients had dyspnea. The mean time from onset to dyspnea was 5 days (range, 1 to 10 days). After the onset of dyspnea, patients usually progressed to respiratory failure or hypoxemia. The mean time from onset to ICU admission was 9.6 days (range, 8 to 11 days) ( Table 1) . All patients had tachypnea when admitted to the ICU, with a mean rate of 43 breaths per minute (range = 38 to 52). Arterial blood gas analysis at ICU admission revealed profound hypoxia, with a mean PaO 2 /FiO 2 of 58.1 (range = 49 to 62.5). White blood cell counts were low or in the normal range. All patients had elevated serum aspartate aminotransferase (AST), lactate dehydrogenase (LDH) and hydroxybutyrate dehydrogenase (HBDH) ( Table 1) . At admission, all patients' levels of immunoglobulin (serum immunoglobulins G and M) and components C3 and C4 were in the normal range. Four patients had lower than normal T-cell subset counts (Table 2) . CXRs revealed multiple bilateral lobar or segment consolidation in the lungs of all five patients, and radiographic lesions progressed rapidly after ICU admission ( Figure 1 ). Three patients were examined by highresolution computed tomography (HRCT). Unilateral or bilateral consolidations and infiltrates were found on HRCT scans of all three of these patients. Consolidations within a single lobe or several lobes with a clear border and air bronchogram were the most common findings on HRCT scans. Nodules, patches, pleural effusion, abscess and a cavity were also seen visualized by HRCT (Figure 2 ). The mean duration from onset to a single-lobe consolidation on CXRs was 2 days (range = 1 to 5 days). The mean duration from the first positive CXR to bilaterally multilobar lung infiltrates was 4.8 days (range = 4 to 7 days). All patients had HAdV-55 viremia. In four of the five patients, it was first detected in endotracheal aspirate (ETA) samples. The time between initial ETA sample collection of adenoviruses and positive results for HAdV-55 nucleic acid in the blood was 1 to 10 days (Table 3) . Virus DNA copies in ETAs were determined for all patients during their ICU stay. The viral load was higher than 1 × 10 8 copies in three patients and 1 × 10 4 in one patient. The viral load became negative in the only patient who survived. In the four patients who did not survive, DNA copies did not decrease, even with antiviral therapy (Figure 3 ). Oxygenation was not maintained with conventional NPPV or IMV support in any of the patients. The mean duration until NPPV failure was 30.8 hours (range = 22 to 48 hours), and the mean time until IMV failure was 6.2 days (range 2 = to 13 days) ( Table 1) . Four patients received venovenous ECMO to maintain oxygen saturation, and one patient refused ECMO support and received high-frequency oscillatory ventilation instead. Table 4 gives the oxygenation data of patients before and after venovenous ECMO support. All patients received antiviral therapy, including acyclovir (10 mg/kg, every 8 hours, intravenous drip), ganciclovir (5 mg/kg, every 12 hours, intravenous drip) and ribavirin (250 mg, twice daily, intravenous drip). Considering that bacterial coinfection may combine with a severe viral infection, broad-spectrum intravenous antibiotics were given to all patients. Tests for bacterial pathogens were negative for only one patient (Table 3) . Four (80%) of the five patients died. Among the four patients receiving venovenous ECMO, only one patient survived. The other four patients died due to ARDS, Aspergillus fumigatus coinfection, septic shock and catheter-related bloodstream infection due to Acinetobacter baumannii, respectively. To the best of our knowledge, this is the first cohort observational study on the clinical characteristics of patients with severe ARDS caused by emergent HAdV-55 infection and also the first on the evaluation of a viral load test for monitoring the reaction to therapy and for prediction of patient outcome. The following are the main findings of this study. (1) HAdV-55 may cause severe ARDS in immunocompetent young men with blood type B. All of our patients were from the same city of Hebei province, northern China. (2) Persistent high fever, dyspnea and rapid progression to respiratory failure within 2 weeks, together with bilateral consolidations and infiltrates at the same time, are the most frequent clinical manifestations of severe HAdV-55induced ARDS. (3) Viral load monitoring may help predict disease severity and patient outcome. (4) The NPPV and IMV failure rates were very high, and ECMO may be the last support method for this group of patients. (5) HAdV-55-induced severe ARDS has a very high mortality rate (80%) despite appropriate respiratory support. Sporadic severe adenoviral infection in healthy adults has historically been described for serotype 4 [11] , serotype 7 [4, 12] and, more recently, serotype 14 in the general population and in military trainees [13, 14] . HAdV-55 was first completely characterized in Shaanxi, China [7] and then reemerged in Hebei, a province close to Beijing, where it caused several cases of acute respiratory disease [9] . It was presumed that HAdV-55 was a recombinant form of the B2 species of HAdV-14 and HAdV-11 [7, 15] due to its sharing a hexon gene with the HAdV-11 and HAdV-14 chassis [16] . The results of our study show that HAdV-55, as an emerging pathogen among immunocompetent adults, may cause severe ARDS. The prevalence of severe fatal adenoviral pneumonia induced by HAdV-55 in our study is somewhat similar to that described by Cao and colleagues [6] . All cases of reported HAdV-55 in our study were from the same city: Baoding, Hebei province, northern China. They occurred between April and July 2013, just partly overlapping or following the influenza epidemic. The patients with severe disease also came from the same region and were treated during a similar time period, which suggests that HAdV-55 may be an important viral pathogen derived from this region. Our study results suggest that the following may be clinical features of ARDS caused by HAdV-55: persistent high fever, rapid progression of dyspnea, need for mechanical ventilation support, elevated AST level and rapid progression from unilateral infiltrates to bilateral consolidations. These clinical features are highly similar to those of ARDS caused by other types of HAdV described in previous reports [6, 9] . Recent studies have shown that the immune system plays a crucial role in the clearance of HAdV viremia and survival of the host [17] . Chen et al. reported that, in the acute phase of HAdV-55 infection, patients with severe disease may have high levels of dendritic cells and Th17 cells [18] . In our study, the only patient who recovered from severe infection had higher T-cell counts. Three of the five patients had relatively low T-cell counts when admitted. Our results suggest that these three patients may have been relatively immunocompromised and that a lower T-cell count may be a risk factor for HAdV-55 infection in young adults. HAdV-55 DNA was previously reported in 41.2% of patients with severe infection [18] . In our study, HAdV-55 DNA was detected and monitored in all patients with severe ARDS. The initial, and trend of, viral load that presented as HAdV-55 DNA copies in the respiratory tract samples and blood may suggest the severity of infection and may predict both the reaction to therapy and patient outcome. The use of mechanical ventilation and ECMO in patients with ARDS caused by HAdV-55 has not been detailed in previous studies. In our cohort, we found that severe HAdV-55 infection could cause a rapid progression of respiratory failure, with a very high failure rate for NPPV and IMV. This failure rate may be a result of the large area of consolidation that induced a severe shunt in the lung, which may lead to lack of response to positive pressure ventilation. For patients with severe ARDS, ECMO should be considered a better choice for oxygenation. Our study has limitations. It is an observational study with no comparison group, so the difference between the severe and modest infections could not be clarified in terms of immune status, clinical features, radiological findings, viral load and treatment effects on respiratory support and antiviral therapy. Sequential dynamic analysis is needed to determine the relationship between HAdV-55 viremia and treatment response.

What is the mortality rate of severe ARDS from human adenovirus type 55 (HAdV-55)?

The Evolutionary Dynamics of the Lion Panthera leo Revealed by Host and Viral Population Genomics https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2572142/ SHA: f2af8027b6801850481d09ad0d4c5eb8e31c7d7f Authors: Antunes, Agostinho; Troyer, Jennifer L.; Roelke, Melody E.; Pecon-Slattery, Jill; Packer, Craig; Winterbach, Christiaan; Winterbach, Hanlie; Hemson, Graham; Frank, Laurence; Stander, Philip; Siefert, Ludwig; Driciru, Margaret; Funston, Paul J.; Alexander, Kathy A.; Prager, Katherine C.; Mills, Gus; Wildt, David; Bush, Mitch; O'Brien, Stephen J.; Johnson, Warren E. Date: 2008-11-07 DOI: 10.1371/journal.pgen.1000251 License: cc0 Abstract: The lion Panthera leo is one of the world's most charismatic carnivores and is one of Africa's key predators. Here, we used a large dataset from 357 lions comprehending 1.13 megabases of sequence data and genotypes from 22 microsatellite loci to characterize its recent evolutionary history. Patterns of molecular genetic variation in multiple maternal (mtDNA), paternal (Y-chromosome), and biparental nuclear (nDNA) genetic markers were compared with patterns of sequence and subtype variation of the lion feline immunodeficiency virus (FIV(Ple)), a lentivirus analogous to human immunodeficiency virus (HIV). In spite of the ability of lions to disperse long distances, patterns of lion genetic diversity suggest substantial population subdivision (mtDNA Φ(ST) = 0.92; nDNA F (ST) = 0.18), and reduced gene flow, which, along with large differences in sero-prevalence of six distinct FIV(Ple) subtypes among lion populations, refute the hypothesis that African lions consist of a single panmictic population. Our results suggest that extant lion populations derive from several Pleistocene refugia in East and Southern Africa (∼324,000–169,000 years ago), which expanded during the Late Pleistocene (∼100,000 years ago) into Central and North Africa and into Asia. During the Pleistocene/Holocene transition (∼14,000–7,000 years), another expansion occurred from southern refugia northwards towards East Africa, causing population interbreeding. In particular, lion and FIV(Ple) variation affirms that the large, well-studied lion population occupying the greater Serengeti Ecosystem is derived from three distinct populations that admixed recently. Text: Lion fossils trace to the Late Pliocene in Eastern Africa and the Early Pleistocene in Eastern and Southern Africa coincident with the flourishing of grasslands ,2-1.5 million years ago [1, 2] . By Mid Pleistocene (,500,000 years ago), lions occupied Europe and by the Late Pleistocene (,130,000-10,000 years ago) lions had the greatest intercontinental distribution for a large land mammal (excluding man), ranging from Africa into Eurasia and the Americas [3] . Lions were extirpated from Europe 2,000 years ago and within the last 150 years from the Middle East and North Africa. Today, there are less than 50,000 free-ranging lions [4] that occur only in sub-Saharan Africa and the Gir Forest, India ( Figure 1A) . Understanding the broader aspects of lion evolutionary history has been hindered by a lack of comprehensive sampling and appropriately informative genetic markers [5] [6] [7] [8] [9] , which in species of modern felids requires large, multigenic data sets due to its generally rapid and very recent speciation [10, 11] . Nevertheless, the unique social ecology of lions [12] [13] [14] and the fact that lions have experienced well-documented infectious disease outbreaks, including canine distemper virus, feline parvovirus, calicivirus, coronavirus, and lion feline immunodeficiency virus (FIV Ple ) [15] [16] [17] [18] provide a good opportunity to study lion evolutionary history using both host and virus genetic information. Indeed, population genetics of transmitted pathogens can accurately reflect the demographic history of their hosts [19, 20] . Unlike other of the 36 cat species, lions have a cooperative social system (prides of 2-18 adult females and 1-9 males) and their populations can have high frequencies of FIV Ple , a lentivirus analogous to human immunodeficiency virus (HIV), which causes AIDS-like immunodeficiency disease in domestic cats. FIV Ple is a retrovirus that integrates into the host genome and is transmitted by cell-to-cell contact, which in felids occurs during mating, fighting and motherto-offspring interactions. Thus, viral dissemination is a function in part of the frequency of contact between infected and naïve lions within and among populations. The virus is quite genetically diverse in lions [15, 18] , offering a unique marker for assessing ongoing lion demographic processes. To unravel lion population demographic history we used a large multigenic dataset. Distinct sets of markers may not necessarily The lion Panthera leo, a formidable carnivore with a complex cooperative social system, has fascinated humanity since pre-historical times, inspiring hundreds of religious and cultural allusions. Here, we use a comprehensive sample of 357 individuals from most of the major lion populations in Africa and Asia. We assayed appropriately informative autosomal, Y-chromosome, and mitochondrial genetic markers, and assessed the prevalence and genetic variation of the lion-specific feline immunodeficiency virus (FIV Ple ), a lentivirus analogous to human immunodeficiency virus (HIV) that causes AIDS-like immunodeficiency disease in domestic cats. We compare the large multigenic dataset from lions with patterns of genetic variation of the FIV Ple to characterize the population-genomic legacy of lions. We refute the hypothesis that African lions consist of a single panmictic population, highlighting the importance of preserving populations in decline rather than prioritizing larger-scale conservation efforts. Interestingly, lion and FIV Ple variation revealed evidence of unsuspected genetic diversity even in the well-studied lion population of the Serengeti Ecosystem, which consists of recently admixed animals derived from three distinct genetic groups. Historical and current geographic distribution of lion, Panthera leo. A three-letter code pointing to a white dotted circle represents the geographic location of the 11 lion populations determined by Bayesian analyses [22] and factorial correspondence analyses [23] of the genetic distinctiveness of 357 lion samples (see text): GIR, Gir Forest, India; UGA, Uganda (Queen Elizabeth National Park); KEN, Kenya (Laikipia), SER, Serengeti National Park, Tanzania; NGC, Ngorongoro Crater, Tanzania; KRU, Kruger National Park, South Africa; BOT-I, southern Botswana and Kalahari, South Africa; BOT-II, northern Botswana; and NAM, Namibia. Green squares represent captive individual samples to explore the relationship of lions from more isolated/ endangered/depleted areas: ATL, Morocco Atlas lions (n = 4); ANG, Angola (n = 2); and ZBW, Zimbabwe (n = 1). Deduced historical expansions (M1 and M2) are represented by red arrows (see text). (B) Haplotype frequencies observed in the 11 lion populations for nDNA (ADA and TF), and mtDNA (12S-16S) genes, paralleled with the FIV Ple serum-prevalence frequencies (black -sero-positive; gray -indeterminate; white -sero-negative). Population sample sizes are indicated within parenthesis. (C) Statistical parsimony networks of lion ADA, TF, and 12S-16S haplotypes. Circle size is proportional to the haplotype frequency and crossbars represent the number of step mutations connecting haplotypes. The mtDNA haplotypes H5 and H6 are shaded gray as they were detected only in the individual samples from ANG, ATL, and ZBW, which do not group in unique population clusters (see text). doi:10.1371/journal.pgen.1000251.g001 yield similar inferences of population history, as coalescent times vary as a function of their pattern of inheritance [21] . There is also a large variance in coalescent times across loci sharing a common pattern of inheritance especially in complex demographical histories (Table 1) . However, the accurate interpretation of the differences among loci can provide a more resolved and coherent population history, affording more-nuanced insights on past demographic processes, levels of admixture, taxonomic issues, and on the most appropriate steps for effective conservation and management of remaining populations. The goal of this study was to assess the evolutionary history of lion by (1) characterizing lion population structure relative to patterns of FIV Ple genetic variation, (2) detect signatures of migration using both host and viral population genomics, and (3) reconstruct lion demographic history and discuss its implication for lion conservation. We assess genetic variation from 357 lions from most of its current distribution, including mitochondrial (mtDNA; 12S-16S, 1,882 bp), nuclear (nDNA) Y-chromosome (SRY, 1,322 bp) and autosomal (ADA, 427 bp; TF, 169 bp) sequences, and 22 microsatellites markers. We further document patterns of FIV Ple variation in lions (FIV Ple pol-RT gene, up to 520 bp). Genetic analyses of 357 lions from throughout the extant species range showed that paternally inherited nDNA (SRY) and maternal inherited (mtDNA) sequence variation was generally low (only one paternal SRY-haplotype and 12 mtDNA haplotypes; p = 0.0066) ( Figure 1 ; Figure S1 ; Tables S1 and S2). The most common mtDNA haplotype H11 was ubiquitous in Uganda/Tanzania and parts of Botswana/South Africa, H1 was common in Southern Africa, and H7 and H8 were unique to Asian lions. The autosomal nDNA sequences showed fairly distinct patterns of variation ( Figure 1 ; Figure S1 ). Of the five ADA haplotypes, A2 was the most common and most-widely distributed. The other four haplotypes, which are derived and much less common, included one (A5) that was fixed in Asian lions. The three TF haplotypes were more widely and evenly distributed. Levels of population subdivision among lions were assessed using microsatellite and sequencing data. Eleven groups were identified using Bayesian analyses [22] and three-dimensional factorial correspondence analyses [23] (Figure 2 ; Table S3 ). Most clusters represented geographically circumscribed populations: Namibia (NAM), Kruger National Park (KRU), Ngorongoro Crater (NGC), Kenya (KEN), Uganda (UGA), and Gir (GIR). Two distinct clusters were found in Botswana, BOT-I that included lions from southern Botswana and Kalahari (South Africa) (F k = 0.24) and BOT-II found exclusively in northern Botswana (F k = 0.18). Surprisingly, three distinct clusters were found in a single geographical locale (approximately 60640 km square) in the large panmyctic population of the Serengeti National Park (SER-I/SER-II/SER-III) (F k = 0.18, 0.21, and 0.15, respectively). Two captive lions from Angola (ANG), one from Zimbabwe (ZBW) and four Morocco Zoo Atlas lions (ATL; presently extinct from the wild) ( Figure 1A) were included in the analyses to explore the relationship of lions from more isolated, endangered, or depleted areas. ANG and ZBW lions were assigned to BOT-II (q = 0.90 and 0.87; 90%CI: 0.47-1.00) and KRU (q = 0.85; 90%CI: 0.52-1.00) (Bayesian analyses [22] ) populations, respectively, as expected based on their geographical proximity. However, these lions differed from BOT-II and KRU by up to 8 mtDNA mutations, sharing haplotypes with the ATL lions (H5 in ANG and H6 in ZBW) ( Figure 1B and 1C). The ATL lions did not group in a unique cluster. Both nDNA and mtDNA pairwise genetic distances among the 11 lion populations showed a significant relationship with geographic distance (R 2 = 0.75; Mantel's test, P = 0.0097; and R 2 = 0.15; Mantel's test, P = 0.0369; respectively) ( Figure 3 ). The significant positive and monotonic correlation across all the scatterplot pairwise comparisons for the nDNA markers (biparental) was consistent with isolation-by-distance across the sampled region. However, the correlation between nDNA F ST and geographic distance considerably decreased when the Asian GIR population was removed (R 2 = 0.19; Mantel's test, P = 0.0065) suggesting that caution should be taken in interpreting the pattern of isolation-by-distance in lions. We further compared linearized F ST estimates [24] plotted both against the geographic distance (model assuming habitat to be arrayed in an infinite onedimensional lattice) and the log geographic distance (model assuming an infinite two-dimensional lattice). The broad distribution of lions might suggest a priori that a two-dimensional isolationby-distance model would provide the best fit for the nDNA data (R 2 = 0.25; Mantel's test, P = 0.0022), but instead the onedimensional isolation-by-distance model performed better (R 2 = 0.71; Mantel's test, P = 0.0476) ( Figure S2 ). The pattern observed for the mtDNA (maternal) was more complex. While there was a significant relationship between mtDNA F ST and geographic distance, there was an inconsistent pattern across broader geographic distances ( Figure 3 ). This is partly due to the fixation or near fixation of haplotype H11 in six populations and the fixation of a very divergent haplotype H4 in KEN population ( Figure 1B and 1C). The removal of the KEN population considerably increased the correlation between mtDNA F ST and geographic distance (R 2 = 0.27; Mantel's test, P = 0.0035). Thus, the null hypothesis of regional equilibrium for mtDNA across the entire sampled region is rejected despite the possibility that isolation-by-distance may occur regionally. These contrasting nDNA and mtDNA results may be indicative of differences in dispersal patterns between males and females, which would be consistent with evidence that females are more phylopatric than males. Alternatively, selection for matrilineally transmitted traits upon which neutral mtDNA alleles hitchhike is possible, given the low values of nucleotide diversity of the mtDNA (p = 0.0066). A similar process has been suggested in whales (p = 0.0007) [25] and African savannah elephants (p = 0.0200) [26] , where both species have female phylopatry and like lions, a matriarchal social structure. However, genetic drift tends to overwhelm selection in small isolated populations, predominantly affecting haploid elements due to its lower effective population size (Table 1) . Therefore, we suggest that the contrasting results obtained for nDNA and mtDNA are more likely further evidence that lion populations underwent severe bottlenecks. The highly structured lion matrilines comprise four monophyletic mtDNA haplo-groups ( Figure 4A ; Figure S3 ). Lineage I consisted of a divergent haplotype H4 from KEN, lineage II was observed in most Southern Africa populations, lineage III was widely distributed from Central and Northern Africa to Asia, and lineage IV occurred in Southern and Eastern Africa. Seroprevalence studies indicate that FIV Ple is endemic in eight of the 11 populations but absent from the Asian GIR lions in India and in Namibia and southern Botswana/Kalahari regions (NAM/ BOT-I) in Southwest Africa ( Figure 1B ). Phylogenetic analysis of the conserved pol-RT region in 117 FIV-infected lions depicted monophyletic lineages [15, 18] that affirm six distinct subtypes (A-F) that are distributed geographically in three distinct patterns ( Figure 4B ; Figure S4 ). First, multiple subtypes may circulate within the same population as exemplified by subtypes A, B and C all ubiquitous within the three Serengeti lion populations (SER-I, SER-II and SER-III) and subtypes A and E within lions of Botswana (BOT-II) ( Figure 4B and 4C and Figure S4 ). Second, unique FIV Ple subtypes may be restricted to one location as subtype F in Kenya, subtype E in Botswana (BOT-II), subtype C in Serengeti, and subtype D in Krugar Park ( Figure 4B and 4C and Figure S4 ). Third, intra-subtype strains cluster geographically, as shown by distinct clades within subtype A that were restricted to lions within Krugar Park, Botswana and Serengeti and within subtype B that corresponded to Uganda, Serengeti and Ngorongoro Crater lions ( Figure 4B and 4C and Figure S4 ). Not unexpectedly, FIV Ple pairwise genetic distances, represented as population F ST among the eight lion FIV-infected populations, were not significantly correlated with geographic distance (R 2 = 0.08; Mantel's test, P = 0.165) (Figure 3 ), and affirms that patterns of viral dissemination do not conform to a strict isolation-by-distance model. Rather, the two distinct clusters observed ( Figure 3 ) reflect the complex distribution of FIV Ple among African lions. Indeed, despite the low geographic distance within East-African lion populations, the FIV Ple genetic divergence showed a broader range in F ST (0.03 to 0.79 for most of first cluster; Figure 3 ). By contrast, approximately half of the range in F ST (0.26 to 0.69 for the second cluster; Figure 3 ) was observed among East and Southern Africa in spite of its large geographic separation. In contrast with the patterns observed in lions, linearized F ST estimates [24] for FIV Ple were better correlated with log geographic distance (two-dimensional lattice model) (R 2 = 0.15) than with geographic distance (one-dimensional model) (R 2 = 0.02), although in both cases the Mantel's test was not significant (P.0.2474) ( Figure S2 ). The mtDNA coalescence dating suggested that the East African lineage I (KEN haplotype H4) had an old origin of ,324,000 years (95% CI: 145,000-502,000). Extant East African populations (KEN/NGC/SER-I/SER-II/SER-III) also showed a slightly significant higher nDNA allelic richness and genetic diversity (Table S4) . Moreover, the FIV Ple subtype diversity was higher in East African clades (exhibiting four out of the six known viral-strains), including the most divergent FIV Ple subtype C ( Figure 4B and 4C). These genetic data from lions and FIV Ple is consistent with the older origin of extant East African lions, which is further supported by the oldest lion fossils discovered in East Africa [1] . Relative to East Africa, Southern lions have a slightly more recent mtDNA coalescence. Lineage II, found in NAM, BOT-II and KRU has an estimated coalescence of 169,000 years (95% CI: 34,000-304,000) and the more widespread lineage IV found in the Southern populations of BOT-I, BOT-II and KRU as well as the Eastern populations of SER (I, II, and III), NGC and UGA, coalesces ,101,000 years ago (95% CI: 11,000-191,000). However, the similar levels of nDNA genetic diversity, the occurrence of an exclusively Southern mtDNA lineage II and highly divergent FIV Ple subtypes, FIV Ple subtype D found only in KRU and subtype E exclusive to BOT-II, suggests that both East and Southern Africa were important refugia for lions during the Pleistocene. Therefore, the co-occurrence of divergent mtDNA haplotypes (6 to 10 mutations; Figure 1B and 1C) in southern populations may be the consequence of further isolation within refugia during colder climatic periods. Contemporary fragmentation of lion populations could further explain the results of nested-clade phylogeographical analysis (NCPA [27] ) ( Figure S5 ), which inferred restricted gene flow with isolation-by-distance between mtDNA haplotypes H9 (BOT-II) and H10 (KRU) (x 2 = 10.00, P = 0.0200), between haplotypes H1 (BOT-II/NAM) and H2 (KRU) (x 2 = 71.00, P#0.0001), and between haplotypes H9-H10 (BOT-II/KRU) and haplotypes H11-H12 (BOT-I/KRU/SER/NGC/UGA) (x 2 = 187.83, P#0.0001). Further isolation within refugia (sub-refugia) may also have occurred in East Africa. This is suggested by the distinctive mtDNA haplotype H4 and the unique FIV Ple subtype F found in the Kenya population, which may have resulted from reduced gene flow across the Rift valley, a scenario that has been suggested for several bovid and carnivore populations (see [28] and references therein). The best example of concordance between host genome markers and viral transmission patterns is observed in the Serengeti National Park in Tanzania. Our previous findings described markedly high levels of FIV Ple subtype A, B and C circulating within the Serengeti lion population to such an extent that 43% of the lions sampled were multiply-infected with two or three subtypes [15, 18] and were hypothesized to represent recent admixture of three formerly separated populations. Such result is confirmed here by lion genomic markers ( Figure 2 ). Further, although lions within the Serengeti can be assigned to one of three populations (SER-I, SER-II or SER-III) by host genomic markers, FIV Ple subtypes are distributed ubiquitously in all three, characteristic of rapid horizontal retroviral transmission subsequent to host population admixture. The possible isolating mechanism remains to be elucidated as there is no apparent barrier to gene flow in this ecosystem. Based on patterns of genetic diversity and phylogenetic analysis of lion nDNA/mtDNA and FIV Ple markers, we propose a scenario of a period of refugia/isolation in the Late Pleistocene followed by two major lion expansions across Africa and Asia. The first expansion, supported by the mtDNA NCPA [27] (x 2 = 690.00, P#0.0001; Figure S5 ), was a long-distance colonization of mtDNA lineage-III (GIR/ATL/ANG/ZBW) around 118,000 years ago (95% CI: 28,000-208,000), with subsequent fragmentation of haplotypes H5-H6 into Central and North Africa and haplotypes H7-H8 into West Asia (M1- Figure 1A) . Support for this initial expansion is also found in nDNA. The ADA haplotype A5 fixed in GIR in also present in KEN, SER-II, and SER-III, suggesting that lions likely colonized West Asia from the East Africa refugia ( Figure 1B) . Such an expansion may have been favored by the start of a warmer and less arid period in Africa 130,000-70,000 years ago [29] . This ''out-of-Africa event'' would have occurred much later than the initial lion expansion through Eurasia based on fossils (,500,000 years ago) [3] . It is likely that multiple lion expansions occurred in the Pleistocene, as occurred with humans [21] . A second, more recent lion expansion probably occurred at the Pleistocene/Holocene transition, this one from Southern Africa toward East Africa (M2- Figure 1A, Figure 3 ). This is reflected in the mtDNA linage IV, where haplotypes present in Southern lions are basal (older) to those found in the East. Overall, mtDNA population nucleotide diversity decreases from Southern to East Africa ( Figure 1B and 1C) , a finding supported by pairwise mismatch analysis [30] (raggedness, r = 0.086; P,0.001). The fixation of mtDNA haplotype H11 in BOT-I (otherwise fixed only in East Africa populations) suggests that the colonizing lions expanded northwards from the Kalahari Desert, which included bush, woodland and savannah habitats during the climatic fluctuations of the Pleistocene [31] . This expansion would have occurred relatively recently as the single rare tip mtDNA haplotype H12, found only in SER-I, is derived from the interior widespread haplotype H11 (,14,000-7,000 years; given one mtDNA substitution every 7,000 years; Table 1 ). This expansion is also supported by FIV Ple subtype A where haplotypes present in Southern lions (KRU and BOT-I) are basal to those found in the East (SER-I, SER-II and SER-III) and a decrease of nucleotidediversity of this FIV Ple subtype is observed from Southern (p = 0.15) to Eastern Africa (p = 0.03) ( Figure 3B ). Interestingly, a similar northward colonization process from Southern Africa has been suggested for some of the lion preys, namely the impala, greater kudu, and wildebeest [32, 33] . If we had restricted our inferences to mtDNA, we might have concluded that East African lion populations, which are fixed or nearly fixed for haplotype H11, went extinct during the Pleistocene/Holocene transition (similar to the well known mega-fauna extinctions of the Late Pleistocene [34] ) and were then colonized by Southern populations. However, our population genomics data better fit a scenario of lion population expansion and interbreeding rather than simple replacement. First, genetic diversity and allelic richness at nDNA are slightly higher in East Africa populations relatively to those in Southern Africa. This is contrary to the expected pattern of population expansion in which there is usually a progressive decline in genetic diversity and allelic richness. Second, SER lions carry two diverse FIV Ple subtypes found only in East Africa (B-C), and not only FIV Ple subtype A, which was presumably introduced in East Africa coincidently with the mtDNA expansion event northwards from South. Third, the East African FIV Ple subtype B found in UGA/SER-I/SER-II/SER-III/NGC showed evidence of a population expansion (raggedness, r = 0.004; P,0.01; F s = 220.37; P,0.00001) and the highest nucleotide diversity observed within FIV Ple subtypes (p = 0.09). Four, the FIV Ple subtype diversity is higher in East African clades (four out of the six viral strains). The utility of FIV Ple pol-RT as a marker of lion population structure and natural history is that it can be informative on a contemporaneous time scale, though it may be less useful at capturing more ancient demographic events. The extreme divergence among FIV Ple subtypes, considered with high sero-prevalence in eight of the 11 lion populations, and combined with patterns of geographic concordance, support the hypothesis that FIV Ple is not a recent emergence within modern lions [35] . Populations that harbor one private FIV Ple subtype such KEN (subtype F), BOT-II (subtype E), and KRU (subtype D) must have been sufficiently isolated for enough time for the virus to evolve into unique subtypes, a result corroborated by the high nDNA and mtDNA genetic structure present in these lion populations ( Figure 4 ). Thus, it is possible that the initial emergence of FIV Ple pre-dates the Late-Pleistocene expansions of contemporary lion populations [36] , but present day distributions are more useful indicators of very recent host population dynamics, a result also observed with FIV Pco in a panmictic population of pumas in western North America [19] . Accurate interpretation of past and contemporary population demographic scenarios is a primary goal for the effective conservation of endangered species. In this study, we found substantial population subdivision, reduced gene flow, and large differences in FIV Ple sequence and sero-prevalence among lion populations, as well as evidence of historic secondary contact between populations ( Figure 3C ; Table S4 to S9). The very low population level of mtDNA nucleotide diversity, the number of haplotypes private to a single population (Figure 1) , and probably also the lack of SRY genetic variation across all male lions (haplotype S1, n = 183) suggests that lion numbers diminished considerably following the Late Pleistocene. The last century reduction in lion distribution further eroded its genomic diversity, and microsatellite variation suggested recent population bottlenecks in seven out of the 11 populations (standardized differences test, P,0.05; Table S5 ) [37] . Although we did not explicitly try to address the adequacy of lion subspecies designations (currently only one African subspecies is widely recognized) [38, 39] , we provided strong evidence that there is no evidence of substantial genetic exchange of matrilines among existing populations as the AMOVA [40] withinpopulation component was uniformly high in all distinct subdivision scenarios (W ST <0.920; P,0.0001; three-six groups; Table S6 ). Similarly, significant population structure was detected from nDNA (F ST = 0.18), with low levels of admixture evident from Bayesian analysis [22] (a = 0.033). Therefore, employing a bottom-up perspective that prioritizes populations, rather than large-scale units (e.g. all African lions), might preserve and maintain lion diversity and evolutionary processes most efficiently [41] . A total of 357 individuals were obtained across most of the lion range in Africa and Asia ( Figure 1A ; Table S1 ). Genetic variation among lion specimens was assessed using maternal (12S and 16S genes), paternal (SRY gene) and bi-parental autosomal (22 microsatelite loci, and the ADA and TF genes) markers (GenBank accession numbers: FJ151632-FJ151652). Analyses of mtDNA in Panthera species are complicated by the presence of a 12.5 kb mtDNA integration into chromosome F2 [42] . Accordingly, mtDNA specific primers were designed for the 12S and 16S genes (Table S2 ) and we used long-range PCR amplification. We designed primers to amplify segments of the ADA (exon 10 and intron 10) and the TF (intron 3) genes (Table S2) , two of the most variable protein loci in lion populations [5] , localized on the domestic cat Felis catus chromosome A3p and C2q, respectively. The Y-chromosome SRY-39UTR gene was also amplified [43] . PCR products were amplified from 50 ng of genomic DNA in a 25 mL reaction system containing 1.5 mM MgCl 2 , 1.0 mM dNTPs, 0.25 units of AmpliTaq Gold DNA polymerase (Applied Biosystems), and 16 PCR buffer II; the amplification protocol was: denaturation 10 min at 95uC, a touch-down cycle of 95uC for 30 s, 52uC for 60 s decreased by 1uC in the next cycle for 10 cycles, 72uC for 120 s, then 35 amplification cycles of 95uC for 30 s, 52uC for 60 s, and 72uC for 120 s, followed by an extension of 10 min at 72uC. PCR products were sequenced on an ABI 377. Sequences were aligned and cleaned using SEQUENCHER (Gene Codes). Twenty two polymorphic microsatellite loci (20 dinucleotide repeats: FCA006, FCA008, FCA014, FCA069, FCA077, FCA085, FCA091, FCA098, FCA105, FCA126, FCA129, FCA139, FCA205, FCA208, FCA211, FCA224, FCA229, FCA230, FCA247, and FCA281; and two tetranucleotide repeats: FCA391 and FCA441) were amplified [44] . Microsatellites were scored in an ABI 377 and analyzed using GENESCAN 2.1 and GENOTYPER 2.5. These loci are located on 11 of the 19 F. catus chromosomes, occurring in different linkage groups or at least 12 centimorgans apart [44, 45] . Western blots using domestic cat and lion FIV as antigen were performed as previously described [46, 47] . The supernatant from virus-infected cells was centrifuged at 200 g for 10 min at 5uC. The resultant supernatant was centrifuged at 150,000 g at 4uC for 2 hours. Pelleted viral proteins were resuspended in 1/20 th of the original volume and total protein content was assayed using the Biorad Protein Assay. Twenty mg of viral protein were run on 4-20% Tris-Glycine gels and transferred to PDVF membranes (BioRad). Membrane strips were exposed 2-12 h to a 1:25 or 1:200 dilution of serum or plasma. After washing, samples were labeled with goat anti-cat HRP or phosphate conjugated antibody (KPL laboratories) at a 1:2000 dilution, washed, and incubated in ECL Western Blotting detection reagents (Amersham Biosciences) for 2 min, then exposed to Lumi-Film Chemiluminescent Detection Film (Boehringer Mannheim) or incubated in BCIP/ NBP phosphatase substrate (KPL laboratories) for 15 min [46] [47] [48] . Results were visualized and scored manually based on the presence and intensity of antibody binding to the p24 gag capsid protein. Nested PCR amplification of partial FIV Ple pol-RT was performed [18, 46] . Briefly, first round PCR reactions used 100 ng of genomic DNA, 2.5 mM MgCl 2 and an annealing temperature of 52uC. Second round PCR reactions used identical conditions with 1-5 ml of first-round product as template. All PCR products were sequenced as described above for lion genetic analyses (GenBank accession numbers: AY549217-AY552683; AY878208-AY878235; FJ225347-FJ225382). We used the GENETIX 4.02 [49] , GENEPOP 3.3 [50] , MICROSAT [51] , and DNASP 4.10 [52] to calculate the following descriptive statistics: (i) percentage of polymorphic loci (P 95 ), number of alleles per locus (A), observed and expected heterozygosity (H E and H O ), and number of unique alleles (A U ); (ii) assess deviations from HWE; (iii) estimate the coefficient of differentiation (F ST ), and (iv) nucleotide (p) and haplotype (h) diversity. We tested the hypothesis that all loci are evolving under neutrality for both the lion and the FIV Ple data. For frequency data, we used the method described by Beaumont and Nichols [53] and implemented in FDIST (http://www.rubic.rdg.ac.uk/ mab/software.html). The F ST values estimated from microsatellite loci plotted against heterozygosity showed that all values fall within the expected 95% confidence limit and consequently no outlier locus were identified. For sequence data (lion nDNA/ mtDNA and FIV Ple pol-RT), we ruled out any significant evidence for genetic hitchhiking and background selection by assessing Fu and Li's D* and F* tests [54] and Fu's F S statistics [55] . A Bayesian clustering method implemented in the program STRUCTURE [22] was used to infer number of populations and assign individual lions to populations based on multilocus genotype (microsatellites) and sequence data (ADA, TF, and mtDNA genes) and without incorporating sample origin. For haploid mtDNA data, each observed haplotype was coded with a unique integer (e.g. 100, 110) for the first allele and missing data for the second (STRUCTURE [22] analyses with or without the mtDNA data were essentially identical). For K population clusters, the program estimates the probability of the data, Pr(X|K), and the probability of individual membership in each cluster using a Markov chain Monte Carlo method under the assumption of Hardy-Weinberg equilibrium (HWE) within each cluster. Initial testing of the HWE in each of the populations defined by the geographic origin of sampling revealed no significant deviation from HW expectations with the exception of SER and BOT population (later subdivided by STRUCTURE [22] in 3 and 2 clusters, respectively; such deviations from HW expectations were interpreted as evidence of further population structuring). We conducted six independent runs with K = 1-20 to guide an empirical estimate of the number of identifiable populations, assuming an admixture model with correlated allele frequencies and with burn-in and replication values set at 30,000 and 10 6 , respectively. STRUCTURE also estimates allele frequencies of a hypothetical ancestral population and an alpha value that measures admixed individuals in the data set. The assignment of admixed individuals to populations using STRUCTURE [22] has been considered in subsequent population analyses. For each population cluster k, the program estimates F k , a quantity analogous to Wright's F ST , but describing the degree of genetic differentiation of population k from the ancestral population. Patterns of gene flow and divergence among populations were described using a variety of tests. First, to visualize subtle relationships among individual autosomal genotypes, threedimensional factorial correspondence analyses [23] (FCA) were performed in GENETIX [49] , which graphically projects the individuals on the factor space defined by the similarity of their allelic states. Second, neighbor-joining (NJ) analyses implementing the Cavalli-Sforza & Edwards' chord genetic distance [56] (D CE ) were estimated in PHYLIP 3.6 [57] , and the tree topology support was assessed by 100 bootstraps. Third, the difference in average H O and A was compared among population groups using a twosided test in FSTAT 2.9.3.2 [58] , which allows to assess the significance of the statistic OS x using 1,000 randomizations. Four, the equilibrium between drift and gene flow was tested using a regression of pairwise F ST on geographic distance matrix among all populations for host nDNA(microsatellites)/mtDNA and FIV Ple data. A Mantel test [59] was used to estimate the 95% upper probability for each matrix correlation. Assuming a stepping stone model of migration where gene flow is more likely between adjacent populations, one can reject the null hypothesis that populations in a region are at equilibrium if (1) there is a nonsignificant association between genetic and geographic distances, and/or (2) a scatterplot of the genetic and geographic distances fails to reveal a positive and monotonic relationship over all distance values of a region [60] . We also evaluated linearized F ST [i.e. F ST /(12F ST )] [24] among populations. We tested two competing models of isolation-by-distance, one assuming the habitat to be arrayed in an infinite one-dimensional lattice and another assuming an infinite two-dimensional lattice. Both models showed that genetic differentiation increased with raw and logtransformed Euclidean distances, respectively [24] . We determined the confidence interval value of the slope of the regression for the nDNA data using a non parametric ABC bootstrap [61] in GENEPOP 4.0 [62] . The demographic history of populations was compared using a variety of estimators based on the coalescence theory. First, signatures of old demographic population expansion were investigated for mtDNA and FIV Ple pol-RT haplotypes using pairwise mismatch distributions [63] in DNASP [52] . The goodness-of-fit of the observed data to a simulated model of expansion was tested with the raggedness (r) index [64] . Second, the occurrence of recent bottlenecks was evaluated for microsatellite data using the method of Cornuet & Luikart [37] in BOTTLENECK [65] and using 10,000 iterations. This approach, which exploits the fact that rare alleles are generally lost first through genetic drift after reduction in population size, employs the standardize differences test, which is the most appropriate and powerful when using 20 or more polymorphic loci [37] . Tests were carried out using the stepwise mutation model (SMM), which is a conservative mutation model for the detection of bottleneck signatures with microsatellites [66] . Third, to discriminate between recurrent gene flow and historical events we used the nested-clade phylogeographical analysis [27, 67] (NCPA) for the mtDNA data. When the nullhypothesis of no correlation between genealogy and geography is rejected, biological inferences are drawn using a priori criteria. The NCPA started with the estimation of a 95% statistical parsimony [68] mtDNA network in TCS 1.20 [69] . Tree ambiguities were further resolved using a coalescence criteria [70] . The network was converted into a series of nested branches (clades) [71, 72] , which were then tested against their geographical locations through a permutational contingency analysis in GEODIS 2.2 [73] . The inferences obtained were also corroborated with the automated implementation of the NCPA in ANECA [74] . To address potential weaknesses in some aspects of the NCPA analysis [75, 76] , we further validated the NCPA inferences with independent methods for detecting restricted gene-flow/isolation-bydistance (using matrix correlation of pairwise F ST and geographic distance) and population expansion (using pairwise mismatch distributions). Four, to test the significance of the total mtDNA genetic variance, we conducted hierarchical analyses of molecular variance [40] (AMOVA) using ARLEQUIN 2.0 [77] . Total genetic variation was partitioned to compare the amount of difference among population groups, among populations within each groups, and within populations. Phylogenetic relationships among mtDNA and FIV Ple pol-RT sequences were assessed using Minimum evolution (ME), Maximum parsimony (MP), and Maximum likelihood (ML) approaches implemented in PAUP [78] . The ME analysis for mtDNA consisted of NJ trees constructed from Kimura two-parameter distances followed by a branch-swapping procedure and for FIV Ple data employed the same parameter estimates as were used in the ML analysis. The MP analysis was conducted using a heuristic search, with random additions of taxa and tree-bisection-reconnection branch swapping. The ML analysis was done after selecting the best evolutionary model fitting the data using MODELTEST 3.7 [79] . Tree topologies reliability was assessed by 100 bootstraps. For the FIV Ple data, the reliability of the tree topology was further assessed through additional analyses using 520 bp of FIV Ple pol-RT sequences in a representative subset of individuals. The time to the most recent common ancestor (TMRCA) for the ADA and TF haplotypes was estimated following Takahata et al. [80] , where we calculate the ratio of the average nucleotide differences within the lion sample to one-half the average nucleotide difference between leopards (P. pardus) and lions and multiplying the ratio by an estimate of the divergence time between lions and leopards (2 million years based on undisputed lion fossils in Africa) [81, 82] . The mtDNA TMRCA was estimated with a linearized tree method in LINTREE [83] and using the equation H = 2mT, where H was the branch height (correlated to the average pairwise distance among haplotypes), m the substitution rate, and T the divergence time. Leopard and snow leopard (P. uncia) sequences were used as outgroups. Inference of the TMRCA for microsatellite loci followed Driscoll et al. [6] where the estimate of microsatellite variance in average allele repeat-size was used as a surrogate for evolutionary time based on the rate of allele range reconstitution subsequent to a severe founder effect. Microsatellite allele variance has been shown to be a reliable estimator for microsatellite evolution and demographic inference in felid species [6] . [24] for lion (nDNA and mtDNA) and FIV Ple (pol-RT) genetic data plotted both against the geographic distance (model assuming habitat to be arrayed in an infinite one-dimensional lattice; one-dimension isolation-by-distance [IBD]) and the log geographic distance (model assuming an infinite two-dimensional lattice; twodimension isolation-by-distance) on geographic distance. Found at: doi:10.1371/journal.pgen.1000251.s002 (0.13 MB PDF) Figure S3 Phylogenetic relationships of the 12S-16S mtDNA lion haplotypes. Neighbour-joining tree of the 1,882 bp 12S-16S mtDNA sequences. Bootstrap values are placed at each branchpoint for the minimum evolution/maximum parsimony/maximum likelihood analyses, respectively (ME/MP/ML). Outgroups: Ppaleopard, Panthera pardus; Pun -snow-leopard, Panthera uncia. The symbol (N) represents nodes with bootstrap support ,50 or an inferred polytomy in the bootstrap 50% majority-rule consensus tree.

When did lions first occupy Europe?

Respiratory Viral Infections in Exacerbation of Chronic Airway Inflammatory Diseases: Novel Mechanisms and Insights From the Upper Airway Epithelium https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7052386/ SHA: 45a566c71056ba4faab425b4f7e9edee6320e4a4 Authors: Tan, Kai Sen; Lim, Rachel Liyu; Liu, Jing; Ong, Hsiao Hui; Tan, Vivian Jiayi; Lim, Hui Fang; Chung, Kian Fan; Adcock, Ian M.; Chow, Vincent T.; Wang, De Yun Date: 2020-02-25 DOI: 10.3389/fcell.2020.00099 License: cc-by Abstract: Respiratory virus infection is one of the major sources of exacerbation of chronic airway inflammatory diseases. These exacerbations are associated with high morbidity and even mortality worldwide. The current understanding on viral-induced exacerbations is that viral infection increases airway inflammation which aggravates disease symptoms. Recent advances in in vitro air-liquid interface 3D cultures, organoid cultures and the use of novel human and animal challenge models have evoked new understandings as to the mechanisms of viral exacerbations. In this review, we will focus on recent novel findings that elucidate how respiratory viral infections alter the epithelial barrier in the airways, the upper airway microbial environment, epigenetic modifications including miRNA modulation, and other changes in immune responses throughout the upper and lower airways. First, we reviewed the prevalence of different respiratory viral infections in causing exacerbations in chronic airway inflammatory diseases. Subsequently we also summarized how recent models have expanded our appreciation of the mechanisms of viral-induced exacerbations. Further we highlighted the importance of the virome within the airway microbiome environment and its impact on subsequent bacterial infection. This review consolidates the understanding of viral induced exacerbation in chronic airway inflammatory diseases and indicates pathways that may be targeted for more effective management of chronic inflammatory diseases. Text: The prevalence of chronic airway inflammatory disease is increasing worldwide especially in developed nations (GBD 2015 Chronic Respiratory Disease Collaborators, 2017 Guan et al., 2018) . This disease is characterized by airway inflammation leading to complications such as coughing, wheezing and shortness of breath. The disease can manifest in both the upper airway (such as chronic rhinosinusitis, CRS) and lower airway (such as asthma and chronic obstructive pulmonary disease, COPD) which greatly affect the patients' quality of life (Calus et al., 2012; Bao et al., 2015) . Treatment and management vary greatly in efficacy due to the complexity and heterogeneity of the disease. This is further complicated by the effect of episodic exacerbations of the disease, defined as worsening of disease symptoms including wheeze, cough, breathlessness and chest tightness (Xepapadaki and Papadopoulos, 2010) . Such exacerbations are due to the effect of enhanced acute airway inflammation impacting upon and worsening the symptoms of the existing disease (Hashimoto et al., 2008; Viniol and Vogelmeier, 2018) . These acute exacerbations are the main cause of morbidity and sometimes mortality in patients, as well as resulting in major economic burdens worldwide. However, due to the complex interactions between the host and the exacerbation agents, the mechanisms of exacerbation may vary considerably in different individuals under various triggers. Acute exacerbations are usually due to the presence of environmental factors such as allergens, pollutants, smoke, cold or dry air and pathogenic microbes in the airway (Gautier and Charpin, 2017; Viniol and Vogelmeier, 2018) . These agents elicit an immune response leading to infiltration of activated immune cells that further release inflammatory mediators that cause acute symptoms such as increased mucus production, cough, wheeze and shortness of breath. Among these agents, viral infection is one of the major drivers of asthma exacerbations accounting for up to 80-90% and 45-80% of exacerbations in children and adults respectively (Grissell et al., 2005; Xepapadaki and Papadopoulos, 2010; Jartti and Gern, 2017; Adeli et al., 2019) . Viral involvement in COPD exacerbation is also equally high, having been detected in 30-80% of acute COPD exacerbations (Kherad et al., 2010; Jafarinejad et al., 2017; Stolz et al., 2019) . Whilst the prevalence of viral exacerbations in CRS is still unclear, its prevalence is likely to be high due to the similar inflammatory nature of these diseases (Rowan et al., 2015; Tan et al., 2017) . One of the reasons for the involvement of respiratory viruses' in exacerbations is their ease of transmission and infection (Kutter et al., 2018) . In addition, the high diversity of the respiratory viruses may also contribute to exacerbations of different nature and severity (Busse et al., 2010; Costa et al., 2014; Jartti and Gern, 2017) . Hence, it is important to identify the exact mechanisms underpinning viral exacerbations in susceptible subjects in order to properly manage exacerbations via supplementary treatments that may alleviate the exacerbation symptoms or prevent severe exacerbations. While the lower airway is the site of dysregulated inflammation in most chronic airway inflammatory diseases, the upper airway remains the first point of contact with sources of exacerbation. Therefore, their interaction with the exacerbation agents may directly contribute to the subsequent responses in the lower airway, in line with the "United Airway" hypothesis. To elucidate the host airway interaction with viruses leading to exacerbations, we thus focus our review on recent findings of viral interaction with the upper airway. We compiled how viral induced changes to the upper airway may contribute to chronic airway inflammatory disease exacerbations, to provide a unified elucidation of the potential exacerbation mechanisms initiated from predominantly upper airway infections. Despite being a major cause of exacerbation, reports linking respiratory viruses to acute exacerbations only start to emerge in the late 1950s (Pattemore et al., 1992) ; with bacterial infections previously considered as the likely culprit for acute exacerbation (Stevens, 1953; Message and Johnston, 2002) . However, with the advent of PCR technology, more viruses were recovered during acute exacerbations events and reports implicating their role emerged in the late 1980s (Message and Johnston, 2002) . Rhinovirus (RV) and respiratory syncytial virus (RSV) are the predominant viruses linked to the development and exacerbation of chronic airway inflammatory diseases (Jartti and Gern, 2017) . Other viruses such as parainfluenza virus (PIV), influenza virus (IFV) and adenovirus (AdV) have also been implicated in acute exacerbations but to a much lesser extent (Johnston et al., 2005; Oliver et al., 2014; Ko et al., 2019) . More recently, other viruses including bocavirus (BoV), human metapneumovirus (HMPV), certain coronavirus (CoV) strains, a specific enterovirus (EV) strain EV-D68, human cytomegalovirus (hCMV) and herpes simplex virus (HSV) have been reported as contributing to acute exacerbations . The common feature these viruses share is that they can infect both the upper and/or lower airway, further increasing the inflammatory conditions in the diseased airway (Mallia and Johnston, 2006; Britto et al., 2017) . Respiratory viruses primarily infect and replicate within airway epithelial cells . During the replication process, the cells release antiviral factors and cytokines that alter local airway inflammation and airway niche (Busse et al., 2010) . In a healthy airway, the inflammation normally leads to type 1 inflammatory responses consisting of activation of an antiviral state and infiltration of antiviral effector cells. This eventually results in the resolution of the inflammatory response and clearance of the viral infection (Vareille et al., 2011; Braciale et al., 2012) . However, in a chronically inflamed airway, the responses against the virus may be impaired or aberrant, causing sustained inflammation and erroneous infiltration, resulting in the exacerbation of their symptoms (Mallia and Johnston, 2006; Dougherty and Fahy, 2009; Busse et al., 2010; Britto et al., 2017; Linden et al., 2019) . This is usually further compounded by the increased susceptibility of chronic airway inflammatory disease patients toward viral respiratory infections, thereby increasing the frequency of exacerbation as a whole (Dougherty and Fahy, 2009; Busse et al., 2010; Linden et al., 2019) . Furthermore, due to the different replication cycles and response against the myriad of respiratory viruses, each respiratory virus may also contribute to exacerbations via different mechanisms that may alter their severity. Hence, this review will focus on compiling and collating the current known mechanisms of viral-induced exacerbation of chronic airway inflammatory diseases; as well as linking the different viral infection pathogenesis to elucidate other potential ways the infection can exacerbate the disease. The review will serve to provide further understanding of viral induced exacerbation to identify potential pathways and pathogenesis mechanisms that may be targeted as supplementary care for management and prevention of exacerbation. Such an approach may be clinically significant due to the current scarcity of antiviral drugs for the management of viral-induced exacerbations. This will improve the quality of life of patients with chronic airway inflammatory diseases. Once the link between viral infection and acute exacerbations of chronic airway inflammatory disease was established, there have been many reports on the mechanisms underlying the exacerbation induced by respiratory viral infection. Upon infecting the host, viruses evoke an inflammatory response as a means of counteracting the infection. Generally, infected airway epithelial cells release type I (IFNα/β) and type III (IFNλ) interferons, cytokines and chemokines such as IL-6, IL-8, IL-12, RANTES, macrophage inflammatory protein 1α (MIP-1α) and monocyte chemotactic protein 1 (MCP-1) (Wark and Gibson, 2006; Matsukura et al., 2013) . These, in turn, enable infiltration of innate immune cells and of professional antigen presenting cells (APCs) that will then in turn release specific mediators to facilitate viral targeting and clearance, including type II interferon (IFNγ), IL-2, IL-4, IL-5, IL-9, and IL-12 (Wark and Gibson, 2006; Singh et al., 2010; Braciale et al., 2012) . These factors heighten local inflammation and the infiltration of granulocytes, T-cells and B-cells (Wark and Gibson, 2006; Braciale et al., 2012) . The increased inflammation, in turn, worsens the symptoms of airway diseases. Additionally, in patients with asthma and patients with CRS with nasal polyp (CRSwNP), viral infections such as RV and RSV promote a Type 2-biased immune response (Becker, 2006; Jackson et al., 2014; Jurak et al., 2018) . This amplifies the basal type 2 inflammation resulting in a greater release of IL-4, IL-5, IL-13, RANTES and eotaxin and a further increase in eosinophilia, a key pathological driver of asthma and CRSwNP (Wark and Gibson, 2006; Singh et al., 2010; Chung et al., 2015; Dunican and Fahy, 2015) . Increased eosinophilia, in turn, worsens the classical symptoms of disease and may further lead to life-threatening conditions due to breathing difficulties. On the other hand, patients with COPD and patients with CRS without nasal polyp (CRSsNP) are more neutrophilic in nature due to the expression of neutrophil chemoattractants such as CXCL9, CXCL10, and CXCL11 (Cukic et al., 2012; Brightling and Greening, 2019) . The pathology of these airway diseases is characterized by airway remodeling due to the presence of remodeling factors such as matrix metalloproteinases (MMPs) released from infiltrating neutrophils (Linden et al., 2019) . Viral infections in such conditions will then cause increase neutrophilic activation; worsening the symptoms and airway remodeling in the airway thereby exacerbating COPD, CRSsNP and even CRSwNP in certain cases (Wang et al., 2009; Tacon et al., 2010; Linden et al., 2019) . An epithelial-centric alarmin pathway around IL-25, IL-33 and thymic stromal lymphopoietin (TSLP), and their interaction with group 2 innate lymphoid cells (ILC2) has also recently been identified (Nagarkar et al., 2012; Hong et al., 2018; Allinne et al., 2019) . IL-25, IL-33 and TSLP are type 2 inflammatory cytokines expressed by the epithelial cells upon injury to the epithelial barrier (Gabryelska et al., 2019; Roan et al., 2019) . ILC2s are a group of lymphoid cells lacking both B and T cell receptors but play a crucial role in secreting type 2 cytokines to perpetuate type 2 inflammation when activated (Scanlon and McKenzie, 2012; Li and Hendriks, 2013) . In the event of viral infection, cell death and injury to the epithelial barrier will also induce the expression of IL-25, IL-33 and TSLP, with heighten expression in an inflamed airway (Allakhverdi et al., 2007; Goldsmith et al., 2012; Byers et al., 2013; Shaw et al., 2013; Beale et al., 2014; Jackson et al., 2014; Uller and Persson, 2018; Ravanetti et al., 2019) . These 3 cytokines then work in concert to activate ILC2s to further secrete type 2 cytokines IL-4, IL-5, and IL-13 which further aggravate the type 2 inflammation in the airway causing acute exacerbation (Camelo et al., 2017) . In the case of COPD, increased ILC2 activation, which retain the capability of differentiating to ILC1, may also further augment the neutrophilic response and further aggravate the exacerbation (Silver et al., 2016) . Interestingly, these factors are not released to any great extent and do not activate an ILC2 response during viral infection in healthy individuals (Yan et al., 2016; Tan et al., 2018a) ; despite augmenting a type 2 exacerbation in chronically inflamed airways (Jurak et al., 2018) . These classical mechanisms of viral induced acute exacerbations are summarized in Figure 1 . As integration of the virology, microbiology and immunology of viral infection becomes more interlinked, additional factors and FIGURE 1 | Current understanding of viral induced exacerbation of chronic airway inflammatory diseases. Upon virus infection in the airway, antiviral state will be activated to clear the invading pathogen from the airway. Immune response and injury factors released from the infected epithelium normally would induce a rapid type 1 immunity that facilitates viral clearance. However, in the inflamed airway, the cytokines and chemokines released instead augmented the inflammation present in the chronically inflamed airway, strengthening the neutrophilic infiltration in COPD airway, and eosinophilic infiltration in the asthmatic airway. The effect is also further compounded by the participation of Th1 and ILC1 cells in the COPD airway; and Th2 and ILC2 cells in the asthmatic airway. Frontiers in Cell and Developmental Biology | www.frontiersin.org mechanisms have been implicated in acute exacerbations during and after viral infection (Murray et al., 2006) . Murray et al. (2006) has underlined the synergistic effect of viral infection with other sensitizing agents in causing more severe acute exacerbations in the airway. This is especially true when not all exacerbation events occurred during the viral infection but may also occur well after viral clearance (Kim et al., 2008; Stolz et al., 2019) in particular the late onset of a bacterial infection (Singanayagam et al., 2018 (Singanayagam et al., , 2019a . In addition, viruses do not need to directly infect the lower airway to cause an acute exacerbation, as the nasal epithelium remains the primary site of most infections. Moreover, not all viral infections of the airway will lead to acute exacerbations, suggesting a more complex interplay between the virus and upper airway epithelium which synergize with the local airway environment in line with the "united airway" hypothesis (Kurai et al., 2013) . On the other hand, viral infections or their components persist in patients with chronic airway inflammatory disease (Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019) . Hence, their presence may further alter the local environment and contribute to current and future exacerbations. Future studies should be performed using metagenomics in addition to PCR analysis to determine the contribution of the microbiome and mycobiome to viral infections. In this review, we highlight recent data regarding viral interactions with the airway epithelium that could also contribute to, or further aggravate, acute exacerbations of chronic airway inflammatory diseases. Patients with chronic airway inflammatory diseases have impaired or reduced ability of viral clearance (Hammond et al., 2015; McKendry et al., 2016; Akbarshahi et al., 2018; Gill et al., 2018; Wang et al., 2018; Singanayagam et al., 2019b) . Their impairment stems from a type 2-skewed inflammatory response which deprives the airway of important type 1 responsive CD8 cells that are responsible for the complete clearance of virusinfected cells (Becker, 2006; McKendry et al., 2016) . This is especially evident in weak type 1 inflammation-inducing viruses such as RV and RSV (Kling et al., 2005; Wood et al., 2011; Ravi et al., 2019) . Additionally, there are also evidence of reduced type I (IFNβ) and III (IFNλ) interferon production due to type 2-skewed inflammation, which contributes to imperfect clearance of the virus resulting in persistence of viral components, or the live virus in the airway epithelium (Contoli et al., 2006; Hwang et al., 2019; Wark, 2019) . Due to the viral components remaining in the airway, antiviral genes such as type I interferons, inflammasome activating factors and cytokines remained activated resulting in prolong airway inflammation (Wood et al., 2011; Essaidi-Laziosi et al., 2018) . These factors enhance granulocyte infiltration thus prolonging the exacerbation symptoms. Such persistent inflammation may also be found within DNA viruses such as AdV, hCMV and HSV, whose infections generally persist longer (Imperiale and Jiang, 2015) , further contributing to chronic activation of inflammation when they infect the airway (Yang et al., 2008; Morimoto et al., 2009; Imperiale and Jiang, 2015; Lan et al., 2016; Tan et al., 2016; Kowalski et al., 2017) . With that note, human papilloma virus (HPV), a DNA virus highly associated with head and neck cancers and respiratory papillomatosis, is also linked with the chronic inflammation that precedes the malignancies (de Visser et al., 2005; Gillison et al., 2012; Bonomi et al., 2014; Fernandes et al., 2015) . Therefore, the role of HPV infection in causing chronic inflammation in the airway and their association to exacerbations of chronic airway inflammatory diseases, which is scarcely explored, should be investigated in the future. Furthermore, viral persistence which lead to continuous expression of antiviral genes may also lead to the development of steroid resistance, which is seen with RV, RSV, and PIV infection (Chi et al., 2011; Ford et al., 2013; Papi et al., 2013) . The use of steroid to suppress the inflammation may also cause the virus to linger longer in the airway due to the lack of antiviral clearance (Kim et al., 2008; Hammond et al., 2015; Hewitt et al., 2016; McKendry et al., 2016; Singanayagam et al., 2019b) . The concomitant development of steroid resistance together with recurring or prolong viral infection thus added considerable burden to the management of acute exacerbation, which should be the future focus of research to resolve the dual complications arising from viral infection. On the other end of the spectrum, viruses that induce strong type 1 inflammation and cell death such as IFV (Yan et al., 2016; Guibas et al., 2018) and certain CoV (including the recently emerged COVID-19 virus) (Tao et al., 2013; Yue et al., 2018; Zhu et al., 2020) , may not cause prolonged inflammation due to strong induction of antiviral clearance. These infections, however, cause massive damage and cell death to the epithelial barrier, so much so that areas of the epithelium may be completely absent post infection (Yan et al., 2016; Tan et al., 2019) . Factors such as RANTES and CXCL10, which recruit immune cells to induce apoptosis, are strongly induced from IFV infected epithelium (Ampomah et al., 2018; Tan et al., 2019) . Additionally, necroptotic factors such as RIP3 further compounds the cell deaths in IFV infected epithelium . The massive cell death induced may result in worsening of the acute exacerbation due to the release of their cellular content into the airway, further evoking an inflammatory response in the airway (Guibas et al., 2018) . Moreover, the destruction of the epithelial barrier may cause further contact with other pathogens and allergens in the airway which may then prolong exacerbations or results in new exacerbations. Epithelial destruction may also promote further epithelial remodeling during its regeneration as viral infection induces the expression of remodeling genes such as MMPs and growth factors . Infections that cause massive destruction of the epithelium, such as IFV, usually result in severe acute exacerbations with non-classical symptoms of chronic airway inflammatory diseases. Fortunately, annual vaccines are available to prevent IFV infections (Vasileiou et al., 2017; Zheng et al., 2018) ; and it is recommended that patients with chronic airway inflammatory disease receive their annual influenza vaccination as the best means to prevent severe IFV induced exacerbation. Another mechanism that viral infections may use to drive acute exacerbations is the induction of vasodilation or tight junction opening factors which may increase the rate of infiltration. Infection with a multitude of respiratory viruses causes disruption of tight junctions with the resulting increased rate of viral infiltration. This also increases the chances of allergens coming into contact with airway immune cells. For example, IFV infection was found to induce oncostatin M (OSM) which causes tight junction opening (Pothoven et al., 2015; Tian et al., 2018) . Similarly, RV and RSV infections usually cause tight junction opening which may also increase the infiltration rate of eosinophils and thus worsening of the classical symptoms of chronic airway inflammatory diseases (Sajjan et al., 2008; Kast et al., 2017; Kim et al., 2018) . In addition, the expression of vasodilating factors and fluid homeostatic factors such as angiopoietin-like 4 (ANGPTL4) and bactericidal/permeabilityincreasing fold-containing family member A1 (BPIFA1) are also associated with viral infections and pneumonia development, which may worsen inflammation in the lower airway Akram et al., 2018) . These factors may serve as targets to prevent viral-induced exacerbations during the management of acute exacerbation of chronic airway inflammatory diseases. Another recent area of interest is the relationship between asthma and COPD exacerbations and their association with the airway microbiome. The development of chronic airway inflammatory diseases is usually linked to specific bacterial species in the microbiome which may thrive in the inflamed airway environment (Diver et al., 2019) . In the event of a viral infection such as RV infection, the effect induced by the virus may destabilize the equilibrium of the microbiome present (Molyneaux et al., 2013; Kloepfer et al., 2014; Kloepfer et al., 2017; Jubinville et al., 2018; van Rijn et al., 2019) . In addition, viral infection may disrupt biofilm colonies in the upper airway (e.g., Streptococcus pneumoniae) microbiome to be release into the lower airway and worsening the inflammation (Marks et al., 2013; Chao et al., 2014) . Moreover, a viral infection may also alter the nutrient profile in the airway through release of previously inaccessible nutrients that will alter bacterial growth (Siegel et al., 2014; Mallia et al., 2018) . Furthermore, the destabilization is further compounded by impaired bacterial immune response, either from direct viral influences, or use of corticosteroids to suppress the exacerbation symptoms (Singanayagam et al., 2018 (Singanayagam et al., , 2019a Wang et al., 2018; Finney et al., 2019) . All these may gradually lead to more far reaching effect when normal flora is replaced with opportunistic pathogens, altering the inflammatory profiles (Teo et al., 2018) . These changes may in turn result in more severe and frequent acute exacerbations due to the interplay between virus and pathogenic bacteria in exacerbating chronic airway inflammatory diseases (Wark et al., 2013; Singanayagam et al., 2018) . To counteract these effects, microbiome-based therapies are in their infancy but have shown efficacy in the treatments of irritable bowel syndrome by restoring the intestinal microbiome (Bakken et al., 2011) . Further research can be done similarly for the airway microbiome to be able to restore the microbiome following disruption by a viral infection. Viral infections can cause the disruption of mucociliary function, an important component of the epithelial barrier. Ciliary proteins FIGURE 2 | Changes in the upper airway epithelium contributing to viral exacerbation in chronic airway inflammatory diseases. The upper airway epithelium is the primary contact/infection site of most respiratory viruses. Therefore, its infection by respiratory viruses may have far reaching consequences in augmenting and synergizing current and future acute exacerbations. The destruction of epithelial barrier, mucociliary function and cell death of the epithelial cells serves to increase contact between environmental triggers with the lower airway and resident immune cells. The opening of tight junction increasing the leakiness further augments the inflammation and exacerbations. In addition, viral infections are usually accompanied with oxidative stress which will further increase the local inflammation in the airway. The dysregulation of inflammation can be further compounded by modulation of miRNAs and epigenetic modification such as DNA methylation and histone modifications that promote dysregulation in inflammation. Finally, the change in the local airway environment and inflammation promotes growth of pathogenic bacteria that may replace the airway microbiome. Furthermore, the inflammatory environment may also disperse upper airway commensals into the lower airway, further causing inflammation and alteration of the lower airway environment, resulting in prolong exacerbation episodes following viral infection. Viral specific trait contributing to exacerbation mechanism (with literature evidence) Oxidative stress ROS production (RV, RSV, IFV, HSV) As RV, RSV, and IFV were the most frequently studied viruses in chronic airway inflammatory diseases, most of the viruses listed are predominantly these viruses. However, the mechanisms stated here may also be applicable to other viruses but may not be listed as they were not implicated in the context of chronic airway inflammatory diseases exacerbation (see text for abbreviations). that aid in the proper function of the motile cilia in the airways are aberrantly expressed in ciliated airway epithelial cells which are the major target for RV infection (Griggs et al., 2017) . Such form of secondary cilia dyskinesia appears to be present with chronic inflammations in the airway, but the exact mechanisms are still unknown (Peng et al., , 2019 Qiu et al., 2018) . Nevertheless, it was found that in viral infection such as IFV, there can be a change in the metabolism of the cells as well as alteration in the ciliary gene expression, mostly in the form of down-regulation of the genes such as dynein axonemal heavy chain 5 (DNAH5) and multiciliate differentiation And DNA synthesis associated cell cycle protein (MCIDAS) (Tan et al., 2018b . The recently emerged Wuhan CoV was also found to reduce ciliary beating in infected airway epithelial cell model (Zhu et al., 2020) . Furthermore, viral infections such as RSV was shown to directly destroy the cilia of the ciliated cells and almost all respiratory viruses infect the ciliated cells (Jumat et al., 2015; Yan et al., 2016; Tan et al., 2018a) . In addition, mucus overproduction may also disrupt the equilibrium of the mucociliary function following viral infection, resulting in symptoms of acute exacerbation (Zhu et al., 2009) . Hence, the disruption of the ciliary movement during viral infection may cause more foreign material and allergen to enter the airway, aggravating the symptoms of acute exacerbation and making it more difficult to manage. The mechanism of the occurrence of secondary cilia dyskinesia can also therefore be explored as a means to limit the effects of viral induced acute exacerbation. MicroRNAs (miRNAs) are short non-coding RNAs involved in post-transcriptional modulation of biological processes, and implicated in a number of diseases (Tan et al., 2014) . miRNAs are found to be induced by viral infections and may play a role in the modulation of antiviral responses and inflammation (Gutierrez et al., 2016; Deng et al., 2017; Feng et al., 2018) . In the case of chronic airway inflammatory diseases, circulating miRNA changes were found to be linked to exacerbation of the diseases (Wardzynska et al., 2020) . Therefore, it is likely that such miRNA changes originated from the infected epithelium and responding immune cells, which may serve to further dysregulate airway inflammation leading to exacerbations. Both IFV and RSV infections has been shown to increase miR-21 and augmented inflammation in experimental murine asthma models, which is reversed with a combination treatment of anti-miR-21 and corticosteroids (Kim et al., 2017) . IFV infection is also shown to increase miR-125a and b, and miR-132 in COPD epithelium which inhibits A20 and MAVS; and p300 and IRF3, respectively, resulting in increased susceptibility to viral infections (Hsu et al., 2016 (Hsu et al., , 2017 . Conversely, miR-22 was shown to be suppressed in asthmatic epithelium in IFV infection which lead to aberrant epithelial response, contributing to exacerbations (Moheimani et al., 2018) . Other than these direct evidence of miRNA changes in contributing to exacerbations, an increased number of miRNAs and other non-coding RNAs responsible for immune modulation are found to be altered following viral infections (Globinska et al., 2014; Feng et al., 2018; Hasegawa et al., 2018) . Hence non-coding RNAs also presents as targets to modulate viral induced airway changes as a means of managing exacerbation of chronic airway inflammatory diseases. Other than miRNA modulation, other epigenetic modification such as DNA methylation may also play a role in exacerbation of chronic airway inflammatory diseases. Recent epigenetic studies have indicated the association of epigenetic modification and chronic airway inflammatory diseases, and that the nasal methylome was shown to be a sensitive marker for airway inflammatory changes (Cardenas et al., 2019; Gomez, 2019) . At the same time, it was also shown that viral infections such as RV and RSV alters DNA methylation and histone modifications in the airway epithelium which may alter inflammatory responses, driving chronic airway inflammatory diseases and exacerbations (McErlean et al., 2014; Pech et al., 2018; Caixia et al., 2019) . In addition, Spalluto et al. (2017) also showed that antiviral factors such as IFNγ epigenetically modifies the viral resistance of epithelial cells. Hence, this may indicate that infections such as RV and RSV that weakly induce antiviral responses may result in an altered inflammatory state contributing to further viral persistence and exacerbation of chronic airway inflammatory diseases (Spalluto et al., 2017) . Finally, viral infection can result in enhanced production of reactive oxygen species (ROS), oxidative stress and mitochondrial dysfunction in the airway epithelium (Kim et al., 2018; Mishra et al., 2018; Wang et al., 2018) . The airway epithelium of patients with chronic airway inflammatory diseases are usually under a state of constant oxidative stress which sustains the inflammation in the airway (Barnes, 2017; van der Vliet et al., 2018) . Viral infections of the respiratory epithelium by viruses such as IFV, RV, RSV and HSV may trigger the further production of ROS as an antiviral mechanism Aizawa et al., 2018; Wang et al., 2018) . Moreover, infiltrating cells in response to the infection such as neutrophils will also trigger respiratory burst as a means of increasing the ROS in the infected region. The increased ROS and oxidative stress in the local environment may serve as a trigger to promote inflammation thereby aggravating the inflammation in the airway (Tiwari et al., 2002) . A summary of potential exacerbation mechanisms and the associated viruses is shown in Figure 2 and Table 1 . While the mechanisms underlying the development and acute exacerbation of chronic airway inflammatory disease is extensively studied for ways to manage and control the disease, a viral infection does more than just causing an acute exacerbation in these patients. A viral-induced acute exacerbation not only induced and worsens the symptoms of the disease, but also may alter the management of the disease or confer resistance toward treatments that worked before. Hence, appreciation of the mechanisms of viral-induced acute exacerbations is of clinical significance to devise strategies to correct viral induce changes that may worsen chronic airway inflammatory disease symptoms. Further studies in natural exacerbations and in viral-challenge models using RNA-sequencing (RNA-seq) or single cell RNA-seq on a range of time-points may provide important information regarding viral pathogenesis and changes induced within the airway of chronic airway inflammatory disease patients to identify novel targets and pathway for improved management of the disease. Subsequent analysis of functions may use epithelial cell models such as the air-liquid interface, in vitro airway epithelial model that has been adapted to studying viral infection and the changes it induced in the airway (Yan et al., 2016; Boda et al., 2018; Tan et al., 2018a) . Animal-based diseased models have also been developed to identify systemic mechanisms of acute exacerbation (Shin, 2016; Gubernatorova et al., 2019; Tanner and Single, 2019) . Furthermore, the humanized mouse model that possess human immune cells may also serves to unravel the immune profile of a viral infection in healthy and diseased condition (Ito et al., 2019; Li and Di Santo, 2019) . For milder viruses, controlled in vivo human infections can be performed for the best mode of verification of the associations of the virus with the proposed mechanism of viral induced acute exacerbations . With the advent of suitable diseased models, the verification of the mechanisms will then provide the necessary continuation of improving the management of viral induced acute exacerbations. In conclusion, viral-induced acute exacerbation of chronic airway inflammatory disease is a significant health and economic burden that needs to be addressed urgently. In view of the scarcity of antiviral-based preventative measures available for only a few viruses and vaccines that are only available for IFV infections, more alternative measures should be explored to improve the management of the disease. Alternative measures targeting novel viral-induced acute exacerbation mechanisms, especially in the upper airway, can serve as supplementary treatments of the currently available management strategies to augment their efficacy. New models including primary human bronchial or nasal epithelial cell cultures, organoids or precision cut lung slices from patients with airways disease rather than healthy subjects can be utilized to define exacerbation mechanisms. These mechanisms can then be validated in small clinical trials in patients with asthma or COPD. Having multiple means of treatment may also reduce the problems that arise from resistance development toward a specific treatment.

Why should future studies be performed using metagenomics in addition to PCR analysis ?

Hantaviruses in the Americas and Their Role as Emerging Pathogens https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3185593/ SHA: efe13a8d42b60ef9f7387ea539a1b2eeb5f80101 Authors: Hjelle, Brian; Torres-Pérez, Fernando Date: 2010-11-25 DOI: 10.3390/v2122559 License: cc-by Abstract: The continued emergence and re-emergence of pathogens represent an ongoing, sometimes major, threat to populations. Hantaviruses (family Bunyaviridae) and their associated human diseases were considered to be confined to Eurasia, but the occurrence of an outbreak in 1993–94 in the southwestern United States led to a great increase in their study among virologists worldwide. Well over 40 hantaviral genotypes have been described, the large majority since 1993, and nearly half of them pathogenic for humans. Hantaviruses cause persistent infections in their reservoir hosts, and in the Americas, human disease is manifest as a cardiopulmonary compromise, hantavirus cardiopulmonary syndrome (HCPS), with case-fatality ratios, for the most common viral serotypes, between 30% and 40%. Habitat disturbance and larger-scale ecological disturbances, perhaps including climate change, are among the factors that may have increased the human caseload of HCPS between 1993 and the present. We consider here the features that influence the structure of host population dynamics that may lead to viral outbreaks, as well as the macromolecular determinants of hantaviruses that have been regarded as having potential contribution to pathogenicity. Text: Emerging pathogens cause new or previously unrecognized diseases, and among them, emerging zoonotic diseases are a major concern among scientists studying infectious diseases at different spatial and temporal scales [1, 2] . Changes in biotic and abiotic conditions may alter population disease dynamics and lead to the emergence of zoonotic infections [3] [4] [5] [6] . During the last decades, several outbreaks of emerging and re-emerging viral pathogens have occurred, affecting both purely-local and worldwide/pandemic involvement of human populations. Among the conspicuous examples are influenza A, Ebola virus, hepatitis C virus, severe adult respiratory distress (SARS), coronavirus, and human immunodeficiency virus, which challenge prevention and control measures of public health systems [7] . In the Americas, the recent outbreak of pandemic influenza A subtype H1N1 became a major target for control due to its rapid spread, and uncertainties in virulence and transmissibility, yet vaccine availability was limited when significant activity occurred in advance of the traditional influenza season [8] . However, in the last century outbreaks of several viral-related diseases have emerged or re-emerged involving arenaviruses and dengue viruses, and more recently, hantaviruses, and the expansion of the geographic range of West Nile virus. Among zoonotic diseases, small mammals are hosts of several pathogenic RNA viruses, especially Arenaviridae and Bunyaviridae: Hantavirus [9] [10] [11] . Hantavirus infections became a concern in the Americas after the description of an outbreak of acute respiratory distress occurred in the Four Corners area in 1993 [12] . The newly recognized disease, hantavirus cardiopulmonary syndrome, HCPS (or hantavirus pulmonary syndrome), was linked to infection by the newly-discovered Sin Nombre virus (SNV), and the rodent Peromyscus maniculatus (deer mouse) was identified as the reservoir [13] . However, hantavirus infections have a much longer history. A review of ancient Chinese writings, dating back to approximately 960 AD, revealed descriptions closely resembling hemorrhagic fever with renal syndrome (HFRS), the syndrome caused by Old World hantaviruses [14] . During the twentieth century, cases of acute febrile disease with renal compromise were described from several Eurasian countries and Japan, often in association with military engagements [15] . HFRS as a distinct syndrome, however, was first brought to the attention of western medicine in association with an outbreak that occurred among United Nations troops during the Korean conflict between 1951 and 1954, where more than 3,200 soldiers were afflicted [16] . It took more than two decades until the etiologic agent, Hantaan virus (HTNV), was isolated from the striped field mouse Apodemus agrarius, detected in part by the binding of antibodies from patient serum samples to the lung tissues of healthy, wild-caught field mice [17, 18] . The virus was later found to represent the type species of a new genus Hantavirus of the family Bunyaviridae, although it was later apparent that the first hantavirus to be isolated was the shrew-borne Thottapalayam virus [19] . The categorization of hantaviruses as belonging to the family Bunyaviridae is due in part to the consistent presence of three RNA genomes that are circularized in vivo as a result of the presence of terminal complementary nucleotides that help fold the genome into a -hairpin‖ morphology, first described for the Uukuniemi phlebovirus [19, 20] . Table 1 is a list of the predominant, serologically distinct pathogenic hantaviruses. Many other named genotypes are described, but such other pathogenic forms are generally closely related to Andes or, in some cases, Sin Nombre virus. During virus maturation, the precursor form GPC is processed using a membrane -bound protease into Gn and Gc, a cleavage that occurs, and appears to be signaled, after the conserved peptide signal WAASA at the C-terminal of Gn [24] . Although the two proteins can be expressed independently through transfection, they can be retained in the wrong cellular compartment (ER or aggresome); they thus must be co-expressed to allow them stability so that the two can be assembled correctly in the Golgi [25, [27] [28] [29] . A number of activities and properties have been identified for the hantavirus envelope glycoproteins, including some features that are suspected to be involved in the pathogenicity of the disease-causing serotypes, a possibility that has engendered experimental attention. The glycoproteins are the known or presumed ligands for at least two distinct cellular receptors, the 3 integrin chain and decay accelerating factor, or DAF [30, 31] ; with gC1qR/p32 also identified as another potential entry receptor [32] . Comparisons with the tick-borne encephalitis virus E protein, led Tischler et al. to consider the Gc glycoprotein as a potential class II fusion protein, perhaps imparting fusion activity to the virion, and this hypothesis has gained support in other studies [33, 34] . Additional activities have been identified with, or claimed to be related to, Gn. For many of these studies, an underlying premise has held that there are differences between the glycoproteins of -pathogenic‖ hantaviruses relative to viruses in the genus that are dubbed to be -non-pathogenic‖. While it is true that it has not yet been possible to link Prospect Hill virus (PHV) to human disease, the absence of evidence for its pathogenicity should perhaps not be equated with the evidence of its absence. One might only consider that the level of disease (e.g., lethargy, fever, proteinuria, and azotemia) associated with infection of nonhuman primates by PHV is not significantly different from that recorded for nonhuman primate models using the known-pathogen Puumala virus (PUUV) [35, 36] . For the purpose of this discussion we will presume that apathogenic hantaviruses are indeed apathogenic. While some studies have suggested that Gn glycoproteins are directed more rapidly into the ubiquitin-proteosome pathway than are apathogenic forms, others have interpreted differences in the handling of Gn glycoproteins across hantavirus species by the ubiquitin-proteosomal system as independent of pathogenicity [37] [38] [39] . Some investigators have directed their efforts toward identifying a differential capacity, either kinetic or in absolute magnitude, in the ability of pathogenic and apathogenic hantaviruses to elicit an interferon response in cells. One premise that emerges is that apathogenic forms would tend to induce an earlier innate response that would render it more likely that the virus would be quickly cleared or rendered less competent in its replication so as to blunt any pathological response in the host [40] [41] [42] . The anti-hantavirus innate response can in some cases be attributed to viral interaction as a ligand of TLR-3, but not in others, and in endothelial cells, it appears not to require more than the viral particle itself, even when introduced in replication-incompetent form [43, 44] . Proteins and mRNAs prominently induced by hantaviruses include MxA and IFIT-1 (ISG-56) and others including some with known or suspected anti-viral activity. Those hantaviruses, often highly pathogenic strains, that fail to induce a potent antiviral response, are suspected or presumed to have a (more) potent interferon-pathway antagonism mechanism relative to other viruses, a mechanism that acts positively to prevent an effective innate response from forming, at least early in infection [42, 45] . Yet some instances are reported wherein highly pathogenic hantaviruses, such as SNV, are also able to induce expression of interferon-stimulated gene mRNAs, even very early in infection, with ISG proteins, as expected, taking longer to appear in the cell [44] . Anti-interferon activities have also been attributed to the NSs protein that may be elaborated in cells infected by serotypes that encode this protein [46] . Other investigators have examined the activities of hantavirus glycoproteins and other proteins that might themselves directly affect some aspects of the pathogenic progression associated with hantavirus infection of humans, such as vascular permeability changes. While early attempts to directly cause increases in permeability of endothelial monolayers with viral particles or viral infection were largely disappointing, hantaviruses have been identified as adversely affecting endothelial migration over substrata and in potentiating VEG-F-induced endothelial permeability [47, 48] . The shorter (50-kD) nucleocapsid or N protein is a structural component of the viral nucleocapsid, along with the genomic viral RNA segments. As an RNA-binding protein that engages the hairpin termini of the genomic segments with high affinity [49, 50] , it limits the access of the RNA to host nucleases and helps to render viral replication a closed process within the cytoplasm. It also acts as a peripheral membrane protein, as does the L protein [51] , an activity that could play a role in its presumed, but not yet demonstrated function as matrix [52] . Until recently, it had not been appreciated that N has a wide variety of other activities, some of which can be linked, not only to fundamental requirements of replication, but also to the interference with an array of the intracellular processes of the normal cell. Thus, an interaction between the amino terminus of the hantavirus N protein and the cellular protein Daxx has been proposed, with the suggestion of potential pro-apoptotic consequences [51] . N is also reported to interact with actin microfilaments, and the SUMO-1 protein [53, 54] . Using reporter-gene based assays, Connie Schmaljohn and her colleagues have reported that Hantaan virus' nucleocapsid protein has an inhibitory role in inflammatory responses mediated by NF kappa B (NF-B). The effects on NF-B expression appeared to be confined to prevention of its nuclear translocation after its attempted activation with lipopolysaccharide, LPS [55] . In the cytoplasm of infected cells, N protein can be found in cellular P bodies where it sequesters and protects 5' caps. It may locate the caps through its interaction with DCP1, a key constituent of P bodies. During hantavirus infection, the viral RNAs become concentrated in P bodies, through their interaction with N and DCP1. The N protein demonstrates preferential protection of mRNAs engineered to prematurely terminate their encoded protein in comparison to native mRNAs [56] . N protein has been increasingly linked to viral replication and translation, sometimes in previously unanticipated ways. It is among a growing family of diverse viral proteins that can serve as a nonspecific -RNA chaperone‖, an activity that should facilitate the L polymerase's access to vRNA for transcription and replication, in that it can transiently dissociate misfolded RNA structures [57] . Some of N protein's effects on translation might not immediately be recognized to be adaptive in nature. It can replace the entire EIF4F translational initiation complex, simultaneously presenting the ribosome with a replacement for the cap-binding activity of eIF 4E, binding to the 43S pre-initiation complex as does eIF 4G, while replacing the helicase activity of eIF 4A, which is presumed to be needed to dissociate higher-order RNA structure [56, 58] . These three factors normally work together to achieve translational initiation. In P bodies, N protein's ability to bind at high affinity to capped native cellular oligoribonucleotides, along with its activity in protecting capped RNAs from degradation likely facilitates the access of capped oligonucleotides for use in transcriptional initiation by L polymerase (-cap snatching‖). Trafficking of N for viral assembly: Classically, N protein in infected cells appears to be clustered or particulate in nature, with a heavy concentration at a single perinuclear location, widely considered to be the Golgi [27] . The N proteins of hantaviruses are found in association with particulate fractions, and confocal microscopy and biochemical-inhibitor studies have shown that N tracks along microtubules but not with actin filaments [52] . The ultimate destination for N, for its assembly into viral particles is the Golgi, and it traffics there via the endoplasmic reticulum-Golgi intermediate complex (ERGIC), also known as vesicular-tubular cluster [52] . A dominant negative inhibitor, dynamitin, associated with dynein-mediated transport, reduced N's accumulation in the Golgi. Later studies suggested that the specific dependence on microtubular transport is specific to Old World hantaviruses such as HTNV, but that the New World hantavirus ANDV is instead associated with actin filaments [59] . However, recent data indicates that microtubular transport is indeed utilized for the New World hantavirus SNV [60] . Hantavirus diseases of man have long been suspected of having an immunopathogenic basis in part because of their relatively long incubation period of 2-3 weeks and the observed temporal association between immunologic derangements and the first appearance of signs and symptoms of hantavirus illness. HFRS and HCPS share many clinical features, leading many investigators to consider them to be, in essence, different manifestations of a similar pathogenic process, differing mainly in the primary target organs of disease expression ( Table 2 ). The pathogenesis of hantavirus infections is the topic of a continuously-updated review in the series UpToDate [61] . By the time symptoms appear in HCPS, both strong antiviral responses, and, for the more virulent viral genotypes, viral RNA can be detected in blood plasma or nucleated blood cells respectively [63, 64] . At least three studies have correlated plasma viral RNA with disease severity for HCPS and HFRS, suggesting that the replication of the virus plays an ongoing and real-time role in viral pathogenesis [65] [66] [67] . Several hallmark pathologic changes have been identified that occur in both HFRS and HCPS. A critical feature of both is a transient (~ 1-5 days) capillary leak involving the kidney and retroperitoneal space in HFRS and the lungs in HCPS. The resulting leakage is exudative in character, with chemical composition high in protein and resembling plasma. The continued experience indicating the strong tissue tropism for endothelial cells, specifically, is among the several factors that make β3 integrin an especially attractive candidate as an important in vivo receptor for hantaviruses. It is likely that hantaviruses arrive at their target tissues through uptake by regional lymph nodes, perhaps with or within an escorting lung histiocyte. The virus seeds local endothelium, where the first few infected cells give rise, ultimately, to a primary viremia, a process that appears to take a long time for hantavirus infections [62, 63] . By the time that secondary viremia emerges, the agents of the more severe forms of HFRS and HCPS have begun to achieve sufficient mass as to induce, through PAMP-PRR interactions and other means, the expression of proinflammatory cytokines [64] . For HCPS, that expression favors the pulmonary bed and lymphoid organs, yet, for unknown reasons, spares the retroperitoneum and, in general, the kidney. In HFRS the situation is reversed, and yet it is often not appreciated that the expected preferential tissue tropism of HFRS-associated viruses and their HCPS-associated counterparts for the renal and pulmonary beds, respectively, is not as one would predict through the manifestations of the two diseases. Local elaboration of inflammatory and chemotactic mediators is considered to be a requirement for the development of systemic disease symptoms, with those abnormalities sometimes culminating in shock and death. Yet it is not hypoxemia, due to the prominent pulmonary edema, that leads to death in most fatal cases of HCPS, but rather intoxication of the heart by as-yet-undefined mediators that leads to the low cardiac output state and the associated shock syndrome [64, 65] . It is tempting to speculate that mediators produced in the lung in connection with the inflammatory infiltrate can percolate through the coronary circulation with minimal dilution in HCPS, a disadvantageous consequence of the close anatomic juxtaposition of the two organs. Thus, at least three classes of potential mechanisms, some overlapping and all certainly nonexclusive of the others, could be presumed to underlie the pathogenesis of HCPS. These include: (1) Innate immune mechanisms. The nature of interactions between hantavirus pathogen-associated molecular patterns (PAMP) with the pattern recognition receptors (PRR) of susceptible endothelial cells are beginning to be clarified. The prototypical HTNV appears to be recognized by TLR-3 [43] . Such an infection has consequences such as increased expression of HLA-DR in dendritic cells [66] and differentiation of monocytes toward dendritic cells [67] . (2) Direct viral effects. The observed correlation between viral load and disease severity leaves the possibility open that hantavirus particles or RNA can themselves have toxic effects on cells or on signaling. Some investigators have favored direct viral toxicity, acting through the inhibition of endothelial cell barrier function, as an explanation for much of the capillary leak, although there is widespread agreement that multiple mechanisms that mediate pathogenesis likely operate simultaneously in the affected patient [68] . A potentially important clue toward the mechanism by which hantavirus infections deplete blood platelets and, in some cases cause hemorrhagic manifestations, was advanced by the recent discovery that pathogenic hantaviruses are able to recruit platelets to adhere to endothelial cell surfaces, with β3 integrin used as a critical binding element [69] . (3) Pathogenic effects caused by the activities of specific viral macromolecules. We have reviewed some of the activities associated with the Gn, Gc and N, virally-encoded polypeptides in previous sections. Testing models of pathogenesis can be done more effectively when there is an animal model that mimics key aspects of the disease. There is no such model that closely mimics HFRS, but animal models exist for both the asymptomatic carriage of PUUV and SNV by their native carrier rodents, the bank vole Myodes glareolus and the deer mouse P. maniculatus; as well as a Syrian hamster model using ANDV or the related Maporal virus from Venezuela, for which an HCPS-mimetic disease is observed [70] [71] [72] [73] . The ANDV-Syrian hamster model has a number of features in common with the human disease, as well as some differences. Unlike the neurologic diseases that have been possible to elicit with HTNV, the hamster model for HCPS appears to be caused by capillary leak that results in pulmonary edema and the production of a pleural effusion with exudative characteristics. Typically the hamsters die between 11 and 14-d post-inoculation, reflecting a slightly accelerated incubation period in comparison to human infections. As with human HCPS, the microscopic examination of the lung reveals abundant fibrin deposition, thickened alveolar septa, and viral antigen expressed abundantly in the microvascular endothelium. ANDV-infected hamsters fitted with physiologic monitoring devices exhibited diminished pulse pressures, tachycardia, and hypotension that appear to closely mimic the shock that is believed to be the proximate cause of demise in patients who succumb to HCPS [65, 74] . Compared to the human disease, ANDV-infected hamsters exhibit exceptionally high titers of live ANDV in their tissues, with much of the viral replication occurring in hepatocytes, which are spared in the human disease. Titers of live ANDV in some cases exceed 10 8 /g, whereas hantavirus isolates from human tissues have been notoriously difficult to obtain. Despite the universal occurrence of mildly-elevated hepatic enzymes in patients with HCPS, hepatic enzymes do not appear to be present at elevated levels in the blood of diseased hamsters even immediately before death [75] . The protracted incubation period associated with hantavirus disease gives the host considerable time to mount a mature immune response against the virus. Thus, in contradistinction to infections of comparable severity and related symptomatology associated with arenaviruses and filoviruses, hantavirus infections of humans are associated with antibody responses of significant titer by the time symptoms commence. Despite this observation, it appears to be possible that natural variation in individual neutralizing antibody responses among patients with SNV infections can be linked to disease severity, suggesting that administration of antiviral antibodies could prove effective therapeutically [76] . In the case of ANDV infection, new evidence has emerged indicating that the apparent clearance of the virus from the blood does not result in the complete removal of antigenic stimulus by the virus, suggesting that the virus may persist, perhaps in some as-yet undetermined immunologically privileged site [77] . A role for T cell-mediated pathological responses in HFRS and HCPS has been the source of speculation for a variety of reasons. The severity of SNV-associated HCPS may have made it more apparent that the onset of pulmonary edema, tachycardia and hypertension seemed to be all but universally temporally associated with the appearance of a spectrum of highly-activated cells of the lymphoid lineage in the peripheral blood. Cells with a close morphologic similarity to these -immunoblasts‖ were detected in the congested, heavy lungs of patients who came to autopsy, as well as in lymphoid organs and in the portal triads [63, [78] [79] [80] . These observations led to speculation that some component of hantavirus pathogenesis could be linked to the appearance of antiviral T cells that could stimulate or contribute to the appearance of a -storm‖ of mediators and the associated capillary leak phenotype. Subsequent studies have borne out the expectation that a significant fraction of the immunoblast population in patients with HCPS are T cells with specificity for specific class I HLA-presented epitopes of viral antigens, including Gn, Gc and N [77, [81] [82] [83] . Presumably, the antiviral activities of such cells, manifested in part through their elaboration of mediators in the affected interstitium, can contribute to the endothelial/capillary leak that lies at the heart of hantavirus pathogenesis. Because early cases of HCPS often came to autopsy, it became possible to examine necropsied tissues for expression of cytokines. The study by Mori et al. (1999) revealed high relative expression of proinflammatory cytokines including TNF, IL-1, IL-6, providing evidence in favor of a -cytokine storm‖ model for pathogenesis [64] . The authors believed, based on the morphology of cytokine-secreting cells, that both monocytes and lymphocytes were contributing to the production of cytokines. That proinflammatory mediators are found in elevated levels in the plasma as well as the renal interstitium of patients with acute hantaviral illness has been recognized for some time as well [84, 85] . While diagnosis of HCPS as well as HFRS is best accomplished with IgM serology, in the acute stage of SNV infection, RT-PCR can also be used if blood cells or blood clot are used instead of plasma or serum, where sensitivity even using nested PCR primers drops to about 70% [86] [87] [88] . In a facility at which many cases of HCPS are treated, the University of New Mexico medical center in Albuquerque, a diagnostic service has long been offered in which the patient's hematologic findings are analyzed to establish the probability that a patient has HCPS. The combination of thrombocytopenia, elevated abundance of -immunoblast‖ lymphocytes, left-shifted polymorphonuclear cell population without strong morphologic evidence for their activation, and elevated hemoglobin or hematocrit values is highly specific for HCPS and allows clinicians the ability to put presumptive-HCPS patients on extracorporeal membrane oxygenation (ECMO), which is believed to have saved many patients from a lethal outcome [89] . Human infection by hantaviruses is thought to follow contact with secretions or excretions produced by infected rodents. In the United States, 538 human infections by hantavirus were reported through late December 2009 [90] , with New Mexico, Arizona and Colorado exhibiting the highest case-loads. While the prototypical central American hantavirus in central America was Rio Segundo virus of Reithrodontomys mexicanus from Costa Rica, the first human disease appeared some years later in Panama, where Choclo virus (CHOV) arose as the etiologic agent and is believed to be responsible for all known cases of HCPS. The fulvous pygmy rice rat Oligoryzomys fulvescens has been identified as the rodent reservoir [91] . In Panama, the first cases of HCPS, albeit with little or no evident cardiac involvement, were reported in 1999, and since then, 106 human infections have occurred with a 26% mortality rate [92] . Serosurveys of mammals in Mexico and Costa Rica have found anti-hantavirus antibodies [93] [94] [95] [96] , and seroprevalences ranging between 0.6 to 1.6% in human populations were reported despite the absence of known HCPS cases [97] . In South America, HCPS cases have been indentified in Argentina, Bolivia, Brazil, Chile, Paraguay and Uruguay, and evidence for human exposure to hantaviruses have also been reported in Venezuela [98] and Perú [99] . In southern South America, ANDV is the main etiologic agent with cases in Chile and Argentina reported since 1995. In Chile, 671 cases of HCPS due to ANDV have occurred during the period 2001-2009 [100] . Since 1995, more than 1,000 HCPS cases have been reported in Argentina [101] ; in Brazil, approximately 1,100 HCPS cases have been identified between 1993 and 2008 [102] . Case-fatality ratios in those three countries have been similar, ranging from 30% (Argentina), 36% (Chile) and 39% (Brazil). Hantavirus infections occur more frequently in men than women, although the male/female ratio is highly variable. For example, Panamanian communities showed a ratio of 55 men to 45 women [103] , while in Chile the ratio is more biased to males (71%) [104] . In the Paraguayan Chaco the male-female ratio approaches 50% [105] . In North America, by December 2009 63% of case-patients were males [90] . All ethnic and racial groups seem to be susceptible to hantavirus infections, and the differences between certain groups (as indigenous and non-indigenous) are more likely correlated with the type habitat where the population resides (e.g., rural versus urban areas). In fact, rural communities account for the highest hantavirus incidences overall and are therefore at higher risk [92, [105] [106] [107] [108] [109] [110] [111] , although the importance of peridomestic settings as a major area of exposure has also been emphasized [112, 113] . The main mechanism by which humans acquire hantavirus infection is by exposure to aerosols of contaminated rodent feces, urine, and saliva [114, 115] . This can occur when humans reside in areas in close proximity to those that rodents inhabit, live in areas infested with rodents, or when rodents invade human settings, which are more frequent in rural habitats. There is a long history of human co-existence with rodents, raising questions about the apparent recent increases in hantavirus-related illnesses, especially HCPS. Other than an apparent association with El Niño southern oscillation (ENSO) events in some regions [116, 117] , the recent increases in incidence of HCPS do not seem to follow a readily-defined temporal or spatial pattern. However, some landscape features such as habitat fragmentation or human-disturbed areas may influence rodent population dynamics and impact viral incidence [118] [119] [120] [121] . Despite the stochasticity associated with contraction of hantavirus infection, certain scenarios have been recognized as posing higher risk. Human activities in poorly ventilated buildings that aerosolize particulates that are then inhaled (i.e., cleaning, shaking rugs, dusting) are frequently identified among patients admitted for HCPS [11, 122] . Outdoor activities are thought to convey lower risk due to lability of hantaviruses to UV radiation and the presumed tendency to be dispersed in wind, although certain environmental conditions seem to maintain the virus for longer periods outside its natural host allowing for indirect transmission [123] . An alternative but uncommon route of virus transmission is by rodent bites [124] [125] [126] . Field workers handling mammals are potentially at higher risk of exposure with hantavirus infections, although when quantified through serosurveys the absolute risk appears rather slight [127] . A new study in Colorado suggests the possibility that a rodent bite may have been the proximate vehicle for outdoor transmission of SNV [128] , which re-emphasizes the use of personal protective equipment during field work activities [129] . As a particular case within hantaviruses, person-to-person transmission has exclusively been documented for the South American Andes virus [130] [131] [132] [133] [134] [135] . The identification of this transmission route has been made using both molecular tools and epidemiological surveys, but the mechanism of interpersonal transmission is not well established. Recent findings show that family clusters and specifically sexual partners share the greater risk of interpersonal transmission, although sexual transmission per se can be neither inferred nor refuted presently [130, 135] . Interestingly, ANDV may also be shed by humans through other biological fluids such as urine [136] , illustrating the particular properties that differentiate this virus from other hantaviruses. Although interpersonal transmission seems to be unique for ANDV, viral RNA of PUUV has been detected in saliva of patients with HFRS, and some patients with SNV-HCPS have viral RNA in tracheal secretions [88, 137] . Hantaviruses in the Americas are naturally hosted by rodents (Muridae and Cricetidae) as well as shrews (Soricidae) and moles (Talpidae) (Figure 1) . Three shrew and one mole species have been reported to host hantaviruses and their pathogenicity for humans remains unknown [22, 138, 139] . At least 15 rodent species have been identified as carriers of different pathogenic hantaviruses, with some South American genotypes such as Castelo do Sonhos (CDSV) or Hu39694 only identified after human infections (Figure 1 ). Hantaviruses typically show high species-specificity and no intermediate host [140] . However, some hantavirus genotypes have been described in the same rodent species. Such is the case of Playa de Oro (OROV) and Catacamas (CATV) identified in Oryzomys couesi [141, 142] , or Maporal (MAPV) and Choclo (CHOV) hosted by O. fulvescens [91, 143] . In North America both Muleshoe and Black Creek Canal hantaviruses have been detected in geographically-distant Sigmodon hispidus [144, 145] . Also, one hantavirus genotype (e.g., Juquitiba-like virus) may be carried by more than one rodent species (O. nigripes, Oxymycterus judex, Akodon montesis). Another example is Laguna Negra virus (LANV) which after being identified in Calomys laucha [146] has also been reported in C. callosus [147] . The rapid increase in the discovery of new hantaviruses and the identification of their hosts does not seem likely to end soon as new small mammal species are screened [95] . This subject is complicated by continued controversy in the criteria for the classification of distinct hantaviruses [148, 149] , which is also tied to host taxonomic classification and taxonomic rearrangements. Cross-species transmission is a major process during spread, emergence, and evolution of RNA viruses [6, 150] . Particularly within hantaviruses, spillover to secondary hosts are increasingly identified as more extensive studies are performed [151] [152] [153] [154] [155] [156] . For example, ANDV is the predominant etiologic agent of HCPS in South America, and O. longicaudatus the main rodent reservoir. Spillover in at least four other rodent species that co-occur with the reservoir have been identified, with Abrothrix longipilis showing the second higher prevalence to ANDV-antibodies, and there is presently no question that the virus is extremely similar genetically between the two host rodents [157, 158] . In North America, spillover of Bayou virus (BAYV) may have occurred from the main reservoir O. palustris to S. hispidus, R. fulvescens, P. leucopus, and B. taylori [159] [160] [161] . Hantavirus spillover is more likely to occur with host populations inhabiting sympatric or syntopic regions [151, 162] , and cross-species transmission would presumably have greater chances of success if the host species are closely related [163] . An interesting exception is found between Oxbow virus (OXBV) and Asama virus (ASAV) in which a host-switch process seemed to have occurred between mammals belonging to two families (Talpidae and Soricidae), likely as a result of alternating and recurrent co-divergence of certain taxa through evolutionary time [138] . Hantaviruses are horizontally transmitted between rodents and are not transmitted by arthropods (unlike other viruses of the family Bunyaviridae). Spillover infection to nonhuman mammals usually results in no onward (or -dead-end‖) transmission, but if humans are infected may result in high morbidity and mortality [122, 164] . During the spring of 1993, an outbreak of patients with HCPS due to SNV occurred in the Four Corners states resulting in more than 60% case-fatality among the initial cases, many involving members of the Navajo tribe [12, 121] . In Panama, an outbreak was reported during 1999-2000 in Los Santos, and 12 cases where identified with three fatalities [165, 166] . This represented the first report of human hantavirus infections in Central America. In South America, the first largest identified outbreak occurred in the Chaco region in northwestern Paraguay during 1995-1996. Seventeen individuals were identified with SNV antibody (ELISA) or were antigen (IHC) positive out of 52 suspected cases [167] . Major outbreaks due to ANDV occurred in 1996 in southern Argentina [131, 134] ; in southern Chile clusters of patients presented with hantavirus illness in 1997 [158] . In Brazil, the first outbreak was identified in the Brazilian Amazon (Maranhão State) in 2000, and involved small villages that resulted in a 13.3% prevalence of those tested (398 total residents) [168] . The factors that trigger hantavirus outbreaks are still poorly understood, probably because they result from several interacting biotic and abiotic features whose key parameters are difficult to model. However, the use of new modeling approaches that involve geographical and environmental features seem to be promising in predicting potential hantavirus outbreaks and/or areas of higher risk [169] [170] [171] [172] . Because hantaviruses are known to be directly transmitted from infected to susceptible hosts, the first natural approach is to relate outbreaks to the ecology of the viral hosts. Hantavirus transmission and persistence in rodent populations depends on several factors that interact to affect ecological dynamics of the host, which in turn is strongly influenced by the behavioral characteristics of individual rodent species, to landscape structure, and environmental features [173, 174] . Viral transmission depends on contact rates among susceptible hosts, and despite the prevailing notion that a higher density increases encounters and hence secondary infected hosts, contrasting patterns relating rodent population size and virus prevalence can be found [175] . In addition, it has been shown that SNV transmission follows a contact heterogeneity pattern, where individuals in the population have different probability of transmitting the infection [176] . The understanding of viral transmission proves to be far more complex when species other than the main reservoir host are incorporated in the model. In fact, recent studies have shown that higher hosts species diversity is correlated with lower infection prevalence in North America for P. maniculatus [177] , in Central America for O. fulvescens (reservoir of Choclo virus) and Zygodontomys brevicauda (reservoir of Calabazo virus) [178] , and in South America for Akodon montensis (reservoir of Jabora virus) [162] . Contact rates vary according to the spatial distribution of populations and seem to be strongly influenced by landscape structure. For example, SNV prevalence in P. maniculatus was higher in landscapes with a higher level of fragmentation of the preferred habitat [179] . In addition, certain properties of the landscape such as elevation, slope, and land cover seem to be useful in detecting areas with persistent SNV infections, and therefore thought to be refugial areas where the virus can be maintained for years [169] . Changes in the natural environment of reservoir species, such as forest fragmentation and habitat loss, may alter population abundance and distribution and lead to hantavirus outbreaks, as observed in the Azurero Peninsula of Panama [118, 119] . Also, differences in the microhabitat, including overstory cover, may lead to differences in the ecological dynamics within populations and affect the rate of exposure to the virus [180] . Differences in hantavirus infections through contrasting landscapes in the latitudinal span have been found in rodent populations of O. longicaudatus in Chile, suggesting that humans are differentially exposed to the virus [107, 181] . Rodent population dynamics are affected by seasonal changes of weather and climate [182, 183] . In the case of the ENSO-associated outbreaks, a complex cascade of events triggered by highly unusual rains in the precedent year have been postulated to result in an increase of primary production and rodent densities, also increasing the likelihood of transmission of the virus to humans, but it has proved difficult to precisely demonstrate the suggested intermediate events such as increased rodent densities in the increased caseload [116, 121, 184] . In South America, effects of climate change and hantavirus outbreaks have not been well studied, despite the knowledge that several rodents species that are reservoirs of emerging diseases have dramatically been affected by events like El Niño [185] . Changes in host population dynamics are also affected by seasonality, which may lead to disease outbreaks when processes that equilibrate rodent populations from season to season are interrupted [186] . Viral emergence may continue to be promoted as human-introduced changes continue to increase in the environment at different geographical scales. Human incursions into previously uncultivated environments may lead to new contacts between rodent reservoirs and humans, increasing the likelihood of contracting infections [187] . These changes may also alter rodent's population structure and dynamics and interspecies interactions creating conditions that may lead to viral outbreaks, viral establishment in new hosts, and emergence of HCPS [102, 162] , even with seemingly slight ecological disturbance to the virus-host system [188] . Certain pathophysiologic characteristics, including thrombocytopenia and shock, of hantavirus diseases of humans, bear substantial similarity to the hemorrhagic fevers induced by other viruses such arenaviruses, filoviruses and flaviviruses, despite sharing essentially no sequence similarities therewith. Such observations raise questions about whether such commonalities in pathogenesis are chance similarities of phenotype, or instead report the presence of common molecular mechanisms among the viruses. In this review we discuss the general properties, discoveries and epidemiology/ecology of the New World forms of pathogenic hantaviruses, and also seek to identify some of the characteristics of the viral macromolecules and immunologic mechanisms that have been proposed as potential direct mediators of the pathogenic events that characterize the human disease HCPS. While it is unlikely that expression of any particular viral protein or RNAs in isolation can be relied upon to replicate key phenotypes of infection by the complete virus, some of the findings have been sufficiently consistent with what is known of the pathogenesis in vivo that they offer plausible first-pass leads in the search for therapeutic targets. We look forward to the mechanistic revelations that will follow the inevitably expanded usage of powerful methods such as deep sequencing, ever-more advanced imaging, and microscopic methods, and animal models that can at last be said to be close mimics of human hantavirus disease.

What do diminished pulse pressures, tachycardia, and hypotension in ANDV infected hamsters appear to closely mimic?

Screening of FDA-Approved Drugs for Inhibitors of Japanese Encephalitis Virus Infection https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5640845/ SHA: 1bd2f6497996fc0fccd8dffd7f84846d3d36f964 Authors: Wang, Shaobo; Liu, Yang; Guo, Jiao; Wang, Peilin; Zhang, Leike; Xiao, Gengfu; Wang, Wei Date: 2017-10-13 DOI: 10.1128/jvi.01055-17 License: cc-by Abstract: Japanese encephalitis virus (JEV), an arthropod-borne flavivirus, is a major cause of acute viral encephalitis in humans. No approved drug is available for the specific treatment of JEV infections, and the available vaccines are not effective against all clinical JEV isolates. In the study described here, a high-throughput screening of an FDA-approved drug library for inhibitors of JEV was performed. Five hit drugs that inhibited JEV infection with a selective index of >10 were identified. The antiviral activities of these five hit drugs against other flavivirus, including Zika virus, were also validated. As three of the five hit drugs were calcium inhibitors, additional types of calcium inhibitors that confirmed that calcium is essential for JEV infection, most likely during viral replication, were utilized. Adaptive mutant analysis uncovered that replacement of Q130, located in transmembrane domain 3 of the nonstructural NS4B protein, which is relatively conserved in flaviviruses, with R or K conferred JEV resistance to manidipine, a voltage-gated Ca(2+) channel (VGCC) inhibitor, without an apparent loss of the viral growth profile. Furthermore, manidipine was indicated to protect mice against JEV-induced lethality by decreasing the viral load in the brain, while it abrogated the histopathological changes associated with JEV infection. This study provides five antiflavivirus candidates and identifies cytoplasmic calcium to be a novel antiviral target for the treatment of JEV infection. The findings reported here provide therapeutic possibilities for combating infections caused by flaviviruses. IMPORTANCE No approved therapy for the treatment of Japanese encephalitis virus infection is currently available. Repurposing of approved drugs would accelerate the development of a therapeutic stratagem. In this study, we screened a library of FDA-approved drugs and identified five hit drugs, especially calcium inhibitors, exerting antiflavivirus activity that blocked viral replication. The in vivo efficacy and toxicity of manidipine were investigated with a mouse model of JEV infection, and the viral target was identified by generating an adaptive mutant. Text: F laviviruses are taxonomically classified in the genus Flavivirus and family Flaviviridae. These viruses comprise over 70 different pathogens, such as Japanese encephalitis virus (JEV), Zika virus (ZIKV), dengue virus (DENV), West Nile virus (WNV), and yellow fever virus (YFV). Most flaviviruses are arthropod borne and cause public health problems worldwide (1) . The development and usage of vaccines against some flaviviruses, such as JEV, YFV, and tick-borne encephalitis virus (TBEV), have decreased the rates of morbidity and mortality from infections caused by these viruses (2) ; however, flavivirus-induced diseases are still pandemic, and few therapies beyond intensive supportive care are currently available. Flaviviruses have an approximately 11-kb positive-stranded RNA genome containing a single open reading frame (ORF) flanked by untranslated regions (UTRs) at both termini. The ORF encodes three structural proteins, including the capsid (C), membrane (premembrane [prM] and membrane [M] ), and envelope (E), and seven nonstructural proteins (NS1, NS2A, NS2B, NS3, NS4A, NS4B, and NS5) (3) . These seven nonstructural proteins participate in viral replication, virion assembly, and virus escape from immune surveillance. To date, no specific antivirals with activity against flaviviruses are available. To address this, we conducted a screen of a library of 1,018 FDA-approved drugs. Since flaviviruses are similar in structure and pathogenesis, we first utilized JEV as the prototype to screen the drug library and subsequently validated the antiviral activities with ZIKV, WNV, and DENV type 2 (DENV-2). The hit drugs identified in this study offer potential new therapies for the treatment of flavivirus infection and disease. Screening of an FDA-approved drug library for inhibitors of JEV infection. Recombinant viral particles (RVPs) with the luciferase-reporting replicon enveloped by the JEV structural proteins were used to select inhibitors, with a focus on those that inhibit virus entry and replication, by a high-throughput screening (HTS) assay (4, 5) . The number of genomic RNA copies of RVP was determined to be 8.4 ϫ 10 6 copies/ml by using a standard curve generated with plasmids carrying the infectious clone. The HTS assay conditions, including the seeding cell density and RVP dose, were optimized to be 10,000 cells per 96-well plate and 20 l (16 copies/cell) RVP for the infective dose, respectively. Under the optimized conditions, the signal-to-basal (S/B) ratio, coefficient of variation (CV), and Z= factor were 38,374, 2.8%, and 0.89, respectively, which demonstrated that the assay was robust and suitable for the large-scale screening of compounds. A schematic of the HTS assay is depicted in Fig. 1B . After three rounds of screening, five hits with a selective index (SI; which is equal to the 50% cytotoxic concentration [CC 50 [/50% inhibitory concentration [IC 50 ]) of Ͼ10 were selected. The CC 50 values of the hit drugs exhibited in Fig. 1B were similar to those previously published for diverse cell systems but determined using different toxicity assays (6) (7) (8) (9) (10) (11) (12) (13) . Three of the hit drugs, manidipine, cilnidipine, and benidipine hydrochloride, were dihydropyridine (DHP) voltage-gated Ca 2ϩ channel (VGCC) antagonists, while pimecrolimus is an inhibitor of inflammatory cytokine secretion and nelfinavir mesylate is an HIV-1 protease blocker. All five drugs exhibited a dose-dependent inhibition of JEV RVP infection (Fig. 1C) . To validate the antiviral effect, hit drugs were purchased from other commercial sources and tested. In the reconfirmation screen, all hit drugs showed antiviral and cytotoxic effects similar to those found in the primary screen. Validation of hit drugs. To verify the results obtained by the luciferase reporter assays, we also investigated the antiviral effect of the five hit drugs on wild-type JEV strain AT31. As expected from the HTS assay, all five drugs robustly inhibited virus production, with a reduction of approximately 4 to 5 log units at the highest concentration and an approximately 1-log-unit decrease with 2.5 M the drugs (Fig. 2B) . A sharp decrease in JEV RNA levels was also detected (Fig. 2C) . The attenuated RNA levels in the high-dose, middle-dose, and low-dose groups were all above 40%. In particular, in the manidipine-treated group, the inhibitory effect was at least 80% compared to that for the control, which showed a strong inhibition of viral replication. Consistent with the inhibition of virus replication and production, expression of the viral structural protein prM was hardly detectable following treatment with the drugs at the high concentration (Fig. 2D) . Overall, the results in Fig. 2 confirmed that the five hit drugs inhibited JEV infection in a dose-dependent manner in vitro. Drugs inhibit JEV infection during viral RNA synthesis. Because RVPs, which have a natural virus-like envelope on the outside and a replicon on the inside, permitted the quantification of JEV productive entry and replication, a time-of-addition experiment was performed to investigate whether the hit drugs blocked the entry step or the replication step. As shown in Fig. 3B , no suppression of luciferase activity by any of the hit drugs was observed when they were used as treatments before infection or during infection or as a virucide, suggesting that these drugs do not inhibit JEV infection either by inactivating the virus directly or by blocking JEV entry. However, these drugs exerted fully inhibitory effects when they were added at 1 h postinfection, suggesting that viral replication was the stage at which these drugs showed inhibitory activity. To confirm this suggestion, we investigated the inhibitory effects of these drugs on the JEV replicon. The highest concentration of manidipine and nelfinavir mesylate tested in baby hamster kidney (BHK-21) cells was adjusted to 5 M and 10 M, respectively. It was shown that all five drugs inhibited JEV RNA synthesis in a dosedependent manner, while neither drug inhibited the initial translation of replicon RNA (5, 14) (Fig. 3C) , confirming that these drugs inhibited JEV infection at the stage of replication. Hit drugs exhibit broad-spectrum antiflavivirus activity. In order to determine whether the antiviral activity of the five hit drugs extended to other flaviviruses, we explored their antiviral effect against ZIKV. Similar to the findings for JEV, the ZIKV titer was decreased by multiple log units when ZIKV was treated with a high concentration of each of the drugs (Fig. 4A) . Moreover, ZIKV exhibited a higher sensitivity to the two calcium channels inhibitors manidipine and cilnidipine than JEV, with no plaque formation being observed at 10 M. Consistent with this result, sharp decreases in the level of replication of ZIKV RNA and the level of expression of viral protein were also detected (Fig. 4A) . Notably, treatment with 5 M manidipine produced a 95% inhibition of viral replication, translation, and viral yields. Taken together, these results indicate that the hit drugs could effectively inhibit ZIKV infection. Since these drugs exhibited their anti-JEV effects at the stage of viral replication, we further tested the effects against WNV and DENV-2 by using WNV and DENV-2 replicons. Similar to the results for JEV, a dose-dependent reduction in the level of WNV replication was observed with the drug treatments. The same phenotype was observed for DENV-2 for all drugs except nelfinavir mesylate, which showed no effect at the concentrations tested ( Fig. 4B and C). Together, these results indicate that the five hit drugs are excellent candidates for broad-spectrum antiflavivirus treatment. Antiviral effect of calcium inhibitors. Since three hit drugs, manidipine, cilnidipine, and benidipine hydrochloride, were DHP VGCC inhibitors, we asked whether other calcium antagonists could block JEV infection. To address this question, we employed four different classes of inhibitors. Verapamil, a prototype phenylalkylamine (PAA) VGCC inhibitor (15) , exhibited a dose-dependent inhibition of JEV on both African Green monkey kidney (Vero) and human hepatocellular carcinoma (Huh-7) cells (Fig. 5) , which was consistent with the inhibitory effects of the DHP inhibitors, suggesting that calcium channels play an important role in JEV infection. Cyclosporine and 2-aminobiphenyl borate (2-APB), which inhibit the efflux of Ca 2ϩ from the mitochondrial and endoplasmic reticulum (ER) pool, respectively (16) (17) (18) (19) , were also found to block JEV infection effectively. Similarly, treatment with the cell-permeant Ca 2ϩ chelator 1,2-bis-(o-aminophenoxy)-ethane-N,N,N=,N=-tetraacetic acid, tetraacetoxymethyl ester (BAPTA-AM), could also suppress JEV infection. Taken together, we concluded that intracellular Ca 2ϩ is essential for JEV infection and cytoplasmic calcium is a potent target for antiflavivirus treatment. Selection and characterization of manidipine-resistant JEV. To identify the viral target of the calcium channel inhibitor, we selected a manidipine-resistant virus by serially passaging JEV in the presence of manidipine. Viruses from passage 20 (P20) showed robust resistance compared with the wild type (WT) (Fig. 6A ). When JEV from P20 was treated with 5 M or 10 M manidipine, the viral titer was about 10-and 100-fold higher than that of the WT, respectively. Individual virus clones were isolated, and two isolates were randomly selected and amplified. An amino acid substitution was observed in two isolated clones, resulting in a glutamine (Q)-to-arginine (R) switch at amino acid position 130 in transmembrane domain 3 (TMD3) of NS4B, i.e., position 2401 of the translated polyprotein in the JEV infectious cDNA clone (Fig. 6B ). Sequence alignment of NS4B indicated that Q130 was conserved in all flaviviruses except YFV, which possessed a lysine at that position (Fig. 6B) . The conserved Q130 of NS4B may account for the sensitivity of JEV, ZIKV, WNV, and DENV-2 to manidipine, as described above (Fig. 4) , while YFV showed resistance to the drug (data not shown). To confirm that the Q130R mutation did confer manidipine resistance and to investigate the role of Q130 in NS4B function, we produced JEV clones with the Q130R, Q130K, Q130E, or Q130A mutation by introducing the desired mutations into the infectious cDNA clone and rescuing the mutant viruses. To investigate the biological properties of the mutant viruses, we first examined the growth kinetics of the rescued viruses. As shown in Fig. 6C , all mutant viruses had an accumulation of infectious virions and reached the highest titer at 60 h postinfection. Infection of the Q130R and Q130K mutant viruses resulted in growth curves similar to the growth curve for the WT (Fig. 6C) , while the Q130E and Q130A mutants produced smaller amounts of viruses between 24 and 60 h. Analysis of the plaque morphology revealed that the plaques of the Q130R, Q130K, and Q130E mutants were similar to the plaques of the WT, whereas the plaques of the Q130A mutant were smaller than those of the WT. We next investigated the sensitivity of the four mutant viruses to manidipine. As shown in Fig. 6D , the Q130R and Q130K mutant viruses were resistant to manidipine. At a 10 M concentration, manidipine efficiently inhibited WT JEV infection and reduced the viral yields by approximately 4 log units, while the Q130R and Q130K mutant viruses were resistant to manidipine and the viral titer decreased less than 2 log units. The Q130A mutant virus demonstrated moderate resistance and a slightly higher Taken together, it could be concluded that Q130 not only is critical for conferring manidipine sensitivity but also is important for JEV replication. The replacement of glutamine with basic amino acids conferred resistance to manidipine without an apparent loss of growth. In vivo efficacy of manidipine. As manidipine exhibited the strongest inhibitory activities on JEV replication as well as ZIKV infection when its activities were compared with those of the five hit drugs (Fig. 2 and 4A) , we further examined the protective effect of manidipine against JEV-induced lethality in a mouse model. As anticipated, mice in the JEV-infected vehicle-treated group started to show symptoms, including limb paralysis, restriction of movement, piloerection, body stiffening, and whole-body tremor, from day 5 postinfection. Within 21 days postinfection, most mice in the JEV-infected group succumbed to the infection, with the mortality rate being 73% (4 out of 15 animals survived). Manidipine treatment following JEV infection reduced the mortality rate to 20% (12 out of 15 animals survived) (Fig. 7A ). Mice treated with manidipine alone or treated with manidipine and infected with JEV showed little abnormal behavior, similar to the findings for the mice in the vehicle-treated group. These results suggest that manidipine provided effective protection against JEVinduced mortality. To further relate these protective effects to the viral load and histopathological changes in the mouse brains, the viral titer was determined and mouse brain sections were collected and assayed at day 5 and day 21 postinfection, since mice started to show symptoms of JEV infection from day 5 postinfection and most of the surviving mice had recovered at day 21. The results indicated that, during the progression of the disease, manidipine treatment significantly reduced the viral load in infected mice compared to that in infected mice not receiving treatment, while no plaques formed in either the manidipine-or vehicle-treated group, and viral loads were undetectable in each group on day 21 postinfection (Fig. 7B) . As JEV was rapidly cleared from the blood after inoculation and was present in the lymphatic system during the preclinical phase, the effects of manidipine on infection of serum and the spleen were evaluated at earlier time points to detect whether the drug reduced the peripheral viral loads (20, 21) . As shown in Fig. 7C , manidipine had little effect on peripheral JEV infection, which indicated that manidipine protected the mice against JEV-induced lethality by decreasing the viral load in the brain. Similarly, apparent damage in the brain, including meningitis, perivascular cuffing, vacuolar degeneration, and glial nodules, was observed in the JEV-infected and vehicle-treated group on day 5 postinfection, while manidipine treatment remarkably alleviated these phenomena (Fig. 7D) . These results indicate that the alleviation of histopathological changes was accompanied by a reduction in the viral load as well as a reduction in the rate of mortality, further confirming the curative effects of manidipine on viral encephalitis. Among the five hit drugs, manidipine, cilnidipine, and benidipine hydrochloride were VGCC inhibitors. It has been well documented in the literature that Ca 2ϩ inhibitors serve to inhibit virus infection at the stage of either entry (15, 22) or replication (18) and even at the stage of budding (23) . To this end, we first reviewed all 21 calcium inhibitors included in the current library of FDA-approved drugs and found that, in addition to the four DHP VGCC inhibitors listed in Fig. 1B , two other calcium inhibitors, i.e., flunarizine dihydrochloride and lomerizine hydrochloride, were also identified to be primary candidates with levels of inhibition of Ͼ90%. Similarly, three calcium channel antagonists, nisoldipine, felodipine, and nicardipine hydrochloride, showed levels of inhibition of 75%, 72%, and 66%, respectively, in the primary screen. Together, 9 of the 21 calcium inhibitors in the library, accounting for nearly half of the calcium inhibitors, exhibited levels of flavivirus inhibition of greater than 50%, suggesting that calcium, especially the calcium channel, is a potential antiviral target. To address this, another type of VGCC inhibitor, verapamil, an FDA-approved drug not yet included in the drug library used in this study, was investigated. Likewise, a Ca 2ϩ chelator, BAPTA-AM, as well as the Ca 2ϩ inhibitors 2-APB and cyclosporine, targeting ER and the mitochondrial Ca 2ϩ channel, respectively, were employed to investigate the response of JEV infection to the decrease in intracellular Ca 2ϩ levels. In line with the activities of the three hit DHP VGCC inhibitor drugs, the additional Ca 2ϩ inhibitors exerted anti-JEV activity, which indicated that Ca 2ϩ is indispensable for JEV infection. Thus, Ca 2ϩ inhibitors might be utilized as effective treatments for flavivirus infection. As the hit drugs exerted full inhibitory activity when they were added posttreatment, we believe that Ca 2ϩ is important for flavivirus genome replication. Furthermore, selection and genetic analysis of drug-resistant viruses revealed that NS4B is the viral target of manidipine. NS4B is part of the viral replication complex and is supposed to anchor the viral replicase to the ER membrane (24) . Meanwhile, the N-terminal 125amino-acid domain of DENV NS4B was indicated to be responsible for inhibition of the immune response (25) . Notably, several structurally distinct compounds have been identified to inhibit flavivirus replication by intensively targeting the TMD of NS4B (26) (27) (28) (29) (30) (31) (32) . It is thus conceivable that inhibitors targeting TMD of NS4B would perturb its function, leading to the suppression of viral RNA replication. In this study, the replacement of Q130 of NS4B with a basic amino acid conferred the resistance effect without suppressing JEV replication, suggesting that position 130 could tolerate a basic amino acid and that the basic amino acid might be involved in the interplay of NS4B with host proteins rather than viral proteins. Moreover, the efficacy and toxicity of manidipine were monitored in vivo, with manidipine demonstrating effective antiviral activity with favorable biocompatibility. However, the dose used in this study was higher than the dose typically used clinically, representing one of the scenarios most commonly encountered in drug repurposing (33, 34) . As manidipine was approved for use for the long-term treatment of hypertension (35, 36) , pulse-dose treatment with manidipine over the shorter period of time required for the treatment of virus infection might be relatively safe. Moreover, use of a combination of manidipine with other Ca 2ϩ inhibitors might improve its therapeutic efficacy, reduce its toxicity, and reduce the risk of resistance development (37) (38) (39) . Besides the three VGCC inhibitors, two hit drugs, pimecrolimus and nelfinavir mesylate, showed equivalent inhibitory activities on the replication of JEV, ZIKV, WNV, and DENV-2. Although there has been no report on the use of pimecrolimus for the treatment of infectious diseases, we showed that it had a robust effect against JEV with an SI of Ͼ32. The maximum plasma concentration (C max ) of nelfinavir mesylate achieved with an adult dose was 3 to 4 g/ml (40) , which was comparable to the IC 50 reported here. Notably, nelfinavir mesylate was confirmed to inhibit herpes simplex virus 1 (HSV-1) and the replication of several other herpesviruses by interfering directly or indirectly with the later steps of virus formation, such as glycoprotein maturation or virion release, other than functioning in herpesviruses protease (41, 42) . Whether nelfinavir mesylate inhibits flavivirus by interference with the virus protease or by other off-target effects is unknown. Understanding of the mechanism of the antiflavivirus effects of these drugs might uncover novel targets of the drugs, providing further insight into the pathogenesis of flaviviruses. Above all, the findings reported here provide novel insights into the molecular mechanisms underlying flavivirus infection and offer new and promising therapeutic possibilities for combating infections caused by flaviviruses. Cells and viruses. BHK-21, SH-SY5Y (human neuroblastoma), Vero, and Huh-7 cells were cultured in Dulbecco modified Eagle medium (HyClone, Logan, UT, USA) supplemented with 10% fetal bovine serum (Gibco, Grand Island, NY, USA). JEV strain AT31, the WNV replicon, and the DENV-2 replicon expressing Renilla luciferase (Rluc) were kindly provided by Bo Zhang, Wuhan Institute of Virology, Chinese Academy of Sciences (CAS), China. JEV replicon recombinant viral particles (RVPs) were generated as previously described (4, 5) . ZIKV strain H/PF/2013, kindly provided by the European Virus Archive Goes Global, was propagated and titrated in Vero cells. Optimization of HTS assay conditions. The cell density and RVP dose were optimized for the HTS assay. Vero cells at different densities (2,500 to 12,500 cells per well) were infected with from 1.25 to 20 l RVPs (1 to 16 copies per well). The appropriate cell density as well as the RVP dose was selected by comparing the S/B ratio, CV, and Z= values under different conditions as previously described (43) . Methyl-␤-cyclodextrin and dimethyl sulfoxide (DMSO) were used as positive and negative controls, respectively. HTS assay of an FDA-approved compound library. A library of 1,018 FDA-approved drugs was purchased from Selleck Chemicals (Houston, TX, USA). The compounds were stored as 10 mM stock solutions in DMSO at 4°C until use. The first round of the HTS assay was carried out as shown in Fig. 1A . The criteria used to identify the primary candidates were no apparent cytotoxicity and an average level of inhibition of Ͼ90% in duplicate wells. The criteria of dose-dependent inhibition and cell viability of Ͼ80% were applied for the reconfirmation screen. Furthermore, the CC 50 of each compound was calculated, and those compounds displaying SIs over 10 were considered hits in this study. Identification of antiviral effects of five hit drugs. The antiviral effects of the drugs were evaluated by quantitative reverse transcription-PCR (qRT-PCR), immunofluorescence assay (IFA), and plaque assay as previously reported (44) (45) (46) (47) . The experimental timeline is depicted in Fig. 2A . To ensure the effectiveness of the hit drugs in flavivirus replication, BHK-21 cells transfected with the JEV, WNV, or DENV-2 replicon were incubated with each drug at the concentrations indicated above, and the luciferase activities were determined 24 h, 48 h, or 72 h later, respectively. Time-of-addition experiment. To evaluate which stage of the JEV life cycle was inhibited by each hit, a time-of-addition experiment was performed as previously described (43) . Vero cells were infected with 20 l RVPs for 1 h (0 to 1 h). The test compounds were incubated with the cells for 1 h before infection (Ϫ1 to 0 h), during infection (0 to 1 h), and for 23 h postinfection (1 to 24 h) (Fig. 3A) . To exclude a possible direct inactivating effect of the drugs, RVPs were incubated with each drug at 37°C for 1 h, and the mixtures were diluted 25-fold to infect Vero cells. Twenty-four hours later, the luciferase activities were determined as described above (Fig. 3A) . Manidipine-resistant virus. Manidipine-resistant virus was generated by passaging of JEV on Vero cells in the presence of manidipine. Passages 1 to 10 used 5 M manidipine, and passages 11 to 20 used 10 M manidipine. As a control, WT virus was passaged in the presence of 2% DMSO in parallel. Passaging was terminated at passage 20, when no further improvement in resistance was detected. Two manidipine-resistant virus isolates were plaque purified and amplified in the presence of manidipine. Viral RNA was extracted, amplified, and purified for sequencing. An infectious cDNA clone of JEV, strain AT31 (pMWJEAT), kindly provided by T. Wakita, Tokyo Metropolitan Institute for Neuroscience, was used to recover WT and mutant viruses as described previously (4) . Virus titers and manidipine sensitivities were determined by plaque assay in Vero cells. Manidipine administration to JEV-infected mice. Adult female BALB/c mice (age, 4 weeks) were kept in the Laboratory Animal Center of Wuhan Institute of Virology, CAS (Wuhan, China). The mice were randomly divided into four groups (30 mice per group): a JEV-infected and vehicle (2% Tween 80 plus 5% DMSO in phosphate-buffered saline [PBS])-treated group, a manidipine-treated group, a JEV-infected and manidipine-treated group, and a vehicle-treated group. For infection, mice were infected intraperitoneally with 5 ϫ 10 6 PFU of JEV strain AT31. For the manidipine and vehicle treatments, mice were injected intraperitoneally with 25 mg/kg of body weight manidipine or PBS with 2% Tween 80 and 5% DMSO, respectively. Treatments were administered twice a day for the first 2 days and then consecutively administered once a day for up to 21 days. Five mice from each group were sacrificed on days 1, 3, and 5 postinfection. Serum, spleen tissue, and brain tissue samples were collected for viral titer determination and histopathology investigation. Fifteen mice were monitored daily for morbidity and mortality. The mice that showed neurological signs of disease were euthanized according to the Regulations for the Administration of Affairs Concerning Experimental Animals in China. The protocols were reviewed and approved by the Laboratory Animal Care and Use Committee at the Wuhan Institute of Virology, CAS (Wuhan, China).

Where is Q130 located in the NS4B protein?

Preparation for Possible Sustained Transmission of 2019 Novel Coronavirus Lessons From Previous Epidemics https://jamanetwork.com/journals/jama/fullarticle/2761285 February 11, 2020 David L. Swerdlow, MD1; Lyn Finelli, DrPH, MS2 Author Affiliations Article Information JAMA. 2020;323(12):1129-1130. doi:10.1001/jama.2020.1960 COVID-19 Resource Center related articles icon Related Articles author interview icon Interviews Audio Interview (25:53) COVID-19 Update From China Transmissibility and severity are the 2 most critical factors that determine the effect of an epidemic. Neither the 2009 pandemic influenza A(H1N1) virus ([H1N1]pdm09) pandemic or the severe acute respiratory syndrome coronavirus (SARS-CoV) or the Middle East respiratory syndrome coronavirus (MERS-CoV) epidemics had the combination of both high transmissibility and severity. Control strategies are driven by this combination. R0, the basic reproduction number, is a commonly used measure of transmissibility and is defined as the number of additional persons one case infects over the course of their illness. An R0 of less than 1 indicates the infection will die out “eventually.” An R0 of greater than 1 indicates the infection has the potential for sustained transmission. For example, influenza A(H1N1)pdm09, first identified in southern California on April 15, 2009, was highly transmissible. By May 5, 2009, influenza A(H1N1)pdm09 had spread to 41 US states and 21 countries.1 While influenza A(H1N1)pdm09 was highly transmissible, it was not severe. Initial estimates of the R0 of influenza A(H1N1)pdm09 were 1.7.2 Although an estimated 201 200 respiratory deaths due to influenza A(H1N1)pdm09 occurred during the first year of the pandemic, the number of deaths per population was 30 times lower than that seen during the 1968 influenza pandemic, 1000 times less than the 1918 pandemic, and even less than typical seasonal influenza epidemics (estimated by the World Health Organization [WHO] to be 250 000 to 500 000 per year, although estimation methods differ).3 Influenza A(H1N1)pdm09 was highly transmissible but not severe. SARS-CoV (2003) and MERS-CoV (2012-current) cause severe disease, but despite the initial R0 estimations of greater than 2.0 for SARS-CoV (indicating sustained and even worldwide transmission could occur), and some large outbreaks, neither were as transmissible as initial concerns suggested. SARS-CoV caused 8098 reported cases and 774 deaths (case-fatality rate, 9.6%) in 37 countries before the epidemic was controlled. Control was thought to have been possible because a high proportion of cases were severe, making it easier to rapidly identify and isolate infected individuals. In addition, the virus was present at lower levels in upper airway secretions. There was no secondary transmission in the United States from the 8 imported cases, although in Toronto, Canada, a single importation is thought to have led to about 400 cases and 44 deaths. Later estimates of R0 were less than 1, indicating that SARS-CoV may not have been capable of sustained transmission, especially in the setting of control measures.4 Similarly, MERS-CoV appears to have high severity and low transmissibility. Since 2012, MERS-CoV has caused 2494 reported cases and 858 deaths (case-fatality rate, 34%) in 27 countries. MERS-CoV has also caused some rapid outbreaks, mainly in hospitals in Saudi Arabia, Jordan, and South Korea, but estimates of MERS-CoV R0 are less than 1, and thus far it has been contained.5 Can a respiratory virus that is both transmissible and severe be contained? In preparation for an influenza pandemic, the US Department of Health and Human Services’ Pandemic Influenza Plan included a combination of nonpharmaceutical (border and school closing, infection control measures) and pharmaceutical (antiviral prophylaxis, vaccines) interventions meant to be used in combination to interrupt or slow influenza transmission. Despite implementation of some of these interventions, influenza A(H1N1)pdm09 spread to 120 countries in 3 months. With the emergence of MERS-CoV in the Middle East, a preparedness plan was developed that included a surveillance plan, laboratory testing, and contact tracing guidance. Infection control guidance was developed for use in health care settings and traveler guidance was developed for the public.6 The US Centers for Disease Control and Prevention (CDC) distributed MERS-CoV polymerase chain reaction test kits to state health departments. Two cases were imported into the United States. Contacts were traced, including household, hospital, and airline contacts. No secondary cases were identified in the United States. MERS-CoV was thought to be severe and control measures relied on recognition of suspect cases. However, during a hospital outbreak in Jeddah, Saudi Arabia, among hospitalized patients only 5 of 53 (9%) health care–associated cases had documented presence in the same room as a patient with MERS.5 Despite the high case-fatality rate (an important measure of severity), MERS cases can be asymptomatic and mild (25% in one outbreak). Although it is not known how often asymptomatic or mildly symptomatic patients transmit MERS, initiating comprehensive measures such as isolating patients suspected of having or having been exposed to the virus and using personal protective equipment when caring for them may be extremely difficult because so many patients have mild and nonspecific symptoms. Is the world ready for a respiratory virus with high transmissibility and severity? After a new influenza virus (H7N9) was identified in China in 2013, a series of modeling articles described the effect of, and level of preparedness for, a severe, single-wave pandemic in the United States.7 In scenarios that used clinical attack rates (the proportion of individuals who become ill with or die from a disease in a population initially uninfected) of 20% to 30% (for comparison the clinical attack rate was 20% in the first year of the 2009 H1N1 pandemic), depending on severity there would be an estimated 669 000 to 4.3 million hospitalizations and an estimated 54 000 to 538 000 deaths without any interventions in the United States. The models suggested that without a vaccine, school closures would be unlikely to affect the pandemic, an estimated 35 000 to 60 000 ventilators would be needed, up to an estimated 7.3 billion surgical masks or respirators would be required, and perhaps most important, if vaccine development did not start before the virus was introduced, it was unlikely that a significant number of hospitalizations and deaths could be averted due to the time it takes to develop, test, manufacture, and distribute a vaccine. It is impossible to know what will happen so early in this novel 2019 coronavirus (2019-nCoV) epidemic. The scope, morbidity, and mortality will depend on the combination of severity and transmissibility. Numerous experts have “nowcasted” how many cases have occurred and forecasted how many cases will likely occur. A recent study suggests rapid person to person transmission can occur.8 Disease modelers have estimated R0 to be 2.2.9 The University of Hong Kong estimates the outbreak could infect more than 150 000 persons per day in China at its peak. Is 2019-nCoV infection severe? To date approximately 14% of cases of 2019-nCoV have been described as severe by WHO, with a case-fatality rate of 2.1%.10 Estimates of severity are usually higher in the beginning of an epidemic due to the identification of the most severely affected cases and decline as the epidemic progresses. However, because many infected persons have not yet recovered and may still die, the case-fatality rate and severity could be underestimated. On January 30, 2020, WHO officially declared the 2019-nCoV epidemic as a Public Health Emergency of International Concern, indicating its concern that countries aside from China could be affected by 2019-nCoV. In preparing for possible sustained transmission of 2019-nCoV beyond China, applicable lessons from previous experiences with epidemics/pandemics of respiratory viruses should be carefully considered to better control and mitigate potential consequences. Influenza preparedness plans have been developed that aim to stop, slow, or limit the spread of an influenza pandemic to the United States. These plans address limiting domestic spread and mitigating disease but also sustaining infrastructure and reducing the adverse effects of the pandemic on the economy and society. These plans would be useful to enact during the 2019-nCoV epidemic should the United States experience sustained transmission. Countries have been successful in the past and there is nothing yet to predict that this time it is likely to be worse. Effective prevention and control will not be easy if there is sustained transmission and will require the full attention of public health, federal and local governments, the private sector, and every citizen. Back to topArticle Information Corresponding Author: David L. Swerdlow, MD, Clinical Epidemiology Lead, Medical Development and Scientific/Clinical Affairs, Pfizer Vaccines, 500 Arcola Rd, Collegeville, PA 19426 ([email protected]). Published Online: February 11, 2020. doi:10.1001/jama.2020.1960 Conflict of Interest Disclosures: Dr Swerdlow reports owning stock and stock options in Pfizer Inc. Dr Swerdlow also reports providing a one-time consultation consisting of an overview of SARS and MERS epidemiology to GLG Consulting and receiving an honorarium. Dr Finelli reports owning stock in Merck and Co. Funding/Support: Pfizer Inc provided salary support for Dr Swerdlow. Role of the Funder/Sponsor: Pfizer Inc reviewed the manuscript and approved the decision to submit the manuscript for publication. References 1. Swerdlow DL, Finelli L, Bridges CB. 2009 H1N1 influenza pandemic: field and epidemiologic investigations in the United States at the start of the first pandemic of the 21st century. Clin Infect Dis. 2011;52(suppl 1):S1-S3. doi:10.1093/cid/ciq005PubMedGoogle ScholarCrossref 2. Balcan D, Hu H, Goncalves B, et al. Seasonal transmission potential and activity peaks of the new influenza A(H1N1): a Monte Carlo likelihood analysis based on human mobility. BMC Medicine. 2009;7(45). doi:10.1186/1741-7015-7-45 3. Dawood FS, Iuliano AD, Reed C, et al. Estimated global mortality associated with the first 12 months of 2009 pandemic influenza A H1N1 virus circulation: a modelling study. Lancet Infect Dis. 2012;12(9):687-695. doi:10.1016/S1473-3099(12)70121-4PubMedGoogle ScholarCrossref 4. Chowell G, Castillo-Chavez C, Fenimore PW, Kribs-Zaleta CM, Arriola L, Hyman JM. Model parameters and outbreak control for SARS. Emerg Infect Dis. 2004;10(7):1258-1263. doi:10.3201/eid1007.030647PubMedGoogle ScholarCrossref 5. Killerby ME, Biggs HM, Midgley CM, Gerber SI, Watson JT. Middle East respiratory syndrome coronavirus transmission. Emerg Infect Dis. 2020;26(2):191-198. doi:10.3201/eid2602.190697PubMedGoogle ScholarCrossref 6. Rasmussen SA, Watson AK, Swerdlow DL. Middle East respiratory syndrome (MERS). Microbiol Spectr. 2016;4(3). doi:10.1128/microbiolspec.EI10-0020-2016PubMedGoogle Scholar 7. Swerdlow DL, Pillai SK, Meltzer MI, eds. CDC modeling efforts in response to a potential public health emergency: influenza A(H7N9) as an example. Clin Infect Dis. 2015;60(suppl):S1-S63. https://academic.oup.com/cid/issue/60/suppl_1.Google Scholar 8. Wang D, Hu B, Hu C, et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus–infected pneumonia in Wuhan, China. JAMA. Published online February 7, 2020. doi:10.1001/jama.2020.1585 ArticlePubMedGoogle Scholar 9. Li Q, Guan X, Wu P, et al. Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia. N Engl J Med. Published online January 29, 2020. doi:10.1056/NEJMoa2001316PubMedGoogle Scholar 10. World Health Organization. Novel coronavirus (2019-nCoV) situation reports. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports/. Accessed February 4, 2020. Comment 2 Comments for this articleEXPAND ALL February 12, 2020 Understanding R and Disease Control Oz Mansoor | Public Health Physician, Wellington The message, that we need to prepare for a pandemic is vital. But the article misreports some key ideas. Firstly, SARS was not controlled "because a high proportion of cases were severe." While that helped , it was because cases were not infectious before some days after symptom onset (usually in the second week of illness). This gave more time for case identification and isolation. And most cases did not pass on infection to anybody, but a few spread to many. When all such individuals were identified and isolated, spread stopped. Unfortunately, the new virusappears to be spreading from people much earlier in the course of illness, and even with mild symptoms - which was never documented for SARS. However, it is not clear that it is any different or better at spread between people, and perhaps with the same pattern of most cases not causing further spread. Secondly, the R0, the basic reproduction number, is correctly described as the average number of infections each case causes. But it lacks two key ideas: 1) the 0 after the R implies the native state, which is a fully susceptible population and without any control measures. R is the effectiive number and can include the impact of control measures. To claim that it was the lack of transmissibility, rather than the control measures that ended SARS, is not based on any evidence. And it ignores the heroic efforts of affected countries. Elimination of SARS demonstrated the potential of globally coordinated collective action, as well as the damage caused by ignorance and prejudice. Most seem to have already forgotten the lessons of SARS.CONFLICT OF INTEREST: Worked for WHO/WPRO in SARS responseREAD MORE February 24, 2020 COVID 19: a global presence and not only a new pathogen? Giuliano Ramadori, Professor of Medicine | University Clinic, Göttingen, Germany In the winter season there comes the time of upper and lower respiratory tract infections characterised by cough, dyspnea and eventually fever (influenza-like illness).Some of the patients, especially older people living alone affected by the disease ,may need hospitalization and eventually intensive care. In many of the cases who are hospitalized nasal and/or tracheal fluid are examined for viral or bacterial agents. Only in less than 50% of the cases influenza viruses are considered to be the cause of the disease.In the rest of the cases diagnostic procedure for human coronaviruses is not performed routinely. One of the fourdifferent Human Coronaviruses (HuCoV: 229E,NL 63,0C43 and HKU1) can however be found in up to 30% ofpatients negative for influenza viruses (1). Chinese scientists in Wuhan, who had to deal with an increasing number of acute respiratory tract diseases resembling viral pneumonia, performed deep sequencing analysis from samples taken from the lower respiratory tract and found a "novel" coronavirus. The sequence of the complete genome was made public. At the same time, however, the notice from Wuhan brought to mind the SARS- and MERS-epidemics. The measures taken by the Chinese- and WHO-authorities are now well known. Recently about 150 new cases have been identified in northern Italy and health authorities are still looking for case 0 (the source). Is it possible that COVID-19 was already existent in Italy -- and not only in Italy but possibly everywhere in the world -- and that newly available nucleotide sequence allows now to find the cause of previously undefined influenza-like illness? REFERENCE 1. Benezit F et al.:Non-influenza respiratory viruses in adult patients admitted with influenza-like illness:a 3- year prospective multicenter study.Infection, 13 february 2020, https://doi.org/10.1007/s15010-019-01388-1).CONFLICT OF INTEREST: None ReportedREAD MORE See More About Global Health Public Health Pulmonary Medicine Infectious Diseases Influenza Download PDF Cite This PermissionsComment CME & MOC Coronavirus Resource Center Trending Opinion is learning has multimedia US Emergency Legal Responses to Novel Coronavirus—Balancing Public Health and Civil Liberties March 24, 2020 Opinion is learning has multimedia 2019 Novel Coronavirus—Important Information for Clinicians March 17, 2020 Research is learning has multimedia Clinical Characteristics of Patients With Novel Coronavirus (2019-nCoV) Infection Hospitalized in Beijing, China March 17, 2020 Select Your Interests JOB LISTINGS ON JAMA CAREER CENTER® ACADEMIC CARDIOLOGIST: HEART FAILURE SPECIALIST Phoenix, Arizona NONINVASIVE CARDIOLOGIST West Grove, Pennsylvania CARDIOLOGIST Phoenixville, Pennsylvania CARDIAC INTENSIVIST FACULTY West Reading, Pennsylvania CLINICAL FACULTY: CARDIOLOGY / ELECTROPHYSIOLOGIST Phoenix, Arizona See more at JAMA Career Center Others Also Liked Coronavirus Dx Emergency Use Authorizations Progressing Rapidly Despite Criticism Madeleine Johnson, 360Dx, 2020 Analysis of therapeutic targets for SARS-CoV-2 and discovery of potential drugs by computational methods Canrong Wu, Acta Pharmaceutica Sinica B, 2020 Commercial Labs Step up Coronavirus Test Efforts After FDA Guidance 360Dx, 2020 Powered by Trending US Emergency Legal Responses to Novel Coronavirus—Balancing Public Health and Civil Liberties JAMA Opinion March 24, 2020 Practical Aspects of Otolaryngologic Clinical Services During the COVID-19 Epidemic JAMA Otolaryngology–Head & Neck Surgery Opinion March 20, 2020 2019 Novel Coronavirus—Important Information for Clinicians JAMA Opinion March 17, 2020 JAMA CONTENT Home New Online Current Issue JOURNAL INFORMATION For Authors Editors & Publishers RSS Contact Us JN Learning / CME Store Apps Jobs Institutions Reprints & Permissions Journal Cover Subscribe Go JAMA Network PUBLICATIONS JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Facial Plastic Surgery JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry (1919-1959) SITES AMA Manual of Style Art and Images in Psychiatry Breast Cancer Screening Guidelines Colorectal Screening Guidelines Declaration of Helsinki Depression Screening Guidelines Evidence-Based Medicine: An Oral History Fishbein Fellowship Genomics and Precision Health Health Disparities Hypertension Guidelines JAMA Network Audio JAMA Network Conferences Machine Learning Med Men Medical Education Opioid Management Guidelines Peer Review Congress Research Ethics Sepsis and Septic Shock Statins and Dyslipidemia Topics and Collections FEATURED ARTICLES ACS Breast Cancer Screening Guideline CDC Guideline for Prescribing Opioids CDC Guideline for Prevention of Surgical Site Infections Consensus Definitions for Sepsis and Septic Shock Global Burden of Cancer, 1990-2016 Global Burden of Disease in Children, 1990-2013 Global Burden of Hypertension, 1990-2015 Global Firearm Mortality, 1990-2016 Health Care Spending in the US and Other High-Income Countries Income and Life Expectancy in the US JNC 8 Guideline for Management of High Blood Pressure President Obama on US Health Care Reform Screening for Colorectal Cancer Screening for Depression in Adults Screening for Prostate Cancer Statins for Primary Prevention of Cardiovascular Disease The State of US Health, 1990-2016 US Burden of Cardiovascular Disease, 1990-2016 WMA Declaration of Helsinki, 7th Revision BLOGS JAMA Health Forum AMA Style Insider INFORMATION FOR Authors Institutions & Librarians Advertisers Subscription Agents Employers & Job Seekers Media JAMA NETWORK PRODUCTS AMA Manual of Style JAMAevidence JN Listen Peer Review Congress JN LEARNING Home CME Quizzes State CME Audio / Podcast Courses Clinical Challenge CME Atrial Fibrillation Course Marijuana Course Penicillin Allergy Course Cervical Cancer Screening Course CME / MOC Reporting Preferences About CME & MOC Help Subscriptions & Renewals Email Subscriptions Update Your Address Contact Us Frequently Asked Questions JAMA CAREER CENTER Physician Job Listings Get the latest from JAMA Email address Sign Up Privacy Policy | Terms of Use Jama Network Logo © 2020 American Medical Association. 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How many ventilators have past studies projected will be required for a pandemic in the United States?

First cases of coronavirus disease 2019 (COVID-19) in the WHO European Region, 24 January to 21 February 2020 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7068164/ SHA: ce358c18aac69fc83c7b2e9a7dca4a43b0f60e2e Authors: Spiteri, Gianfranco; Fielding, James; Diercke, Michaela; Campese, Christine; Enouf, Vincent; Gaymard, Alexandre; Bella, Antonino; Sognamiglio, Paola; Sierra Moros, Maria José; Riutort, Antonio Nicolau; Demina, Yulia V.; Mahieu, Romain; Broas, Markku; Bengnér, Malin; Buda, Silke; Schilling, Julia; Filleul, Laurent; Lepoutre, Agnès; Saura, Christine; Mailles, Alexandra; Levy-Bruhl, Daniel; Coignard, Bruno; Bernard-Stoecklin, Sibylle; Behillil, Sylvie; van der Werf, Sylvie; Valette, Martine; Lina, Bruno; Riccardo, Flavia; Nicastri, Emanuele; Casas, Inmaculada; Larrauri, Amparo; Salom Castell, Magdalena; Pozo, Francisco; Maksyutov, Rinat A.; Martin, Charlotte; Van Ranst, Marc; Bossuyt, Nathalie; Siira, Lotta; Sane, Jussi; Tegmark-Wisell, Karin; Palmérus, Maria; Broberg, Eeva K.; Beauté, Julien; Jorgensen, Pernille; Bundle, Nick; Pereyaslov, Dmitriy; Adlhoch, Cornelia; Pukkila, Jukka; Pebody, Richard; Olsen, Sonja; Ciancio, Bruno Christian Date: 2020-03-05 DOI: 10.2807/1560-7917.es.2020.25.9.2000178 License: cc-by Abstract: In the WHO European Region, COVID-19 surveillance was implemented 27 January 2020. We detail the first European cases. As at 21 February, nine European countries reported 47 cases. Among 38 cases studied, 21 were linked to two clusters in Germany and France, 14 were infected in China. Median case age was 42 years; 25 were male. Late detection of the clusters’ index cases delayed isolation of further local cases. As at 5 March, there were 4,250 cases. Text: In the WHO European Region, COVID-19 surveillance was implemented 27 January 2020. We detail the first European cases. As at 21 February, nine European countries reported 47 cases. Among 38 cases studied, 21 were linked to two clusters in Germany and France, 14 were infected in China. Median case age was 42 years; 25 were male. Late detection of the clusters' index cases delayed isolation of further local cases. As at 5 March, there were 4,250 cases. A cluster of pneumonia of unknown origin was identified in Wuhan, China, in December 2019 [1] . On 12 January 2020, Chinese authorities shared the sequence of a novel coronavirus termed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) isolated from some clustered cases [2] . Since then, the disease caused by SARS-CoV-2 has been named coronavirus disease 2019 (COVID -19) . As at 21 February 2020, the virus had spread rapidly mostly within China but also to 28 other countries, including in the World Health Organization (WHO) European Region [3] [4] [5] . Here we describe the epidemiology of the first cases of COVID-19 in this region, excluding cases reported in the United Kingdom (UK), as at 21 February 2020. The study includes a comparison between cases detected among travellers from China and cases whose infection was acquired due to subsequent local transmission. On 27 January 2020, the European Centre for Disease Prevention and Control (ECDC) and the WHO Regional Office for Europe asked countries to complete a WHO standard COVID-19 case report form for all confirmed and probable cases according to WHO criteria [6] [7] [8] . The overall aim of surveillance at this time was to support the global strategy of containment of COVID-19 with rapid identification and follow-up of cases linked to affected countries in order to minimise onward transmission. The surveillance objectives were to: describe the key epidemiological and clinical characteristics of COVID-19 cases detected in Europe; inform country preparedness; and improve further case detection and management. Data collected included demographics, history of recent travel to affected areas, close contact with a probable or confirmed COVID-19 case, underlying conditions, signs and symptoms of disease at onset, type of specimens from which the virus was detected, and clinical outcome. The WHO case definition was adopted for surveillance: a confirmed case was a person with laboratory confirmation of SARS-CoV-2 infection (ECDC recommended two separate SARS-CoV-2 RT-PCR tests), irrespective of clinical signs and symptoms, whereas a probable case was a suspect case for whom testing for SARS-CoV-2 was inconclusive or positive using a pan-coronavirus assay [8] . By 31 January 2020, 47 laboratories in 31 countries, including 38 laboratories in 24 European Union and European Economic Area (EU/EEA) countries, had diagnostic capability for SARS-CoV-2 available (close to 60% of countries in the WHO European Region), with cross-border shipment arrangements in place for many of those lacking domestic testing capacity. The remaining six EU/EEA countries were expected to have diagnostic testing available by mid-February [9] . As at 09:00 on 21 February 2020, 47 confirmed cases of COVID-19 were reported in the WHO European Region and one of these cases had died [4] . Data on 38 of these cases (i.e. all except the nine reported in the UK) are included in this analysis. The first three cases detected were reported in France on 24 January 2020 and had onset of symptoms on 17, 19 and 23 January respectively [10] . The first death was reported on 15 February in France. As at 21 February, nine countries had reported cases ( Figure) : Belgium (1), Finland (1), France (12), Germany (16), Italy (3), Russia (2), Spain (2), Sweden (1) and the UK (9 -not included further). The place of infection (assessed at national level based on an incubation period presumed to be up to 14 days [11] , travel history and contact with probable or confirmed cases as per the case definition) was reported for 35 cases (missing for three cases), of whom 14 were infected in China (Hubei province: 10 cases; Shandong province: one case; province not reported for three cases). The remaining 21 cases were infected in Europe. Of these, 14 were linked to a cluster in Bavaria, Germany, and seven to a cluster in Haute-Savoie, France [12, 13] . Cases from the Bavarian cluster were reported from Germany and Spain, whereas cases from the Haute-Savoie cluster were reported from France All but two cases were hospitalised (35 of 37 where information on hospitalisation was reported), although it is likely that most were hospitalised to isolate the person rather than because of severe disease. The time from onset of symptoms to hospitalisation (and isolation) ranged between 0 and 10 days with a mean of 3.7 days (reported for 29 cases). The mean number of days to hospitalisation was 2.5 days for cases imported from China, but 4.6 days for those infected in Europe. This was mostly a result of delays in identifying the index cases of the two clusters in France and Germany. In the German cluster, for example, the first three cases detected locally were hospitalised in a mean of 5.7 days, whereas the following six took only a mean of 2 days to be hospitalised. Symptoms at the point of diagnosis were reported for 31 cases. Two cases were asymptomatic and remained so until tested negative. The asymptomatic cases were tested as part of screening following repatriation and during contact tracing respectively. Of the remaining 29, 20 reported fever, 14 reported cough and eight reported weakness. Additional symptoms reported included headaches (6 cases), sore throat (2), rhinorrhoea (2), shortness of breath (2), myalgia (1), diarrhoea (1) and nausea (1). Fever was reported as the sole symptom for nine cases. In 16 of 29 symptomatic cases, the symptoms at diagnosis were consistent with the case definition for acute respiratory infection [16] , although it is possible that cases presented additional symptoms after diagnosis and these were not reported. Data on pre-existing conditions were reported for seven cases; five had no pre-existing conditions while one was reported to be obese and one had pre-existing cardiac disease. No data on clinical signs e.g. dyspnea etc. were reported for any of the 38 cases. All hospitalised cases had a benign clinical evolution except four, two reported in Italy and two reported in France, all of whom developed viral pneumonia. All three cases who were aged 65 years or over were admitted to intensive care and required respiratory support and one French case died. The case who died was hospitalised for 21 days and required intensive care and mechanical ventilation for 19 days. The duration of hospitalisation was reported for 16 cases with a median of 13 days (range: 8-23 days). As at 21 February 2020, four cases were still hospitalised. All cases were confirmed according to specific assays targeting at least two separate genes (envelope (E) gene as a screening test and RNA-dependent RNA polymerase (RdRp) gene or nucleoprotein (N) gene for confirmation) [8, 17] . The specimen types tested were reported for 27 cases: 15 had positive nasopharyngeal swabs, nine had positive throat swabs, three cases had positive sputum, two had a positive nasal swab, one case had a positive nasopharyngeal aspirate and one a positive endotracheal aspirate. As at 09:00 on 21 February, few COVID-19 cases had been detected in Europe compared with Asia. However the situation is rapidly developing, with a large outbreak recently identified in northern Italy, with transmission in several municipalities and at least two deaths [18] . As at 5 March 2020, there are 4,250 cases including 113 deaths reported among 38 countries in the WHO European region [19] . In our analysis of early cases, we observed transmission in two broad contexts: sporadic cases among travellers from China (14 cases) and cases who acquired infection due to subsequent local transmission in Europe (21 cases). Our analysis shows that the time from symptom onset to hospitalisation/case isolation was about 3 days longer for locally acquired cases than for imported cases. People returning from affected areas are likely to have a low threshold to seek care and be tested when symptomatic, however delays in identifying the index cases of the two clusters in France and Germany meant that locally acquired cases took longer to be detected and isolated. Once the exposure is determined and contacts identified and quarantined (171 contacts in France and 200 in Germany for the clusters in Haute-Savoie and Bavaria, respectively), further cases are likely to be rapidly detected and isolated when they develop symptoms [15, 20] . In the German cluster, for example, the first three cases detected locally were hospitalised in a mean of 5.7 days, whereas the following six were hospitalised after a mean of 2 days. Locally acquired cases require significant resources for contact tracing and quarantine, and countries should be prepared to allocate considerable public health resources during the containment phase, should local clusters emerge in their population. In addition, prompt sharing of information on cases and contacts through international notification systems such as the International Health Regulations (IHR) mechanism and the European Commission's European Early Warning and Response System is essential to contain international spread of infection. All of the imported cases had a history of travel to China. This was consistent with the epidemiological situation in Asia, and supported the recommendation for testing of suspected cases with travel history to China and potentially other areas of presumed ongoing community transmission. The situation has evolved rapidly since then, however, and the number of countries reporting COVID-19 transmission increased rapidly, notably with a large outbreak in northern Italy with 3,089 cases reported as at 5 March [18, 19] . Testing of suspected cases based on geographical risk of importation needs to be complemented with additional approaches to ensure early detection of local circulation of COVID-19, including through testing of severe acute respiratory infections in hospitals irrespectively of travel history as recommended in the WHO case definition updated on 27 February 2020 [21] . The clinical presentation observed in the cases in Europe is that of an acute respiratory infection. However, of the 31 cases with information on symptoms, 20 cases presented with fever and nine cases presented only with fever and no other symptoms. These findings, which are consistent with other published case series, have prompted ECDC to include fever among several clinical signs or symptoms indicative for the suspected case definition. Three cases were aged 65 years or over. All required admission to intensive care and were tourists (imported cases). These findings could reflect the average older age of the tourist population compared with the local contacts exposed to infection in Europe and do not allow us to draw any conclusion on the proportion of severe cases that we could expect in the general population of Europe. Despite this, the finding of older individuals being at higher risk of a severe clinical course is consistent with the evidence from Chinese case series published so far although the majority of infections in China have been mild [22, 23] . This preliminary analysis is based on the first reported cases of COVID-19 cases in the WHO European Region. Given the small sample size, and limited completeness for some variables, all the results presented should be interpreted with caution. With increasing numbers of cases in Europe, data from surveillance and investigations in the region can build on the evidence from countries in Asia experiencing more widespread transmission particularly on disease spectrum and the proportion of infections with severe outcome [22] . Understanding the infection-severity is critical to help plan for the impact on the healthcare system and the wider population. Serological studies are vital to understand the proportion of cases who are asymptomatic. Hospital-based surveillance could help estimate the incidence of severe cases and identify risk factors for severity and death. Established hospital surveillance systems that are in place for influenza and other diseases in Europe may be expanded for this purpose. In addition, a number of countries in Europe are adapting and, in some cases, already using existing sentinel primary care based surveillance systems for influenza to detect community transmission of SARS-CoV-2. This approach will be used globally to help identify evidence of widespread community transmission and, should the virus spread and containment no longer be deemed feasible, to monitor intensity of disease transmission, trends and its geographical spread. Additional research is needed to complement surveillance data to build knowledge on the infectious period, modes of transmission, basic and effective reproduction numbers, and effectiveness of prevention and case management options also in settings outside of China. Such special studies are being conducted globally, including a cohort study on citizens repatriated from China to Europe, with the aim to extrapolate disease incidence and risk factors for infection in areas with community transmission. Countries together with ECDC and WHO, should use all opportunities to address these questions in a coordinated fashion at the European and global level. provided input to the outline, multiple versions of the manuscript and gave approval to the final draft.

What symptoms were reported?

Improved Pharmacological and Structural Properties of HIV Fusion Inhibitor AP3 over Enfuvirtide: Highlighting Advantages of Artificial Peptide Strategy https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4541410/ SHA: f2fcc16391f946c99717b63ec9a24e5384aac381 Authors: Zhu, Xiaojie; Zhu, Yun; Ye, Sheng; Wang, Qian; Xu, Wei; Su, Shan; Sun, Zhiwu; Yu, Fei; Liu, Qi; Wang, Chao; Zhang, Tianhong; Zhang, Zhenqing; Zhang, Xiaoyan; Xu, Jianqing; Du, Lanying; Liu, Keliang; Lu, Lu; Zhang, Rongguang; Jiang, Shibo Date: 2015-08-19 DOI: 10.1038/srep13028 License: cc-by Abstract: Enfuvirtide (T20), is the first HIV fusion inhibitor approved for treatment of HIV/AIDS patients who fail to respond to the current antiretroviral drugs. However, its clinical application is limited because of short half-life, drug resistance and cross-reactivity with the preexisting antibodies in HIV-infected patients. Using an artificial peptide strategy, we designed a peptide with non-native protein sequence, AP3, which exhibited potent antiviral activity against a broad spectrum of HIV-1 strains, including those resistant to T20, and had remarkably longer in vivo half-life than T20. While the preexisting antibodies in HIV-infected patients significantly suppressed T20’s antiviral activity, these antibodies neither recognized AP3, nor attenuated its anti-HIV-1 activity. Structurally different from T20, AP3 could fold into single-helix and interact with gp41 NHR. The two residues, Met and Thr, at the N-terminus of AP3 form a hook-like structure to stabilize interaction between AP3 and NHR helices. Therefore, AP3 has potential for further development as a new HIV fusion inhibitor with improved antiviral efficacy, resistance profile and pharmacological properties over enfuvirtide. Meanwhile, this study highlighted the advantages of artificially designed peptides, and confirmed that this strategy could be used in developing artificial peptide-based viral fusion inhibitors against HIV and other enveloped viruses. Text: The sequences of gp41 NHR-or CHR-derived peptides. The residues corresponding to the NHR pocket region are marked in red. The residues for the PBD are marked in blue, and the MT-hook residues adjacent to the N terminus of PBD are marked in green. 5HRu peptide consists of 5 copies of artificial sequence template (AEELAKK) underlined. The mutant residues in PBD of AP2 and AP3 were highlighted in pink. (b) The inhibitory activity of AP1, AP2, AP3 and T20 on infection by HIV-1 IIIB (subtype B, X4) in MT-2 cells (left panel) by HIV-1 Bal (subtype B, R5) in M7 cells (right panel). Each sample was tested in triplicate and the experiment was repeated twice. The data are presented as means ± SD. Scientific RepoRts | 5:13028 | DOi: 10 .1038/srep13028 To address these obstacles, many efforts have been made to optimize T20 and gp41 CHR-derived peptides. Some of these peptides have better inhibitory activities against T20-resistant strains and/or longer half-life than T20. However, they still have the problem to cross-react with the preexisting antibodies in the sera of HIV-infected patients because they contain some native CHR sequences. Based on the universal artificial peptide template of 5HRu, we previously designed the artificial peptides of AP1 (PBD-m4HR) and AP2 (PBDtrp-m4HR), and have made preliminary research on their inhibitory activity against HIV-1 Env-mediated cell-cell fusion 16 . In the present study, we designed a new artificial peptide, AP3 (Fig. 1a) , aiming to apply the "M-T hook" structure to stabilize the interaction of the artificial peptide with the hydrophobic pocket on the gp41 NHR trimer 17, 18 . After comprehensively studying its antiviral activity, biochemical property, crystal structure, functional mechanism, in vivo half-life and, for the first time, the effect of preexisting antibodies in the sera of HIV-infected patients, we found that the newly designed artificial peptide, AP3, exhibited improved antiviral activity, drug resistance profile and pharmacological properties over T20. Particularly, the preexisting antibodies in the sera of HIV-infected patients did not suppress, but enhanced the anti-HIV-1 activity of AP3. These results suggest that AP3 has potential for development as a new anti-HIV drug and confirm that this strategy can be used for designing artificial antiviral peptides against other enveloped viruses, such as SARS-CoV 19 , MERS-CoV 20 , and paramyxovirus 21 . AP3 inhibited HIV-1 infection with higher potency than T20. Our previously designed artificial peptides AP1 and AP2 could inhibit HIV-1 Env-mediated cell-cell membrane fusion 16 . He and colleagues reported that adding two amino acids of Met and Thr to the N-terminus of a CHR-peptide could enhance their anti-HIV-1 activity 17, 18 . Here we designed a new artificial peptide, AP3, by adding Met and Thr to the N-terminus of AP2 (Fig. 1a) . We then compared AP3 with AP1, AP2 and T20 for their anti-HIV-1 activity against divergent HIV-1 strains, including the laboratory-adapted viruses, IIIB (subtype B, X4) and Bal (subtype B, R5), and a series of primary HIV-1 isolates, as well as the T20-resistant strains. As shown in Fig. 1b , AP3 exhibited higher inhibitory activities on infection by HIV-1 IIIB and HIV-1 Bal strains (IC 50 : 3.06 and 15.09 nM, respectively) than AP1 (IC 50 : 86.25 and 396.14 nM, respectively), AP2 (IC 50 : 23.05 and 49.95 nM, respectively), and T20 (IC 50 : 13.63 and 30.21 nM, respectively). The inhibitory activity of AP3 on infection by divergent primary HIV-1 isolates with distinct genotypes (subtypes A -E and group O) and phenotypes (R5 and X4) was also higher than that of AP2 and T20 (Table 1) . While T20 was not effective against T20-resistant HIV-1 strains at the concentration as high as 2,000 nM, AP3 could effectively inhibit infection of these strains with IC 50 in the range of 13 ~ 90 nM, which was about 2-to 4-fold more effective than AP2 (Table 1 ). These results indicate that the artificial peptide AP3 has remarkably improved anti-HIV-1 activity against a broad spectrum of HIV-1 strains, including T20resistant variants, over T20 and the artificial peptides AP1 and AP2. The preexisting antibodies in HIV-1-infected patients neither recognized AP3, nor attenuated its anti-HIV-1 activity. Previous studies have shown that the preexisting antibodies in HIV-1-infected patients, including those cross-reacting with T20 and those specific for the binding sites of T20 in gp120 (e.g., the C1 and V3 loop regions) and gp41 (e.g., the NHR domain), could significantly block the fusion inhibitory activity of T20 14, 15 . Here we investigated the influence of preexisting antibodies against AP3 peptide. As shown in Fig. 2a , both T20 and C46 reacted with the antibodies in sera from five HIV-1-infected patients; however, none of the three artificial peptides AP1, AP2 and AP3 was recognized by the preexisting antibodies. The inhibitory activity of T20 on HIV-1 IIIB infection was reduced about 1.9-fold to > 3.6-fold in the presence of the sera from HIV-1-infected patients ( Fig. 2b and Supplementary Table S1), confirming that the preexisting antibodies in sera of HIV/AIDS patients can attenuate the anti-HIV-1 activity of T20 14, 15 . However, none of the artificial peptides in the present study showed significant decrease of anti-HIV-1 activity in the presence of patients' sera. Instead, the antiviral activity of AP3 increased in the presence of antisera from HIV-1-infected patients ( Fig. 2b and Supplementary Table S1), suggesting that anti-HIV-1 antibodies actually enhanced the anti-HIV-1 activity of AP3, possibly because the binding of the antibodies to some sites in gp120 or gp41 promote the interaction of AP3 with viral gp41 NHR region. AP3 had longer half-life than T20. Although T20 has shown efficacy in inhibiting HIV-1 infection, its major weakness lies in its short half-life in plasma (about 2 h) [22] [23] [24] . As a result, T20 has to be administered subcutaneously twice daily at 90 mg per dose, often causing serious injection-site reactions 25, 26 . Here, we performed pharmacokinetic studies by intravenous administration of AP3, AP2, and T20, respectively, to SD rat at a dose of 1 mg/kg, in order to compare their in vivo circulation time. As expected, T20 exhibited a shorter half-life and lower AUC (0-t) from systemic circulation, while AP3 and AP2 demonstrated much higher concentration and longer circulation time ( Table 2 ). The pharmacokinetic profiles of AP3 and AP2 fit a non-compartment model. The pharmacokinetic parameters were calculated with PK Solver. The in vivo elimination half-life of AP3 (t 1/2 = 6.02 h) was about 2.8-fold longer than that of T20 (t 1/2 = 1.57 h). This result provided the theoretical basis for reducing the injection frequency and dose of the fusion inhibitor, in conjugation with the improved antiviral potency of AP3. Therefore, replacement of T20 with AP3 may significantly reduce injection-site reactions and the drug cost, which would promote the clinical applications of the HIV fusion inhibitor in resource-poor regions or countries. AP3 was much more resistant than T20 to proteolytic degradation by proteinase K and rat liver homogenate. We compared the stability of T20 and AP3 in the presence of proteinase K (a broad-spectrum serine proteinase) and rat liver homogenate. After treatment with 20 ng/mL of proteinase K for 2 h at 37 °C, only 29% of the parental T20 peptide remained, as detected by LC-MS analysis. Under the same condition, AP3 retained 100% of its prototype (Fig. 3a ). In addition, AP3 showed a significantly enhanced in vitro metabolic stability over T20 in the presence of liver homogenate (Fig. 3b) . These results indicate that the artificial peptide AP3 is much more resistant to proteolytic degradation than the natural peptide T20, which may contribute to its significant longer in vivo half-life than T20 as described above. AP3 formed stable α-helical complex and block gp41 6-HB formation. To investigate the antiviral mechanism of AP3, the thermal stability of AP3/N36 complex was compared with that of AP1/N36, AP2/N36, T20/N36, and C34/N36 complexes by circular-dichroism (CD) spectroscopy 27 . Because T20 lacks the pocket-binding domain (PBD), the T20/N36 complex did not show a typical α -helical conformation, in consistence with our previous studies 8, 9 . Similar to the α -helicity of C34/N36 complex 3 , the AP1/N36, AP2/N36 and AP3/N36 complexes all formed a saddle-shaped negative peak at 208 nm and 222 nm, indicating their α -helical structures (Fig. 4a) Fig. 4b) , indicating that the α -helical complex formed by AP3 and N36 is the most stable among the four complexes. Then we compared the inhibitory activity of AP3 with that of AP1 and AP2 on 6-HB formation between C34 and N36. Since T20 cannot block 6-HB formation 8, 9 , we used a small-molecule HIV-1 fusion inhibitor, ADS-J1 28, 29 , to replace T20 as a control of 6-HB inhibition. As expected, ADS-J1 could effectively inhibit 6-HB formation with IC 50 of 2.75 μ M 8, 9, [27] [28] [29] . AP3 was highly effective against 6-HB formation in a dose-dependent manner with an IC 50 value of 0.24 μ M, about 30-and 15-fold more potent than AP1 and AP2, respectively (Fig. 4c) , confirming that AP3 can potently block gp41 6-HB fusion core formation, thus inhibiting HIV-1 fusion with the target cell membrane. Structural basis for the potent fusion inhibitory activity of the artificial peptide AP3. To elucidate the molecular determinants of these artificial peptides, we successfully solved all three complex structures of AP1/AP2/AP3 peptides binding with gp41 NHR. For AP1 and AP2, an optimized linker Each sample was tested in triplicate and the experiment was repeated twice. The data are presented as means ± SD. *P < 0.05, **P < 0.01, ***P < 0.001. "SGGRGG" was used to assemble the NHR and the artificial peptide into a single recombinant protein (N36-L6-AP1 or N36-L6-AP2). However, a similar strategy failed on the crystallization of AP3; therefore, we decided to cocrystallize the synthetic peptide N45 and AP3 peptide, and eventually the complex crystals were obtained. Interestingly, the crystals of three different inhibitors belong to three distinctive space groups: P2 1 for N36-L6-AP1, R32 for N36-L6-AP2, and P6 3 for N45/AP3. As expected, the NHR portions in three structures all form a trimeric core, while the AP1, AP2 or AP3 portion folds into a single-helix conformation and binds to NHR-trimer to form a typical 6-HB, similar to that of the HIV-1 gp41 core structure formed by the native CHR peptide C34 and N36 (Fig. 5a) . Also, the conserved hydrophobic residues, such as W43, W46 and I50, in the artificial peptides were deeply buried into the hydrophobic Table 2 . Pharmacokinetic parameters of AP2, AP3 and T20 following intravenous administration at 1 mg/kg in male SD rats (n = 2). Figure 3 . Sensitivity of AP3 and T20 to proteolytic degradation by proteinase K and rat liver homogenate. (a) After digestion by proteinase K at pH 7.2 and (b) rat liver homogenate, the residual amount of AP3 and T20 was detected by LC-MS analysis. The experiment was performed in triplicate and the data are presented as means ± SD. The inhibition of AP1, AP2, AP3, T20 and ADS-J1 against 6-HB formation between N36 and C34 was detected by ELISA using the 6-HB-specific mAb NC-1. Each sample was tested in triplicate, and the data are presented as means ± SD. grooves formed between each pair of NHR helices, similar to the corresponding residues of W628, W631 and I635 in the native gp41 CHR (Fig. 5b) . AP peptides exhibited better affinity against gp41 natural CHR. In C34, which contains the natural CHR sequence from W628 to L661, no strong interaction between I642 and Q565 in the viral gp41 NHR-CHR complex was found (Fig. 5c) . However, in the corresponding sequence (from W43 to K76) of AP1 and AP2, a hydrogen bond was established between S57 (corresponding to I642 in CHR) and Q18 (corresponding to Q565 in NHR) in N36-L6-AP1, N36-L6-AP2 and N45/AP3. Thus, S57 in AP1/AP2/AP3 plays a role in stabilizing the interactions between the artificial peptide inhibitor and its NHR target, resulting in their stronger binding affinity. Moreover, in NHR-CHR, L567 and L568 on two adjacent NHRs form a hydrophobic groove, in which T639 is buried (Supplementary Fig. S1a ). However, in N36-L6-AP1, N36-L6-AP2 and N45/AP3, I54 (corresponding to T639 in CHR) can strongly bind to L20 and L21 through fully hydrophobic side chain interactions. Similarly, the interaction of I64 (corresponding to S659 in CHR) with L10 and L11 (corresponding to L567 and L568 in NHR, respectively) in N36-L6-AP1, N36-L6-AP2 and N45/AP3 has been significantly enhanced ( Supplementary Fig. S1b ). Like the gp41 CHR helix, the helices of AP1, AP2 and AP3 also have two different sides, a hydrophobic side facing toward the NHR and a hydrophilic one facing outward. It is expected that the enhancement of the hydrophilicity of the exposed side of the inhibitors can increase their antiviral activity and solubility. To achieve this goal, the amino acid residues with hydrophobicity, or low hydrophilicity, like N637, S640, L641 and S644 in CHR, were changed to the amino acid residues with high hydrophilicity, like E52, K55, K56 and E59 in AP1, AP2 and AP3, respectively. Moreover, the hydrophobic residue M629 in CHR was replaced with a hydrophilic residue E44 in AP2 and AP3 ( Supplementary Fig. S2 ). These hydrophilic residues, such as glutamic acid and lysine, can increase the solubility of whole peptide and, hence, stabilize the complex formed by the inhibitor and its target. It has been proved that the EE-KK double salt bridge can stabilize helix conformation 30 . We have identified this kind of interaction between i and i + 3 or i + 4 positions on the three complex structures. In N36-L6-AP1, R48 interacts with E45 and E52 to form a salt bridge network. In N36-L6-AP2, E45 interacts with K48, and E52 binds to K56, while in N45/AP3, K69 binds to E66 ( Supplementary Fig. S2 ). These strong salt bridges formed by the oppositely charged residues stabilize AP peptide conformation, bringing its inhibitory effect into full play. As previously reported, addition of the "M-T hook" to the CHR peptides C34 and sifuvertide could dramatically improve the anti-HIV-1 activity 17, 18 . As expected, the N-terminal Met and Thr of AP3 forms a hook-like structure (Fig. 5d) . The hydrophobic methionine side chain of M41 accommodates the groove between AP3 and NHR helices, capping the hydrophobic pocket. This interaction leads to a series of conformational changes. The main chain of AP3 at W43 moves 1.91 Å closer to NHR compared to AP2 (Supplementary Fig. S3 ). The side chain of W43 in AP3 flips around 90 degrees and is buried deeper than that of AP2. The side chain of E44 turns back to interact to D47, but the E45 side chain turns back from K48 and interacts with T42. Therefore, this M-T hook structure could further stabilize the binding between AP3 and NHR target. Enfuvirtide, also known as T20, was approved by the U.S. FDA as the first HIV entry inhibitor-based antiviral drug for use with other anti-HIV medicines to treat HIV-1 infected adults and children at ages 6-16 years 23,31,32 (http://www.fuzeon.com). Although T20 is an indispensable anti-HIV drug for HIV/ AIDS patients who have failed to respond to the current antiretroviral therapeutics, its shortcomings have limited its clinical application. T20 has lower anti-HIV activity and shorter half-life than other CHR peptides containing PBD, such as C34 and C38 8, 9, 33 . In addition, T20-resistant HIV-1 variants emerged shortly (e.g., 14 days) after its use in patients 34 . Most of the T20-resistant viruses carried mutations in the GIV motif (residues 36-45: GIVQQQNNLL) in the gp41 NHR domain 10, [34] [35] [36] [37] [38] . The lack of PBD contributes to the major weaknesses of T20 described above. Since the conserved hydrophobic pocket in the gp41 NHR-trimer plays a critical role in stabilizing the interaction between the gp41 NHR and CHR and formation of the fusogenic 6-HB core 1, 39, 40 , the PBD-containing CHR-peptide, like C34, can bind to viral gp41 trimer more strongly and stably, thus possessing more potent anti-HIV activity than T20, a CHR peptide without PBD 8, 9 . In the absence of PBD, T20 mainly interacts with the middle region of the NHR domain containing the GIV motif. Therefore, a virus with mutations in this motif is generally resistant to T20 10, [34] [35] [36] [37] [38] . Compared with other anti-HIV drugs, another weakness of T20 is its cross-reactivity with the preexisting antibodies in HIV-1-infected patients. Besides gp41, T20 could also bind to some regions in gp120. The preexisting antibodies specific for the T20's binding sites in gp120 and gp41 may indirectly suppress the anti-HIV activity of T20 14, 15 . Addition of PBD to the N-terminus of T20, such as T-1249, could significantly improve the anti-HIV-1 potency, half-life and drug-resistance profile 33, [41] [42] [43] . Addition of M-T hook structure to the N-terminus of a PBD-containing CHR-peptides, such as MT-C34 or MT-SFT, could further increase the anti-HIV-1 activity of the corresponding CHR-peptides 17, 18 . Deletion of the GIV-motif-binding domain from a CHR-peptide, such as CP621-652 and CP32M, is another effective approach to increase the genetic barrier to drug resistance 44, 45 . However, none of the above approaches is effective in preventing the cross-reaction of T20 with the preexisting anti-gp41 antibodies in HIV/AIDS patients, since the above-modified peptides mainly contain the native sequences of the HIV-1 gp41 CHR domain. Our previous studies have shown that AP1 and AP2, artificial peptides with non-native protein sequences, could form coiled-coil structure to interact with gp41 NHR and inhibit HIV-1 Env-mediated cell-cell fusion 16 . In the present study, we designed a new artificial peptide, AP3, by adding M-T hook structure to the N-terminus of AP2 (Fig. 1a) , followed by investigating the influence of preexisting anti-gp41 antibodies in HIV-infected patients on AP3, using AP1, AP2 and T20 as controls. We demonstrated that sera of HIV-infected patients could bind to T20 and significantly reduce its potency against HIV-1. However, these same serum samples did not interact with the three artificial peptides and hardly impaired their antiviral activity. Surprisingly, the antibodies in the sera could even enhance AP3's anti-HIV-1 activity (Fig. 2a,b and Supplementary Table S1 ). These results confirmed, for the first time, that replacement of the native viral sequence in T20 with an artificial sequence is an effective approach to overcome a key shortcoming of T20 whereby its anti-HIV activity could be attenuated by preexisting anti-gp41 antibodies in HIV/AIDS patients. It is worthwhile to explore why the antibodies in the sera is able to enhance the anti-HIV-1 activity of AP3. Our recent study has demonstrated that T20's anti-HIV-1 activity is enhanced by a non-neutralizing antibody directed against the NHR domain of the HIV-1 gp41 46 . We thus hypothesize that some of the anti-gp41 antibodies in HIV/AIDS patients may bind to a site in NHR domain adjacent to the AP3's binding region, resulting in increased interaction between AP3 and NHR-trimer and enhanced antiviral activity of AP3. We then compared the inhibitory activity of AP3 with M-T hook and T20/AP2 without M-T hook on infection by divergent HIV-1 strains. AP3 was more effective than either AP2 or T20 in inhibiting infection by the laboratory-adapted strains and the primary isolates of HIV-1, including those resistant to T20 (Fig. 1b, Table 1 ). One may question whether AP3 can also induce drug-resistant viruses in patients if it is used in clinics to treat HIV-infected patients. We believe that AP3 is expected to have much higher genetic barrier to resistance than T20 because AP3 contains PBD, while T20 lacks PBD. Dwyer et al. 33 used T2544, a PBD-containing CHR-peptide, to carry out a passaging experiment, using T20 as a control. They demonstrated that T20 could induce a mutant virus with high resistance (81-fold) to T20 in about 1 month, while T2544 failed to induce a resistant strain in more than 2 months in culture. After extending the passaging experiment for almost 8 months, they identified one strain with a weak resistance (8.3-fold) to T-2544, and the related mutation sites were not in the gp41 pocket region, suggesting that the PBD-containing CHR-peptides, including AP3, may have difficulty to induce drug-resistance. AP3 also had longer half-life than T20 (Table 2) , possibly because the artificial peptide AP3 is less sensitive to the proteolytic enzymes than T20 with native viral protein sequence. Removal of the proteolytic enzymes' cleavage sites in AP3 peptide is expected to further extend its half-life. These results confirmed that replacement of native protein sequence with artificial sequence and addition of the M-T hook to the PBD-containing peptide is a sound strategy for designing HIV fusion inhibitory peptides with improved antiviral activity and pharmacological properties when compared to T20. Since the three-dimensional structures of AP peptides had not been investigated before the present study, the optimization of these artificial peptide inhibitors could not be performed rationally. Our structural studies of the artificial peptides AP1/AP2/AP3 in complex with NHR showed that AP peptides, just like the CHR peptide C34, could bind to gp41 NHR to form a canonical 6-HB structure (Fig. 5a) . It is well known that a deep hydrophobic pocket exists in each groove on the surface of the viral gp41 NHR trimer. The hydrophobic residues I635, W631 and W628 in the gp41 CHR bind with the hydrophobic residues in the wall of this pocket, resulting in the formation of stable 6-HB by the strong interaction between CHR and NHR. This important feature has been well preserved in the AP1/AP2/AP3 6-HB structures (Fig. 5b) , which may account for the potent HIV-1 fusion inhibitory activities of these artificial peptides. A new hydrogen bond, which was established between S57 and Q18 in AP1/AP2/AP3 complexes, does not exist in the viral gp41 CHR-NHR complex, suggesting that S57 may play an important role in stabilizing the interactions between the peptide and NHR, resulting in binding affinities of AP1/AP2/AP3 that are stronger than those of HIV-1 gp41 CHR to NHR. Furthermore, the EE-KK double salt bridge formed between the i and i + 4 positions in the AP1/AP2/AP3 structures could stabilize helix conformation and increase the inhibitory effect of these peptides. Compared with AP1, triple-site mutations were introduced in AP2 and AP3, i.e. M44E, R48K and E49K. Those substitutions not only increase solubility of the peptide, but also trigger a series of rearrangements of certain intrahelical salt bridges to improve the stability of CHR helix structure and HIV-1 fusion inhibitory activity. M-T hook was previously demonstrated to be an effective step toward increasing the stable interaction between a CHR-peptide and the HIV-1 gp41 pocket 17, 18 . Therefore, AP2 was further optimized by incorporating Met and Thr at its N-terminus. CD spectroscopy and thermal denaturation results both indicate that the incorporation of M-T hook contribute to the formation of a more stable 6-HB core structure between AP3 (M-T hook-optimized AP2) and N36. In addition, the EE-KK double salt bridge formed between i and i + 4 positions in the N36-L6-AP3 structure contributed to increased CHR helix and 6-HB stability, resulting in improved potency of AP3, as has been noted in studies of CHR-peptides with EE-KK double mutations 30, 33, 47, 48 . Also, the HIV-1 fusion activity and half-life of AP2 may have been strengthened and extended, respectively, by the addition of M-T hook in the design of AP3. In conclusion, AP3, an artificial peptide with both PBD and M-T hook structures, exhibited improved anti-HIV-1 activity and drug-resistance profile, as well as prolonged half-life. Moreover, it did not react with the preexisting antibodies in the sera of HIV/AIDS patients. Consequently, its antiviral activity Scientific RepoRts | 5:13028 | DOi: 10.1038/srep13028 was not significantly affected by these antibodies. Therefore, AP3 shows promise as a candidate for further development as a new HIV fusion inhibitor for clinical use. This study also provides important structure and activity information for the rational design of novel artificially peptide inhibitors. Besides, our results highlighted the advantages of artificially designed peptides and confirmed that this strategy could be widely used in development of artificial peptide-based virus fusion inhibitors against HIV-1 and other enveloped viruses with class I membrane fusion proteins, such as SARS-CoV 19 , MERS-CoV 20 , and paramyxovirus 49 . Ethics statement. This study did not involve human experimentation; the only human materials used were serum samples obtained from HIV-1-infected individuals with the approval by the Ethics Committee of the Shanghai Public Health Clinical Center, Fudan University (Protocol No. SPHCC-125-2). The methods were carried out in accordance with the approved guidelines. All of these sera samples came from adults; no minor was involved in this study. Written informed consent for the use of the clinical specimens was obtained from all patients involved in this study. Peptide synthesis. A panel of peptides (Fig. 1a) , including T20, C34, C46, AP1, AP2, AP3, as well as NHR-derived N-peptides, N36 and N45, were synthesized with a standard solid-phase FMOC method, as described previously 8, 50 . All peptides were acetylated at the N terminus and amidated at the C terminus. The peptides were found to be about 95% pure by HPLC and were identified by mass spectrometry (Perseptive Biosystems, Framingham, MA, USA). Concentrations of the peptides were determined by UV absorbance and a theoretically calculated molar-extinction coefficient based on tryptophan and tyrosine residues. Qualification assay. Chromatographic analyses were performed using an ODS-C8 column (5 μ m, 100 mm × 2.0 mm ID) kept at ambient temperature. The mobile phase was composed of acetonitrile-water-formic acid in the ratio of 50:50:0.1 (v/v/v) at a flow rate of 0.3 mL/min. The sample injection volume was 10 μ L. Acetonitrile was HPLC grade, and other chemical reagents and solvents were analytical grade. A Thermo TSQ Quantum Discovery MAX triple-quadruple tandem mass spectrometer equipped with ESI source (San Jose, CA) and Surveyor LC pump were used for LC-MS analysis. Data acquisition and data processing were performed by using Xcalibur software and LCQuan 2.0 data analysis program (Thermo Finnigan), respectively. Optimized MS parameters were as below: 4800 V spray voltage, 40.0 psi sheath gas pressure, 1.0 psi auxiliary valve flow, and 300 °C of capillary temperature. When running collision-induced dissociation (CID), the pressure was set to 1.5 mTorr. The selected reaction monitoring (SRM) mode was used for AP3 while the selected ion monitoring (SIM) mode was preformed for T20. The following transitions were recorded: m/z 670.5 for AP3, m/z 1498.6 for T20. The masses of synthetic peptides T20, AP1, AP2 and AP3 were determined by MALDI-TOF-MS (Supplementary Fig. S4 and S5 ). Expression and purification of fusion protein N36-L6-AP1 and N36-L6-AP2. Using overlapping PCR, the DNA fragment encoding AP1 or AP2 peptide was attached to the 3′-end of the cDNA of gp41 NHR ("N36", 546-581), with a short linker ("L6", SGGRGG) between them. Then, the whole sequence was subcloned into the pET-28a vector (Novagen, USA) with an artificial SUMO-tag between the N-terminal His-tag and the target protein. The pET-28a-SUMO-N36-L6-AP1-or pET-28a-SUMO-N36-L6-AP2-transformed E. coli cells were induced by adding 1 mM IPTG and incubating overnight at 16 °C. Fusion protein was purified by Ni-NTA affinity resin (Qiagen, Valencia, CA, USA), and the His-SUMO-tag was cleaved off by Ulp1 enzyme treatment at 4 °C for 2 h. The purified N36-L6-AP1 or N36-L6-AP2 was applied onto a Superdex-75 gel filtration column (GE Healthcare, Piscataway, NJ, USA). Fractions containing N36-L6-AP1 or N36-L6-AP2 trimer were collected and concentrated to different concentrations by ultrafiltration. Crystallization, data collection, and structure determination. The fusion protein N36-L6-AP1 was crystallized at 16 °C using the hanging drop, vapor-diffusion method. The drops were set on a siliconized cover clip by equilibrating a mixture containing 1 μ l protein solution (25 mg/ml N36-L6-AP1 trimer in 20 mM Tris-HCl pH 8.0 and 150 mM NaCl) and 1 μ l reservoir solution (0.1 M Tris-HCl pH 8.5, 32% (w/v) PEG3350, and 0.2 M MgCl 2 ) against a 400 μ l reservoir solution. After one week, single crystals formed and were flash frozen by liquid nitrogen for future data collection. Fusion protein N36-L6-AP2 was crystallized in a similar way with a different reservoir solution (0.1 M Tris-HCl pH 8.0, 34% (w/v) PEG3350, and 0.2 M MgCl 2 ). To obtain the complex crystal of AP3 and NHR, synthesized AP3 was first mixed with peptide N45 at 1:1 molar ratio and then applied onto a Superdex-75 gel filtration column (GE Healthcare, Piscataway, NJ, USA) to isolate the formed 6-HB. Fractions containing N45/AP3 trimer were collected and concentrated to 30 mg/ml, then crystallized at 16 °C using the hanging drop, vapor-diffusion method.The drops were set on a siliconized cover clip by equilibrating a mixture containing 1 μ l protein solution (20 mM Tris-HCl pH 8.0 and 150 mM NaCl) and 1 μ l reservoir solution (0.2 M Ammonium Sulfate, 0.1 M Bis-Tris pH 6.5, and 25% w/v PEG 3350) against a 400 μ l reservoir solution. After 3 days, single crystals formed and were flash frozen by liquid nitrogen for future data collection. The datasets of N36-L6-AP1 were collected at 100 K at beamline 19-ID of the Advanced Photon Source (Argonne National Laboratory, USA). The datasets of N36-L6-AP2 were collected on an in-house x-ray source (MicroMax 007 x-ray generator, Rigaku, Japan) at the Institute of Biophysics, ChineseAcademy of Sciences. The datasets of AP3/N45 complex crystals were collected at beamline BL-19U1 of the Shanghai Synchrotron Radiation Facility, China. X-ray diffraction data were integrated and scaled using the HKL2000 program 51 . The phasing problem of all three structures was solved by the molecular replacement method using PHENIX.phaser 52 with a crystal structure of HIV gp41 NHR-CHR (PDB entry: 1SZT) as a search model. The final models were manually adjusted in COOT 53 and refined with PHENIX.refine 54 . All coordinates were deposited in the Protein Data Bank (N36-L6-AP1: 5CMU; N36-L6-AP2: 5CN0; and N45/AP3: 5CMZ). The statistics of data collection and structure refinement are given in Supplementary Table S2 . Determination of the cross-reactivity of the native and artificial peptides with the preexisting antibodies in HIV-1-infected patients by sandwich ELISA. A sandwich ELISA was conducted to determine the cross-reactivity of the peptides with the preexisting antibodies in HIV-1-infected patients. T20, C46, AP1, AP2 and AP3 were coated onto the wells of 96-well polystyrene plates (Costar, Corning Inc., Corning, NY) at 10 μ g/ml. The wells were then blocked with 1% gelatin, followed by addition of 50 μ l of serially diluted sera from HIV-1-infected patients and incubation at 37 °C for 1 h. Then, HRP-labeled goat-anti-human IgG (Abcam, UK) and TMB were added sequentially. A450 was determined with an ELISA reader (Ultra 384, Tecan). patients. Inhibition of peptides on HIV-1 IIIB (subtype B, X4)infection in the presence of HIV-1-infected patients' sera was determined as previously described 55 . Briefly, each peptide was mixed with serially diluted serum from an HIV-1-infected patient at room temperature for 30 min. Next, the mixture of peptide/serum and HIV-1 (100 TCID 50 ) were added to MT-2 cells (1 × 10 5 /ml) in RPMI 1640 medium containing 10% FBS. After incubation at 37 °C overnight, the culture supernatants were replaced with fresh culture medium. On the fourth day post-infection, culture supernatants were collected for detection of p24 antigen by ELISA. CD Spectroscopy and Thermal Midpoint Analysis. The secondary structure of AP1, AP2 or AP3 peptides mixed with N36 was analyzed by CD spectroscopy as previously described 56 . Briefly, each peptide or peptide mixture was dissolved in phosphate-buffered saline (PBS: 50 mM sodium phosphate and 150 mM NaCl, pH 7.2) at the final concentration of 10 μ M and incubated at 37 °C for 30 min before cooling down to 4 °C. The CD spectra of each sample were acquired on a Jasco spectropolarimeter (Model J-815, Jasco Inc., Japan) at 4 °C using a 5 nm bandwidth, 0.1 nm resolution, 0.1 cm path length, and an average time of 5.0 sec. Spectra were corrected by the subtraction of a blank corresponding to the solvent composition of each sample. Thermal midpoint analysis was used to determine the temperature at which 50% of the 6-HB formed by the CHR and NHR would decompose. It was monitored at 222 nm from 4 °C to 98 °C by applying a thermal gradient of 5 °C/min. The melting curve was smoothed, and the midpoint of the thermal unfolding transition (Tm) values was calculated using Jasco software utilities as described above. Inhibition of gp41 six-helix bundle formation by sandwich ELISA. Inhibition of gp41 six-helix bundle formation by a testing peptide was determined with a sandwich ELISA described previously 57 . Briefly, a testing peptide (ADS-J1 as a control) at graded concentrations was preincubated with peptide N36 (1 μ M) at 37 °C for 30 min, followed by the addition of peptide C34 (1 μ M) and incubation at 37 °C for another 30 min. The mixture was added to a 96-well polystyrene plate (Costar, Corning Inc., Corning, NY) precoated with anti-N36/C34 antibodies (2 μ g/ml) purified from mouse antisera specifically against the gp41 six-helix bundle 58 . Then, mAb NC-1, HRP-labeled rabbit-anti-mouse IgG (Sigma), and TMB were added in order. A450 was determined by an ELISA reader (Ultra 384, Tecan). Inhibition activities of AP1, AP2, and AP3 on HIV-1 infection were determined as previously described 57 . For inhibition of HIV-1 IIIB (subtype B, X4) infection,100 TCID 50 of the virus was added to 1 × 10 5 /ml MT-2 cells in RPMI 1640 medium containing 10% FBS in the presence or absence of the test peptide overnight. Then, the culture supernatants were changed to fresh media. On the fourth day post-infection, culture supernatants were collected for detection of p24 antigen by ELISA. For inhibition of infection by the HIV-1 strain Bal (subtype B, R5), M7 cells (1 × 10 5 /ml) were precultured overnight and infected with Bal at 100 TCID 50 in the presence or absence of the test peptide or protein overnight. Then, the culture supernatants were changed to fresh media. On the fourth day post-infection, the culture supernatants were discarded, and fresh media were complemented again. The supernatants were collected on the seventh day post-infection and tested for p24 antigen by ELISA as previously described 55 . The percent inhibition of p24 production was calculated. Analysis of the half-life of peptide inhibitors. Four male SD rats weighing approximately 200 g each were obtained from the Shanghai Medical School Animal Center and were used for the half-life assay. Animals were treated in accordance with the Animal Welfare Act and the "Guide for the Care and Use of Laboratory Animals" (NIH Publication 86-23, revised 1985). Either AP2 or AP3 was intravenously injected at the concentration of 1 mg/ml. After injection, blood samples were acquired from rat orbit at several time points (8 and 30 min and 1.5, 3, 6, 9, 12, and 24 h after peptide injection) and placed in clean tubes. To study the pharmacokinetics of AP2 and AP3 in rats and provide experimental evidence for the possible pharmacokinetics in human, a double-antibody sandwich ELISA method was established for rapid determination of AP2 and AP3 in rat plasma. Briefly, 96-well polystyrene plates (Costar, Corning Inc., Corning, NY) were precoated with antibody against AP2 or AP3 (5 μ g/ml) purified from rabbit anti-sera 59 . They were then preincubated with serum samples diluted 20 times at 37 °C for 1 h, followed by the addition of anti-AP2 or anti-AP3 antibody (1:1000) purified from mouse antisera specifically against AP2 or AP3 59 at 37 °C for another 1 h. Then, HRP-labeled rabbit-anti-mouse IgG (Sigma, USA) and TMB were added in order. Absorbance at 450 nm was determined by an ELISA reader (Ultra 384, Tecan). The standard peptide parameters were obtained first. Then, the plasma peptide concentrations were determined as a function of time, and the half-life was calculated by using PK Solver for Microsoft Excel to obtain pharmacokinetic parameters. Assessment of sensitivity of peptides to proteolytic digestion by proteinase K and proteolytic enzymes in liver homogenate. The peptides (10 μ g/mL) were prepared in PBS pH 7.2 containing 20 ng/ml proteinase K. The resulting mixture were incubated at 37 °C in a water bath and taken out at different time intervals (0, 5, 15, 30, 60, 120 minutes), followed by quenching the samples with ethyl alcohol and quantitating the peptides by LC-MS analysis as described above. To test the sensitivity of peptides to the proteolytic enzymes in liver homogenate, 3 male SD rats (250 ± 20 g) were sacrificed under anesthesia. The whole liver was quickly removed from each rat, washed in ice-cold PBS (50 mM, pH 7.2), weighed and cut into small pieces, which were resuspended in PBS to 100 mg wet liver tissue/2.5 ml PBS. The samples were pooled and homogenized, followed by centrifugation at 9,000 g for 20 min at 4 °C. The supernatants were collected. The test peptides were added to the liver homogenate at a final concentration of 10 μ g/ml. The resulting mixture was incubated 37 °C in a water bath, and the residue peptides in the mixture were quantitated as described above.

What is the serum half-life of T20?

SARS to novel coronavirus – old lessons and new lessons https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7026896/ SHA: 5d254ed178c092d3639ce70ae9653593acc471f9 Authors: McCloskey, Brian; Heymann, David L. Date: 2020-02-05 DOI: 10.1017/s0950268820000254 License: cc-by Abstract: The response to the novel coronavirus outbreak in China suggests that many of the lessons from the 2003 SARS epidemic have been implemented and the response improved as a consequence. Nevertheless some questions remain and not all lessons have been successful. The national and international response demonstrates the complex link between public health, science and politics when an outbreak threatens to impact on global economies and reputations. The unprecedented measures implemented in China are a bold attempt to control the outbreak – we need to understand their effectiveness to balance costs and benefits for similar events in the future. Text: On 29 December 2019 clinicians in a hospital in Wuhan City, China noticed a clustering of cases of unusual pneumonia (with the first case identified at that time on 12 December) with an apparent link to a market that sells live fish, poultry and animals to the public. This event was reported to the World Health Organisation (WHO) on 31 December [1]. Within 4 weeks, by 26 January 2020, the causative organism had been identified as a novel coronavirus, the genome of the virus had been sequenced and published, reverse transcription polymerase chain reaction tests had been developed, the WHO R&D Blueprint had been activated to accelerate diagnostics, therapeutics and vaccine development and a candidate vaccine was ready for initial laboratory testing. Currently Chinese health authorities are building a 1000 bed hospital in Wuhan in 10 days. By 26 January also, almost 50 million people in Wuhan and neighbouring cities had effectively been placed in quarantine while the WHO had determined that the event should not yet be declared as a Public Health Emergency of International Concern (PHEIC) [2] and had recommended no specific travel restrictions. The WHO have emphasised the importance of exit screening at ports in countries showing transmission of the novel coronavirus and have provided guidance for countries implementing entry screening at airports while acknowledging that evidence for the effectiveness of entry screening is equivocal. This response is one of the swiftest, coordinated global responses to an emerging infectious disease the world has seen in modern times, but is it the appropriate response, will it be effective and is it sustainable? According to the situation report published by the WHO on 28 January 2020 [3], a total of 2798 confirmed 2019-nCoV cases have been reported globally; of these, 2761 cases were from China, including Hong Kong (8 cases), Macau (5) and Taipei (4). Thirty-seven confirmed cases have been reported outside of China in eleven countries in Europe, North America, Australia and Asia; of these 37 exported cases, 36 had a travel history from China or an epidemiological link to a case from China. Of the confirmed cases in China, 461 have been reported as severely ill, with 80 deaths to date. This outbreak and the response to it illustrate some key issues about how global preparedness and response capacity for outbreaks have evolved over almost two decades since the severe acute respiratory syndrome (SARS) epidemic of 2002/3 and what lessons have, or have not, been learned. It also raises questions about the impact these lessons have had on the way agencies and governments respond to these events and about the role of the WHO and the International Health Regulations (IHR). One of the critical lessons from the SARS experience was the absolute necessity to be able to coordinate the international resources that are available in an outbreak and to get them focussed on identifying priorities and solving problems. The WHO established the means to do this for SARS and it has since been further developed and integrated into global preparedness, especially after the West Africa Ebola epidemic. Organisations such as the Global Outbreak Alert and Response Network (GOARN), the Coalition for Epidemic Preparedness Innovations (CEPI), the Global Research Collaboration For Infectious Disease Preparedness (GloPID-R) and the Global Initiative on Sharing All Influenza Data (GISAID) have been supported by the WHO Research Blueprint and its Global Coordinating Mechanism to provide a forum where those with the expertise and capacity to contribute to managing new threats can come together both between and during outbreaks to develop innovative solutions to emerging problems. This global coordination has been active in the novel coronavirus outbreak. WHO's response system includes three virtual groups based on those developed for SARS to collate real time information to inform real time guidelines, and a first candidate vaccine is ready for laboratory testing within 4 weeks of the virus being identified. Another key factor in successfully preventing and managing emerging threats is the rapid and transparent sharing of information between countries and agencies. There was extensive criticism of China for its perceived failure to share information about the emerging SARS infection early enough in the outbreak to allow countries to prepare and respond. There were similar concerns about information sharing as Middle East Respiratory Syndrome (MERS) emerged and evolved in the Middle East in 2012, particularly in Saudi Arabia, and about the emergence of Ebola in West Africa in 2014. On this occasion information sharing seems to have been rapid and effective (while recognising that the information available in the early stages of an outbreak is always less than the global community would like). The WHO was notified of the original clustering within days and the full genomic sequence of the new virus was published less than 2 weeks after the cluster was first detected. The WHO has expressed its satisfaction with the actions of the Chinese authorities in sharing information with the WHO. Working with journalists and the media to help them understand the science and epidemiology, particularly in a fast moving event, will improve risk communication to the public and reduce inappropriate concerns and panic. While reporting of this outbreak shows signs of the efforts of epidemiologists, infectious disease experts, national and international public health agencies and others engaging with journalists, there are also signs that this is not yet achieving it's goal. For example, the public perception is that the increase in case numbers reported daily by the Chinese authorities represents a daily escalation in the epidemic while the reality is that these numbers are also the result of active, aggressive, case finding in China and some of these cases are 'old' cases newly recognised as being due to the novel coronavirus. Similarly the virus is usually described by the media as 'deadly' and although this is true in the sense that it has caused deaths, the nuances of uncertain case fatality rates in the early stages of an outbreak are not being communicated. The current estimated case fatality rate seems to be around 3% which is significant but not comparable to the 10% rate for SARS or 34% reported for MERS. These misperceptions are still driving public anxiety. To supplement formal reporting mechanisms between countries and with WHO (including the IHR), the use of informal mechanisms such as media and social media reports was advocated in the light of the SARS experience. There are now globally several systems that provide collated information from informal reporting including networks of experts and scanning of media and social media. These contribute to, and amplify, epidemic intelligence and are being integrated with national and international surveillance systems. The value, and the challenges, of this additional source of information has been evident in the current outbreak. The value comes from ensuring that early indications of cases beyond the initial outbreak city have been detected and can supplement the global risk assessment and monitoring of the evolution of the outbreak. The challenges lie in the volume and diversity of the information available and the relative lack of verification mechanisms, such that one of these systems (ProMed) has commented that it was becoming increasingly difficult to assimilate the information being supplied [4] and to make meaningful interpretations. Early in the outbreak it was reported that health workers had not been infected. This was reassuring because it is health workers who many times, and inadvertently, amplify transmission. Failure to wash hands between patients, for example, can result not only in autoinfection, but also in infection of patients hospitalised for other causes when they provide care. Autoinfection is not only a risk for the health worker, but also for their families and the communities in which they live, depending on the transmissibility and means of transmission. More recently infection, and at least one death, in health workers has been confirmed. Although not unexpected this does add to the epidemiological risk. A characteristic of the SARS outbreak was the variability of transmissibility between cases and the occurrence of 'superspreading events' where a case infected significantly more contacts than the average. This was also seen with MERS in the outbreak in the Republic of Korea (RoK). In this current novel coronavirus outbreak, such superspreading events have not been documented but the epidemiology is still not clear. Confirming whether or not this is happening must be an urgent task for the Chinese investigation. Modellers have suggested reproductive rates (R 0 ) of 3.8 (95% confidence interval, 3.6-4.0) [5] and 2.6 (1.5-3.5) [6] ; R 0 for SARS was estimated at around 3 in the absence of control measures [7] . The economic impact of major outbreaks can be substantial for the affected country. This was seen clearly in SARS, MERS in RoK and Ebola in West Africa. One analyst estimates that the current coronavirus outbreak's likely impact will range from a 0.8% cut to real GDP if the epidemic is controlled within 3 months, to a 1.9% cost to GDP if the epidemic lasts 9 months [8] . This may increase substantially in the light of the extended restrictions on movement, and therefore trade and commerce, within China. The emergence of a significant respiratory illness linked to a novel coronavirus represents a test of the global capacity to detect and mange emerging disease threats. Its emergence in China adds an additional dimension in the light of previous experience with SARS. The timing of the outbreak immediately before the Chinese Lunar New Year with its attendant population movements adds extra risk and urgency to the response. The rapid sharing of information in this outbreak and the speed of the coordinated response both in the country and internationally suggest that lessons have been learned from SARS that improve global capacity. The international networks and forums that now exist have facilitated the bringing together of expertise from around the world to focus research and development efforts and maximise the impact. At this early stage in the outbreak information remains incomplete and key clinical and epidemiological questions have not yet been answered, but the deficit seems to be due more to the constraints of investigating an emerging disease than to any unwillingness to engage and share information with partners. There are some indications of areas where further improvement is necessary. The global media response to the unfolding events has been relatively balanced and informed but the nuances of the evolving situation have not been critically examined in partnership with the media and as a result the public perception of the risk may be exaggeratedalthough it of course remains possible that the outbreak will develop in a way that matches up to the perceived risk. The lack of appreciation of the uncertainties in determining a meaningful case fatality rate and the significance of ascertainment bias at the beginning of an outbreak, along with the impact of aggressive case finding on case numbers, are examples of where understanding could be improved. This is always a challenging process when balancing the resources focussed on analysing the situation on the ground with resources directed at interpreting the information for journalists but in SARS, the R 0 was seen to decrease in response to information reaching the public and the public then adopting risk reduction actions [6] ; so accurate public risk communication is critical to success. It would be helpful to find a forum where this can be explored with the media community after the event. The increase in access to early information from diverse sources including media and social media adds an important dimension to identifying and tracking new events globally and is a key part of the overall epidemic intelligence system. However, it is also a potential source of disinformation. When, as has been seen in this outbreak, the volume of information coming in exceeds any capacity to collate and analyse it and to attempt to cross-reference and verify separate items, there is a risk that the information fuels speculation and media and public concern. Again there is a fine balance between information that encourages appropriate risk avoidance actions and information that encourages inappropriate actions; however the public health is usually better served by more information rather than less. The role of a declaration of a PHEIC in managing a serious outbreak has been questioned in the light of Ebola in West Africa and in the Democratic Republic of Congo [9] and has been challenged again with this outbreak. The binary nature of a PHEIC declaration (either an event is a PHEIC or it isn'tthere are no intermediate options) and the specificity of the three defined criteria for a PHEIC have caused difficulty for Emergency Committees in considering whether a given event should be a PHEIC. The lack of a clear understanding of what a PHEIC declaration is meant to achieve adds to the Emergency Committee's difficulties, as does the relative paucity of clinical and epidemiological answers at this stage of the investigation. In this instance the Emergency Committee were divided in coming to a conclusion but decided on balance that the current situation, although an emergency, should not as yet be declared a PHEIC [2]. As with Ebola in the DRC, there has been criticism of the WHO for this decision but, as with Ebola, it is not immediately clear what would be different in the response if a PHEIC was declared. The WHO is working on improving the way in which Emergency Committees develop their advice for the Director General but, as recommended by this Emergency Committee and the post-Ebola IHR Review Committee in 2015, the development of an intermediate alert alongside WHO's risk assessment process may be helpful. A key function of a PHEIC declaration is that it is the (only) gateway to the WHO Temporary Recommendations on possible travel and trade restrictions to limit international spread of a disease. In this case several countries globally had already implemented entry screening at airports and China had begun closing down international travel from Wuhan before the Emergency Committee had finished their deliberations. While the WHO would not, and could not, interfere with the sovereign decisions of member states, the lack of influence on travel and trade decisions could prove problematic. Alongside the speed of the response in this outbreak, we have seen dramatic changes in the scale of the response. The imposition of very extensive quarantine measures on millions of people as an attempt to break the transmission of the virus is unprecedented. We do not know whether they will be effective; indeed we do not know how we will determine if they have been effectivewhat end point can we measure that will provide an answer to that question? If recent suggestions that people infected with this coronavirus may be infectious while incubating or asymptomatic, and the reports that up to 5 m people left Wuhan before the travel restrictions were imposed, are confirmed, the efficacy of these control measures will be more challenged. Given the likely impact on at least the Chinese economy and probably the global economy, it will be important to understand the role and the effectiveness of public health measures on this scale for the future. However, the imposition of these dramatic measures does also raise a wider question: if there is an impact from these measures, what other countries would (or could) implement such measures? Would other countries accept the self-imposed economic damage that China has accepted to try and contain this outbreak? Is it reasonable to consider that national governments would close down public transport into and out of London, New York or Paris in the week before Christmas even if it were shown to be an effective control measure? These decisions and questions cross the interface between public health, science and politics. The response to this outbreak in China was inevitably influenced by the historical reaction to the country's response to SARS and the world's suspicion of China's lack of cooperation at that time. The current response is therefore framed within a context of not wanting to be seen to be behaving in the same way with this event. This may indicate another impact of the SARS (and MERS and Ebola) experience on the response to subsequent outbreaksa tendency to look at worst case scenarios and respond accordingly and a fear of 'getting it wrong'. This can deter leaders at all levels, from outbreak teams to national governments, from making judgements when all the information they would like is not available in case those judgments turn out to be wrong when the full information becomes available. In emergency response it is generally better to over-react and then scale back if necessary rather than under-react and then act too late. Response should be on a 'no regrets' basismake the best decisions possible on the basis of the best information and science available at the time but do not judge or criticise if later information suggests a different course of action. The early response must recognise what is known and what is not known and look at what of the unknowns can reasonably be estimated by reference to previous outbreaks, similar pathogens, early reporting and modelling, etc. The risk assessment and response can then be modified and refined as information on the unknowns evolves. Key to that approach, however, is confidence that decisions will not be criticised based on information that was not available at the time. It is also important to be ready to change decisions when the available information changessomething that both scientists and politicians can find difficult. In that context, China should not be judged for implementing what might appear to be extreme measures but China should also be prepared to discontinue the measures quickly if evidence suggests they are not the best way to solve the problem. By closing airports the international spread from Wuhan may be decreased, but success will depend on how effective the measures really are at stopping people moving out of the affected area as well as on the behaviour of the virus. As always, only time will tellbut time is scarce.

Where did SARS-CoV-2 originate?

Frontiers in antiviral therapy and immunotherapy https://doi.org/10.1002/cti2.1115 SHA: facbfdfa7189ca9ff83dc30e5d241ab22e962dbf Authors: Heaton, Steven M Date: 2020 DOI: 10.1002/cti2.1115 License: cc-by Abstract: nan Text: Globally, recent decades have witnessed a growing disjunction, a 'Valley of Death' 1,2 no less, between broadening strides in fundamental biomedical research and their incommensurate reach into the clinic. Plumbing work on research funding and development pipelines through recent changes in the structure of government funding, 2 new public and private joint ventures and specialist undergraduate and postgraduate courses now aim to incorporate pathways to translation at the earliest stages. Reflecting this shift, the number of biomedical research publications targeting 'translational' concepts has increased exponentially, up 1800% between 2003 and 2014 3 and continuing to rise rapidly up to the present day. Fuelled by the availability of new research technologies, as well as changing disease, cost and other pressing issues of our time, further growth in this exciting space will undoubtedly continue. Despite recent advances in the therapeutic control of immune function and viral infection, current therapies are often challenging to develop, expensive to deploy and readily select for resistance-conferring mutants. Shaped by the hostvirus immunological 'arms race' and tempered in the forge of deep time, the biodiversity of our world is increasingly being harnessed for new biotechnologies and therapeutics. Simultaneously, a shift towards host-oriented antiviral therapies is currently underway. In this Clinical & Translational Immunology Special Feature, I illustrate a strategic vision integrating these themes to create new, effective, economical and robust antiviral therapies and immunotherapies, with both the realities and the opportunities afforded to researchers working in our changing world squarely in mind. Opening this CTI Special Feature, I outline ways these issues may be solved by creatively leveraging the so-called 'strengths' of viruses. Viral RNA polymerisation and reverse transcription enable resistance to treatment by conferring extraordinary genetic diversity. However, these exact processes ultimately restrict viral infectivity by strongly limiting virus genome sizes and their incorporation of new information. I coin this evolutionary dilemma the 'information economy paradox'. Many viruses attempt to resolve this by manipulating multifunctional or multitasking host cell proteins (MMHPs), thereby maximising host subversion and viral infectivity at minimal informational cost. 4 I argue this exposes an 'Achilles Heel' that may be safely targeted via host-oriented therapies to impose devastating informational and fitness barriers on escape mutant selection. Furthermore, since MMHPs are often conserved targets within and between virus families, MMHP-targeting therapies may exhibit both robust and broadspectrum antiviral efficacy. Achieving this through drug repurposing will break the vicious cycle of escalating therapeutic development costs and trivial escape mutant selection, both quickly and in multiple places. I also discuss alternative posttranslational and RNA-based antiviral approaches, designer vaccines, immunotherapy and the emerging field of neo-virology. 4 I anticipate international efforts in these areas over the coming decade will enable the tapping of useful new biological functions and processes, methods for controlling infection, and the deployment of symbiotic or subclinical viruses in new therapies and biotechnologies that are so crucially needed. Upon infection, pathogens stimulate expression of numerous host inflammatory factors that support recruitment and activation of immune cells. On the flip side, this same process also causes immunopathology when prolonged or deregulated. 5 In their contribution to this Special Feature, Yoshinaga and Takeuchi review endogenous RNA-binding proteins (RBPs) that post-transcriptionally control expression of crucial inflammatory factors in various tissues and their potential therapeutic applications. 6 These RBPs include tristetraprolin and AUF1, which promote degradation of AU-rich element (ARE)-containing mRNA; members of the Roquin and Regnase families, which respectively promote or effect degradation of mRNAs harbouring stem-loop structures; and the increasingly apparent role of the RNA methylation machinery in controlling inflammatory mRNA stability. These activities take place in various subcellular compartments and are differentially regulated during infection. In this way, mRNA-destabilising RBPs constitute a 'brake' on the immune system, which may ultimately be toggled therapeutically. I anticipate continued efforts in this area will lead to new methods of regaining control over inflammation in autoimmunity, selectively enhancing immunity in immunotherapy, and modulating RNA synthesis and virus replication during infection. Another mRNA under post-transcriptional regulation by Regnase-1 and Roquin is Furin, which encodes a conserved proprotein convertase crucial in human health and disease. Furin, along with other PCSK family members, is widely implicated in immune regulation, cancer and the entry, maturation or release of a broad array of evolutionarily diverse viruses including human papillomavirus (HPV), influenza (IAV), Ebola (EboV), dengue (DenV) and human immunodeficiency virus (HIV). Here, Braun and Sauter review the roles of furin in these processes, as well as the history and future of furin-targeting therapeutics. 7 They also discuss their recent work revealing how two IFN-cinducible factors exhibit broad-spectrum inhibition of IAV, measles (MV), zika (ZikV) and HIV by suppressing furin activity. 8 Over the coming decade, I expect to see an ever-finer spatiotemporal resolution of host-oriented therapies to achieve safe, effective and broad-spectrum yet costeffective therapies for clinical use. The increasing abundance of affordable, sensitive, high-throughput genome sequencing technologies has led to a recent boom in metagenomics and the cataloguing of the microbiome of our world. The MinION nanopore sequencer is one of the latest innovations in this space, enabling direct sequencing in a miniature form factor with only minimal sample preparation and a consumer-grade laptop computer. Nakagawa and colleagues here report on their latest experiments using this system, further improving its performance for use in resource-poor contexts for meningitis diagnoses. 9 While direct sequencing of viral genomic RNA is challenging, this system was recently used to directly sequence an RNA virus genome (IAV) for the first time. 10 I anticipate further improvements in the performance of such devices over the coming decade will transform virus surveillance efforts, the importance of which was underscored by the recent EboV and novel coronavirus (nCoV / COVID-19) outbreaks, enabling rapid deployment of antiviral treatments that take resistance-conferring mutations into account. Decades of basic immunology research have provided a near-complete picture of the main armaments in the human antiviral arsenal. Nevertheless, this focus on mammalian defences and pathologies has sidelined examination of the types and roles of viruses and antiviral defences that exist throughout our biosphere. One case in point is the CRISPR/Cas antiviral immune system of prokaryotes, which is now repurposed as a revolutionary gene-editing biotechnology in plants and animals. 11 Another is the ancient lineage of nucleocytosolic large DNA viruses (NCLDVs), which are emerging human pathogens that possess enormous genomes of up to several megabases in size encoding hundreds of proteins with unique and unknown functions. 12 Moreover, hundreds of human-and avian-infective viruses such as IAV strain H5N1 are known, but recent efforts indicate the true number may be in the millions and many harbour zoonotic potential. 13 It is increasingly clear that host-virus interactions have generated truly vast yet poorly understood and untapped biodiversity. Closing this Special Feature, Watanabe and Kawaoka elaborate on neo-virology, an emerging field engaged in cataloguing and characterising this biodiversity through a global consortium. 14 I predict these efforts will unlock a vast wealth of currently unexplored biodiversity, leading to biotechnologies and treatments that leverage the host-virus interactions developed throughout evolution. When biomedical innovations fall into the 'Valley of Death', patients who are therefore not reached all too often fall with them. Being entrusted with the resources and expectation to conceive, deliver and communicate dividends to society is both cherished and eagerly pursued at every stage of our careers. Nevertheless, the road to research translation is winding and is built on a foundation of basic research. Supporting industry-academia collaboration and nurturing talent and skills in the Indo-Pacific region are two of the four pillars of the National Innovation and Science Agenda. 2 These frame Australia's Medical Research and Innovation Priorities, which include antimicrobial resistance, global health and health security, drug repurposing and translational research infrastructure, 15 capturing many of the key elements of this CTI Special Feature. Establishing durable international relationships that integrate diverse expertise is essential to delivering these outcomes. To this end, NHMRC has recently taken steps under the International Engagement Strategy 16 to increase cooperation with its counterparts overseas. These include the Japan Agency for Medical Research and Development (AMED), tasked with translating the biomedical research output of that country. Given the reciprocal efforts at accelerating bilateral engagement currently underway, 17 the prospects for new areas of international cooperation and mobility have never been more exciting nor urgent. With the above in mind, all contributions to this CTI Special Feature I have selected from research presented by fellow invitees to the 2018 Awaji International Forum on Infection and Immunity (AIFII) and 2017 Consortium of Biological Sciences (ConBio) conferences in Japan. Both Australia and Japan have strong traditions in immunology and related disciplines, and I predict that the quantity, quality and importance of our bilateral cooperation will accelerate rapidly over the short to medium term. By expanding and cooperatively leveraging our respective research strengths, our efforts may yet solve the many pressing disease, cost and other sustainability issues of our time.

What is included in RBPs?

Chikungunya: A Potentially Emerging Epidemic? https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2860491/ SHA: f7c3160bef4169d29e2a8bdd79dd6e9056d4774c Authors: Thiboutot, Michelle M.; Kannan, Senthil; Kawalekar, Omkar U.; Shedlock, Devon J.; Khan, Amir S.; Sarangan, Gopalsamy; Srikanth, Padma; Weiner, David B.; Muthumani, Karuppiah Date: 2010-04-27 DOI: 10.1371/journal.pntd.0000623 License: cc-by Abstract: Chikungunya virus is a mosquito-borne emerging pathogen that has a major health impact in humans and causes fever disease, headache, rash, nausea, vomiting, myalgia, and arthralgia. Indigenous to tropical Africa, recent large outbreaks have been reported in parts of South East Asia and several of its neighboring islands in 2005–07 and in Europe in 2007. Furthermore, positive cases have been confirmed in the United States in travelers returning from known outbreak areas. Currently, there is no vaccine or antiviral treatment. With the threat of an emerging global pandemic, the peculiar problems associated with the more immediate and seasonal epidemics warrant the development of an effective vaccine. In this review, we summarize the evidence supporting these concepts. Text: Chikungunya virus (CHIKV), a mosquito-borne pathogen listed by National Institute of Allergy and Infectious Diseases (NIAID) as a Category C Priority Pathogen that causes Chikungunya fever (CHIKF), has been spreading throughout Asia, Africa, and parts of Europe in recent times [1, 2, 3] . CHIKV is an arthropod-borne virus (arbovirus) and is transmitted to humans primarily by Aedes aegypti, the infamous yellow fever propagator [4, 5] . CHIKV infection is marked by severe joint pain, contorting its victims into unusual postures [6] . The disease gets its name from the Kimakonde vernacular language of Tanzania and Mozambique, and the word chikungunya means ''that which contorts or bends up'' and translates in Swahili to ''the illness of the bended walker'' [7, 8, 9] . In Africa, CHIKV is maintained in a sylvatic cycle among forest-dwelling Aedes spp. mosquitoes, wild primates, squirrels, birds, and rodents ( Figure 1 ) [10] . In Asia, the disease is vectored by Ae. aegypti and Ae. albopictus [11] . Transmission in Asia occurs in an urban cycle whereby the mosquito spreads the disease from an infected human to an uninfected human, following an epidemiological pattern similar to dengue fever [12] . The 2005-2006 epidemic of CHIKV in La Reunion islands in the Indian Ocean, spurred the discovery of a new vector species, Ae. albopictus [5] . Wrecking over one-third of the island's population, this epidemic peaked its devastation between January and February 2006, when over 46,000 cases came into light every week, including 284 deaths [5, 13] . Ae. albopictus is common in urban areas of the United States and is already flourishing in 36 states, raising grave concerns to the immunologically naive populace of the United States [14] . Accordingly, this review elaborately details the epidemiology and global expansion of CHIKV, describes its clinical features and pathogenesis and its symptoms and complications, and finally nominates a possible vaccine approach against CHIKV infection. CHIKV has been isolated into three genotypes based on phylogenetic studies. These genotypes, based on the gene sequences of an Envelope protein (E1), are Asian, East/Central/ South African, and West African [4, 11, 15] . Using phylogenetic models, Cherian et al. estimate that the Asian genotype of CHIKV emerged between 50 and 310 y ago, and the West and East African genotypes diverged between 100 and 840 y ago [15] . Since then, CHIKV has come a long way, with several mutations incorporated, and has continued to wreak epidemics in several regions. Recent activities of CHIKV include the Indian epidemic in 2005-2006, which was followed by a sudden explosion of cases in 2007. An estimated 1.3 million people across 13 states were reported to be infected in India [12, 16] , and CHIKV was also widespread in Malaysia, Sri Lanka, and Indonesia [17] . In July-August of 2007, CHIKV was reported in Italy, probably brought in by travelers from CHIKV-prone regions of India, Africa, and Indian Ocean islands such as Mauritius, Madagascar, and Seychelles. Few of the Italian isolates were found to have evolved from the Kerala isolate, which was associated with a A226V shift in E1 gene that represents a successful evolutionary adaptation in the mosquito vector similar to the ones observed in Reunion Island [2, 18, 19] . In recent times, with an increase in global travel, the risk for spreading CHIKV to non-endemic regions has heightened [1] . Several travelers have brought CHIKV home with them after visiting areas with actively infected populations [12, 20] . Such cases have been documented in European countries, Australia, Asia, and the United States [8, 21] . The United States has already reported at least twelve cases of travel-associated CHIKV, while France has reported 850 cases, and the United Kingdom 93 [8, 14] . Beyond this, CHIKV-infected travelers have also been diagnosed in Australia, Belgium, Canada, Czech Republic, French Guiana, Germany, Hong Kong, Italy, Japan, Kenya, Malaysia, Martinique, Norway, Switzerland, and Sri Lanka [21] . Some travelers were viremic, worrying public health officials about the spread of CHIKV to new areas [1, 8] . The incubation time for CHIKV is relatively short, requiring only 2-6 d with symptoms usually appearing 4-7 d post-infection [22] . Vazeille et al. detected CHIKV in the salivary glands of Ae. albopictus only 2 d after infection [5] . Upon infection, CHIKF tends to present itself in two phases. The first stage is acute, while the second stage, experienced by most but not all, is persistent, causing disabling polyarthritis. Characteristics of the acute phase include an abrupt onset of fever, arthralgia, and in some cases, maculopapular rash [6, 23] . The acute phase causes such intense joint and muscular pain that makes movement very difficult and prostrates its victims [6, 20] . Ninety-five percent of infected adults are symptomatic after infection, and of these, most become disabled for weeks to months as a result of decreased dexterity, loss of mobility, and delayed reaction. Eighteen months after disease onset, 40% of patients are found to still have anti-CHIKV IgM [6, 18, 23, 24] . The chronic stage of CHIKF is characterized by polyarthralgia that can last from weeks to years beyond the acute stage [6] . CHIKV has been shown to attack fibroblasts, explaining the involvement of muscles, joints, and skin connective tissues. The high number of nociceptive nerve endings found within the joints and muscle connective tissues can explain pain associated with CHIKF [25, 26] . More than 50% of patients who suffer from severe CHIKF are over 65 y old, and more than 33% of them die. Most adults who suffer from severe CHIKF have underlying medical conditions [6, 24, 27] . The other group that is disproportionately affected by severe CHIKV is children. Other complications associated with CHIKV, from most common to least common, include respiratory failure, cardiovascular decompensation, meningoencephalitis, severe acute hepatitis, severe cutaneous effects, other central nervous system problems, and kidney failure [6, 18, 20, 23, 24, 26, 27] . CHIKV undertakes a complex replication cycle upon host infection (Figure 2 ), which makes its genome susceptible to mutations [28, 29] . For instance, Ae. aegypti, responsible for epidemics in Kenya, Comoros, and Seychelles, carried CHIKV with an alanine in the 226 position of the E1 gene (E1-A226) [4, 18] . However, when the virus struck La Reunion Islands, a decline in population of Ae. aegypti, due to massive dichlorodiphenyltrichloroethane usage and dearth of Ae. albopictus species' www.plosntds.org population, resulted in an ecological pressure, favoring replacement of alanine at position 226 with valine (E1-A226V) [5] . This mutation allowed CHIKV's secondary vector species, Ae. albopictus, to supplement Ae. aegypti as its primary vector [5] . Within a year, the E1-A226V mutation was present in La Reunion Island, and Ae. albopictus apparently vectored the large epidemic infecting 34% of La Reunion Island's population [5] . All of the CHIKV strains isolated from Mayotte carried the E1-A226V mutation, and the mutation was also found in Madagascar in 2007 [5] . The E1-A226V mutation was not present at the beginning of the Indian Ocean Islands outbreak (before September 2005). However, more than 90% of later viral strains found there had incorporated the mutation (December-March 2006), indicating a genotype switch during the winter season [5, 18, 20] . The E1-A226V mutation also enabled an increase in infectivity of Ae. albopictus when compared to its infectivity of Ae. aegypti [4, 11, 18, 30] , and with several factors taken together, Ae. albopictus has become the new preferred and more lethal vector for CHIKV [4, 5, 11] . In fact, Tsetsarkin et al. found that a Green Fluorescent Protein tagged E1-A226V virus was 100 times more infective to Ae. albopictus than it was to Ae. aegypti [4] . In all the Indian Ocean Islands, Ae. albopictus became the main vector for CHIKV within 1-2 y after CHIKV was introduced to the region [31] . Of note is that Ae. aegypti has most likely been established in North America for over 300 y, while Ae. albopictus has been in many areas of the US, since 1985, primarily in Florida [32] and since then has expanded its range in the country. Reiskind et al. set out to determine if Ae. aegypti and Ae. albopictus mosquitoes captured in Florida were susceptible to CHIKV infection by a La Reunion isolate [32] . Each mosquito tested was highly susceptible to infection by a full-length infectious clone of the La Réunion Island isolate, CHIKV LR2006 OPY1 strain. Even though the Ae. albopictus strains were more susceptible to infection, overall ecology and differences in human biting patterns need to be studied further Characteristically, there are two rounds of translation: (+) sense genomic RNA (49S9 = 11.7 kb) acts directly as mRNA and is partially translated (59 end) to produce non-structural proteins (nsp's). These proteins are responsible for replication and formation of a complementary (2) strand, the template for further (+) strand synthesis. Subgenomic mRNA (26 S = 4.1 kb) replication occurs through the synthesis of full-length (2) intermediate RNA, which is regulated by nsp4 and p123 precursor in early infection and later by mature nsp's. Translation of the newly synthesized sub-genomic RNA results in production of structural proteins such as Capsid and protein E2-6k-E1 (from 39 end of genome). Assembly occurs at the cell surface, and the envelope is acquired as the virus buds from the cell and release and maturation almost simultaneous occurred. Replication occurs in the cytoplasm and is very rapid (,4 h) [28, 29] . doi:10.1371/journal.pntd.0000623.g002 www.plosntds.org to gain a more accurate understanding of a potential CHIKV epidemic in the US [32] . During the 7 d preceding birth, no human mother has been reported to transmit the disease vertically. However, about 50% of newborns delivered while the mother was infected with CHIKV contracted the disease from their mother, despite the method of delivery. Furthermore, there have been instances of CHIKV transmission from mother to fetus causing congenital illness and fetal death [33] . During the 2005-2006 La Reunion Island outbreaks, Ramful et al. discovered that mothers could transmit CHIKV to their progeny during the perinatal period (Day 24 to Day +1) [33, 34] , and it is associated with a high degree of morbidity. By mean Day 4 of life, all of the neonates were symptomatic for CHIKV, exhibiting common CHIKF symptoms. Six neonates were confirmed to have contracted CHIKV and developed mengoencephalitis. Of those mothers who, during the La Reunion Island epidemic, were infected long before delivery, only three fetal deaths were reported [12, 33] . Ramful et al. theorized that motherto-child transmission most likely happens transplacentally shortly before delivery [33] . A similar study by Gerardin et al. reported nineteen cases of neonatal infection associated with intrapartum maternal viremia that progressed to develop encephalitis owing to vertical transmission from infected mothers [34] . Clinical and epidemiological similarities with dengue fever make CHIKV diagnosis difficult, which may lead physicians to misdiagnose CHIKV as dengue fever; therefore, the incidence of CHIKV may actually be higher than currently believed (Table 1 ) [6, 12, 35] . The amount of time elapsed since disease onset is the most critical parameter when choosing a diagnostic test. CHIKV can be detected and isolated by culturing with mosquito cells (C6/36), Vero cells (mammalian), or in mice [26] . However, this method can take at least a week and only achieves a high sensitivity during the viremic phase, which usually only lasts up to 48 h after the bite. Five days post-infection, the viral isolation approach has a low sensitivity but is still the preferred method for detecting the CHIKV strain [12, 26, 31, 35] . RT-PCR on the other hand is a faster and more sensitive method that can be used within the first week of disease onset [26] , and it is currently the most sensitive method for detecting and quantifying viral mRNA [4, 36] . Classic serological detection, by assays such as ELISA [37] , immunofluorescence [5, 38] , complement binding, and haemagglutination inhibition [39] , constitutes the second diagnostic tool used for biological diagnosis of CHIKV infection. These proven techniques are useful for detection of Antigen in mosquitoes during epidemiological studies. These assays detect virus-specific IgM and IgG, however the sensitivity and specificity of these assays has been poorly characterized. Viral competence, or the potential of viral infection and transmission, is an important parameter that can be quantified by ELISA, viral culture, and PCR. A study by Ng et al. showed biomarkers indicative of severe CHIKV infection [40] . They found decreased levels of RANTES and increased levels of Interleukin-6 (IL-6) and Interleukin-1b (IL-1b) that could be sued for CHIKV detection in patients as indicators of CHIKV-driven cytokine storm. Couderc et al. demonstrate another cytokine, type-I IFN, as a key player in the progression to CHIKV infection [26] . Using an IFN-a/b null mouse model, they demonstrated evidence of muscles, joints, and skin as privileged CHIKV targets, which is consistent with human pathology. Although Ng et al. concluded that RANTES levels were significantly suppressed in severe CHIKF patients [40] , interestingly, an increase in levels of RANTES has been observed in dengue infection [41] . Since the symptoms of CHIKF mimic those of dengue fever, results obtained from this study strongly suggest that RANTES could be a potential distinctive biomarker that differentiates between these two clinically similar diseases. There are no approved antiviral treatments currently available for CHIKV [1, 3, 12, 42] . Currently, CHIKF is treated symptomatically, usually with non-steroidal anti-inflammatory drugs or steroids, bed rest, and fluids. Movement and mild exercise are thought to decrease stiffness and morning arthralgia, but heavy exercise may exacerbate rheumatic symptoms. Corticosteroids may be used in cases of debilitating chronic CHIKV infection. There is a debate about the appropriateness of chloroquine as treatment for unresolved, non-steroidal anti-inflammatory drugresistant arthritis [43] . A study showed that viral production was www.plosntds.org drastically reduced at 16 h post-infection after treatment with 100 mM dec-RVKR-cmk (Decanoyl-Arg-Val-Lys-Arg-chloromethylketone), a furine inhibitor [42, 44] . Chloroquine acted by raising the pH, blocking low pH-dependent entry of virus into the cell. It is important to note that dec-RVKR-cmk or chloroquine only inhibited viral spreading from cell to cell, not CHIKV replication once it had entered the cell [43] . However, most would agree that the best weapon against CHIKV is prevention. A live CHIKV vaccine developed by the United States reached phase II clinical trial encompassing 59 healthy volunteers [45] . Eight percent of the volunteers experienced transient arthralgia, while 98% of the volunteers had seroconversion [45] . However, live CHIKV vaccines are still questionable. One cannot discount the risk of a live vaccine possibly inducing chronic rheumatism. Also, there is the question as to whether widespread use among the public could trigger mosquito transmission or lead to chronic infection or viral reversion [1] . An alternative approach would be to produce a chimeric vaccine against CHIKV. Wang et al. developed a chimeric alphavirus vaccine that is uniformly attenuated and does not cause reactogenicity in mice [3] . Three different versions of this vaccine were made using three different backbone vectors: Venezuelan equine encephalitis virus (VEEV) attenuated vaccine strain T-83, naturally attenuated eastern equine encephalitis virus (EEEV), and attenuated Sindbis virus (SINV). In short, CHIKV structural proteins were engineered into the backbones of the aforementioned vaccines to produce the chimeras [3] . These chimeras were found to stimulate a strong humoral immunity, and even at doses of 5.3-5.8 log 10 PFU, they did not trigger reactogenicity. When vaccinated mice were challenged with CHIKV, neither adult nor neonatal mice gained weight, had fever, or displayed signs of neurological illness. Upon comparison of the chimeras with the Army181/25 vaccine, the Army vaccine resulted in higher levels of viremia and replication in the joints of neonatal mice. Because the joints are known targets of CHIKV, Wang et al. noted their vaccine might avoid the negative reactogenic side effects of the Army vaccine. After being subcutaneously vaccinated with 5.3-5.8 log 10 PFU of the chimeric vaccines, mice produced strong neutralizing antibody titers. The VEEV and EEEV chimeras yielded higher neutralizing antibody titers than the SINV chimera without being more virulent. On top of this, the VEEV and EEEV CHIKV chimeras seemed to be more immunogenic than the Army vaccine despite the chimeras' lower viremia and replication in the joints of neonatal mice [3] . Tiwari et al. [46] adopted a different strategy using formalin inactivated CHIKV in combination with alhydrogel (Aluminum Hydroxide) as an adjuvant. This study clearly suggests that this vaccine elicits both humoral and cell-mediated immune responses in mice, providing its immunogenic potential. A recent study by Couderc et al. [47] showed passive immunization as a potential treatment for CHIKV infection. Using purified immunoglobulin extracted from convalescent CHIKV patients, they demonstrated effective neutralizing activity against CHIKV infection both in vitro and in vivo. This thereby establishes a potential preventive and therapeutic approach to combat CHIKV infection. Pathogenesis studies conducted with related alpha virus, like RRV, have shown the role of macrophages in persistence on infection [48] . They also demonstrated the role of RRV-specific CD8 T cells in clearing viral load in infected patients, thereby warranting similar investigations with CHIKV and the importance of investigating a cell-mediated immune response-based vaccine against CHIKV [49] . There are always certain risks associated with live attenuated or inactivated viral vaccines [50] . One way to avoid these potential problems is to construct a consensus-based DNA vaccine. DNA based vaccines have an improved safety profile as compared to live or attenuated vaccines [51, 52] . A consequence of CHIKV's rapid evolution is difficulty in constructing a vaccine that will be able to Figure 3 . Levels of CHIKV-specific IgG in mice immunized with CHIKV vaccines. Each group of C57BL/6 mice (n = 5) was immunized with 12.5 mg of pVax1 control vector or CHIKV vaccine plasmids as indicated at 0 and 2 wk. Mice were bled 2 wk after each immunization, and each group's serum pool was diluted to 1:100 and 1:500 for reaction with specific vaccine constructs. Serum was incubated for 1 h at 37uC on 96-well plates coated with 2 mg/ml of respective CHIKV peptides, and antibody was detected using anti-mouse IgG-HRP and OD was measured at 405 nm. doi:10.1371/journal.pntd.0000623.g003 www.plosntds.org effectively protect large populations from multiple strains of the virus. One of the strengths of DNA consensus vaccines is its ability to induce cross-reactive immune responses against the three distinct phylogenetic groups of CHIKV. Also DNA-based vaccines can be produced more rapidly than protein-based vaccines. Recently, Muthumani et al. constructed a vaccine that was shown to induce both humoral and cellular immunity in vivo in 3-4-wk-old female C57/BL6 mice [49] . These mice were immunized using an in vivo electroporation method to deliver the vaccine into the quadriceps muscle. The consensus construct was designed against E1, E2, and the core protein capsid. To design the construct, they aligned 21 sequences of CHIKV isolated between 1952 and 2006, using strains from differing countries, including La Reunion Island. The most common nucleotide among the sequences was chosen at each position to be used in the consensus construct, taking care not to alter the reading frame. They conducted codon and RNA optimization, added a strong Kozak sequence, and substituted signal peptide with an immunoglobulin E leader sequence to improve vaccine efficacy. After immunizing the mice, spleens were harvested along with serum and tested to determine antibody titer. After three immunizations, consensus E1, E2, and C vaccines were shown to induce T-cell immune responses leading to strong IFN-c responses and proliferation in C57/BL6 mice. Furthermore, when compared with control mice, immunized mice had higher total IgG levels as well as higher anti-E1 specific, anti-E2 specific, and anti-C specific IgG antibodies, suggesting a strong humoral immune response ( Figure 3 ) and also specificity for the antigens encoded in the vaccine constructs ( Figure 4 ). Because of its promising results and the need for a safer vaccine, this consensus DNA vaccine deserves further investigation. Determining longevity of protective effects of the vaccine and persistence of antibody and IFN-c responses could be the next step of investigation. Challenged studies of immunized mice must also be carried out. CHIKV mosquito-borne disease has caused massive outbreaks for at least half a century but is no longer confined to the www.plosntds.org developing nations. It began to encroach into the boundaries of the developing world. As a result, the NIAID has designated CHIKV as a Category C pathogen alongside the influenza and SARS-CoV viruses [3] . Realization of the potential severity of this disease is exigent; for instance, if used as a biological weapon, the world economy could be severely crippled; if enough members of the armed forces were to become infected during a military deployment, military operations could be significantly affected. Efforts to monitor the disease will only provide minimal warning in a global society, and steps to prevent the morbidity and mortality associated with pandemic are imperative [21, 31] . Despite the gravity of its infectious potency and the fear of it being a potential biological weapon, there is currently no vaccine for CHIKV infections. Live attenuated vaccine trials were carried out in 2000, but funding for the project was discontinued. Newer approaches such as DNA vaccines appear promising over conventional strategies like live attenuated or inactivated virus and thus call for further investigation. Recent advances such electroporation delivery and incorporation of adjuvants has boosted DNA vaccine efficacy [51, 53] . Despite the low antibody response to DNA vaccines, other numerous advantages have overshadowed these minor drawbacks (Table 2) , the most important one being the ability to induce both humoral and cellular immune responses [51, 54] . Judging by recent success, such as the immunogenic construct developed by Muthumani et al., DNA vaccines could play a major role in combating CHIKV [49] . Vaccines are literally a critical component of CHIKV disease control and therefore research in this area is highly encouraged. The dramatic spread of dengue viruses (DENV) throughout tropical America since 1980 via the same vectors and human hosts underscores the risk to public health in the Americas. The adverse events associated with the current live vaccine are well documented [55] . Realizing these drawbacks, earnest efforts should be taken to develop new strategies to forestall further spread and complications.

What does Chikungunya mean in Swahili?

Community-acquired pneumonia in children — a changing spectrum of disease https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5608782/ SHA: eecb946b106a94f26a79a964f0160e8e16f79f42 Authors: le Roux, David M.; Zar, Heather J. Date: 2017-09-21 DOI: 10.1007/s00247-017-3827-8 License: cc-by Abstract: Pneumonia remains the leading cause of death in children outside the neonatal period, despite advances in prevention and management. Over the last 20 years, there has been a substantial decrease in the incidence of childhood pneumonia and pneumonia-associated mortality. New conjugate vaccines against Haemophilus influenzae type b and Streptococcus pneumoniae have contributed to decreases in radiologic, clinical and complicated pneumonia cases and have reduced hospitalization and mortality. The importance of co-infections with multiple pathogens and the predominance of viral-associated disease are emerging. Better access to effective preventative and management strategies is needed in low- and middle-income countries, while new strategies are needed to address the residual burden of disease once these have been implemented. Text: Pneumonia has been the leading cause of death in children younger than 5 years for decades. Although there have been substantial decreases in overall child mortality and in pneumonia-specific mortality, pneumonia remains the major single cause of death in children outside the neonatal period, causing approximately 900,000 of the estimated 6.3 million child deaths in 2013 [1] . Substantial advances have occurred in the understanding of risk factors and etiology of pneumonia, in development of standardized case definitions, and in prevention with the production of improved vaccines and in treatment. Such advances have led to changes in the epidemiology, etiology and mortality from childhood pneumonia. However in many areas access to these interventions remains sub-optimal, with large inequities between and within countries and regions. In this paper we review the impact of recent preventative and management advances in pneumonia epidemiology, etiology, radiologic presentation and outcome in children. The overall burden of childhood pneumonia has been reduced substantially over the last decade, despite an increase in the global childhood population from 605 million in 2000 to 664 million in 2015 [2] . Recent data suggest that there has been a 25% decrease in the incidence of pneumonia, from 0.29 episodes per child year in low-and middle-income countries in 2000, to 0.22 episodes per child year in 2010 [3] . This is substantiated by a 58% decrease in pneumonia-associated disability-adjusted life years between 1990 and 2013, from 186 million to 78 million as estimated in the Global Burden of Disease study [1] . Pneumonia deaths decreased from 1.8 million in 2000 to 900,000 in 2013 [1] . These data do not reflect the full impact of increasingly widespread use of pneumococcal conjugate vaccine in low-and middle-income countries because the incidence of pneumonia and number of deaths are likely to decrease still further as a result of this widespread intervention [4] . Notwithstanding this progress, there remains a disproportionate burden of disease in low-and middle-income countries, where more than 90% of pneumonia cases and deaths occur. The incidence in high-income countries is estimated at 0.015 episodes per child year, compared to 0.22 episodes per child year in low-and middle-income countries [3] . On average, 1 in 66 children in high-income countries is affected by pneumonia per year, compared to 1 in 5 children in low-and middle-income countries. Even within low-and middleincome countries there are regional inequities and challenges with access to health care services: up to 81% of severe pneumonia deaths occur outside a hospital [5] . In addition to a higher incidence of pneumonia, the case fatality rate is estimated to be almost 10-fold higher in low-and middle-income countries as compared to high-income countries [3, 5] . Childhood pneumonia can also lead to significant morbidity and chronic disease. Early life pneumonia can impair longterm lung health by decreasing lung function [6] . Severe or recurrent pneumonia can have a worse effect on lung function; increasing evidence suggests that chronic obstructive pulmonary disease might be related to early childhood pneumonia [7, 8] . A meta-analysis of the risk of long-term outcomes after childhood pneumonia categorized chronic respiratory sequelae into major (restrictive lung disease, obstructive lung disease, bronchiectasis) and minor (chronic bronchitis, asthma, abnormal pulmonary function) groups [9] . The risk of developing at least one of the major sequelae was estimated as 6% after an ambulatory pneumonia event and 14% after an episode of hospitalized pneumonia. Because respiratory diseases affect almost 1 billion people globally and are a major cause of mortality and morbidity [10] , childhood pneumonia might contribute to substantial morbidity across the life course. Chest radiologic changes have been considered the gold standard for defining a pneumonia event [11] because clinical findings can be subjective and clinical definitions of pneumonia can be nonspecific. In 2005, to aid in defining outcomes of pneumococcal vaccine studies, the World Health Organization's (WHO) standardized chest radiograph description defined a group of children who were considered most likely to have pneumococcal pneumonia [12] . The term "end-point consolidation" was described as a dense or fluffy opacity that occupies a portion or whole of a lobe, or the entire lung. "Other infiltrate" included linear and patchy densities, peribronchial thickening, minor patchy infiltrates that are not of sufficient magnitude to constitute primary end-point consolidation, and small areas of atelectasis that in children can be difficult to distinguish from consolidation. "Primary end-point pneumonia" included either end-point consolidation or a pleural effusion associated with a pulmonary parenchymal infiltrate (including "other" infiltrate). Widespread use of pneumococcal conjugate vaccination and Haemophilus influenzae type B conjugate vaccination has decreased the incidence of radiologic pneumonia. In a review of four randomized controlled trials and two case-control studies of Haemophilus influenzae type B conjugate vaccination in high-burden communities, the vaccination was associated with an 18% decrease in radiologic pneumonia [13] . Introduction of pneumococcal conjugate vaccination was associated with a 26% decrease in radiologic pneumonia in California between 1995 and 1998 [14] . In vaccine efficacy trials in low-and middle-income countries, pneumococcal conjugate vaccination reduced radiologic pneumonia by 37% in the Gambia [15] , 25% in South Africa [16] and 26% in the Philippines [17] . The WHO radiologic case definition was not intended to distinguish bacterial from viral etiology but rather to define a sub-set of pneumonia cases in which pneumococcal infection was considered more likely and to provide a set of standardized definitions through which researchers could achieve broad agreement in reporting chest radiographs. However, despite widespread field utilization, there are concerns regarding inter-observer repeatability. There has been good consensus for the description of lobar consolidation but significant disagreement on the description of patchy and perihilar infiltrates [18, 19] . In addition, many children with clinically severe lung disease do not have primary end-point pneumonia: in one pre-pneumococcal conjugate vaccination study, only 34% of children hospitalized with pneumonia had primary end-point pneumonia [20] . A revised case definition of "presumed bacterial pneumonia" has been introduced, and this definition includes pneumonia cases with WHO-defined alveolar consolidation, as well as those with other abnormal chest radiograph infiltrates and a serum C-reactive protein of at least 40 mg/L [21, 22] . This definition has been shown to have greater sensitivity than the original WHO radiologic definition of primary end-point pneumonia for detecting the burden of pneumonia prevented by pneumococcal conjugate vaccination [23] . Using the revised definition, the 10-valent pneumococcal conjugate vaccine (pneumococcal conjugate vaccination-10), had a vaccine efficacy of 22% in preventing presumed bacterial pneumonia in young children in South America [22] , and pneumococcal conjugate vaccination-13 had a vaccine efficacy of 39% in preventing presumed bacterial pneumonia in children older than 16 weeks who were not infected with human immunodeficiency virus (HIV) in South Africa [21] . Thus there is convincing evidence that pneumococcal conjugate vaccination decreases the incidence of radiologic pneumonia; however there is no evidence to suggest that pneumococcal conjugate vaccination modifies the radiologic appearance of pneumococcal pneumonia. Empyema is a rare complication of pneumonia. An increased incidence of empyema in children was noted in some high-income countries following pneumococcal conjugate vaccination-7 introduction, and this was attributed to pneumococcal serotypes not included in pneumococcal conjugate vaccination-7, especially 3 and 19A [24] . In the United States, evidence from a national hospital database suggests that the incidence of empyema increased 1.9-fold between 1996 and 2008 [25] . In Australia, the incidence rate ratio increased by 1.4 times when comparing the pre-pneumococcal conjugate vaccination-7 period (1998 to 2004) to the post-pneumococcal conjugate vaccination-7 period (2005 to 2010) [26] . In Scotland, incidence of empyema in children rose from 6.5 per million between 1981 and 1998, to 66 per million in 2005 [27] . These trends have been reversed since the introduction of pneumococcal conjugate vaccination-13. Data from the United States suggest that empyema decreased by 50% in children younger than 5 years [28] ; similarly, data from the United Kingdom and Scotland showed substantial reduction in pediatric empyema following pneumococcal conjugate vaccination-13 introduction [29, 30] . Several national guidelines from high-income countries, as well as the WHO recommendations for low-and middleincome countries, recommend that chest radiography should not be routinely performed in children with ambulatory pneumonia [31] [32] [33] . Indications for chest radiography include hospitalization, severe hypoxemia or respiratory distress, failed initial antibiotic therapy, or suspicion for other diseases (tuberculosis, inhaled foreign body) or complications. However, point-of-care lung ultrasound is emerging as a promising modality for diagnosing childhood pneumonia [34] . In addition to the effect on radiologic pneumonia, pneumococcal conjugate vaccination reduces the risk of hospitalization from viral-associated pneumonia, probably by reducing bacterial-viral co-infections resulting in severe disease and hospitalization [35] . An analysis of ecological and observational studies of pneumonia incidence in different age groups soon after introduction of pneumococcal conjugate vaccination-7 in Canada, Italy, Australia, Poland and the United States showed decreases in all-cause pneumonia hospitalizations ranging from 15% to 65% [36] . In the United States after pneumococcal conjugate vaccination-13 replaced pneumococcal conjugate vaccination-7, there was a further 17% decrease in hospitalizations for pneumonia among children eligible for the vaccination, and a further 12% decrease among unvaccinated adults [28] . A systematic review of etiology studies prior to availability of new conjugate vaccines confirmed S. pneumoniae and H. influenzae type B as the most important bacterial causes of pneumonia, with Staphylococcus aureus and Klebsiella pneumoniae associated with some severe cases. Respiratory syncytial virus was the leading viral cause, identified in 15-40% of pneumonia cases, followed by influenza A and B, parainfluenza, human metapneumovirus and adenovirus [37] . More recent meta-analyses of etiology data suggest a changing pathogen profile, with increasing recognition that clinical pneumonia is caused by the sequential or concurrent interaction of more than one organism. Severe disease in particular is often caused by multiple pathogens. With high coverage of pneumococcal conjugate vaccination and Haemophilus influenzae type B conjugate vaccination, viral pathogens increasingly predominate [38] . In recent case-control studies, at least one virus was detected in 87% of clinical pneumonia cases in South Africa [39] , while viruses were detected in 81% of radiologic pneumonia cases in Sweden [40] . In a large multi-center study in the United States, viral pathogens were detected in 73% of children hospitalized with radiologic pneumonia, while bacteria were detected in only 15% of cases [41] . A meta-analysis of 23 case-control studies of viral etiology in radiologically confirmed pneumonia in children, completed up to 2014, reported good evidence of causal attribution for respiratory syncytial virus, influenza, metapneumovirus and parainfluenza virus [42] . However there was no consistent evidence that many other commonly described viruses, including rhinovirus, adenovirus, bocavirus and coronavirus, were more commonly isolated from cases than from controls. Further attribution of bacterial etiology is difficult because it is often not possible to distinguish colonizing from pathogenic bacteria when they are isolated from nasal specimens [43] . Another etiology is pertussis. In the last decade there has also been a resurgence in pertussis cases, especially in highincome countries [44] . Because pertussis immunity after acellular pertussis vaccination is less long-lasting than immunity after wild-type infection or whole-cell vaccination, many women of child-bearing age have waning pertussis antibody levels. Their infants might therefore be born with low transplacental anti-pertussis immunoglobulin G levels, making them susceptible to pertussis infection before completion of the primary vaccination series [45] . In 2014, more than 40,000 pertussis cases were reported to the Centers for Disease Control and Prevention in the United States; in some states, population-based incidence rates are higher than at any time in the last 70 years [44] . In contrast, most low-and middleincome countries use whole-cell pertussis vaccines and the numbers of pertussis cases in those countries were stable or decreasing until 2015 [46] . However recent evidence from South Africa (where the acellular vaccine is used) shows an appreciable incidence of pertussis among infants presenting with acute pneumonia: 2% of clinical pneumonia cases among infants enrolled in a birth cohort were caused by pertussis [39] , and 3.7% of infants and young children presenting to a tertiary academic hospital had evidence of pertussis infection [47] . Similarly, childhood tuberculosis is a major cause of morbidity and mortality in many low-and middle-income countries, and Mycobacterium tuberculosis has increasingly been recognized as a pathogen in acute pneumonia in children living in high tuberculosis-prevalence settings. Postmortem studies of children dying from acute respiratory illness have commonly reported M. tuberculosis [48, 49] . A recent systematic review of tuberculosis as a comorbidity of childhood pneumonia reported culture-confirmed disease in about 8% of cases [50] . Because intrathoracic tuberculosis disease is only culture-confirmed in a minority of cases, the true burden could be even higher; tuberculosis could therefore be an important contributor to childhood pneumonia incidence and mortality in high-prevalence areas. Childhood pneumonia and clinically severe disease result from a complex interaction of host and environmental risk factors [37] . Because of the effectiveness of pneumococcal conjugate vaccination and Haemophilus influenzae type B conjugate vaccination for prevention of radiologic and clinical pneumonia, incomplete or inadequate vaccination must be considered as a major preventable risk factor for childhood pneumonia. Other risk factors include low birth weight, which is associated with 3.2 times increased odds of severe pneumonia in low-and middle-income countries, and 1.8 times increased odds in high-income countries [51] . Similarly, lack of exclusive breastfeeding for the first 4 months of life increases odds of severe pneumonia by 2.7 times in low-and middle-income countries and 1.3 times in highincome countries. Markers of undernutrition are strong risk factors for pneumonia in low-and middle-income countries only, with highly significant odds ratios for underweight for age (4.5), stunting (2.6) and wasting (2.8) . Household crowding has uniform risk, with odds ratios between 1.9 and 2.3 in both low-and middle-income countries and high-income countries. Indoor air pollution from use of solid or biomass fuels increases odds of pneumonia by 1.6 times; lack of measles vaccination by the end of the first year of age increases odds of pneumonia by 1.8 times [51] . It is estimated that the prevalence of these critical risk factors in low-and middle-income countries decreased by 25% between 2000 and 2010, contributing to reductions in pneumonia incidence and mortality in low-and middle-income countries, even in countries where conjugate vaccines have not been available [3] . The single strongest risk factor for pneumonia is HIV infection, which is especially prevalent in children in sub-Saharan Africa. HIV-infected children have 6 times increased odds of developing severe pneumonia or of death compared to HIV-uninfected children [52] . Since the effective prevention of mother-to-child transmission of HIV, there is a growing population of HIV-exposed children who are uninfected; their excess risk of pneumonia, compared to HIV unexposed children, has been described as 1.3-to 3.4-fold higher [53] [54] [55] [56] [57] . The pneumococcal conjugate vaccination and Haemophilus influenzae type B conjugate vaccination have been effective tools to decrease pneumonia incidence, severity and mortality [58, 59] . However, equitable coverage and access to vaccines remains sub-optimal. By the end of 2015, Haemophilus influenzae type B conjugate vaccination had been introduced in 73 countries, with global coverage estimated at 68%. However, inequities are still apparent among regions: in the Americas coverage is estimated at 90%, while in the Western Pacific it is only 25%. By 2015, pneumococcal conjugate vaccination had been introduced into 54 countries, with global coverage of 35% for three doses of pneumococcal conjugate vaccination for infant populations [60] . To address this issue, the WHO's Global Vaccine Access Plan initiative was launched to make life-saving vaccines more equitably available. In addition to securing guarantees for financing of vaccines, the program objectives include building political will in low-and middle-income countries to commit to immunization as a priority, social marketing to individuals and communities, strengthening health systems and promoting relevant local research and development innovations [61] . Maternal vaccination to prevent disease in the youngest infants has been shown to be effective for tetanus, influenza and pertussis [62] . Influenza vaccination during pregnancy is safe, provides reasonable maternal protection against influenza, and also protects infants for a limited period from confirmed influenza infection (vaccine efficacy 63% in Bangladesh [63] and 50.4% in South Africa [64] ). However as antibody levels drop sharply after birth, infant protection does not persist much beyond 8 weeks [65] . Recently respiratory syncytial virus vaccination in pregnancy has been shown to be safe and immunogenic, and a phase-3 clinical trial of efficacy at preventing respiratory syncytial virus disease in infants is under way [66] . Within a decade, respiratory syncytial virus in infancy might be vaccine-preventable, with further decreases in pneumonia incidence, morbidity and mortality [67] . Improved access to health care, better nutrition and improved living conditions might contribute to further decreases in childhood pneumonia burden. The WHO Integrated Global Action Plan for diarrhea and pneumonia highlights many opportunities to protect, prevent and treat children [68] . Breastfeeding rates can be improved by programs that combine education and counseling interventions in homes, communities and health facilities, and by promotion of baby-friendly hospitals [69] . Improved home ventilation, cleaner cooking fuels and reduction in exposure to cigarette smoke are essential interventions to reduce the incidence and severity of pneumonia [70, 71] . Prevention of pediatric HIV is possible by providing interventions to prevent mother-to-child transmission [72] . Early infant HIV testing and early initiation of antiretroviral therapy and cotrimoxazole prophylaxis can substantially reduce the incidence of community-acquired pneumonia among HIV-infected children [73] . Community-based interventions reduce pneumonia mortality and have the indirect effect of improved-careseeking behavior [58] . If these cost-effective interventions were scaled up, it is estimated that 67% of pneumonia deaths in lowand middle-income countries could be prevented by 2025 [58] . Case management of pneumonia is a strategy by which severity of disease is classified as severe or non-severe. All children receive early, appropriate oral antibiotics, and severe cases are referred for parenteral antibiotics. When implemented in highburden areas before the availability of conjugate vaccines, case management as part of Integrated Management of Childhood Illness was associated with a 27% decrease in overall child mortality, and 42% decrease in pneumonia-specific mortality [74] . However the predominance of viral causes of pneumonia and low case fatality have prompted concern about overuse of antibiotics. Several randomized controlled trials comparing oral antibiotics to placebo for non-severe pneumonia have been performed [75] [76] [77] and others are ongoing [78] . In two studies, performed in Denmark and in India, outcomes of antibiotic and placebo treatments were equivalent [76, 77] . In the third study, in Pakistan, there was a non-significant 24% vs. 20% rate of failure in the placebo group, which was deemed to be non-equivalent to the antibiotic group [75] . Furthermore, because WHO-classified non-severe pneumonia and bronchiolitis might be considered within a spectrum of lower respiratory disease, many children with clinical pneumonia could actually have viral bronchiolitis, for which antibiotics are not beneficial [79] . This has been reflected in British [33] and Spanish [31] national pneumonia guidelines, which do not recommend routine antibiotic treatment for children younger than 2 years with evidence of pneumococcal conjugate vaccination who present with non-severe pneumonia. The United States' national guidelines recommend withholding antibiotics in children up to age 5 years presenting with non-severe pneumonia [32] . However, given the high mortality from pneumonia in low-and middle-income countries, the lack of easy access to care, and the high prevalence of risk factors for severe disease, revised World Health Organization pneumonia guidelines still recommend antibiotic treatment for all children who meet the WHO pneumonia case definitions [80] . Use of supplemental oxygen is life-saving, but this is not universally available in low-and middle-income countries; it is estimated that use of supplemental oxygen systems could reduce mortality of children with hypoxic pneumonia by 20% [81] . Identifying systems capacity to increase availability of oxygen in health facilities, and identifying barriers to further implementation are among the top 15 priorities for future childhood pneumonia research [82] . However, up to 81% of pneumonia deaths in 2010 occurred outside health facilities [5] , so there are major challenges with access to health services and health-seeking behavior of vulnerable populations. Identifying and changing the barriers to accessing health care is an important area with the potential to impact the survival and health of the most vulnerable children [82] . Much progress has been made in decreasing deaths caused by childhood pneumonia. Improved socioeconomic status and vaccinations, primarily the conjugate vaccines (against Haemophilus influenzae and pneumococcus), have led to substantial reductions in the incidence and severity of childhood pneumonia. Stronger strategies to prevent and manage HIV have reduced HIV-associated pneumonia deaths. However, despite the substantial changes in incidence, etiology and radiology globally, there remain inequities in access to care and availability of effective interventions, especially in low-and middle-income countries. Effective interventions need to be more widely available and new interventions developed for the residual burden of childhood pneumonia.

Case Fatality Rates for Childhood Pneumonia in high income vs low and middle income countries.

Severe Acute Respiratory Syndrome Coronavirus Viroporin 3a Activates the NLRP3 Inflammasome https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6361828/ SHA: f02d0c1e8b0109648e578662dc250abe349a033c Authors: Chen, I-Yin; Moriyama, Miyu; Chang, Ming-Fu; Ichinohe, Takeshi Date: 2019-01-29 DOI: 10.3389/fmicb.2019.00050 License: cc-by Abstract: Nod-like receptor family, pyrin domain-containing 3 (NLRP3) regulates the secretion of proinflammatory cytokines interleukin 1 beta (IL-1β) and IL-18. We previously showed that influenza virus M2 or encephalomyocarditis virus (EMCV) 2B proteins stimulate IL-1β secretion following activation of the NLRP3 inflammasome. However, the mechanism by which severe acute respiratory syndrome coronavirus (SARS-CoV) activates the NLRP3 inflammasome remains unknown. Here, we provide direct evidence that SARS-CoV 3a protein activates the NLRP3 inflammasome in lipopolysaccharide-primed macrophages. SARS-CoV 3a was sufficient to cause the NLRP3 inflammasome activation. The ion channel activity of the 3a protein was essential for 3a-mediated IL-1β secretion. While cells uninfected or infected with a lentivirus expressing a 3a protein defective in ion channel activity expressed NLRP3 uniformly throughout the cytoplasm, NLRP3 was redistributed to the perinuclear space in cells infected with a lentivirus expressing the 3a protein. K(+) efflux and mitochondrial reactive oxygen species were important for SARS-CoV 3a-induced NLRP3 inflammasome activation. These results highlight the importance of viroporins, transmembrane pore-forming viral proteins, in virus-induced NLRP3 inflammasome activation. Text: Severe acute respiratory syndrome coronavirus (SARS-CoV), a member of the genus Betacoronavirus within the family Coronaviridae, is an enveloped virus with a single-stranded positive-sense RNA genome of approximately 30 kb in length. The 5 two-thirds of the genome encodes large polyprotein precursors, open reading frame (ORF) 1 and ORF1b, which are proteolytically cleaved to generate 16 non-structural proteins (Tan et al., 2005) . The 3 one-third of the genome encodes four structural proteins, spike (S), envelope (E), matrix (M) and nucleocapsid (N), and non-structural proteins, along with a set of accessory proteins (3a, 3b, 6, 7a, 7b, 8a, 8b, and 9b) (Perlman and Dandekar, 2005; Tan et al., 2005) . SARS-CoV is the etiological agent of SARS (Drosten et al., 2003; Fouchier et al., 2003; Ksiazek et al., 2003; Kuiken et al., 2003; Peiris et al., 2003) . At least 8,098 laboratory-confirmed cases of human infection, with a fatality rate of 9.6%, were reported to the World Health Organization from November 2002 to July 2003. High levels of proinflammatory cytokines, including tumor necrosis factor (TNF)-α, interleukin (IL)-1β, and IL-6, were detected in autopsy tissues from SARS patients (He et al., 2006) . Although dysregulation of inflammatory cytokines may be involved in lung injury and the pathogenesis of SARS-CoV, the underlying molecular mechanisms are not fully understood. The innate immune systems utilizes pattern recognition receptors (PRRs) to detect pathogen-associated molecular patterns (Medzhitov, 2001; Kawai and Akira, 2010) . Recognition of virus infection plays an important role in limiting virus replication at the early stages of infection. Nod-like receptor family, pyrin domain-containing 3 (NLRP3) is activated by a wide variety of stimuli, including virus infection (Bauernfeind et al., 2011) . Four models describing activation of the NLRP3 inflammasome have been proposed thus far (Hornung and Latz, 2010; Schroder et al., 2010; Tschopp and Schroder, 2010) . First, the disturbances in intracellular ionic concentrations, including K + efflux and Ca 2+ influx, play an important role (Fernandes-Alnemri et al., 2007; Petrilli et al., 2007; Arlehamn et al., 2010; Ichinohe et al., 2010; Ito et al., 2012; Murakami et al., 2012; Munoz-Planillo et al., 2013) . Second, cathepsin B and L, which are specific lysosomal cysteine proteases, are though to play a role after phagocytosis of cholesterol crystals (Duewell et al., 2010) , fibrillar peptide amyloid-beta , silica crystals, and aluminum salts . Third is the release of reactive oxygen species (ROS) or mitochondrial DNA from damaged mitochondria (Zhou et al., , 2011 Nakahira et al., 2011; Shimada et al., 2012) . Finally, viral RNA or RNA cleavage products generated by RNase L activate the NLRP3 inflammasome via the DExD/H-box helicase, DHX33 (Allen et al., 2009; Mitoma et al., 2013; Chen et al., 2014; Chakrabarti et al., 2015) . Upon activation, the NLRP3 is recruited to the mitochondria via association with mitochondrial antiviral signaling (MAVS) or mitofusin 2 expressed on the outer mitochondrial membrane Subramanian et al., 2013) ; these molecules then recruit the apoptosis-associated speck-like protein containing a caspase recruitment domain (ASC) and pro-caspase-1 to form the NLRP3 inflammasome. This event activates the downstream molecule, caspase-1, which catalyzes the proteolytic processing of pro-IL-1β and pro-IL-18 into their active forms and stimulates their secretion (Kayagaki et al., 2015; Shi et al., 2015) . It is increasingly evident that NLRP3 detects RNA viruses by sensing the cellular damage or distress induced by viroporins (Ichinohe et al., 2010; Ito et al., 2012; Triantafilou et al., 2013; Nieto-Torres et al., 2015) , transmembrane pore-forming proteins, encoded by certain RNA viruses; these proteins alter membrane permeability to ions by forming membrane channels (Tan et al., 2005; Chen and Ichinohe, 2015) . A recent study shows that the SARS-CoV E protein, which comprise only 76 amino acids, forms Ca 2+ -permeable ion channels and activates the NLRP3 inflammasome (Nieto-Torres et al., 2015) . Although the E and 3a proteins of SARS-CoV, which comprise 274 amino acids and contain three transmembrane domains (Zeng et al., 2004; Lu et al., 2006) , are thought to act as Na + /K + and K + channels, respectively (Wilson et al., 2004; Lu et al., 2006; Torres et al., 2007; Parthasarathy et al., 2008; Pervushin et al., 2009; Wang et al., 2011) , the role of the 3a protein in activating the NLRP3 inflammasome remains unknown. Here, we examined the role of the 3a protein in activating the NLRP3 inflammasome. Six-week-old female C57BL/6 mice were purchased from The Jackson Laboratory. All animal experiments were approved by the Animal Committees of the Institute of Medical Science (The University of Tokyo). Bone marrow-derived macrophages (BMMs) were prepared as described previously (Ichinohe et al., 2009) . In brief, bone marrow was obtained from the tibia and femur by flushing with Dulbecco's modified Eagle's medium (DMEM; Nacalai Tesque). Bone marrow cells were cultured for 5 days in DMEM supplemented with 30% L929 cell supernatant containing macrophage colony-stimulating factor, 10% heat-inactivated fetal bovine serum (FBS), and L-glutamine (2 mM) at 37 • C/5% CO 2 . HEK293FT cells (a human embryonic kidney cell line) and HeLa cells (a human epithelial carcinoma cell line) were maintained in DMEM supplemented with 10% FBS, penicillin (100 units/ml), and streptomycin (100 µg/ml) (Nacalai Tesque). MDCK cells (Madin-Darby canine kidney cells) and HT-1080 cells (a human fibrosarcoma cell line) were grown in Eagle's minimal essential medium (E-MEM; Nacalai Tesque) supplemented with 10% FBS, penicillin (100 units/ml), and streptomycin (100 µg/ml) (Nacalai Tesque). Influenza A virus strain A/PR8 (H1N1) was grown at 35 • C for 2 days in the allantoic cavities of 10-day-old fertile chicken eggs (Ichinohe et al., 2009) . The viral titer was quantified in a standard plaque assay using MDCK cells (Pang et al., 2013) . Plasmids cDNAs encoding the E and M proteins of SARS-CoV Frankfurt 1 strain (Matsuyama et al., 2005) were obtained by reverse transcription and PCR of total RNA extracted from SARS-CoVinfected Vero cells, followed by PCR amplification using specific primers. pcDNA3.1D-3a-V5His was provided by Ming-Fu Chang (National Taiwan University College of Medicine, Taipei, Taiwan). To generate the plasmids pLenti6-E-V5His, pLenti6-3a-V5His, and pLenti-M-V5His, cDNA fragments of E, 3a, and M were amplified from pcDNA3.1D-E-V5His, pcDNA3.1D-3a-V5His, and pcDNA3.1D-M-V5His using specific primer sets and then ligated into pLenti6-TOPO vectors (Invitrogen). To generate plasmids pCA7-flag-E, pCA7-flag-3a, and pCA7flag-M, pCA7-HA-E, pCA7-HA-3a, and pCA7-HA-M, cDNA fragments of E, 3a, and M were amplified from pcDNA3.1D-E-V5His, pcDNA3.1D-3a-V5His, and pcDNA3.1D-M-V5His using specific primer sets, digested with EcoR I and Not I, and subcloned into the EcoR I-Not I sites of the pCA7-flag-ASC plasmid or pCA7-HA-M2 plasmid, respectively (Ito et al., 2012) . To construct plasmids expressing the E mutant V25F, the mutated E fragments were amplified by inverse PCR with wildtype E-containing plasmids and specific primer sets. The PCR products were cleaved by Dpn I, ligated in a ligase-and T4 kinase-containing reaction and then transformed into DH5α competent cells (TOYOBO). To construct plasmids expressing the 3a mutant 3a-CS, fragments were amplified from wildtype 3a-containing plasmids using 3a-specific primer sets and transformed as described above. HEK293FT cells were seeded in 24-well cluster plates and transfected with 1 µg pLenti6-E/3a/M-V5His, pLenti-GFP (green fluorescent protein), or pLenti-M2 using polyethylenimine (PEI) Max. At 24 h post-transfection, the cells were lysed with RIPA buffer (50 mM Tris-HCl, 1% NP-40, 0.05% sodium dodecyl sulfate (SDS), 150 mM NaCl and 1 mM EDTA). And the lysates were subjected to SDS-polyacrylamide gel electrophoresis (PAGE) followed by electroblotting onto polyvinylidene difluoride (PVDF) membranes. The membranes were incubated over night with mouse anti-V5-tag (R960-25, Invitrogen), mouse anti-influenza A virus M2 (14C2, Abcam), mouse anti-GFP (GF200, Nacalai Tesque), or rabbit antitubulin (DM1A, Santa Cruz) antibodies, followed by horseradish peroxide-conjugated anti-mouse IgG (Jackson Immuno Research Laboratories) or anti-rabbit IgG (Invitrogen). After washing 3 times with washing buffer (0.05% Tween-20/PBS), the membranes were exposed using Chemi-Lumi One Super (Nacalai Tesque), and the chemiluminescent signals were captured by an ImageQuant LAS-4000 mini apparatus (GE Healthcare). To generate lentiviruses expressing V5-tagged SARS-CoV E, 3a, and M proteins, the full-length cDNA encoding each viral protein was cloned into the pLenti6.3/V5-TOPO vector (Invitrogen) using the following primers: SARS-CoV E forward, 5 -caccatgtactcattcgtttcgga-3 , and reverse, 5 -gaccagaagatcaggaactc-3 ; SARS-CoV 3a forward, 5caccatggatttgtttatgagatt-3 , and reverse, 5 -caaaggcacgctagtagtcg-3 ; SARS-CoV M forward, 5 -caccatggcagacaacggtactat-3 , and reverse, 5 -ctgtactagcaaagcaatat-3 . Sub-confluent monolayers of HEK293FT cells seeded in a collagen-coated dish (10 cm in diameter) were transfected with 3 µg of pLenti6.3/V5-TOPO vector expressing each viral protein or EGFP together with ViraPower Packaging Mix (Invitrogen) using Lipofectamine 2000 (Invitrogen). The supernatants containing lentiviruses were harvested and filtered through a 0.45 µm filter (Millipore) at 72-96 h post-transfection (Ito et al., 2012) . The lentiviral titer was then quantified using HT-1080 cells as described previously . Bone marrow-derived macrophages were plated at a density of 8 × 10 5 in 24-well plate and infected with A/PR8 influenza virus or lentivirus at a multiplicity of infection (MOI) of 5 or 0.2 for 1 h, respectively. Then, BMMs were stimulated with 1 µg/ml of LPS and cultured for additional 23 h in complete media. Supernatants were collected at 24 h post-infection and centrifuged to remove cell debris. The amount of IL-1β in the supernatants was measured in an enzyme-linked immunosorbent assay (ELISA) using paired antibodies (eBioscience) (Ichinohe et al., 2010 . To clarify the cellular localization of the wild-type and mutant 3a proteins of SARS-CoV, HeLa cells were cultured on coverslips and transfected with 1 µg of pCA7-flag-3a or pCD7-flag-3a-CS together with 0.5 µg of ER-mCherry or DsRed-Golgi (Ito et al., 2012) . At 24 h post-transfection, cells were fixed with 4% paraformaldehyde and permeabilized with 1% Triton X-100/PBS. After washing with PBS and blocking with 4% BSA/PBS, the cells were incubated with a mouse anti-flag antibody (M2, Sigma) followed by incubation with Alexa Fluor 488-conjugated goat anti-mouse IgG (H+L) (Life Technologies). To observe the cellular distribution of NLRP3 in the E-or 3a-expressing cells, HeLa cells were cultured on coverslips and transfected with 1 µg of pCA7-HA-E, pCA7-HA-EV25F, pCA7-HA-3a, pCA7-HA-3a-CS, or pCA7 control vector together with 0.5 µg of pCA7-NLRP3. At 24 h post-transfection, cells were fixed and permeabilized with 4% paraformaldehyde and 1% Triton X-100/PBS. After washing and blocking, the cells were incubated with rabbit anti-HA (561, MBL) and mouse anti-NLRP3 (Cryo-2; AdipoGen) antibodies, followed by Alexa Fluor 488-conjugated goat anti-rabbit IgG (H+L) and Alexa Fluor 568-conjugated goat anti-mouse IgG (H+L) (Life Technologies). Fluorescent signals were observed by confocal microscopy (A1R + , Nikon). Statistical significance was tested using a two-tailed Student's t-test. P-values < 0.05 were considered statistically significant. We previously demonstrated that the influenza virus M2 protein (a proton-selective ion channel), its H37G mutant (which has lost its proton selectivity and enables the transport of other cations such as Na + and K + ), and the EMCV 2B protein (a Ca 2+ channel) stimulates NLRP3 inflammasome-mediated IL-1β secretion (Ichinohe et al., 2010; Ito et al., 2012) . In addition, the SARS-CoV E protein acts as a Ca 2+ -permeable ion channels that activates the NLRP3 inflammasome (Nieto- Torres et al., 2015) . The fact that 3a protein of SARS-CoV acts as viroporin prompted us to examine whether it also triggers inflammasome activation. Thus, we first generated lentivirus plasmids expressing V5-tagged proteins and confirmed their expression in HEK293FT cells by immunoblot analysis (Figures 1A-C) . We next transduced lipopolysaccharide (LPS)-primed BMMs with the lentiviruses expressing the SARS-CoV E, 3a, M, influenza virus M2, or EMCV 2B proteins. Consistent with previous reports (Ichinohe et al., Figure 1D) . Similarly, the lentiviruses expressing the SARS-CoV E or 3a proteins stimulated IL-1β release from LPS-primed BMMs ( Figure 1D) . Furthermore, IL-1β secretion from LPSprimed BMMs co-infected with E-and 3a-expressing lentiviruses was significantly higher than that from SARS-CoV E-expressing lentivirus-infected cells ( Figure 1E) . These data indicated that the expression of SARS-CoV viroporin 3a is sufficient to stimulate IL-1β secretion by LPS-primed BMMs. Previous studies demonstrated that the N-terminal 40 amino acids of the SARS-CoV E protein are important for ion channel formation, and that mutations N15A and V25F [located in the transmembrane domain (from amino acid residues 7-38)] prevent ion conductivity (Wilson et al., 2004; Torres et al., 2007; Verdia-Baguena et al., 2012) . In addition, the SARS-CoV 3a protein contains a cysteine-rich domain (amino acid residues 127-133) that is involved in the formation of a homodimer to generate the ion channel (Lu et al., 2006; Chan et al., 2009) . Thus, mutation of the cysteine-rich domain blocks the ion conductivity by the 3a protein (Chan et al., 2009) . To this end, we substituted amino acids Cys-127, Cys-130, and Cys-133 within the cysteine-rich domain of the SARS-CoV 3a protein with serine to generate a lentivirus expressing the ion channel activity-loss mutant, 3a-CS (Chan et al., 2009; Figure 2A) . To test whether the ion channel activity of the SARS-CoV 3a protein is required to stimulate secretion of IL-1β, we transduced LPSprimed BMMs with lentiviruses expressing the SARS-CoV E, V25F, 3a, 3a-CS, or M proteins. Consistent with a previous report (Nieto -Torres et al., 2015) , we found that the V25F mutant lentivirus failed to stimulate IL-1β release from BMMs ( Figure 2B) . Notably, the 3a-CS mutant completely abrogated IL-1β secretion (Figure 2B) , suggesting that the ion channel activity of the 3a protein is required for SARS-CoV 3a-induced IL-1β secretion. FIGURE 4 | NLRP3 inflammasome activation by SARS-CoV 3a. HeLa cells were transfected with the expression plasmid encoding NLRP3 and that encoding HA-tagged SARS-CoV 3a, 3a-CS, E, or V25F, and by with a confocal microscope. Scale bars, 10 µm. Data are representative of at least three independent experiments. Next, we determined the subcellular localization of the SARS-CoV 3a protein using confocal microscopy. When the SARS-CoV Cell-free supernatants were collected at 24 h (lentiviruses) or 6 h (ATP) post-infection or stimulation, and analyzed for IL-1β by ELISA. Data are representative of at least three independent experiments, and indicate the mean ± SD; * * P < 0.01 and * * * P < 0.001. 3a protein was expressed in HeLa cells, we observed two main distribution patterns. Consistent with previous reports (Yu et al., 2004; Yuan et al., 2005) , the 3a protein localized to the Golgi apparatus ( Figure 3A ). In addition, the 3a proteins concentrated in spot structures, which mainly localized to the endoplasmic reticulum (ER) (Figure 3B ). By contrast, the 3a-CS mutant was concentrated in the Golgi apparatus rather than in the ER and did not form spot structures (Figures 3A,B) . We next examined the intracellular localization of NLRP3. Activation of the NLRP3 inflammasome led to a redistribution from the cytosol to the perinuclear space, a process considered as a hallmark of NLRP3 activation (Zhou et al., 2011; Ito et al., 2012; Johnson et al., 2013; Moriyama et al., 2016) . Although cells expressing the ion channel activity-loss mutants 3a-CS or V25F uniformly expressed NLRP3 throughout the cytoplasm, it was redistributed to the perinuclear region in SARS-CoV 3a-or E-expressing cells (Figure 4) . Together, these data provide evidence that the ion channel activity of the SARS-CoV 3a protein is essential for triggering the NLRP3 inflammasome. Both K + Efflux and ROS Production Are Involved in the IL-1β Release Induced by the SARS-CoV 3a Protein Finally, we investigated the mechanism by which SARS-CoV 3a triggers NLRP3 inflammasome activation. A previous study showed that the 3a protein of SARS-CoV acts as a K + channel (Lu et al., 2006) . In addition, K + efflux is a well-known activator of the NLRP3 inflammasome (Mariathasan et al., 2006; Petrilli et al., 2007) . These observations prompted us to examine whether K + efflux is required for 3a-mediated IL-1β secretion. To this end, BMMs in K + -rich medium were infected with influenza A virus or lentiviruses expressing the SARS-CoV E or 3a proteins. In agreement with a previous result (Ichinohe et al., 2010) , we found that IL-1β secretion caused by influenza virus was completely blocked when the extracellular K + concentration was increased to 130 mM ( Figure 5A) . The inhibitory effect of the K + -rich medium was also observed when cells were stimulated with lentiviruses expressing the SARS-CoV E or 3a proteins ( Figure 5B ). Since mitochondrial ROS are important for NLRP3 inflammasome activation (Nakahira et al., 2011; Zhou et al., 2011) , we next stimulated BMMs with extracellular ATP or lentiviruses expressing the SARS-CoV E or 3a proteins in the presence or absence of the antioxidant, Mito-TEMPO, a scavenger that is specific for mitochondrial ROS Trnka et al., 2009) . As reported previously (Nakahira et al., 2011; Ito et al., 2012) , treatment of BMMs with Mito-TEMPO completely blocked IL-1β secretion in response to ATP ( Figure 6A) . Similarly, IL-1β release induced by the SARS-CoV E and 3a proteins was significantly inhibited by Mito-TEMPO ( Figure 6B) . These observations indicate that the SARS-CoV 3a protein disrupts intracellular ionic concentrations and causes mitochondrial damages, thereby activating the NLRP3 inflammasome. In summary, we found that the ion channel activity of SARS-CoV 3a protein is essential for activation of the NLRP3 inflammasome. In addition, both K + efflux and mitochondrial ROS production are required for SARS-CoV 3a-mediated IL-1β secretion. Thus far, several models have been proposed to explain NLRP3 inflammasome activation by RNA viruses. First, viral RNA or RNA cleavage products generated by RNase L activate the NLRP3 inflammasome via the DExD/H-box helicase, DHX33 (Allen et al., 2009; Mitoma et al., 2013; Chen et al., 2014; Chakrabarti et al., 2015) . Second, viroporins encoded by RNA viruses activates the NLRP3 inflammasome (Ichinohe et al., 2010; Ito et al., 2012; Triantafilou et al., 2013; Nieto-Torres et al., 2015) . In the case of influenza virus, the proton-selective M2 ion channel in the acidic trans-Golgi network activates the NLRP3 inflammasome (Ichinohe et al., 2010) . Interestingly, an M2 mutant in which histidine was substituted with glycine at position 37 (H37G), causing loss of proton selectivity, enables transport of other cations (i.e., Na + and K + ), thereby leading to enhanced secretion of IL-1β from LPS-primed BMMs and dendritic cells when compared with the wild-type M2 protein. In addition, the 2B proteins of EMCV, poliovirus, enterovirus 71 (EV71), and human rhinovirus (a member of the Picornaviridae family) triggers NLRP3 inflammasome activation by inducing Ca 2+ flux from the ER and Golgi compartments (Ito et al., 2012; Triantafilou et al., 2013) . Furthermore, hepatitis C virus stimulates NLRP3 inflammasome-mediated IL-1β production though its p7 viroporin (Negash et al., 2013; Farag et al., 2017) . Third, a recent study has demonstrated that the 3D protein of EV71 directly interacts with NLRP3 to facilitate the assembly of NLRP3 inflammasome complex (Wang et al., 2017) . In the case of SARS-CoV, the viroporin E forms forms Ca 2+permeable ion channels and activates the NLRP3 inflammasome (Nieto-Torres et al., 2015) . In addition, another viroporin 3a was found to induce NLRP3 inflammasome activation (Yue et al., 2018) . Although alanine substitution at Cys-133, which is required for dimer or tetramer formation (Lu et al., 2006) , still allows activation of the NLRP3 inflammasome by interacting with caspase-1 (Yue et al., 2018) , the ion channel activity-loss mutant 3a-CS (Cys-to-Ser substitution at positions Cys-127, Cys-130, and Cys-133) (Chan et al., 2009 ) completely abrogated IL-1β secretion from LPS-primed BMMs, suggesting that the 3a protein of SARS-CoV has the ability to induce the NLRP3 inflammasome activation by multiple mechanisms. Previous studies show that the 3a protein of SARS-CoV is localized to the plasma membrane (Minakshi and Padhan, 2014) and acts as a K + channel (Lu et al., 2006) , thereby (presumably) stimulating the K + efflux at the plasma membrane. Indeed, we found that IL-1β secretion caused by the 3a protein was significantly inhibited when the extracellular K + concentration increased to 130 mM. Although it remains unclear whether another viroporin 8a of SARS-CoV (Castano-Rodriguez et al., 2018) activates the NLRP3 inflammasome, these data highlights the importance of viroporins in SARS-CoV-induced NLRP3 inflammasome activation. A better understanding of the mechanism that governs the NLRP3 inflammasome will facilitate the development of more effective interventions for the treatment of infectious diseases and increase our understanding of viral pathogenesis.

How many amino acids are in the SARS-CoV E protein?

Genomic characterization of the 2019 novel human-pathogenic coronavirus isolated from a patient with atypical pneumonia after visiting Wuhan https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7067204/ SHA: c097a8a9a543d69c34f10e5c3fd78019e560026a Authors: Chan, Jasper Fuk-Woo; Kok, Kin-Hang; Zhu, Zheng; Chu, Hin; To, Kelvin Kai-Wang; Yuan, Shuofeng; Yuen, Kwok-Yung Date: 2020-01-28 DOI: 10.1080/22221751.2020.1719902 License: cc-by Abstract: A mysterious outbreak of atypical pneumonia in late 2019 was traced to a seafood wholesale market in Wuhan of China. Within a few weeks, a novel coronavirus tentatively named as 2019 novel coronavirus (2019-nCoV) was announced by the World Health Organization. We performed bioinformatics analysis on a virus genome from a patient with 2019-nCoV infection and compared it with other related coronavirus genomes. Overall, the genome of 2019-nCoV has 89% nucleotide identity with bat SARS-like-CoVZXC21 and 82% with that of human SARS-CoV. The phylogenetic trees of their orf1a/b, Spike, Envelope, Membrane and Nucleoprotein also clustered closely with those of the bat, civet and human SARS coronaviruses. However, the external subdomain of Spike’s receptor binding domain of 2019-nCoV shares only 40% amino acid identity with other SARS-related coronaviruses. Remarkably, its orf3b encodes a completely novel short protein. Furthermore, its new orf8 likely encodes a secreted protein with an alpha-helix, following with a beta-sheet(s) containing six strands. Learning from the roles of civet in SARS and camel in MERS, hunting for the animal source of 2019-nCoV and its more ancestral virus would be important for understanding the origin and evolution of this novel lineage B betacoronavirus. These findings provide the basis for starting further studies on the pathogenesis, and optimizing the design of diagnostic, antiviral and vaccination strategies for this emerging infection. Text: Coronaviruses (CoVs) are enveloped, positive-sense, single-stranded RNA viruses that belong to the subfamily Coronavirinae, family Coronavirdiae, order Nidovirales. There are four genera of CoVs, namely, Alphacoronavirus (αCoV), Betacoronavirus (βCoV), Deltacoronavirus (δCoV), and Gammacoronavirus (γCoV) [1] . Evolutionary analyses have shown that bats and rodents are the gene sources of most αCoVs and βCoVs, while avian species are the gene sources of most δCoVs and γCoVs. CoVs have repeatedly crossed species barriers and some have emerged as important human pathogens. The best-known examples include severe acute respiratory syndrome CoV (SARS-CoV) which emerged in China in 2002-2003 to cause a large-scale epidemic with about 8000 infections and 800 deaths, and Middle East respiratory syndrome CoV (MERS-CoV) which has caused a persistent epidemic in the Arabian Peninsula since 2012 [2, 3] . In both of these epidemics, these viruses have likely originated from bats and then jumped into another amplification mammalian host [the Himalayan palm civet (Paguma larvata) for SARS-CoV and the dromedary camel (Camelus dromedarius) for MERS-CoV] before crossing species barriers to infect humans. Prior to December 2019, 6 CoVs were known to infect human, including 2 αCoV (HCoV-229E and HKU-NL63) and 4 βCoV (HCoV-OC43 [ HCoV-OC43 and HCoV-HKU1 usually cause self-limiting upper respiratory infections in immunocompetent hosts and occasionally lower respiratory tract infections in immunocompromised hosts and elderly [4] . In contrast, SARS-CoV (lineage B βCoV) and MERS-CoV (lineage C βCoV) may cause severe lower respiratory tract infection with acute respiratory distress syndrome and extrapulmonary manifestations, such as diarrhea, lymphopenia, deranged liver and renal function tests, and multiorgan dysfunction syndrome, among both immunocompetent and immunocompromised hosts with mortality rates of ∼10% and ∼35%, respectively [5, 6] . On 31 December 2019, the World Health Organization (WHO) was informed of cases of pneumonia of unknown cause in Wuhan City, Hubei Province, China [7] . Subsequent virological testing showed that a novel CoV was detected in these patients. As of 16 January 2020, 43 patients have been diagnosed to have infection with this novel CoV, including two exported cases of mild pneumonia in Thailand and Japan [8, 9] . The earliest date of symptom onset was 1 December 2019 [10] . The symptomatology of these patients included fever, malaise, dry cough, and dyspnea. Among 41 patients admitted to a designated hospital in Wuhan, 13 (32%) required intensive care and 6 (15%) died. All 41 patients had pneumonia with abnormal findings on chest computerized tomography scans [10] . We recently reported a familial cluster of 2019-nCoV infection in a Shenzhen family with travel history to Wuhan [11] . In the present study, we analyzed a 2019-nCoV complete genome from a patient in this familial cluster and compared it with the genomes of related βCoVs to provide insights into the potential source and control strategies. The complete genome sequence of 2019-nCoV HKU-SZ-005b was available at GenBank (accession no. MN975262) ( Table 1 ). The representative complete genomes of other related βCoVs strains collected from human or mammals were included for comparative analysis. These included strains collected from human, bats, and Himalayan palm civet between 2003 and 2018, with one 229E coronavirus strain as the outgroup. Phylogenetic tree construction by the neighbour joining method was performed using MEGA X software, with bootstrap values being calculated from 1000 trees [12] . The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (1000 replicates) was shown next to the branches [13] . The tree was drawn to scale, with branch lengths in the same units as those of the evolutionary distances used to infer the phylogenetic tree. The evolutionary distances were computed using the Poisson correction method and were in the units of the number of amino acid substitutions per site [14] . All ambiguous positions were removed for each sequence pair (pairwise deletion option). Evolutionary analyses were conducted in MEGA X [15] . Multiple alignment was performed using CLUSTAL 2.1 and further visualized using BOX-SHADE 3.21. Structural analysis of orf8 was performed using PSI-blast-based secondary structure PREDiction (PSIPRED) [16] . For the prediction of protein secondary structure including beta sheet, alpha helix, and coil, initial amino acid sequences were input and analysed using neural networking and its own algorithm. Predicted structures were visualized and highlighted on the BOX-SHADE alignment. Prediction of transmembrane domains was performed using the TMHMM 2.0 server (http://www.cbs.dtu.dk/services/TMHMM/). Secondary structure prediction in the 5 ′ -untranslated region (UTR) and 3 ′ -UTR was performed using the RNAfold WebServer (http://rna.tbi.univie.ac.at/cgi-bin/ RNAWebSuite/RNAfold.cgi) with minimum free energy (MFE) and partition function in Fold algorithms and Table 2 . Putative functions and proteolytic cleavage sites of 16 nonstructural proteins in orf1a/b as predicted by bioinformatics. Putative function/domain Amino acid position Putative cleave site complex with nsp3 and 6: DMV formation complex with nsp3 and 4: DMV formation short peptide at the end of orf1a basic options. The human SARS-CoV 5 ′ -and 3 ′ -UTR were used as references to adjust the prediction results. The single-stranded RNA genome of the 2019-nCoV was 29891 nucleotides in size, encoding 9860 amino acids. The G + C content was 38%. Similar to other (Table 2 ). There are no remarkable differences between the orfs and nsps of 2019-nCoV with those of SARS-CoV (Table 3) . The major distinction between SARSr-CoV and SARS-CoV is in orf3b, Spike and orf8 but especially variable in Spike S1 and orf8 which were previously shown to be recombination hot spots. Spike glycoprotein comprised of S1 and S2 subunits. The S1 subunit contains a signal peptide, followed by an N-terminal domain (NTD) and receptor-binding domain (RBD), while the S2 subunit contains conserved fusion peptide (FP), heptad repeat (HR) 1 and 2, transmembrane domain (TM), and cytoplasmic domain (CP). We found that the S2 subunit of 2019-nCoV is highly conserved and shares 99% identity with those of the two bat SARS-like CoVs (SL-CoV ZXC21 and ZC45) and human SARS-CoV (Figure 2 ). Thus the broad spectrum antiviral peptides against S2 would be an important preventive and treatment modality for testing in animal models before clinical trials [18] . Though the S1 subunit of 2019-nCoV shares around 70% identity to that of the two bat SARS-like CoVs and human SARS-CoV (Figure 3(A) ), the core domain of RBD (excluding the external subdomain) are highly conserved (Figure 3(B) ). Most of the amino acid differences of RBD are located in the external subdomain, which is responsible for the direct interaction with the host receptor. Further investigation of this soluble variable external subdomain region will reveal its receptor usage, interspecies transmission and pathogenesis. Unlike 2019-nCoV and human SARS-CoV, most known bat SARSr-CoVs have two stretches of deletions in the spike receptor binding domain (RBD) when compared with that of human SARS-CoV. But some Yunnan strains such as the WIV1 had no such deletions and can use human ACE2 as a cellular entry receptor. It is interesting to note that the two bat SARS-related coronavirus ZXC21 and ZC45, being closest to 2019-nCoV, can infect suckling rats and cause inflammation in the brain tissue, and pathological changes in lung & intestine. However, these two viruses could not be isolated in Vero E6 cells and were not investigated further. The two retained deletion sites in the Spike genes of ZXC21 and ZC45 may lessen their likelihood of jumping species barriers imposed by receptor specificity. A novel short putative protein with 4 helices and no homology to existing SARS-CoV or SARS-r-CoV protein was found within Orf3b ( Figure 4 ). It is notable that SARS-CoV deletion mutants lacking orf3b replicate to levels similar to those of wildtype virus in several cell types [19] , suggesting that orf3b is dispensable for viral replication in vitro. But orf3b may have a role in viral pathogenicity as Vero E6 but not 293T cells transfected with a construct expressing Orf3b underwent necrosis as early as 6 h after transfection and underwent simultaneous necrosis and apoptosis at later time points [20] . Orf3b was also shown to inhibit expression of IFN-β at synthesis and signalling [21] . Subsequently, orf3b homologues identified from three bat SARSrelated-CoV strains were C-terminally truncated and lacked the C-terminal nucleus localization signal of SARS-CoV [22] . IFN antagonist activity analysis demonstrated that one SARS-related-CoV orf3b still possessed IFN antagonist and IRF3-modulating activities. These results indicated that different orf3b proteins display different IFN antagonist activities and this function is independent of the protein's nuclear localization, suggesting a potential link between bat SARS-related-CoV orf3b function and pathogenesis. The importance of this new protein in 2019-nCoV will require further validation and study. Orf8 orf8 is an accessory protein found in the Betacoronavirus lineage B coronaviruses. Human SARS-CoVs isolated from early-phase patients, all civet SARS-CoVs, and other bat SARS-related CoVs contain fulllength orf8 [23] . However, a 29-nucleotide deletion, Bat SL-CoV ZXC21 2018 Bat which causes the split of full length of orf8 into putative orf8a and orf8b, has been found in all SARS-CoV isolated from mid-and late-phase human patients [24] . In addition, we have previously identified two bat SARS-related-CoV (Bat-CoV YNLF_31C and YNLF_34C) and proposed that the original SARS-CoV full-length orf8 is acquired from these two bat SARS-related-CoV [25] . Since the SARS-CoV is the closest human pathogenic virus to the 2019-nCoV, we performed phylogenetic analysis and multiple alignments to investigate the orf8 amino acid sequences. The orf8 protein sequences used in the analysis derived from early phase SARS-CoV that includes full-length orf8 (human SARS-CoV GZ02), the mid-and late-phase SARS-CoV that includes the split orf8b (human SARS-CoV Tor2), civet SARS-CoV (paguma SARS-CoV), two bat SARS-related-CoV containing full-length orf8 (bat-CoV YNLF_31C and YNLF_34C), 2019-nCoV, the other two closest bat SARS-related-CoV to 2019-nCoV SL-CoV ZXC21 and ZC45), and bat SARS-related-CoV HKU3-1 ( Figure 5(A) ). As expected, orf8 derived from 2019-nCoV belongs to the group that includes the closest genome sequences of bat SARS-related-CoV ZXC21 and ZC45. Interestingly, the new 2019-nCoV orf8 is distant from the conserved orf8 or Figure 5(B) ) which was shown to trigger intracellular stress pathways and activates NLRP3 inflammasomes [26] , but this is absent in this novel orf8 of 2019-nCoV. Based on a secondary structure prediction, this novel orf8 has a high possibility to form a protein with an alpha-helix, following with a betasheet(s) containing six strands ( Figure 5(C) ). The genome of 2019-nCoV has overall 89% nucleotide identity with bat SARS-related-CoV SL-CoVZXC21 (MG772934.1), and 82% with human SARS-CoV BJ01 2003 (AY278488) and human SARS-CoV Tor2 (AY274119). The phylogenetic trees constructed using the amino acid sequences of orf1a/b and the 4 structural genes (S, E, M, and N) were shown (Figure 6(A-E) ). For all these 5 genes, the 2019-nCoV was clustered with lineage B βCoVs. It was most closely related to the bat SARS-related CoVs ZXC21 and ZC45 found in Chinese horseshoe As shown in Figure 7 (A-C), the SARS-CoV 5 ′ -UTR contains SL1, SL2, SL3, SL4, S5, SL5A, SL5B, SL5C, SL6, SL7, and SL8. The SL3 contains trans-cis motif [27] . The SL1, SL2, SL3, SL4, S5, SL5A, SL5B, and SL5C structures were similar among the 2019-nCoV, human SARS-CoV and the bat SARS-related ZC45. In the 2019-nCoV, part of the S5 found was inside Figure 7 Continued the orf1a/b (marked in red), which was similar to SARS-CoV. In bat SARS-related CoV ZC45, the S5 was not found inside orf1a/b. The 2019-nCoV had the same SL6, SL7, and SL8 as SARS-CoV, and an additional stem loop. Bat SARS-related CoV ZC45 did not have the SARS-COV SL6-like stem loop. Instead, it possessed two other stem loops in this region. All three strains had similar SL7 and SL8. The bat SARS-like CoV ZC45 also had an additional stem loop between SL7 and SL8. Overall, the 5 ′ -UTR of 2019-nCoV was more similar to that of SARS-CoV than the bat SARS-related CoV ZC 45. The biological relevance and effects of virulence of the 5 ′ -UTR structures should be investigated further. The 2019-nCoV had various 3 ′ -UTR structures, including BSL, S1, S2, S3, S4, L1, L2, L3, and HVR (Figure 7(D-F) ). The 3 ′ -UTR was conserved among 2019-nCoV, human SARS-CoV and SARS-related CoVs [27] . In summary, 2019-nCoV is a novel lineage B Betacoronavirus closely related to bat SARS-related coronaviruses. It also has unique genomic features which deserves further investigation to ascertain their roles in viral replication cycle and pathogenesis. More animal sampling to determine its natural animal reservoir and intermediate animal host in the market is important. This will shed light on the evolutionary history of this emerging coronavirus which has jumped into human after the other two zoonotic Betacoroanviruses, SARS-CoV and MERS-CoV.

What is the RNA of the 2019-nCOV?