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PV generation and load profile data of net zero energy homes in South Australia

Fig. 1 shows the general configuration of a Net Zero Energy home.The data is supplied in two separate Excel files: one containing raw half-hourly load data for a region in South Australia, and the other containing PV generation and load data scaled down for a single home.Examining the second file will reveal that it consists of three variables: PV power generation of a 3 kWp system, residential load demand of a typical home, and ambient temperature.Both the PV generation and load data are represented in kW.The data are specifically filtered for the state of South Australia and represent hourly data for a full year.The data contain information related to PV generation pattern of SA region, for example, the minimum PV generation occurs during the middle of the calendar year due to winter.The load data provide insight into the electricity usage pattern of South Australian homes.For example, electricity demand peaks at certain times in the early and late days of a year due to high summer temperatures, which increases the air-conditioning load.Fig. 2 illustrates the average daily PV generation and load profile of a typical SA household for the year 2015.This clearly demonstrates significant mismatch between the PV generation and load patterns, which provides the justification for using battery storage with PV systems .In Fig. 1, the exported power and the imported power represent the amount of power exchanged between the home and the grid due to the mismatch.The power balance equation at the point of common coupling is shown in Fig. 1.When the PV generation is higher than the load demand then PEXP > 0 and PIMP = 0.When the PV generation is lower than the load demand then PEXP = 0 and PIMP > 0.Because the dataset relate to a NZE Home, the overall PV generated energy and the energy consumed by the home is the same over a year.Therefore, the annual exported energy is the same as the annual imported energy.The method used to produce the data is given in the next section.The method to produce PV generation and residential load profile is given below.Hourly PV generation and ambient temperature data have been derived using web platform Renewable ninja .To generate the data the following information is supplied to the system:Location of the PV system,Target PV system capacity,PV system loss,These factors can be varied according to system requirement.Additional features such as tracking type can be included.The data presented in this article is an hourly PV generation profile of the year 2015.Note that the data downloaded from this source is for the UTC time zone.However, South Australian time is 10.5 hours ahead of UTC time during the month of January.Therefore, a few rows of data from the bottom of the generated file need to be shifted to the top rows to align with South Australian time.The above steps can be used to generate household load and PV generation data for other Australian states.

This paper presents the hourly Photovoltaic (PV) generation and residential load profiles of a typical South Australian Net Zero Energy (NZE) home. These data are used in the research article entitled “Energy Cost Minimization for Net Zero Energy Homes through Optimal Sizing of Battery Storage System” Sharma et al., 2019. The PV generation data is derived using the publicly accessible renewable ninja web platform by feeding information such as the region of interest, PV system capacity, losses and tilt angle. The raw load profile data is sourced from the Australian Energy Market Operator (AEMO) website, which is further processed and filtered to match the household load requirement. The processing of data has been carried out using Microsoft Excel and MATLAB software. The experimental method used to obtain the required data from the downloaded raw dataset is described in this paper. While the data is generated for the state of South Australia (SA), the method described here can be used to produce datasets for any other Australian state.

The synthesis of recombinant membrane proteins in yeast for structural studies

The first crystal structures of mammalian membrane proteins derived from recombinant sources were solved in 2005 using protein that had been produced in yeast cells: the rabbit Ca2+-ATPase, SERCA1a, was overexpressed in Saccharomyces cerevisiae and the rat voltage-dependent potassium ion channel, Kv1.2 was produced in Pichia pastoris .Since then, several other host cells have been used for eukaryotic membrane protein production including Escherichia coli, baculovirus-infected insect cells and mammalian cell-lines .Whilst all host systems have advantages and disadvantages, yeasts have remained a consistently-popular choice in the eukaryotic membrane protein field .As microbes, they are quick, easy and cheap to culture; as eukaryotes they are able to post-translationally process eukaryotic membrane proteins.Very recent crystal structures of recombinant transmembrane proteins produced in yeast include those of human aquaporin 2, chicken bestrophin-1, the human TRAAK channel, human leukotriene C4 synthase, an algal P-glycoprotein homologue and mouse P-glycoprotein using P. pastoris-derived samples; the structures of the Arabidopsis thaliana NRT1.1 nitrate transporter, a fungal plant pathogen TMEM16 lipid scramblase and the yeast mitochondrial ADP/ATP carrier were solved using recombinant protein produced in S. cerevisiae.Despite these successes, the overall rate of progress in membrane protein structural biology has, until very recently, been markedly slower than that in the soluble protein field .However, recent experimental breakthroughs mean that the gap is set to narrow.For example, the use of stabilizing mutants has had a revolutionary impact on increasing the crystallization propensity of some membrane protein targets , while incorporating fusion partner proteins such as T4 lyzozyme1 has been particularly important in structural studies of G protein-coupled receptors.From the perspective of the host cell, our improved understanding of cellular pathways controlling translation and protein folding, and how they influence functional recombinant protein yields, means it is now possible to select better expression strains; this knowledge has also allowed a more strategic approach to cell culture in order to maximise the productivity of each cell .Finally, new methods for extracting and solubilizing membrane proteins from the cell membrane using styrene maleic anhydride co-polymers have enabled traditional detergents to be circumvented .The benefits of this approach include improved thermostability of the solubilized protein and retention of protein–lipid interactions that are normally disrupted during detergent-extraction .This review focuses on current approaches to optimizing expression plasmids, yeast strains and culture conditions, as well as the extraction and purification of functional membrane proteins for crystallization trials using detergents and SMA co-polymers.Over 1500 species of yeast are known, but only very few of them have been employed as host organisms for the production of recombinant proteins .The two most widely used for recombinant membrane protein production are S. cerevisiae and P. pastoris.These single-celled, eukaryotic microbes grow quickly in complex or defined media in formats ranging from multi-well plates to shake flasks and bioreactors of various sizes .P. pastoris has the advantage of being able to grow to very high cell densities and therefore has the potential to produce large amounts of recombinant membrane protein for structural analysis.This yeast has also been important in generating high-resolution GPCR crystal structures such as the adenosine A2A and the histamine H1 receptors.However, because it is a strictly aerobic organism, the full benefits of P. pastoris are achievable only if it is cultured under highly-aerated conditions; this is usually only possible in continuously-stirred tank bioreactors.S. cerevisiae has the advantage that its genetics are better understood and that it is supported by a more extensive literature than P. pastoris.This has led to the development of a much wider range of tools and strains for improved membrane protein production.Consequently, projects requiring specialized strains may benefit from using S. cerevisiae as the host.Notably, the structure of the histamine H1 receptor was obtained from protein produced in P. pastoris, although initial screening to define the best expression construct was performed in S. cerevisiae .This is presumably because of the greater range of molecular biological tools available for S. cerevisiae at the screening stage, coupled with the superior yield characteristics of P. pastoris when cultured at larger scale in bioreactors.In principle, many of the tools established for S. cerevisiae could be transferred to P. pastoris combining the strengths of both yeast species, although such work would be time-consuming.In our laboratory, we often start with P. pastoris and, if the production is not straightforward, turn to S. cerevisiae to troubleshoot thereby benefitting from the best attributes of the two hosts .Having decided which yeast species will be used as the recombinant host, a suitable expression plasmid needs to be selected or designed.Table 1 lists examples of common plasmids that are used for recombinant protein production in S. cerevisiae and P. pastoris, while Sections 3.1–3.3 briefly review three key elements of such plasmids: the promoter; the nature of any tags and the codon sequence.Typically, episomal plasmids are used for expression in S. cerevisiae, but the expression cassette is integrated into the genome of P. pastoris.These continuing preferences may have resulted from the replication of early successes using particular plasmid/species combinations.Since the P. pastoris system depends upon very strong promoters, only a few copies of the gene are required to obtain sufficient levels of mRNA.In contrast, in S. cerevisiae, the promoter can be 10- to 100-fold weaker so the use of episomal plasmids with high copy numbers is advantageous; episomal plasmids are available for P. pastoris, but are not yet widely used in structural biology projects.Auxotrophic markers are routinely used in S. cerevisiae plasmids to select for successfully-transformed yeast cells.Notably, the yield of the recombinant insulin analogue precursor protein was increased sevenfold simply by using the selection marker URA3 instead of LEU2 .Truncations in the promoters of auxotrophic marker genes can further increase recombinant protein yields: by decreasing the promoter length, transcription of the marker gene on the plasmid is reduced and the cell compensates by increasing the plasmid copy number .A truncated LEU2 promoter was recently used to increase the yields of nine different transporters, including NRT1.1 .Most recombinant expression systems employed in structural biology pipelines depend upon strong, inducible promoters to drive high rates of mRNA synthesis.For example, the strong S. cerevisiae promoter, PGAL1, is induced with galactose while PAOX1 is induced with methanol .In choosing a strong promoter, the idea is that transcription should not be rate limiting.However, high mRNA synthesis rates may be countered by high rates of mRNA degradation .Moreover, evidence from prokaryotic expression systems suggests that acquired mutations that lower promoter efficiency lead to improved functional yields of membrane proteins for some, but not all, targets .In a separate study, a series of E. coli strains that had been evolved to improve their yield characteristics were found to have a mutation in the hns gene, which has a role in transcriptional silencing .Together, these results support an emerging view that a suitable balance between mRNA and protein synthesis rates is desirable, although how this might be achieved in practice is not yet understood; one possibility might be a system based on slow, constitutive expression .It has been proposed that the ideal inducible system would completely uncouple cell growth from recombinant synthesis, which requires the host cell to remain metabolically capable of transcription and translation in a growth-arrested state.In this scenario, all metabolic fluxes would be diverted to the production of recombinant protein .While this approach is yet to be demonstrated for membrane protein production in yeast cells, soluble chloramphenicol acetyltransferase was produced to more than 40% of total cell protein in E. coli suggesting that this may be a strategy worth exploring in yeast.Indeed, growth rates often decline dramatically upon induction of yeast cultures, in part achieving this state.When wild-type P. pastoris cells were cultured in methanol, it was found that a higher proportion of the total mRNA pool was associated with two or more ribosomes compared to the same cells cultured in any other non-inducing growth condition .This observation suggests that high recombinant protein yields in methanol-grown cells are due not just to promoter strength, but also to the global response of P. pastoris to growth on methanol .However, PAOX1-driven expression is leaky; the recent characterization of pre-induction expression under the control of PAOX1 indicates that the uncoupling of growth and protein synthesis in P. pastoris cells has not yet been achieved.The response of a series of inducible S. cerevisiae promoters to different carbon sources has also been studied ; this type of careful analysis of promoter expression patterns now opens up opportunities for dynamic regulation of recombinant protein production in S. cerevisiae.In addition to the open reading frame of the gene of interest, a typical expression plasmid will usually incorporate a number of other sequences in its expression cassette.The S. cerevisiae α-mating factor signal sequence is a common addition to commercial expression plasmids because it is believed to correctly-target recombinant membrane proteins to the yeast membrane.For example, its presence had a positive impact on the yield of the mouse 5-HT5A serotonin receptor but dramatically reduced expression of the histamine H1 receptor .Alternative signal sequences have been used such as the STE2 leader sequence of the fungal GPCR, Ste2p .Many expression plasmids contain tags as part of their DNA sequence, and it is straightforward to add a range of others by gene synthesis or polymerase chain reaction.Frequently-used tags for recombinant membrane proteins are polyhistidine, green fluorescent protein and T4L.These and others have been reviewed extensively elsewhere .Briefly, polyhistidine tags are routinely fused to recombinantly-produced membrane proteins to facilitate rapid purification by metal chelate chromatography using Ni–NTA resins.In many cases, the tag is not removed prior to crystallization trials, although protease cleavage sites can be engineered into the expression plasmid if this is desired .GFP tags are used differently, typically to assess functional yield or homogeneity of the purified recombinant protein prior to crystallization trials.In the former case, caution must be exercised because GFP tags remain fluorescent in eukaryotic cells irrespective of whether the partner membrane protein is correctly folded in the plasma membrane .GFP is therefore an inappropriate marker to assess the folding status of recombinant membrane proteins produced in yeast prior to extraction, although it is still useful in analyzing the stability of a purified membrane protein by fluorescence size-exclusion chromatography .Finally, most GPCR crystal structures have been obtained using a fusion protein strategy where the flexible third intracellular loop is replaced by T4L, with modified T4L variants having been developed to optimize crystal quality or promote alternative packing interactions .Overall, the precise combination and location of any tags needs to be decided based upon their proposed use and the biochemistry of the recombinant membrane protein.The sequence of an mRNA transcript is critically important in determining the rate and accuracy of translation meaning that optimal design of the corresponding DNA expression plasmid is essential to the success of a recombinant protein production experiment.Each organism is known to have a preference for some of the 64 available codons over others, but the biological reason for this is not yet clear.One idea is that each codon is decoded at a different rate: codons that are decoded quickly will be more resource efficient , while slower decoding will allow time for proper post-translational folding and translocation .Another idea is that different codons are read with different accuracy, which might affect proteolysis and degradation .Codon optimization involves manipulating the sequence of an ORF in order to maximize its expression.Several companies offer codon optimization services that account for codon bias in the host cell, mRNA GC content and secondary structure while minimizing sites such as internal ribosome entry sites or premature polyA sites that may negatively affect gene expression.However, there is no guarantee that recombinant protein yields will be increased, as demonstrated for the production of two membrane proteins in E. coli .In contrast, careful codon optimization of the mouse P-glycoprotein gene for expression in P. pastoris led to substantially more recombinant protein compared to expression from the wild-type gene .It has been proposed that the mRNA sequence around the translation start site has a bigger influence on membrane protein yields than codon choice in the rest of the ORF both in E. coli and P. pastoris since strong mRNA structure in this region could affect translation initiation and therefore protein production .The use of degenerate PCR primers to optimize the codon sequence around the start codon therefore offers one approach to improving the expression plasmid .As mentioned in Section 2, a wide range of S. cerevisiae resources are available, including comprehensive strain collections from which potential expression hosts can be selected.These resources are supported by a wealth of information in the Saccharomyces Genome Database.The yeast deletion collections comprise over 21,000 mutant strains with precise start-to-stop deletions of the approximately 6000 S. cerevisiae ORFs .The collections include heterozygous and homozygous diploids as well as haploids of both MATa and MATα mating types.Individual strains or the complete collection can be obtained from Euroscarf or the American Type Culture Collection.Complementing this, Dharmacon sells the Yeast Tet-Promoters Hughes Collection with 800 essential yeast genes under control of a tetracycline-regulated promoter that permits experimental regulation of essential genes.A number of specifically-engineered S. cerevisiae strains also exist including those with “humanized” sterol and glycosylation pathways .Notably, protease-deficient strains are a consistently-popular choice in membrane protein structural biology projects.Use of specific strains from these collections offers the potential to gain mechanistic insight into the molecular bottlenecks that preclude high recombinant protein yields; we and others have used transcriptome analysis to guide strain selection.In an early study we were able to identify genes that were up-regulated under high yielding conditions for our target membrane protein but down-regulated under low yielding conditions or vice versa .This enabled us to select four high yielding strains: srb5Δ, spt3Δ, gcn5Δ and yTHCBMS1.The use of the spt3Δ strain resulted in the largest yields of Fps1p in shake-flasks.When the yTHCBMS1 strain was cultured in the presence of 0.5 μg/ml doxycycline, yields were increased by 30-fold in shake-flasks and over 70-fold in bioreactors compared with wild-type cells .Using the strains srb5Δ and gcn5Δ, Fps1p yields were increased 5- and 10-fold over wild-type, respectively.While these strains were originally selected to optimize Fps1p yields, we also noted generic advantages in that functional yields of the adenosine A2A receptor and soluble GFP could be doubled using them .This suggests that both general and target-specific effects are likely to occur during recombinant protein production in yeast.It would be desirable to be able to distinguish between the two, but this remains challenging because of the limited number of studies that have been done, including those in yeast.Specific metabolic pathways have been targeted in order to increase functional recombinant protein yields in yeast cells.For example, exploiting the global cellular stress response to misfolded proteins has been investigated as a route to improving functional yields of recombinant proteins for structural studies ; it has even been argued that exposure to mild stress may enhance tolerance to a future stressful stimulus such as that imposed during recombinant protein production.A recent study of recombinant GPCR production in S. cerevisiae demonstrated that mislocalized proteins were associated with the endoplasmic reticulum chaperone, BiP , providing opportunities to regulate the chaperone network.The unfolded protein response and the heat shock response have also been examined; tuning expression levels to avoid or minimize UPR induction has previously been shown to increase functional membrane protein yields , while the HSR activates chaperones and the proteasome in order to relieve stress.HSR up-regulation has specifically been used to increase recombinant yields of soluble α-amylase in S. cerevisiae, but did not increase the yield of a recombinant human insulin precursor .Overall, studies such as these demonstrate that the manipulation of stress responses may influence recombinant protein yields in yeast, but that the magnitude of any effect is protein specific.P. pastoris expression plasmids are usually integrated into the yeast genome to produce a stable production strain.Since it is not possible to precisely control the number of copies that integrate, the optimal clone must be selected experimentally.One approach is to screen on increasing concentrations of antibiotic to obtain so-called “jackpot” clones.Although the results in Fig. 3 suggest a correlation between the copy number of the integrated expression cassette and the final yield of recombinant protein, this is not always the case .Sometimes clones with lower copy numbers are more productive, suggesting that the cellular machinery is overwhelmed in jackpot clones.Consistent with this idea, adenosine A2A receptor yields were increased 1.8-fold when the corresponding gene was co-expressed in P. pastoris with the stress-response gene HAC1 ; Hac1 drives transcription of UPR genes.In contrast to the situation in S. cerevisiae, many fewer P. pastoris strains are available in which to integrate the expression plasmid for the generation of a recombinant production strain.The wild-type strain, X33, the histidine auxotroph GS115, and the slow-methanol-utilization strain KM71H, have all been used to produce membrane proteins for structural studies .Protease-deficient strains such as SMD1163, which lacks proteinase A and proteinase B, are also available.The structures of recombinant membrane proteins produced using P. pastoris that were published in 2014 and 2015 were all produced in one of the three mutant strains, SMD1163, KM71H and GS115 .Notably production of human aquaporin 2 was actually done using an engineered GS115 strain in which the native aquaporin gene, AQY1, was deleted.In all these strains, P. pastoris post-translationally glycosylates membrane proteins by adding core8-2 groups, but not the higher-order structures found in humans and other mammals; compared to S. cerevisiae, the mannose chains also tend to be shorter.However, the effects of these non-native modifications are not necessarily detrimental and need to be assessed for each individual protein .Indeed, the high-resolution structure of a glycosylated form of the Caenorhabditis elegans P-glycoprotein demonstrates that yeast glycosylation does not necessarily hinder crystal formation .Nonetheless, in order to overcome potential bottlenecks in producing, purifying, characterizing and crystallizing human proteins in yeast, engineered strains have been developed including strains with “humanized” glycosylation and sterol pathways.The yeast membrane differs in composition from that of mammalian membranes.This is likely to be highly relevant to subsequent structural and functional studies of recombinant membrane proteins produced in yeast because lipids have a particularly important role in the normal function of membrane proteins; they contribute to membrane fluidity and may directly interact with membrane proteins.In an attempt to “humanize” the yeast membrane, yeast strains have been developed that synthesize cholesterol rather than the native yeast sterol, ergosterol.This was achieved by replacing the ERG5 and ERG6 genes of the ergosterol biosynthetic pathway with the mammalian genes DHRC24 and DHRC7 , respectively.The gene products of DHRC7 and DHRC24 were identified as key enzymes that saturate sterol intermediates at positions C7 and C24 in cholesterol synthesis.Erg5p introduces a double bond at position C22 and Erg6p adds a methyl group at position C24 in the ergosterol biosynthetic pathway and therefore competes with the gene product of DHRC24 for its substrate.The yeast tryptophan permease, Tat2p, was unable to function in a yeast strain producing only ergosterol intermediates, but in a cholesterol-producing strain activity was recovered to almost wild-type levels.Localization to the plasma membrane also appeared to correlate with the function of Tat2p .The yeast ABC transporter, Pdr12p, although correctly localized to the plasma membrane, was inactive in a cholesterol-producing strain because of the lack of the key methyl group at position C24 .A similar scenario was observed with the function of yeast Can1p: the protein was localized to the plasma membrane regardless of the sterol produced, but function was lost when ergosterol production was disrupted .The native yeast GPCR, Ste2p, which is involved in signal transduction, partially retained its function when cholesterol was produced instead of ergosterol.The agonist of Ste2p, MFα, retained potency on this receptor in both wild-type and cholesterol-producing strains.However, the efficacy appeared to be only half of that observed in the wild-type strain .A positive outcome was observed when the human Na,K-ATPase α3β1 isoform was expressed in a cholesterol-producing P. pastoris strain : there was an improvement in recombinant yield and radio-ligand binding on intact cells, with the number of ligand binding sites in the cholesterol-producing strain increasing 2.5- to 4-fold compared to wild-type and protease deficient strains, respectively, both of which are ergosterol-containing .Overall, studies on native yeast membrane proteins suggest that cell viability is not impaired in “humanized” yeast cells, although growth rates and densities are somewhat affected.However, this is likely to be an acceptable trade-off in return for higher yields of functional protein.Since a relatively small number of heterologous membrane proteins have been produced in cholesterol-producing yeast strains to date, potential exists to further optimize functional yields by using them.Recovering functional protein from recombinant host cells is dependent upon their capacity to synthesize an authentically-folded polypeptide.This requires the proper functioning of the transcription, translation and folding pathways .During a recombinant protein production experiment, the maintenance and processing of an expression plasmid places a substantial metabolic burden on a cell, which means that these pathways must operate under abnormally stressful conditions .A popular strategy to mitigate this burden is to decrease culture temperature; however, transcription, translation, polypeptide folding rates and membrane composition are also affected by low temperature stress .This probably explains why increased yields are not always observed experimentally using that approach.Furthermore, many other variables are likely to affect yields including the composition of the growth medium, the pH and oxygenation of the culture, the inducer concentration and the point of induction.Yeast cells grow quickly in complex or defined media; the selection and composition of suitable broths have been discussed elsewhere .While higher yields are typically achieved in complex media, more control is possible in selective media, such as the ability to incorporate selenomethionine for anomalous dispersion phasing in both S. cerevisiae and P. pastoris .The transcriptional and translational machinery of a cell respond to its growth rate, which is strongly affected by nutrient availability .For example, several inducible and constitutive S. cerevisiae promoters have recently been characterised following growth on different carbon sources and across the diauxic shift in glucose batch cultivation .The study demonstrates that constitutive promoters differ in their response to different carbon sources and that expression under their control decreases as glucose is depleted and cells enter the diauxic shift .Changes in nutrient source have also been found to alter the transcriptome and the global translational capacity of P. pastoris .As discussed in Section 3.1, when P. pastoris cells were cultured in methanol, the majority of the total mRNA pool was associated with two or more ribosomes per mRNA.Methanol is used to induce protein production under the control of PAOX1 in this yeast, suggesting that high recombinant protein yields may be associated with the global response of P. pastoris to methanol as well as promoter activity .Several small molecules, sometimes referred to as chemical chaperones, have been investigated for their ability to enhance functional membrane protein yields.Specific improvements in yield have been reported following addition to recombinant yeast cultures of dimethyl sulphoxide, glycerol, histidine and protein-specific ligands .The effects of antifoams on protein yield, which are added to prevent foaming in bioreactor cultures, are discussed separately in Section 5.3.The solvent DMSO has numerous biological applications, and is routinely used as a cryoprotectant and a drug vehicle .Addition of DMSO to yeast cultures producing membrane proteins has been reported to have a positive effect on yield .DMSO added at 2.5% v/v more than doubled the yield of 9 GPCRs produced in P. pastoris, with improvements of up to 6-fold .In another study, the production in S. cerevisiae of a range of transporters fused to GFP was enhanced on average by 30% following DMSO addition .However, DMSO has also been reported to have no effect or, in some cases, negative effects upon membrane protein yields .The underlying mechanisms are incompletely understood; DMSO is known to increase membrane permeability and cross membranes itself .It has also been shown to upregulate the transcription of genes involved in lipid biosynthesis and increase phospholipid levels in S. cerevisiae .When DMSO is added with stabilizing ligands, it may therefore improve the ability of these compounds to pass through the membrane and reach receptors in compartments within the cell .Glycerol has been added to S. cerevisiae cultures producing human P-glycoprotein; at 10% v/v, yields were improved by up to 3.3-fold .Glycerol is not as membrane-permeable as DMSO , so is thought to exert its effects by stabilizing protein conformation .However, in another study, glycerol addition had a negative impact upon the yields of several membrane proteins produced in S. cerevisiae .When producing recombinant membrane proteins such as GPCRs, ligands may be added at saturating concentrations to boost yields.Functional yields of GPCRs such as the β2-adrenergic receptor were tripled, the 5HT5A receptor doubled and the adenosine A2A receptor doubled by adding receptor-specific ligands.An optimization study demonstrated that addition of ligand could improve functional yields of 18 out of 20 GPCRs, with increases of up to 7-fold.However, a decrease in Bmax was observed for two of the receptors investigated .It is thought that ligands able to pass through the plasma membrane may bind to receptors as they fold during biosynthesis, thereby stabilizing them in the correctly-folded state.As a result, the level of functional receptors expressed at the plasma membrane is increased .The amino acid, histidine, has been shown to double yields of some GPCRs when added to cultures at 0.04 mg/mL.Notably, its addition positively influenced fewer receptors than other additives such as DMSO .Histidine addition did not have any effect upon the growth of the cells; instead it has been suggested that improved protein yields may result from its ability to protect yeast from oxidative stress .Overall, it is clear that the use of a range of additives has improved recombinant membrane protein yields for diverse targets.In some cases additive effects have been synergistic, while in others their addition has been detrimental .It is therefore important to systematically investigate the effects of additives on a case-by-case basis.Membrane protein production is often done in bioreactors in order to obtain the large quantities of protein required for crystallization trials.Use of bioreactors enables the tight control of critical parameters, such as culture temperature, pH and the level of dissolved oxygen, thereby enabling the design of highly-reproducible bioprocesses.The most efficient way to select the optimal combination of these parameters is to use a design of experiments approach .DoE applies a structured test design to determine how combining input parameters set at different levels affects the output.This efficient test design means that all experimental combinations do not need to be tested in order to derive the empirical relationship between the input parameters and protein yield in the form of a deterministic equation.The DoE approach is therefore a highly efficient way to obtain a quantitative understanding of how each factor and its interaction with all other factors affect final protein yield .While this strategy is ideally executed in a bioreactor format, even in shake flasks yields can be improved by careful control of culture conditions .One of the most important parameters in bioreactor cultures, especially of P. pastoris cells, is appropriate oxygenation.This is achieved by vigorous stirring and sparging of gases, which usually leads to foaming.The addition of chemical antifoaming agents is therefore required to manage and prevent the formation of foam.As additives to the process, these chemicals can affect both host cells and the recombinant proteins being produced; yields can be affected by the type of antifoam used, the concentration added, and whether production is undertaken in small shake flasks or in larger-scale bioreactors.Although the biological effects of antifoams are not well understood, they have been shown to affect the volumetric mass oxygen transfer coefficient , influence growth rates of yeast and are thought to alter membrane permeability .While it was possible to more than double the yield of soluble GFP secreted by P. pastoris cells following the optimization of antifoam addition, the same conditions had detrimental effects on the functional yield of a recombinant GPCR produced in yeast.These findings highlight the importance of investigating the effects of antifoam addition; this often disregarded experimental parameter can significantly affect recombinant protein yields.In Section 3.1, we highlighted the fact that most recombinant expression systems employed in structural biology pipelines depend upon strong, inducible promoters.All promoters are known to vary in activity over time as well as in response to different carbon sources, which means that the timing of induction can be critical in obtaining the highest yields of functional protein; these parameters must be empirically determined.The response of a series of inducible S. cerevisiae promoters to different carbon sources has been studied providing a framework for these types of experiments.We previously demonstrated the major impact of the induction regime on the yield of secreted GFP from P. pastoris cultures, showing the importance of matching the composition of the methanol feedstock to the metabolic activity of the cells .PAOX1 is induced on methanol; however, when glucose was the pre-induction carbon source, the adenosine A2A receptor and GFP were still produced in the pre-induction phases of bioreactor cultures .This study also reveals that a range of recombinant membrane proteins can be detected in the pre-induction phases of P. pastoris cultures when grown in bioreactors, but not shake-flasks.The results of all these investigations suggest that a DoE approach to selecting and optimizing induction phase conditions might be a particularly effective method of maximizing recombinant protein yields.The first steps in isolating a recombinant membrane protein are to break open the host cells and harvest the membranes.In yeast this requires breaking the cell wall, which needs harsher conditions than those typically used for insect, mammalian or E. coli cells.Typical methods for achieving this include high pressure or homogenization using glass beads shaken at high frequency followed by differential centrifugation .In isolating a recombinant membrane protein from yeast membranes , the goal is to maintain structural integrity and functionality.Depending on the protein target, this can be an extremely difficult task.However approaches are available to optimize the extraction process and the environment into which the target protein is being transferred.Traditionally, detergents have been used for membrane protein extraction, purification and crystallization; the general principles have been reviewed extensively elsewhere .Popular detergents include the non-ionic n-octyl-β-d-gluocopyranoside, n-decyl-β-d-maltopyranoside and n-dodecyl-β-d-maltopyranoside .Interestingly, the most commonly-used detergents to date are the same for yeast as for other expression systems, despite the differences in membrane composition.Optimization of detergent and buffer conditions must be done for each individual target membrane protein by assessment of protein stability and monodispersity.Unfortunately, membrane protein aggregation is a relatively common occurrence in these studies since detergents do not provide an exact mimic of the lipid environment in which the protein natively resides.Alternative amphiphiles have been designed to overcome these limitations and include novel compounds such as maltose neopentyl glycol .It has been suggested that for some target membrane proteins, MNG provides increased protein stability in comparison to detergents such as DM .One useful technique to assess membrane protein stability prior to crystallization trials exploits a thiol-specific fluorochrome, N-maleimide, which enables the investigator to assess the thermal stability of a recombinant membrane protein in a high-throughput format, therefore requiring small amounts of purified material .In order to use this assay, the target membrane protein must have cysteine residues buried within the hydrophobic interior.Such residues bind thiol-specific CPM upon temperature-induced protein unfolding.CPM is essentially non-fluorescent until it reacts with a cysteine residue; therefore fluorescence can be recorded over time to determine the rate of protein unfolding.The influence of detergent type and concentration, salt concentration, pH, glycerol content and lipid addition on stability can all be investigated.Several studies have found a correlation between protein stability and the likelihood of obtaining well-ordered crystals for high resolution structure determination .When determining the structure of a protein it is important to demonstrate that it is functional.For many membrane proteins, measuring function in the detergent-solubilized state can be difficult, either due to detergent effects or because both ‘sides’ of the membrane are accessible.Therefore reconstitution of detergent-solubilized proteins into proteoliposomes is needed.Typically this involves the following steps: preparation of liposomes comprised of the desired lipids; destabilization of the liposomes with a detergent; mixing of detergent-purified protein with the liposomes; and removal of detergent using methods such as adsorption onto Bio-Beads SM-2 resin or dialysis .Several proteins expressed in S. cerevisiae or P. pastoris have been reconstituted into proteoliposomes and studied, showing that proteins produced in yeast are fully functional and comparable to those expressed in other cell systems.Although all crystal structures of membrane proteins to date, including those synthesized in yeast, have used detergents for extraction of the protein from the lipid bilayer, the use of detergents is not without problems.As mentioned in Section 6.1, screening for conditions and detergents that effectively extract the protein yet retain structure and stability can be difficult, time consuming and expensive.The environment produced by a detergent micelle does not fully mimic the lipid bilayer environment, as not only does the bilayer provide lateral pressure to stabilize the protein structure but interactions between the protein and its annular lipids can affect protein function.Notably, the most effective detergents for extraction are often not the best detergents for crystal formation.Recently a new detergent-free method for extraction of membrane proteins has emerged using SMA co-polymers.The SMA inserts into biological membranes and forms small discs of lipid bilayer surrounded by the polymer, termed SMALPs , also known as lipodisqs or native nanodiscs .Membrane proteins within the SMALPs retain their annular lipid bilayer environment , yet the particles are small, stable and water soluble, allowing standard affinity chromatography methods to be used to purify a protein of interest .To date this approach has been successfully applied to a wide range of transmembrane proteins from many different expression systems including both S. cerevisiae and P. pastoris , for protein targets including GPCRs, ABC transporters and ion channels.Proteins within SMALPs have been shown to retain functional activity .The small size of the particle and lack of interference from the polymer scaffold mean the SMALPs are ideal for many spectroscopic and biophysical techniques .Importantly for structural studies, SMALP-encapsulated proteins have been found to be significantly more thermostable, less prone to aggregation, and easier to concentrate than detergent-solubilized proteins .The importance of maintaining the lipid bilayer environment and lateral pressure is highlighted in Fig. 5d.When the adenosine A2A receptor is extracted from P. pastoris membranes with detergent, it is necessary to supplement with the cholesterol analogue, cholesterol hemisuccinate, in order to retain any binding activity.However when the SMA co-polymer is used to extract the receptor, there is no requirement for CHS suggesting that it is not the cholesterol per se that is required for function of this protein, but some stabilizing interaction with lipids.Although as yet, there are no reports of SMALP-encapsulated proteins being used to generate protein crystals, they have been used in both negative stain and cryo-single particle electron microscopy .With recent technological and analytical advances within the field of electron microscopy the possibility of high resolution membrane protein structures using electron microscopy has become a reality ; SMALPs offer the ability for these structures to be obtained without stripping away the membrane environment from a transmembrane protein.Yeast has an important role to play in membrane protein structural biology projects; since S. cerevisiae and P. pastoris are particularly amenable to genetic study, new insight may emerge that can lead to the design of improved experiments.One challenge is to identify which experimental parameters discussed in Sections 3–5, above, should be the focus in devising a production trial for a novel target.This is particularly demanding since these parameters may affect both host-cell- and target-protein-specific responses.Our understanding of the interlinked processes of transcription, translation and protein folding offers new opportunities to improve functional yields of recombinant membrane proteins through strain selection and the choice of suitable culture conditions using DoE.Coupled with new approaches to extraction and solubilization, it is likely that the pace of solving new membrane protein structures is set to increase in the foreseeable future.

Historically, recombinant membrane protein production has been a major challenge meaning that many fewer membrane protein structures have been published than those of soluble proteins. However, there has been a recent, almost exponential increase in the number of membrane protein structures being deposited in the Protein Data Bank. This suggests that empirical methods are now available that can ensure the required protein supply for these difficult targets. This review focuses on methods that are available for protein production in yeast, which is an important source of recombinant eukaryotic membrane proteins. We provide an overview of approaches to optimize the expression plasmid, host cell and culture conditions, as well as the extraction and purification of functional protein for crystallization trials in preparation for structural studies.

ACP-DL: A Deep Learning Long Short-Term Memory Model to Predict Anticancer Peptides Using High-Efficiency Feature Representation

Cancer is one of the most devastating killers of human beings, accounting for millions of deaths around the world each year.1,2,Conventional physical and chemical methods, including targeted therapy, chemotherapy, and radiation therapy, remain the principle modes to treat cancer, which focus on killing the diseased cells, but normal cells are also adversely affected.3,4,More obviously, these treatments are expensive and inefficient, which means there is an urgent need to develop novel efficient measures to solve this deadly disease.5,The discovery of anticancer peptides, a kind of short peptide generally with a length less than 50 amino acids and most of which are derived from antimicrobial peptides, often cationic in nature, has led to the emergence of a novel alternative therapy to treat cancer.ACPs open a promising perspective for cancer treatment, and they have various attractive advantages,6,7 including high specificity, ease of synthesis and modification, low production cost, and so on.8,ACPs could interact with the anionic cell membrane components of only cancer cells, and, for this reason, they can selectively kill cancer cells with almost no harmful effect on normal cells.4,9,In addition, few ACPs, e.g., cell-penetrating peptides or peptide drugs, inhibit the cell cycle or any other functionality.Thus, they are safer than traditional broad-spectrum drugs, which have become the most competitive choice as therapeutics compared to small molecules and antibodies.In recent years, ACP therapeutics have been extensively explored and used to fight various tumor types across different phases of preclinical and clinical trials.10–14,However, only a few of them can eventually be employed for clinical treatment.Furthermore, it’s time-consuming, expensive, and lab-limited to identify potential new ACPs by experiment.With the huge therapeutic importance of ACPs, there is an urgent need to develop highly efficient prediction techniques.Some notable research has been reported in the prediction of ACPs.15,Tyagi et al.16 developed a support vector machine model using amino acid composition and dipeptide composition as input features on experimentally confirmed anticancer peptides and random peptides derived from the Swiss-Prot database.Hajisharifi et al.17 also reported an SVM model using Chou’s18,19 pseudo AAC and the local alignment kernel-based method.Vijayakumar and Ptv20 proposed that, between ACPs and non-ACPs, there was no significant difference in AAC observed.Also, they presented a novel encoding measure, which achieved better predictive performance than AAC-based features, considering both compositional information and centroidal, distributional measures of amino acids.Shortly afterward, based on the optimal g-gap dipeptide components, by exploring the correlation between long-range residues and sequence-order effects, Chen et al.21 described iACP, which exhibited the best predictive performance at that time.More recently, Wei et al.22 developed a sequence-based predictor called ACPred-FL, which uses two-step feature selection and seven different feature representation methods.According to the cognition of the short length of ACPs, it’s difficult to exploit the efficient features of many mature feature representation methods, which are widely used on protein sequences.23,With the rapid growth of the number of ACPs that has been identified experimentally, by machine learning, and by bioinformatics research,24–40 the computational prediction methods of ACPs still need further development.In this study, we proposed a deep learning long short-term memory neural network model to predict anticancer peptides, which we named ACP-DL.The efficient features exploited from peptides sequences are fed as input to train the LSTM model.More specifically, peptide sequences are transformed by k-mer sparse matrix of the reduced amino acid alphabet,41,42 which is a 2D matrix, and retained almost complete sequence order and amino acid component details.Meanwhile, peptide sequence are also converted by a binary profile feature,43 which can be regarded as one-hot encoding of categorical variables and has been suggested to be an efficient feature extraction technique.16,22,Finally, these features are fed into our LSTM model to predict new anticancer peptides.To further evaluate the performance of our model, we evaluated the ACP-DL on two novel benchmark datasets.We also compared the purposed ACP-DL with existing state-of-the-art machine-learning models, e.g., SVM,44,45 Random Forest,46and Naive Bayes.47,The 5-fold cross-validation experimental results showed that our method is suitable for the anticancer prediction mission with notable prediction performance.The workflow of ACP-DL is show in Figure 1.Above all, we compared the different distributions of amino acids in anticancer peptides, non-anticancer peptides, and all peptides in datasets ACP740 and ACP240.The results for ACP740 are shown in Figure 2, the composition of all 20 amino acids in these peptides were counted and compared.Certain residues, including Cys, Phe, Gly, His, Ile, Asn, Ser, and Tyr, were found to be abundant in anticancer peptides compared to non-anticancer peptides, while Glu, Leu, Met, Gln, Arg, and Trp were abundant in non-anticancer peptides compared to anticancer peptides.Similarly, as shown in Figure 3, in dataset ACP240, the Phe, His, Ile, and Lys were abundant in anticancer peptides.Since terminal residues play essential roles in biological functions of peptides.First, we executed our model ACP-DL on the ACP740 and ACP240 datasets to evaluate its ability of predicting anticancer peptides.The 5-fold cross-validation details are offered in Tables 1 and 2.The average accuracy of 5-fold cross-validation on ACP740 was 81.48% with 3.12% SD, the average sensitivity was 82.61% with 3.36% SD, the average specificity was 80.59% with 4.01% SD, the mean precision was 82.41% with 3.81% SD, and the Matthews correlation coefficient was 63.05% with 6.23% SD.ACP-DL showed an outstanding capability to identify anticancer peptides, performed an area under the receiver operating characteristic curve of 0.894, as shown in Figure 4A, and has achieved the best performance on the ACP740 dataset among all comparison methods.The mean accuracy of 5-fold cross-validation on ACP240 was 85.42%, the average Sens was 84.62%, the average Spec was 89.94%, the mean Prec was 80.28%, and the MCC was 71.44%; and, the AUC of ACP-DL was 0.906, as shown in Figure 4C.In general, the performance of the deep learning model will become better with the increase in the scale of data, and the model that can achieve good results on smaller datasets will also achieve good results on huger data.Actually, the data scale of anticancer peptides is not very large, so we didn’t implement a neural network model with very complex architecture; and, to a certain extent, the 5-fold cross-validation is not conducive to the neural network model, because it further reduces the amount of training data.It is noteworthy that, although the dataset ACP240 was smaller than ACP740, our model ACP-DL still performed very well.The experimental results of rigorous cross-validation on benchmark dataset ACP740 and dataset ACP240 confirmed that our model has a good capability to predict anticancer peptides.To evaluate the ability of the purposed method, we further compared ACP-DL with other widely used machine-learning models on the same benchmark datasets, including ACP740 and ACP240.Here we have selected the SVM, RF, and NB models, and we built them using the same cross-validation datasets.The implementation of these three machine-learning models comes from Scikit-learn,48 and they were tested with default parameters.Since these methods were evaluated using the same evaluation criteria, the comparison model and deep learning model ACP-DL results are shown in Table 3 and Figures 4 and 5.ACP-DL obtained the most significant performance among the contrasted methods.Table 3 shows the details of the comparison.In the ACP740 dataset, our method ACP-DL significantly outperformed other methods with an accuracy of 81.48%, a Sens of 82.61%, a Spec of 80.59%, a Prec of 82.41%, an MCC of 63.05%, and an AUC of 0.894.ACP-DL increased the accuracy by over 5%, the MCC by over 10%, and the AUC by more than 5%, respectively.In the dataset ACP240, ACP-DL also performed remarkably with an accuracy of 85.42%, a Sens of 84.62%, a Spec of 89.94%, a Prec of 80.28%, an MCC of 71.44%, and an AUC of 0.906.ACP-DL improved the accuracy by over 8%, the Spec by over 10%, the MCC by over 14%, and the AUC by more than 5%, respectively.Obviously, the deep learning model shows its power, and our model is suitable for anticancer peptide identification and prediction.ACP-DL is a competitive model used to predict anticancer peptides and accelerate related research.The comparison experiment results proved our assumption.In this study, we proposed a deep learning LSTM model to predict potential anticancer peptides using high-efficiency feature representation.More specifically, we developed an efficient feature representation approach by integrating binary profile feature and k-mer sparse matrix of reduced amino acid alphabet feature to fully exploit peptide sequence information.Then we implemented a deep LSTM model to automatically learn how to identify anticancer peptides and non-anticancer peptides.To the best of our knowledge, this is the first time that the deep LSTM model has been applied to predict anticancer peptides.Meanwhile, to evaluate the capability of the proposed method, we further compared ACP-DL with widely used machine-learning models in the same benchmark datasets, including ACP740 and ACP240; experimental results on the 5-fold cross-validation show that the proposed method achieved outstanding performance compared with existing methods, on benchmark dataset ACP740 with 81.48% accuracy at the AUC of 0.894 and on dataset ACP240 with an accuracy of 85.42% at the Spec of 89.94 and the AUC of 0.906, respectively.The improvement is mainly from the deep LSTM model’s model parameter optimization and effective feature representation from original peptide sequences.In addition, we have contributed two novel anticancer peptide benchmark datasets, ACP740 and ACP240, in this work.It is anticipated that ACP-DL will become a very useful high-throughput and cost-effective tool, being widely used in anticancer peptide prediction as well as cancer research.Further, as demonstrated in a series of recent publications in developing new prediction methods,49–51 user-friendly and publicly accessible web servers will significantly enhance their impacts.It is our wish to be able to provide in the future a web server for the prediction method presented in this paper.In this study, we proposed a novel deep learning LSTM model to predict anticancer peptides, named ACP-DL, using high-efficiency features provided by k-mer sparse matrix and the binary profile feature.Furthermore, we evaluated ACP-DL’s predictive performance of anticancer peptides in benchmark datasets ACP740 and ACP240.Moreover, we compared ACP-DL with three widely used machine-learning models in the same datasets, including SVM,44 RF,46 and NB,47 to prove the robustness and effectiveness of the proposed method.Eventually, we made a summary, analysis, and discussion of the ACP-DL.We constructed two novel benchmark datasets in this work for ACP identification, named ACP740 and ACP240.As previous studies suggested, the new datasets comprised both positive and negative datasets, while positive samples were experimentally validated ACPs and AMPs without anticancer function were collected as negative samples.We selected 388 samples as the initial positive data on the basis of Chen et al.’s21 and Wei et al.’s24 studies, of which 138 were from Chen et al.’s work and 250 were from Wei et al.’s work.Correspondingly, the initial negative data were 456 samples, of which 206 were from Chen et al.’s work and 250 were from Wei et al.’s work, respectively.To avoid the bias of dataset, the widely used tool CD-HIT52 was further used to remove those peptides sequences with a similarity of more than 90%.As a result, we finally obtained a dataset containing 740 samples, of which 376 were positive samples and 364 were negative samples.As the same procedure, to validate the generalization ability of the predictive model, we further constructed an additional dataset, named ACP240, which initially included 129 experimentally validated anticancer peptide samples as the positive dataset and 111 AMPs without anticancer functions as the negative dataset, respectively.Moreover, those sequences with a similarity of more than 90% were removed using the popular tool CD-HIT.52,The similarity setting was consistent with previous studies.21,22,The CD-HIT is available at http://weizhong-lab.ucsd.edu/cdhit-web-server.There was no overlap between dataset ACP740 and dataset ACP240, and these two datasets are both non-redundant datasets.The two benchmark datasets are publicly available at https://github.com/haichengyi/ACP-DL.We also encoded the peptide sequence by using the k-mer sparse matrix previously proposed.41,In detail, its k-1 consecutive nucleotides and k consecutive nucleotides are regarded as a unit.3-mer of peptides is composed of 3 amino acids.53,First the 20 amino acids were reduced into 7 groups based on their dipole moments and side chain volume: Ala, Gly, and Val; Ile, Leu, Phe, and Pro; Tyr, Met, Thr, and Ser; His, Asn, Gln, and Tpr; Arg and Lys; Asp and Glu; and Cys.16,54,55,So, the peptide sequence was reduced into a 7-letter alphabet.Then we scanned each peptide sequence from left to right, stepping one amino acid at a time, which is considered the characteristics of each amino acid.LSTM is an improvement of a recurrent neural network, which is mainly used in the natural language processing and speech recognition field.57–59,Different from a traditional neural network, an RNN can take advantage of sequence information.Theoretically, it can utilize the information of arbitrary length sequence; but, because of the problem of vanishing gradient in the network structure, it can only retrospectively utilize the information on time steps that are close to it in practical applications.To solve this problem, LSTM was presented with specially designed network architecture, which can learn long-term dependency information naturally.A general architecture of LSTM is composed of an input gate, a forget gate, an update gate, and a memory block.The improvement of LSTM is mainly from incorporating a memory cell that accepts the network to learn when to forget previous hidden states and when to update hidden states, according to the input information through time.It uses dedicated storage units to store information.To our knowledge, the deep LSTM model was first applied to predict novel anticancer peptides in this work.LSTM selectively passes information through a gate unit, which mainly is by means of a sigmoid neural layer and a dot multiplication operation.Each element of the sigmoid layer output is a real number between 0 and 1, representing the weight that the corresponding information passes through.For example, 0 means no information is allowed, and 1 means let all information pass.The implementation of the deep learning model is based on the Keras framework, which is capable of running on top of TensorFlow, Theano, or CNTK and is supported on both GPUs and CPUs.It was developed with a focus on enabling fast experimentation.61,H.-C.Y. and Z.-H.Y. conceived the algorithm, carried out analyses, prepared the datasets, carried out experiments, and wrote the manuscript.Other authors designed, performed, and analyzed experiments and wrote the manuscript.All authors read and approved the final manuscript.The authors declare no competing interests.

Cancer is a well-known killer of human beings, which has led to countless deaths and misery. Anticancer peptides open a promising perspective for cancer treatment, and they have various attractive advantages. Conventional wet experiments are expensive and inefficient for finding and identifying novel anticancer peptides. There is an urgent need to develop a novel computational method to predict novel anticancer peptides. In this study, we propose a deep learning long short-term memory (LSTM) neural network model, ACP-DL, to effectively predict novel anticancer peptides. More specifically, to fully exploit peptide sequence information, we developed an efficient feature representation approach by integrating binary profile feature and k-mer sparse matrix of the reduced amino acid alphabet. Then we implemented a deep LSTM model to automatically learn how to identify anticancer peptides and non-anticancer peptides. To our knowledge, this is the first time that the deep LSTM model has been applied to predict anticancer peptides. It was demonstrated by cross-validation experiments that the proposed ACP-DL remarkably outperformed other comparison methods with high accuracy and satisfied specificity on benchmark datasets. In addition, we also contributed two new anticancer peptides benchmark datasets, ACP740 and ACP240, in this work. The source code and datasets are available at https://github.com/haichengyi/ACP-DL.

Influence of alkalinity and temperature on photosynthetic biogas upgrading efficiency in high rate algal ponds

Biogas from the anaerobic digestion of organic matter constitutes a promising renewable energy vector for the production of heat and power in households and industry .Raw biogas is mainly composed of CH4, CO2 and other components at lower concentrations such as H2S, oxygen, nitrogen, siloxanes, ammonia and halogenated hydrocarbons .The high content of CO2 significantly reduces the specific calorific value of biogas, increases its transportation costs and promotes emissions of CO and hydrocarbons during combustion.On the other hand, H2S is a toxic and malodorous gas that severely reduces the lifespan of the biogas storage structures, pipelines, boilers and internal combustion engines .The removal of these biogas pollutants is mandatory in order to comply with the technical specifications required for biogas injection into natural gas grids or use as a vehicle fuel .State-of-the-art physical/chemical or biological technologies for CO2 removal often need a previous H2S cleaning step, while the few technologies capable of simultaneously removing CO2 and H2S from biogas exhibit a high energy and chemicals consumption, which limits their economic and environmental sustainability for biogas upgrading .In this context, algal-bacterial symbiosis represents a cost-effective and environmentally friendly platform for the simultaneous removal of CO2 and H2S from raw biogas in a single step process .Photosynthetic biogas upgrading in algal-bacterial photobioreactors is based on the light-driven CO2 consumption by microalgae coupled to the oxidation of H2S to either elemental sulfur or sulfate by sulfur-oxidizing bacteria using the oxygen photosynthetically produced .The environmental and economic sustainability of the process can be boosted with the integration of wastewater treatment in the photobioreactor devoted to biogas upgrading .In this regard, digestate or domestic wastewater can be used as an inexpensive nutrient source for microalgae and bacteria growth during photosynthetic biogas upgrading, which in turn would reduce the costs associated to nutrients removal .Recent investigations have focused on the optimization of the simultaneous biogas upgrading and digestate treatment in photobioreactors.These studies have identified the optimum photobioreactor configuration , the strategies for minimizing oxygen concentration in the biomethane and the influence of light intensity, wavelength and photoperiod regime on the final quality of the upgraded biogas under indoors conditions .Unfortunately, most of these previous works did not result in a biomethane composition complying with the specifications of most European regulations due to the limited CO2 mass transfer rates from the raw biogas to the aqueous phase .In this context, a recent study conducted outdoors in a high rate algal pond interconnected to an external absorption column for the simultaneous treatment of biogas and centrate suggested that both alkalinity and temperature in the algal-bacterial broth can play a key role on the final biomethane quality .Indeed, culture broth alkalinity determines the kinetics of both microalgae growth in the HRAP and CO2/H2S absorption in the absorption column .Likewise, culture broth temperature directly impacts on the gas/liquid equilibria and biomass growth kinetics .However, despite the relevance of these environmental parameters on the performance of photosynthetic biogas upgrading, no study has evaluated to date the effect of alkalinity and temperature on the final quality of biomethane in algal-bacterial photobioreactors.This work systematically evaluated the influence of inorganic carbon concentration and temperature in the cultivation broth on biomethane quality in a 180 L HRAP interconnected to a 2.5 L absorption column via external recirculation of the settled cultivation broth under indoor conditions.The tested inorganic carbon concentrations are typically encountered in high and medium strength digestates and domestic wastewater, respectively, while the tested temperatures are representative of spring-autumn and summer seasons in temperate climates.A synthetic gas mixture composed of CO2, H2S and CH4, was used in this study as a model biogas.Centrate was collected from the anaerobically digested sludge-dehydrating centrifuges at Valladolid wastewater treatment plant and stored at 4 °C prior to use.The average centrate composition was as follows: inorganic carbon = 459 ± 83 mg L−1, total nitrogen = 576 ± 77 mg L−1 and S-SO42− = 4.7 ± 3.4 mg L−1.NH4Cl was added to the raw centrate to a final TN concentration of 1719 ± 235 mg L−1 in order to simulate a high-strength digestate and thus minimize the flow rate of centrate used in the pilot plant.The experimental set-up was located at the Department of Chemical Engineering and Environmental Technology at Valladolid University.The set-up consisted of a 180 L HRAP with an illuminated surface of 1.2 m2 divided by a central wall in two water channels.The HRAP was interconnected to a 2.5 L absorption column via external liquid recirculation of the supernatant of the algal-bacterial cultivation broth from a 10 L conical settler coupled to the HRAP.The remaining algal bacterial biomass collected at the bottom of the settler was continuously recirculated to the HRAP in order to avoid the development of anaerobic conditions in the settler due to an excessive biomass accumulation.The HRAP cultivation broth was continuously agitated by a 6-blade paddlewheel at an internal recirculation velocity of ≈20 cm s−1.A photosynthetic active radiation of 1350 ± 660 μmol m−2 s−1 at the HRAP surface was provided by six high-intensity LED PCBs operated in a 12 h:12 h light/dark regime.Six operational conditions were tested in order to assess the influence of alkalinity and temperature on biomethane quality.The influence of IC concentrations of 1500, 500 and 100 mg L−1 was evaluated in stages I–II, III–IV and V–VI, respectively, while a temperature of 35 °C was maintained during stages I, III and V and a temperature of 12 °C during stages II, IV and VI.The HRAP was initially filled with an aqueous solution containing a mixture of NaHCO3 and Na2CO3 before inoculation to adjust the initial IC concentration to the corresponding concentration set in the operational stage.The IC concentration of the digestate fed to the HRAP during each operational stage was also adjusted accordingly.Thus, IC concentrations of 1500 and 500 mg L−1 were obtained by addition of NaHCO3 to the raw centrate, while IC concentrations of 100 mg L−1 were achieved via an initial centrate acidification with HCl aqueous solution to a final pH of 5.5 in order to remove IC by air-aided CO2 stripping followed by NaHCO3 addition to adjust the IC concentration.The temperature of the HRAP cultivation broth was controlled with an external heat exchanger.A consortium of microalgae/cyanobacteria from outdoors HRAPs treating centrate and domestic wastewater at the Department of Chemical Engineering and Environmental Technology at Valladolid University and at the WWTP of Chiclana de la Frontera, respectively, was used as inoculum in each operational stage.During the illuminated periods, the HRAP was fed with the modified digestate as a nutrient source at a flow rate of 2 L d−1, while synthetic biogas was sparged into the absorption column under co-current flow operation at a flow rate of 4.9 L h−1 and a recycling liquid flow rate to biogas flow rate ratio of 0.5 .Tap water was continuously supplied in order to compensate water evaporation losses.A biomass productivity of 7.5 g dry matter m−2 d−1 was set in the six operational stages evaluated by controlling the biomass harvesting rate.The algal-bacterial biomass was harvested by sedimentation after coagulation-flocculation via addition of the polyacrylamide-based flocculant Chemifloc CV-300 .This operational strategy resulted in a process operation without effluent.Approximately two weeks after the beginning of each stage, the system had already achieved a steady state, which was confirmed by the negligible variation of most parameters during the rest of the stage.The ambient and cultivation broth temperatures, the flow rates of digestate, tap water and external liquid recycling, and the dissolved oxygen concentration in the cultivation broth were monitored three times per week during the illuminated and dark periods.The PAR was measured at the HRAP surface at the beginning of each stage.Gas samples of 100 μL from the raw and upgraded biogas were drawn three times per week in order to monitor the CO2, H2S, CH4, O2 and N2 concentrations.The inlet and outlet biogas flow rates at the absorption column were also measured to accurately determine CO2 and H2S removals.Liquid samples of 100 mL of digestate and cultivation broth were drawn three times per week and filtered through 0.20 μm nylon filters to monitor pH, dissolved IC, TN and SO42−.In addition, liquid samples of 20 mL were also drawn three times per week from the cultivation broth to monitor the TSS concentration.Unfortunately, no analysis of the microbial population structure was conducted in this study.The DO concentration and temperature were monitored with an OXI 330i oximeter, while a pH meter Eutech Cyberscan pH 510 was used for pH determination.The PAR at the HRAP surface was recorded with a LI-250A lightmeter.CO2, H2S, O2, N2 and CH4 gas concentrations were analysed using a Varian CP-3800 GC-TCD according to Posadas et al. .The dissolved IC and TN concentrations were determined using a Shimadzu TOC-VCSH analyser equipped with a TNM-1 chemiluminescence module.SO4−2 concentration was measured by HPLC-IC according to Posadas et al. , while the determination of TSS concentration was carried out according to standard methods .The ambient and cultivation broth temperatures, pH, cultivation broth TSS concentrations, the flow rates of digestate, tap water and external liquid recycling, the dissolved oxygen concentration, and the flowrate and composition of biogas were obtained under steady state operation.CO2-REs and H2S-REs were calculated according to based on duplicate measurements of the biogas and biomethane composition.The results here presented were provided as the average values along with their corresponding standard deviation.A t-student statistical analysis was performed in order to determine the statistically significant differences between the pH value at the bottom and the top of the absorption column.In addition, the t-student test was applied to determine the effect of temperature at the different alkalinities tested.Finally, a one-way ANOVA was performed to determine the effect of alkalinity and temperature on the quality of the biomethane produced along the six operational stages.The average water loss by evaporation in the HRAP during process operation at 35 °C was 15.9 ± 1.2 L d−1 m−2, while this value decreased to 1.9 ± 0.4 L d−1 m−2 at 12 °C.The maximum evaporation rate recorded in this study was ~1.8 times higher than the maximum reported by Posadas et al. in a similar outdoors HRAP during summer in a temperate climate and ~2.6 times higher than the highest value estimated by Guieysse et al. in an arid location.The high water losses here recorded were caused by the high and constant temperatures of the cultivation broth throughout the entire day and the high turbulence induced by the oversized paddlewheel typical in lab-scale systems .On the other hand, the lower temperature prevented water losses, the minimum value recorded being in the range obtained by Posadas et al. in a similar outdoors HRAP during spring in a temperate climate.The average DO concentrations in the cultivation broth during the illuminated period were 10.1 ± 2.1, 14.4 ± 0.9, 13.5 ± 0.8, 16.6 ± 1.9, 8.8 ± 0.8 and 16.5 ± 1.7 mg O2 L−1 during stages I, II, III, IV, V and VI, respectively; while the DO concentrations during the dark period averaged 1.3 ± 0.5, 6.2 ± 1.2, 3.7 ± 0.1, 7.0 ± 0.9, 4.6 ± 0.6 and 10.0 ± 0.5 mg O2 L−1 in stages I to VI, respectively.The higher DO concentrations recorded at 12 °C were attributed to the increased oxygen solubility at low temperatures .No pernicious effect of these DO concentrations on microalgae activity was expected since inhibition of photosynthesis typically occurs above 25 mg O2 L−1, and the values remained within the optimal range to support nutrients and CO2 bioassimilation .The average pHs in the HRAP during stages I, II, III, IV, V and VI were 11.0 ± 0.0, 10.5 ± 0.3, 10.5 ± 0.4, 9.7 ± 0.2, 7.2 ± 0.3 and 7.5 ± 0.2, respectively.These findings confirmed that the influence of the IC concentration in the cultivation broth was higher than that of the temperature on the steady state pH of the cultivation broth, which was in accordance with previous results from Posadas et al. .Moreover, the highest pH values here recorded matched those observed by Toledo-Cervantes et al. during the simultaneous treatment of biogas and digestate in a similar experimental set-up, while Lebrero et al. reported comparable pHs to the lowest values obtained in this study when evaluating biogas upgrading in a transparent PVC column photobioreactor.A higher pH in the cultivation broth enhances the mass transfer rate of the acidic gases from biogas to the liquid phase, which ultimately results in higher upgrading performances as discussed below .TSS concentrations of 0.4–0.5 g L−1 were recorded during process operation at both high and medium alkalinity.Thus, the biomass concentration in the cultivation broth at the imposed biomass productivity during stages I to IV was representative of the operation of conventional outdoor raceways, where TSS concentration typically ranges from 0.3 to 0.5 g L−1 .However, the biomass concentration and productivity, during stages V and VI, decreased to 0.2 g TSS L−1 and 5–7 g dry matter m−2 d−1 respectively, due to the lower carbon load supplied in the feed and the lower CO2 mass transfer in the absorption column mediated by the low pH of the cultivation broth.Average CO2-REs of 99.3 ± 0.1, 97.8 ± 0.8, 48.3 ± 3.6, 50.6 ± 3.0, 30.8 ± 3.6 and 41.5 ± 2.0% were recorded during stages I, II, III, IV, V and VI, respectively.During stages I and II, the high CO2 mass transfer rates between the biogas and the liquid phase were promoted by the high pH and high buffer capacity of the cultivation broth.The initial pH of the system was roughly maintained in the cultivation broth of the HRAP and along the absorption column as a result of the high alkalinity of the digestate.During stages III and IV, a slight decrease in the pH of the cultivation broth from the initial value occurred as a result of biogas absorption in the column due to both the acidic nature of CO2 and H2S and the lower buffer capacity of the media, thus resulting in lower CO2-REs.This effect was more pronounced in stages V and VI, where the low buffer capacity of the cultivation broth was unable to maintain a constant and high pH, which resulted in the lowest CO2-REs recorded in this experiment.The pH of the cultivation broth significantly differed between the bottom and the top of the absorption column at medium and low alkalinity.Higher L/G ratios would have avoided these high pH variations along the absorption column.Nevertheless, a lower biomethane quality would be expected at high L/G ratios as a result of the enhanced O2 and N2 stripping from the recycling cultivation broth to the upgraded biogas .These data was in accordance to Lebrero et al. , who reported an average CO2-RE of 23% at a pH 7 and of 62% when the pH of the cultivation broth was increased up to 8.1.Overall, these results showed the relevance of inorganic carbon concentration to maintain a high pH in the scrubbing cultivation broth during biogas upgrading.On the other hand, a negligible effect of the temperature on CO2-RE was found at high and medium alkalinity.However, the higher CO2 solubility at lower temperatures resulted in a higher CO2-RE at 12 °C compared to that achieved at 35 °C under low alkalinity.This suggests that, despite the lower alkalinity of the cultivation broth could be partially compensated with the decrease in temperature, the latter mediated a major effect on CO2 mass transfer.C-CO2 desorption ratios, defined as the ratio between the mass flow rate of IC desorbed from the cultivation broth and the total mass flow rate of IC supplied to the system and considering a carbon content of 50% in the microalgal biomass , of 51, 50, 2 and 4% were recorded in stages I, II, III and IV, respectively.However, a negligible C-CO2 desorption was estimated at low alkalinities as a result of the low CO2 mass transfer in the absorption column and low IC input via centrate addition, which ultimately resulted in process operation under carbon limiting conditions.The highest CO2 desorption rates obtained during stages I and II were associated to the high IC concentration in the cultivation broth, which supported a positive CO2 concentration gradient to the atmosphere even though IC was mainly in the form of CO32−.On the contrary, IC was preferentially used by microalgae rather than removed by stripping despite the low pH prevailing in the cultivation broth at low alkalinity.These results agreed with those reported by Meier et al. , who identified stripping as the main mechanism responsible for carbon removal in a 50 L photobioreactor fed with a mineral medium and connected to a bubble column.Similarly, Alcántara et al. observed a 49% CO2 loss by desorption in a comparable 180 L HRAP interconnected to an absorption column during the simultaneous treatment of biogas and centrate.Average H2S-REs of 96.4 ± 2.9, 100 ± 0, 93.4 ± 2.6, 94.7 ± 1.9, 66.2 ± 6.9 and 80.3 ± 3.9% were recorded during stages I, II, III, IV, V and VI, respectively."The higher H2S-REs compared to CO2-REs were attributed to the higher dimensionless Henry's Law constants of H2S, defined as the ratio between the aqueous phase concentration of H2S or CO2 and its gas phase concentration .The highest H2S removals were achieved at the highest alkalinities, corresponding to the highest pH along the absorption column.Similarly, Franco-Morgado et al. obtained H2S-RE of 99.5 ± 0.5% during the operation of a HRAP interconnected to an absorption column using a highly carbonated medium at a pH of 9.5.On the other hand, the low pH in the cultivation broth together with the large decrease in pH in the absorption column under low alkalinity caused the poor H2S removal recorded.These results were in accordance with those reported by Bahr et al. , who observed a significant deterioration in the H2S-RE from 100% to 80% when the pH in the absorption column decreased from 7 to 5.4 in a similar HRAP-absorption column system.No significant effect of the temperature was observed at high-medium alkalinity on the removal of H2S.On the contrary, higher H2S-REs were recorded at 12 °C under low alkalinity likely due to the increase in the aqueous solubility of H2S.H2S oxidation ratios of 100%, 87% and 94% were obtained at 35 °C during stages I, III and V, respectively.However, an incomplete oxidation of H2S occurred at 12 °C, resulting in ratios of 55%, 67% and 33% during stages II, IV and VI, respectively.The remaining sulfur being most likely present as S-intermediates or biomass.Incomplete H2S oxidation was also reported by Toledo-Cervantes et al. , who estimated than only 40% of the absorbed H2S was oxidized to SO42− in a similar experimental set-up.Interestingly, the high DO concentrations in the cultivation broth at 12 °C did not result in higher H2S oxidation ratios likely due to the lower microbial activity at low temperatures.An average CH4 content of 98.9 ± 0.2, 98.2 ± 1.0, 80.9 ± 0.8, 82.5 ± 1.2, 75.9 ± 0.7 and 79.2 ± 0.7% was obtained in the final biomethane during stages I, II, III, IV, V and VI, respectively.The high CH4 contents in stages I and II were attributed to the high absorption efficiency of CO2 and H2S and the limited desorption of N2 and O2.Furthermore, a negligible CH4 absorption in the absorption column was observed along the six operational stages, with average losses of 2.8 ± 3.4% regardless of the alkalinity or temperature.Posadas et al. obtained slightly lower CH4 losses in an outdoors HRAP, while CH4 losses of 4.9 ± 2.4% were reported by Toledo-Cervantes et al. in a similar indoors system.At this point it should be pointed out that the composition of the biomethane produced in stages I and II complied with most European regulations for biogas injection into natural gas grids or use as autogas in terms of content of CH4 and CO2 < 2.5–4% .In fact, the CO2 content in the upgraded biogas accounted for 0.3 ± 0.1, 0.9 ± 0.3, 18.4 ± 1.0, 16.9 ± 0.8, 23.0 ± 0.9 and 20.3 ± 0.6% during stages I, II, III, IV, V and VI, respectively.During stages I to IV, H2S concentrations below 0.03% were recorded in the upgraded biogas, which complied with EU regulations.Moreover, no significant differences in O2 and N2 content of the upgraded biogas were observed during the six operational stages, which also matched the levels required by most European regulations.These results might be explained by the low L/G ratio applied during the study, which entailed a limited O2 and N2 stripping from the cultivation broth to the biomethane in the absorption column .No significant effect of the microalgae population structure on the removals of CO2 and H2S, and on the stripping of N2 or O2, was expected above a certain photosynthetic activity threshold.In our particular study, the control of the biomass productivity guaranteed a constant rate of photosynthetic activity along the process regardless of the microalgae species dominant.In addition, previous works have consistently reported no-correlation between the dominant microalgae species and biogas upgrading performance .The alkalinity of the cultivation broth was here identified as a key environmental parameter influencing biomethane quality.A negligible effect of the temperature on the quality of the upgraded biogas was recorded at high-medium alkalinity, while temperature played a significant role on biomethane quality at low alkalinity.Biomethane composition complied with most European regulations for biogas injection into natural gas grids or use as a vehicle fuel when photosynthetic biogas upgrading was carried out at high alkalinity.In addition, this study also revealed that low alkalinity media might induce inorganic carbon limitation, which ultimately decreases the CO2 mass transfer from biogas as a result of a rapid acidification of the scrubbing cultivation broth in the absorption column.

Algal-bacterial photobioreactors have emerged as a cost-effective platform for biogas upgrading. The influence on biomethane quality of the inorganic carbon concentration (1500, 500 and 100 mg L−1) and temperature (12 and 35 °C) of the cultivation broth was evaluated in a 180 L high rate algal pond (HRAP) interconnected to a 2.5 L absorption column via settled broth recirculation. The highest CO2 and H2S removal efficiencies (REs) from biogas were recorded at the highest alkalinity (CO2-REs of 99.3 ± 0.1 and 97.8 ± 0.8% and H2S-REs of 96.4 ± 2.9 and 100 ± 0% at 12 and 35 °C, respectively), which resulted in CH4 concentrations of 98.9 ± 0.2 and 98.2 ± 1.0% at 12 and 35 °C, respectively, in the upgraded biogas. At the lowest alkalinity, the best upgrading performance was observed at 12 °C (CO2 and H2S-REs of 41.5 ± 2.0 and 80.3 ± 3.9%, respectively). The low recycling liquid to biogas ratio applied (0.5) resulted in a negligible O2 stripping regardless of the alkalinity and temperature, which entailed a biomethane O2 content ranging from 0 to 0.2 ± 0.3%.

The genome of the protozoan parasite Cystoisospora suis and a reverse vaccinology approach to identify vaccine candidates

Cystoisospora suis is a protozoan parasite of the phylum Apicomplexa.This phylum contains almost exclusively obligate endoparasites of animals, including species of great medical and veterinary relevance such as Plasmodium falciparum and Toxoplasma gondii.According to recent reevaluations of the coccidian phylogeny, the position of C. suis in the family Sarcocystidae constitutes an outgroup of the cluster containing the genera Neospora, Hammondia and Toxoplasma.The closest outgroup genus of C. suis in the family Sarcocystidae is Sarcocystis, while the closest outgroup family of Sarcocystidae is Eimeridae, which contains the genus Eimeria.Cystoisospora suis is responsible for neonatal porcine coccidiosis, a diarrheal disease of suckling piglets that causes significant economic losses in swine production worldwide.The disease is commonly controlled with the triazinone toltrazuril, but drug costs and pressure to reduce the use of drugs in livestock production are increasing.Furthermore, resistance to toltrazuril has been described among parasites of the genus Eimeria and must be considered likely for C. suis.To date, no other drugs have been shown to be effective if administered in a way that is compatible with field use.Alternative options for the control of this pathogen include vaccination.Immunological control measures commonly provide longer lasting protection than chemotherapeutic interventions and do not leave chemical residues in the host or the environment.Vaccine development for apicomplexan parasites has been hindered in part by their relatively complex life cycles and lack of in vitro and in vivo models for screening.Moreover, many apicomplexans have proved capable of evading immune killing by targeting immunoprivileged sites or through extensive antigenic diversity.Vaccination against some apicomplexan parasites such as the Eimeria spp. that infect chickens has been possible using formulations of live unmodified or attenuated parasites, but vaccine production requires passage and amplification in live animals with implications for cost, biosafety and animal welfare.For other protozoan parasites, success has been harder to achieve.However, progress towards antigen identification that could lead to development of recombinant or vectored vaccines has improved for several coccidian parasites in recent years.To date, no vaccine has been developed against C. suis although previous studies have shown that cellular and humoral immune responses are induced upon infection, PhD Thesis, Auburn University, USA; Worliczek et al., 2010a; Schwarz et al., 2013; Gabner et al., 2014) and that superinfection of sows ante partum with high doses of oocysts can confer partial maternal protection.Since the use of live, virulent vaccines in large amounts is not practical and attenuated lines are not currently available for C. suis, a systematic search for proteins with antigenic properties is required to find appropriate vaccine candidates for testing and antigenic characterisation.A key step towards the identification of appropriate antigens for many apicomplexans has been the availability of genomic data, urging the development of a C. suis genome sequence assembly.The approach of finding vaccine candidates using a genome sequence has been termed “reverse vaccinology”.This strategy has become a powerful way to identify proteins that can elicit an antigenic response with relevance to host/pathogen interaction.Reverse vaccinology is based on in silico screening of protein sequences to search for motifs and structural features responsible for inducing an immune response.Examples include transmembrane domains, signal peptides for excretion or surface membrane targeting and binding sites for Major Histocompatibility Complex proteins.While this method has been successfully applied in bacterial pathogens, only a few studies have been performed on eukaryotic pathogens.Examples include the apicomplexan species T. gondii and Neospora caninum, as well as helminths such as Schistosoma.Recently, the program Vacceed was developed, providing a high-throughput pipeline specifically designed to identify vaccine candidates in eukaryotic pathogens.This program was tested on the coccidian T. gondii, where it showed an accuracy of 97% in identifying proteins that corresponded to previously validated vaccine candidates.In this work, we applied the reverse vaccinology paradigm to identify potential vaccine candidates in C. suis.To accomplish this, we used Illumina Next Generation Sequencing technology to sequence the C. suis genome and annotate protein-coding genes by combining ab initio and orthology predictions with gene models derived from a C. suis merozoite RNA-Seq library.Additionally, the annotation was manually curated at single gene resolution, greatly enhancing the quality of the gene models.Vacceed was then applied to perform a genome-wide screen for potential immunogenic proteins and identified 1,168 proteins with a high immunogenicity score.Finally, we validated the immunogenic potential of a C. suis-specific 42 kDa transmembrane protein by performing an independent immunoblot analysis using positive polyclonal sera from infected piglets.These results show how reverse vaccinology, combined with comparative genomics and transcriptomics, can be applied to a eukaryotic pathogen to guide the identification of novel vaccine candidates as a starting point to develop a vaccine against C. suis.Moreover, the C. suis genome represents the first genomic sequence available for a member of the Cystoisospora group and it might serve as a reference for future studies involving Cystoisospora spp.For preparation of genomic DNA for sequencing, C. suis oocysts were isolated from experimentally infected piglets, left to sporulate in potassium bichromate and purified using a caesium chloride gradient.DNA was extracted from 2.5 × 106 washed and pelleted sporulated oocysts using a Peqlab Microspin tissue DNA kit following the manufacturer’s instructions.RNAse A digestion was performed on the DNA before final purification.Cystoisospora suis merozoites were maintained in IPEC-J2 cells as described earlier.Free merozoites were harvested by collecting supernatant 6 days p.i. and purified on a Percoll® density gradient.Purified merozoites were then filtered through Partec CellTrics® disposable filters, washed twice with PBS and pelleted by centrifugation at 1000g for 10 min.Total RNA was extracted from purified pelleted merozoites using a QIAamp RNA blood mini kit according to the manufacturer’s instructions and was quantified using a spectrophotometer.A total of 80 million paired-end 100 bp reads were generated from an Illumina HiSeq 2000 platform.A genome sequence was assembled into contigs with CLC Genomics Workbench version 7.5 using default parameters.In this study, we did not attempt to assemble chromosomes, as the main interest was in identifying proteins to screen for immunogenic features.To remove possible contaminants, we aligned the contigs against the non-redundant RefSeq protein database using BLASTN with default parameters and removed all contigs with a hit to non-apicomplexan organisms.Alignment of C. suis contigs to N. caninum and T. gondii genomes was performed with PROmer version 3.0.7.Repetitive content was computed using RepeatMasker version 4.0.5.Genes were annotated using Maker version 2.31.8, which is a pipeline that combines different annotation tracks into a final set of gene models.Each annotation track was produced using the following programs:Augustus is an ab initio gene predictor that can be trained with an accurate set of gene models, if available.To construct the training set we started from the Cufflinks gene track, as it was based on the most accurate evidence for transcription, namely RNA-Seq data.Transcript sequences from the Cufflinks track were given to ORFPredictor version 2.3 to predict the location of each Coding Sequence.Transcripts were then filtered according to the following criteria: transcripts with incomplete CDS were removed; in the case of multiple isoforms, the isoform with the longest CDS was retained; genes without introns were removed; genes with at least one exon made entirely of untranslated regions were removed; genes separated by at least 500 bp from the previous or next gene were retained; genes with containing ambiguous nucleotides in the upstream or downstream 500 bp flanking regions were removed.The resulting gene set was used to train Augustus version 3.01 to generate a new species model that was provided as a species parameter for Augustus to predict gene locations on the contigs and generate the final track.Snap is an ab initio gene predictor.We used the same training set constructed for Augustus to train Snap version 2006-07-28 and generate the parameters file that was provided to Snap together with the contigs to produce the gene track.Exonerate is a homology based gene predictor which generates gene models based on the assumption that gene sequence and structure is conserved among closely related species.To create this gene track, protein sequences from Eimeria tenella, N. caninum, Hammondia hammondi and T. gondii were downloaded from ToxoDB release 24 and aligned to the contigs with Exonerate version 2.2.0.Cufflinks is an evidence-based gene predictor which constructs gene models from RNA-Seq data.Total RNA was extracted from merozoites harvested from an in vitro culture 6 days p.i. and sent for sequencing to GATC Biotech AG using an Illumina sequencer HiSeq 2500 to generate paired-end reads of 100 bp.A combined reference including the pig and C. suis genome was created, as the raw reads were likely to contain residual pig RNAs from the cell culture from which the merozoites were extracted.The Sus scrofa genome version 10.2 was downloaded from Ensembl and concatenated to the C. suis contigs.Reads were mapped to this combined reference with TopHat version 2.1.0.Reads mapped to the pig genome were filtered out and the resulting BAM file was provided to Cufflinks version 2.2.1 to reconstruct gene models.Cufflinks additionally computes the expression level of each gene using the standard FPKM measure, which normalises the number of reads mapped to a gene by gene length and the total number of reads in the dataset).Transcripts with low expression were removed and the output was converted to GFF3 format using the cufflinks2gff3 script from the Maker pipeline.Additionally, junctions derived from TopHat were converted to GFF3 with the tophat2gff3 script from Maker and added to the Cufflinks GFF3 to get the final track.After generating the four gene tracks, Maker was run to generate the uncurated Maker track with the following parameters that were provided in the configuration file:genome = FASTA with contigs,augustus_species = species name from the Augustus training,snaphmm = HMM parameter file from the Snap training,protein = FASTA with proteins from E. tenella, N. caninum, H. hammondi and T. gondii,Furthermore, these additional parameters were specified:.The BAM file from TopHat, the four gene tracks and the uncurated Maker gene track were loaded into the Integrative Genomics Viewer.Each gene was independently curated in the following way: in the case of incongruences among tracks, priority was given to the Cufflinks evidence, followed by Exonerate, Augustus and Snap.We decided to prioritise Augustus over Snap, as Snap produced a high number of very short terminal exons, which we cautiously regarded as unreliable.Gene models corresponding to lowly expressed genes were resolved only in some cases, as their exon–intron structure was often very fragmented.The genome and annotation of C. suis are available in the National Center for Biotechnology Information, database under the accession number PRJNA341953.To assess the quality of the annotation, cuffcompare was run to compute the fraction of final gene models confirmed by each type of evidence.To evaluate the completeness of the annotation, the core eukaryotic proteins from Parra et al. were downloaded from http://korflab.ucdavis.edu/datasets/cegma/core/core.fa and aligned to the contigs with BLASTP, retaining only the best hit.Eukaryotic core proteins that did not align to contigs were further analysed to check for their presence in the RNA-Seq dataset using the following steps: RNA-Seq reads from merozoites were de novo assembled into transcripts using Oases with default parameters; unaligned proteins were aligned to the assembled transcripts with TBLASTN.Functional annotation was performed with Blast2GO version 3.1.3 on the protein sequences using the following steps: blastp-fast was run against the local version of the nr database downloaded on 01 December, 2015 and used to generate a first version of the Gene Ontology functional annotation; InterProScan was run and results were merged to the initial GO annotation in order to extend it, ANNEX was run to further augment the GO annotation.The final functional annotation also allowed the identification of additional transposable elements that were not found by RepeatMasker.Initially, 403 protein sequences containing unknown amino acids were filtered out.The software Vacceed was used to identify potential immunogenic proteins.Vacceed implements a machine learning approach that combines independent sources of evidence for immunogenic features computed by different tools: WoLf PSORT, SignalP, TargetP, Phobius, TMHMM, MHC-I Binding and MHC-II Binding.The program finally assigns a score between 0 and 1 to each protein to rank the protein from low immunogenicity to high.We excluded MHC-II Binding in our analyses as data about MHC-II allele binding affinity is unavailable for the pig.Moreover, prior to running Vacceed the tool MHC-I Binding was trained using known immunogenic proteins as input.Since no known immunogenic proteins were available for C. suis, we used T. gondii proteins from Goodswen et al. to train MHC-I Binding for affinity against Swine Leukocyte Antigens alleles, as T. gondii was the closest relative for which a dataset of immunogenic and non-immunogenic proteins was available.Moreover, as the computational burden of the MHC-I Binding predictor was very high, we divided the Vacceed analysis into two steps: we ran Vacceed on all the protein sequences using each tool except MHC-I Binding and ranked the proteins according to score; we selected all proteins with a score > 0.75 and reran Vacceed on this subset using all the tools, including MHC-I Binding.Finally, from the score distribution obtained after step ii, we selected as candidates only proteins with a score ⩾ 0.998, corresponding to the top 25% of the score distribution.To assign orthologs of C. suis genes in the other coccidian species, protein sequences from E. tenella, Sarcocystis neurona, N. caninum, H. hammondi and T. gondii were downloaded from ToxoDB release 24 and clustered with the C. suis proteins using the Orthology MAtrix software with parameters LengthTol = 0.30.For genes for which OMA did not detect an ortholog in any species, we manually screened for its presence in the ToxoDB database by looking at the section “Orthologs and Paralogs within ToxoDB” within the gene entry.For 2D gel electrophoresis, 6 × 106 merozoites harvested from cell culture were purified and concentrated as described above and directly dissolved in DIGE buffer DTT, 20 mM Tris) and centrifuged again at 20,000g for 10 min at 4 °C.The protein concentration of each lysate was determined by Bradford assay.For separation in the first dimension, an aliquot of 40 μg of protein was diluted in 300 μl of rehydration solution CHAPS, 12.7 mM DTT, 2% immobilised pH gradient buffer, 0.002% bromophenol blue) and used to rehydrate 13 cm IPG strips with a non-linear gradient pH 3–10 for 18 h at room temperature.Isoelectric focusing was carried out using a Multiphor II electrophoresis chamber.After IEF, the IPG strips were equilibrated with 10 mg/ml of DTT in equilibration buffer SDS, 0.002% bromophenol blue, 1.5 M Tris–HCl) for 20 min and further incubated in the same buffer for another 20 min, replacing DTT with 25 mg/ml of iodoacetamide.The IPG strips were then washed with deionised water.In the second dimension, SDS–PAGE was performed using vertical slab gels under reducing conditions at 15 mA for 15 min, followed by 25 mA in a Protean II electrophoresis chamber.Each gel was stained with silver and scanned using the program ImageMaster™ 2D platinum v.7.0.Proteins separated by 2D gel electrophoresis were transferred onto a Trans-Blot® nitrocellulose membrane for 150 min at 35 V, 150 mA and 6 W on a Nova Blot semi-dry transfer system.The membranes were dried overnight in the dark at room temperature.The next day, blots were blocked for 1 h using 2% BSA.After three washes with TTBS buffer, the blots were incubated with porcine anti-C.suis serum diluted in TTBS buffer under gentle agitation at room temperature for 30 min.After rinsing in TTBS for 15 min, blots were exposed to biotinylated goat anti-pig IgG as secondary antibody for 30 min at room temperature, incubated with ABC solution and finally detected by 3,3′-5,5′-tetramethylbenzidine.Pre-colostral sera from non-infected piglets served as negative controls.The spot of interest from 2D gel was excised, washed, destained, reduced with DTT and alkylated with iodoacetamide.In-gel digestion was performed with trypsin according to Shevchenko et al. with a final trypsin concentration of 20 ng/μl in 50 mM aqueous ammonium bicarbonate and 5 mM calcium chloride.Dried peptides were reconstituted in 10 μl of 0.1% trifluoroacetic acid.Nano-HPLC separation was performed on an Ultimate 3000 RSLC system.Sample pre-concentration and desalting were accomplished with a 5 mm Acclaim PepMap μ-precolumn with a flow rate of 5 μl/min using a loading solution in 0.05% aqueous TFA).The separation was performed on a 25 cm Acclaim PepMap C18 column with a flow rate of 300 nl/min.The gradient started with 4% B and increased to 35% B over 120 min.It was followed by a washing step with 90% B for 5 min.Mobile Phase A consisted of mQH2O with 0.1% formic acid.The injection volume was 1 μl partial loop injection mode.For mass spectrometric analysis, the LC was directly coupled to a high-resolution quadrupole time of flight mass spectrometer was used.For information-dependent data acquisition, MS1 spectra were collected in the range 400–1500 m/z.The 25 most intense precursors with charge state 2–4 which exceeded 100 counts per second were selected for fragmentation, and MS2 spectra were collected in the range 100–1800 m/z for 110 ms. The precursor ions were dynamically excluded from reselection for 12 s.The nano-HPLC system was regulated by Chromeleon 8.8 and the MS by Analyst Software 1.7.Processed spectra were searched via the software Protein Pilot against T. gondii extracted from the UniProt_TREMBL database as well as against our C. suis protein database using the following search parameters: Global modification: Cysteine Alkylation with Iodacetamide, Search effort: rapid, FDR analysis: Yes.Proteins with more than two matching peptides at > 95% confidence were selected.We generated a total of 14,776 contigs from 80 M paired-end Illumina reads.The assembly had an N50 of 29,979 bp, with minimum and maximum contig lengths of 89 bp and 285,055 bp. A graphical distribution of contig lengths is shown in Fig. 1A. To exclude bacterial and other contaminations, we aligned the contigs to the nr database and retained 14,630 contigs without matches to other organisms outside the Apicomplexa and covering a length of 83.6 Mb.We used this set of contigs for the remainder of the analyses.In Table 1 we report a comparison about genome size and GC content between C. suis and other coccidians.To investigate evolutionary divergence from other coccidians we aligned the C. suis contigs to the genomes of the closest relatives, T. gondii and N. caninum, in the coccidian phylogeny.Only 27.8% and 28.1% of the C. suis bases aligned, respectively, to T. gondii and N. caninum.This translated into 722 C. suis contigs successfully aligned to T. gondii, covering a total of 3,143 C. suis genes, and 733 contigs aligned to N. caninum, including 3,195 C. suis genes.The GC content of the C. suis genome was 50.0%, similar to that of other coccidian species.Among repetitive sequences 5.07% were simple repeats, 1.84% low complexity regions and 0.02% small RNAs.We identified 93 genes associated with transposons distributed as follows: 58 genes from the ty3-gypsy subclass, six tf2 retrotransposons, one FOG transposon and 28 genes encoding for retrotransposon accessory proteins, such as gag/pol.Most apicomplexan parasites possess a special organelle called the apicoplast, which is a plastid acquired through secondary horizontal transfer from an algal ancestor and has functions related to secondary metabolism.We attempted to identify contigs corresponding to the apicoplast genome.By aligning the T. gondii apicoplast sequence to the C. suis contigs using BLASTN, we found that 89.4% of the T. gondii sequence mapped to three C. suis contigs.Most of the apicoplast sequence was covered by contig 294, with additional short fragments located on contigs 1252 and 6453.Gene annotation confirmed the apicoplast origin of these contigs, with a total of 25 protein-coding genes all well conserved with T. gondii.The genes encode for 14 ribosomal proteins, the elongation factor tu, orf c, d, e, f, the caseinolytic protease C, four RNA polymerases and the cysteine desulfurase activator complex subunit.Four of the ribosomal proteins contain a premature stop codon, which might suggest that they are pseudogenes.Finally, five T. gondii genes did not have an ortholog in the C. suis apicoplast.To further characterise apicoplast genes we looked at RNA-Seq expression data from merozoites, however we found none of the genes to be expressed in either C. suis or T. gondii at this stage.The complete list of apicoplast genes is available in Supplementary Table S3.To annotate protein-coding genes we applied the MAKER pipeline, as outlined in Section 2.3, followed by a manual curation of each individual gene.To quantify the effect of curation we computed the total number of genes, the percentage of exonic base pairs overlapping with exons generated from the RNA-Seq dataset) and the percentage of genes with UTRs before and after curation.The initial uncurated annotation contained 10,065 genes with a nucleotide level sensitivity of 68.8%, 5,553 genes with 5′ UTRs and 4,911 genes with 3′ UTRs.After curation, we obtained 11,572 genes, a nucleotide level sensitivity of 85.1%, 9,806 genes with 5′ UTRs and 8,485 genes with 3′ UTRs.These results showed that our curation greatly enhanced the quality of the annotation by increasing the concordance with transcriptional evidence and the number of genes with UTRs.The total gene number appears to be higher in C. suis compared with other coccidians, although lowly expressed genes might represent transcriptional noise and artificially increase the total gene count.To test this hypothesis, we removed genes with FPKM < 1, as this threshold is commonly used to distinguish transcriptional noise from real transcription.We detected 1,207 genes with FPKM < 1, implying that lowly expressed genes can only partially account for the higher number of genes in C. suis compared with other species.To verify the completeness of our annotation, we checked for the presence of eukaryotic core genes that should be conserved in all eukaryotes.From 458 eukaryotic core genes, 396 were present in our gene catalogue.We looked for the presence of the missing genes in filtered contigs and in transcripts reconstructed de novo from the RNA-Seq dataset.Of the remaining 62 eukaryotic core genes, none were found in the filtered contigs and 16 were found in the de novo assembled transcripts.A total of 46 eukaryotic core genes was thus not found in our C. suis data.To shed light on whether these might constitute missing annotations or genuine losses, we looked for their presence in the proteomes of the coccidian species listed in Table 1.We found 29 genes present in at least one coccidian species.The remaining 17 genes were not found in any species, and thus were likely true gene losses in this clade.If this set of genes is excluded from the initial list of eukaryotic core genes a final completeness of/ * 100 = 93% is obtained.To further assess the quality of the annotation we assigned to each gene a binary vector summarizing the kind of evidence that was used to annotate it.Generally, the most reliable source of annotation is transcription evidence, followed by orthology inference and ab initio predictions.Of a total of 11,572 genes, 10,452 were supported by transcription evidence.Among the remaining 1,120 genes, 435 were supported by orthology evidence and 338 only by ab initio predictors.Finally, 347 genes were only partially supported by any kind of evidence and would require further curation.Next, we predicted CDS for 11,545 genes using ORFPredictor.We classified genes according to the completeness of the CDS as follows: 7,801 genes had a complete CDS, i.e. with start and stop codon; 1,964 had only a start codon, 1,161 only a stop codon and 619 were CDS fragments, i.e. without start and stop codons.The complete annotation of C. suis including exons, introns, CDS and UTRs is available at the NCBI database under accession number PRJNA341953.Finally, the phylogenetic distribution of C. suis genes was assessed using OrthoMCL and found that 6,387 of the protein coding genes were assigned to an ortholog group.We applied the tool Vacceed to screen the C. suis proteome for vaccine candidates.Due to the high computation time of the MHC-I Binding tool, the Vacceed analysis was divided into two steps.Fig. 3A shows the score distribution after the first step of Vacceed.This resulted in a clearly bimodal distribution from which 2,905 proteins with a score > 0.75 were selected.During the second step of Vacceed we refined this set including the MHC-I Binding tool and obtained 1,168 final candidate proteins.By looking at the classification of candidates by functional class, we observed that most of the proteins had no annotated function or contained transmembrane domains but had unknown function.Among proteins with known function, the most abundant were channels and transporters, followed by proteins involved in metabolism and biosynthesis.Notable was the presence of apicomplexan-specific secretory organelles proteins such as rhoptry kinases, microneme and dense granule proteins, which are involved in parasite motility, attachment, invasion and re-modelling of the intracellular parasite environment.Next, we wanted to establish the contribution of different immunogenic features in defining a protein as immunogenic, as Vacceed utilises different sources of evidence from various tools: WoLf PSORT to predict protein subcellular localisation; SignalP, TargetP, Phobius for detecting transmembrane domains; TMHMM and MHC-I Binding.For this analysis, we excluded MHC-I Binding due to its high computational burden.For each tool, Vacceed was run on the C. suis proteome using all other tools except for the tool in question and the score distributions were compared with the one computed using all tools.All correlations were very high, showing that removing any one of the tools from Vacceed did not significantly affect the score distribution.However, it appeared that TMHMM was the program that contributed most to the immunogenic score since, when it was omitted, the correlation with the score computed with all tools was the lowest.Vaccine candidates in coccidians can point to homologous proteins that may also induce immune protection against other coccidians.For this reason, we collected all tested vaccine candidates from an extensive screen of the published literature and from the VIOLIN database for E. tenella, S. neurona, N. caninum and T. gondii.No previously tested candidates were found for H. hammondi.We found 13 candidates for E. tenella, one for S. neurona, 19 for N. caninum and 43 for T. gondii, giving a union set of 58 total candidates.These mostly included apicomplexan-specific secretory organelle proteins and surface antigens, which are known to be directly involved in the invasion process, but also a heterogeneous set of other proteins with no overrepresented function.Out of the 58 candidates, 34 were also present in the C. suis proteome and seven overlapped with our set of C. suis vaccine candidates, namely proteins orthologous to microneme proteins TgMIC8 and TgMIC13, the dense granule antigen TgGRA1, the surface antigen TgSAG1, the cyclophilin CyP, the immune mapped protein IMP1 and the protein disulphide isomerase PDI.This enrichment of previously described vaccine candidates in our Vacceed set was highly significant.Notably, most previously described rhoptry and dense granule candidate vaccine proteins were absent in C. suis, as they originated before the split of T. gondii, H. hammondi and N. caninum.On the other hand, many microneme proteins were present in C. suis but they were not classified as vaccine candidates in our study: three of those had a Vacceed score that was only slightly lower than our threshold, while the remaining three had a very low score.Proteins that are phylogenetically restricted to C. suis or conserved only among closely related species might be more attractive candidates for experimental testing, since proteins with homologs and conserved epitopes in the host might induce an unwanted autoimmune response.To study the evolutionary conservation of vaccine candidates, we applied OrthoMCL and classified proteins according to taxonomic levels.Approximately 28% of the proteins were very conserved in eukaryotes or shared by all organisms, another 29% were restricted to coccidians, apicomplexans or alveolates, while the largest fraction was not assigned to any ortholog group.If one would exclude the most conserved proteins from in vitro testing, there is still a large set of candidates to be investigated.By looking at GO functional enrichment of apicomplexan-specific and coccidian-specific proteins we observed a significant overrepresentation for “calcium ion binding” and “protein kinase activity”.Similarly, for coccidian-specific proteins the most prominent functional terms were “calcium ion binding” and “cyclic-nucleotide phosphodiesterase activity”.It is usually assumed that highly expressed genes are more likely to induce a sustained immunogenic response compared with lowly expressed ones.To gain insight into the expression of genes encoding for immunogenic proteins we performed an RNA-Seq experiment using polyadenylated RNA purified from C. suis merozoites, as those constitute the primary intracellular reproductive stage of C. suis and interact directly with the host during invasion.Genes with functions related to invasion were the most highly expressed and included surface antigens, apicomplexan-specific secretory organelles, cell adhesion and motility, and parasitophorous vacuole-related genes.When looking at the total ranked set of candidates according to expression level, we observed that more than 50% of the highly expressed candidates had unknown functions.Other proteins such as transporter abcg89, cytochrome b and c, were highly expressed and well characterised, but phylogenetically highly conserved and thus less suitable for further experimentation.Finally, 13 uncharacterised genes with very high expression and C. suis specificity might constitute attractive candidates for in vitro testing.To produce a more stringent list of candidates, we selected from the 1,168 vaccine candidates only those proteins that were highly expressed in merozoites and without orthologs outside the Apicomplexa.This resulted in a set of 220 proteins.These include 152 proteins with unknown function, of which 88 contain transmembrane domains.Additionally, there were 17 surface antigens related to the TgSAG/SRS gene families, 12 apicomplexan-specific secretory organelles proteins including orthologues of TgAMA1, TgMIC6, TgMIC13, TgROP6, TgROP12, TgROP27, TgROP32, nine proteins involved in metabolism and biosynthesis, seven channels and transporters proteins and three proteins related to cell adhesion.For the complete list of candidates see Supplementary Table S5.To test whether vaccine candidate proteins interact with the host immune system and induce an immune response during C. suis infection, we performed a 2D immunoblot experiment using positive polyclonal sera from experimentally infected piglets.We resolved crude lysates from cultured C. suis merozoites using a broad range of 2D gels.These revealed 18 spots that were easily visualised by silver staining, likely corresponding to the most highly expressed proteins in the merozoite proteome.To detect proteins that are recognised as antigenic by serum antibodies of infected hosts, we performed an immunoblot of the 2D gel probed with highly positive sera from experimentally infected piglets.This revealed one immuno-reactive spot, whereas no reactive spots were detected in the immunoblot probed with precolostral sera.Protein sequencing by mass spectrometry showed that the spot corresponded to a set of eight proteins.Remarkably, one of these proteins overlapped with our set of vaccine candidates.This protein had no annotated function, but showed a very high expression level and was C. suis-specific, according the OrthoMCL orthology assignment, making it a very attractive vaccine candidate.The protein was predicted to be short, with a molecular weight of 42 kDa and encoded by a single-exon gene located on contig 2816.To further characterise this protein we analysed its sequence using Phobius, which identified two transmembrane domains interspersed by a short cytoplasmic region and followed by a longer extracellular tail.Screening of this protein with the B-cell epitope predictor from the IEDB Analysis Resource tools revealed the presence of several putative epitopes along the sequence.No additional information about the function of CSUI_005805 was currently available, as this protein lacks orthologs in other organisms.By virtue of all its features such as a high Vacceed score, high expression, species specificity and in vitro immunoreactivity, we conclude that the CSUI_005805 protein constitutes an attractive vaccine candidate for further experimental testing.In this study, we sequenced, assembled and annotated the genome of C. suis, an apicomplexan species of worldwide veterinary relevance.We used this new resource to predict a panel of putative vaccine candidate proteins, which hopefully will serve to develop a novel subunit vaccine.We performed this analysis by combining in silico predictors of protein immunogenicity with transcription and comparative genomics data.Comparison with publicly accessible genome sequences for other coccidian species identified a relatively large assembly for C. suis, with only S. neurona found to be bigger.To understand whether this discrepancy was due to expansion of intergenic regions within the C. suis genome, we compared the length of genic and intergenic regions in C. suis and T. gondii and found no significant difference in proportions between the two species.Similarly, the proportions of exon to intron lengths were also not significantly different between the two species.Finally, repetitive regions were also not responsible for the difference in genome size.However, we caution that different genome assembly technologies and annotation strategies for different species might bias the comparison of genomic features among assemblies.Features such as average GC content were also consistent with those reported for other coccidians.Alignment of the C. suis contigs to assemblies representing the closest coccidian relatives showed that less than 30% of the C. suis assembly could be aligned to T. gondii or N. caninum.It has previously been shown that 90% of the N. caninum contig base pairs could be aligned to the T. gondii assembly, indicating a greater evolutionary divergence between C. suis and T. gondii.The next step will be to estimate the evolutionary divergence in millions of years between C. suis and its sister species.Our annotation of the predicted C. suis transcriptome suggests more than 11,000 genes, considerably higher than most other coccidians, but consistent with the larger genome size.We evaluated the quality of our annotation using a range of metrics and found 86% of the core eukaryotic genes to be present.Notably, if we exclude core eukaryotic genes that are absent in the whole coccidian clade, thus also expected to be absent in C. suis, this proportion rises to 93%.Expression data validated 90% of the gene models, pointing to a high degree of completeness and reliability of the gene structures.Additionally, we performed a gene-by-gene manual curation, which greatly enhanced the quality of the annotation, by increasing the concordance with transcriptional evidence by almost 20% and the number of genes with UTRs by ∼30%.The larger number of genes predicted compared with most other coccidians might be a consequence of more orphan genes, supported by the fact that more than 40% of the C. suis genes could not be assigned to any orthologous group.Alternatively, fragmented gene models might have resulted in an overestimation of gene number.However, gene numbers in other coccidians might also have been underestimated since most recent RNA-Seq based annotations have not yet been incorporated into ToxoDB.In our annotation, most of the genes identified had coding potential.While non-coding RNAs were not explicitly annotated, it is likely that polyadenylated non-coding RNA, such as long non-coding RNAs, constitute a minor fraction of the gene catalogue of C. suis, as also previously shown for T. gondii.Another feature specific to the apicomplexan clade is the presence of the apicoplast organelle in most of its members, except the gregarine-like Cryptosporidium.Two contigs were found to contain most the C. suis apicoplast genome, confirming the presence of this organelle in this species.Comparison with T. gondii revealed a high level of conservation for the C. suis apicoplast genes, although some genes contained premature stop codons, implying a recent pseudogenisation event.This phenomenon has also been described in T. gondii, where it was suggested that internal stop codons might be interpreted as tryptophan coding by the translation machinery.The absence of transcripts derived from these genes within the RNA-Seq data preclude confirmation for C. suis since transcription may simply have been low in the single merozoite lifecycle stage sampled.Consistent with these results, T. gondii orthologs of C. suis apicoplast genes also had very low expression levels in 3 days p.i. merozoites according to the expression data from the ToxoDB database.Screening the predicted C. suis proteome, 1,169 putative vaccine candidates were identified using the software Vacceed.We further characterised the candidates according to function, conservation, expression and overlap with candidates that had been tested in other coccidians.Most of the candidates were annotated as of unknown function and remarkably many had no orthologs in other coccidian species.Such diversity might be due to accelerated evolution of proteins that interact with the immune system of the host, as formerly reported for other apicomplexan species.Vaccine candidate proteins involved in host interaction and invasion such as apicomplexan-specific secretory organelles proteins, surface antigens and cell adhesion proteins were highly expressed in merozoites, as might have been expected given their function in the C. suis lifecycle.Interestingly, when vaccine candidates identified in other coccidian species were compared, only 22% of the candidates with orthologs in C. suis had a high Vacceed score.To understand why some known candidates had a low score in C. suis we looked at the partial scores from the various tools that constitute the Vacceed pipeline.Many proteins had very low partial scores, indicating the absence of specific signals for membrane, secretion or MHC1-binding epitopes.Additionally, when we looked at protein domains from the InterPro database we did not find any domain related to membrane, secretion or interaction with the immune system.This indicates that membrane-related signals might not always be required features for an anticoccidial vaccine candidate.A relatively high proportion of candidates identified in other coccidians had no orthologs in C. suis.By looking at the phylogenetic patterns of these proteins, these candidates were found to be either specific to E. tenella or they were proteins that originated just before the split of N. caninum, H. hammondi and T. gondii, mostly rhoptry kinases, dense granules and some surface antigens of the SRS family.These results also reflect the likely fast evolution of immune-related proteins.Finally, by overlapping the vaccine candidates obtained by Vacceed with proteins identified from an immunoblot experiment of pig serum, we pinpointed a promising new vaccine candidate corresponding to a 42 kDa transmembrane protein with unknown function.However, only a few proteins were recognised by positive sera from infected piglets.More sensitive detection methods or increased amounts of proteins on the gel would certainly reveal more positive spots.To further confirm the usefulness of candidates identified by reverse vaccinology and immunoblotting, recombinant proteins must be generated and characterised in vitro and in vivo in further experiments,In summary, we combined reverse vaccinology with transcriptomics and comparative genomics to identify a list of vaccine candidate proteins for further experimental testing.In order to restrict this set of candidates, new indicators of immunogenicity could be incorporated into the Vacceed pipeline, which is feasible due to the modularity of this tool.Studies on putatively immunogenic proteins of C. suis will also greatly enhance our understanding of the immune mechanisms underlying protection in porcine cystoisosporosis.Lastly, the genome and annotation of C. suis constitute a new step in the genomic era of apicomplexans.As the genus Cystoisospora can also be found in other hosts such as dogs, cats and humans, we anticipate that these resources will help to unravel the evolutionary mechanisms of host specificity in apicomplexan parasites.

Vaccine development targeting protozoan parasites remains challenging, partly due to the complex interactions between these eukaryotes and the host immune system. Reverse vaccinology is a promising approach for direct screening of genome sequence assemblies for new vaccine candidate proteins. Here, we applied this paradigm to Cystoisospora suis, an apicomplexan parasite that causes enteritis and diarrhea in suckling piglets and economic losses in pig production worldwide. Using Next Generation Sequencing we produced an ∼84 Mb sequence assembly for the C. suis genome, making it the first available reference for the genus Cystoisospora. Then, we derived a manually curated annotation of more than 11,000 protein-coding genes and applied the tool Vacceed to identify 1,168 vaccine candidates by screening the predicted C. suis proteome. To refine the set of candidates, we looked at proteins that are highly expressed in merozoites and specific to apicomplexans. The stringent set of candidates included 220 proteins, among which were 152 proteins with unknown function, 17 surface antigens of the SAG and SRS gene families, 12 proteins of the apicomplexan-specific secretory organelles including AMA1, MIC6, MIC13, ROP6, ROP12, ROP27, ROP32 and three proteins related to cell adhesion. Finally, we demonstrated in vitro the immunogenic potential of a C. suis-specific 42 kDa transmembrane protein, which might constitute an attractive candidate for further testing.

Drivers and emerging innovations in knowledge-based destinations: Towards a research agenda

Understanding the drivers and the typologies of innovation in destinations represents one of the main challenges for academics, policy makers and managers who are called on to define the evolutionary process of tourism in the complexity of human-technology interaction.The phenomenon of innovation has been receiving increasing attention in tourism research for the last 10 years and is considered to be a key factor in the competitiveness and sustainability of enterprises, organisations and destinations.Although innovation is an emerging topic of research, and innovation in destinations has been recognised as one of the main drivers of local development, the existing studies are fragmented; tourism innovation remains an empty buzzword that is extremely fragmented and largely ignored, and it lacks a specific theoretical framework.Several papers emphasise the key role of information and communication technologies in innovative processes, but technologies represent only a small part of the innovation drivers of the ‘complex world’ of destinations in which diverse actors interact; these actors are influenced by the social, economic and political factors of the destination and/or region and generate multidimensional and unusual forms of innovation.Considering the complexity of the tourism experience, which is co-created by the interaction among tourists, destination organisations and the local community, diverse forms of innovation can emerge and present new challenges for research on innovation in tourism destinations, in which human-technology interaction can play a significant role.New ways of thinking and interpreting tourism and innovation, including destination management, can capitalise on the connections between technological and societal changes by emphasising the local contexts in which innovation is nurtured.Innovation is a contextual process embedded in a geographical space.The literature considers a destination as a local innovation system in which public and private actors generate a co-evolutionary process of innovation that is dynamically influenced by the spatial dimension.Geographical proximity creates virtuous circles among knowledge, collective innovativeness and pervasive innovation and generates spill-over effects.Studies in diverse research fields − such as regional development, local systems of innovation, sociology, entrepreneurship and knowledge management − can advance the debate on innovation in destinations by combining the tourism theoretical domain with other conceptual frameworks in which local contexts play a significant role.This critical review paper aims to contribute to the debate on innovation in destinations, an emerging stream of research, by cross-fertilising diverse theoretical domains and proposing an integrated theoretical framework.This framework was constructed by adopting an integrative literature review as a useful research method for the emerging streams of research to discuss and integrate the existing fragmented studies of diverse research fields coherent with the destination management theoretical framework and to identify new challenges for research on innovation in tourism destinations.This critical review paper proposes an overarching theoretical framework for innovation in knowledge-based destinations.The paper identifies four forms of innovation in destinations – namely, experience co-creation, smart destinations, e-participative governance and social innovation − as a result of the synergies among four destination actors, the learning process and knowledge sharing that are facilitated by social capital and ICT platforms.The discussion and conclusion present the theoretical advances attained by this exploratory analysis of destination innovation and offer avenues for future research and challenges that should be explored by academics, policy makers and destination managers.This research was built on an integrative literature review, as useful qualitative research for emerging research topics would benefit from a holistic conceptualisation and synthesis of the literature.This method has consistently been adopted by other studies in tourism research.The integrative literature review is a distinctive form of research that integrates the existing literature and explores new knowledge through reviews, discussions, critiques and syntheses that allow a comprehensive literature review or a reconceptualisation of the existing frameworks.An integrative literature review differs from a systematic literature review because an integrative review can encompass any work design, with the implication that it is less standardised than a systematic review.The integrative review follows the conceptual structuring of the research topic, organised around the main concepts of the review topic, and provides a map in which the main concepts and streams of research have been connected.The work design used for this study consists of the literature that addresses the following nine concepts related to innovation in tourism destinations: innovation in tourism research; knowledge management and innovation in tourism local contexts; ICT infrastructures; social capital; political and institutional actors; destination management organisations; the local community; and local firms.The existing literature has discussed these nine concepts separately or as pairs but not all together, thus failing to cross-fertilise these diverse theoretical domains.This methodology allows the design of a conceptual framework that describes innovation in destinations and defines a preliminary research agenda that poses “provocative questions and provides direction for future research”.The research follows two phases.The first, through an analysis of the literature on innovation in tourism research, examines the main topic through a critical analysis and deconstructs and reconstructs works in the literature.Although the papers analysed in this phase present a classification and review of tourism innovation, thereby opening up spaces for new avenues of research, they present some limitations in capturing the complexity of innovation in tourism destinations."In the second phase, the main topic identified in the first phase is cross-fertilised and synthesised with different theoretical domains considering several seminal papers to examine the topic's ideas and concepts and proceed with a critical analysis.The literature, including various theoretical and empirical studies, has been organised by the main topics and is summarised in specific tables.The phenomenon of tourism innovation has gained relevance in academic research in recent years and has intensified the debate on the typologies of innovation and the drivers of innovativeness."Following Schumpeter's seminal classification of innovation, in which innovation can be interpreted as something ‘new’, as new or improved products, new production processes, new markets, new supply sources and new forms of organisation, scholars have introduced the concept of innovation and related classifications in diverse fields of research.From this perspective, innovation concerns the process of problem-solving and generating new ideas; however, it also requires the acceptance and implementation of processes, products, or services that involve the capacity to change or adapt.In this integrative literature review, only the following three articles address a review of the literature in tourism innovation and an analysis of the types of innovation that attempt to conceptualise this theoretical domain: ‘A review of innovation research in tourism’, with 432 citations; ‘100 innovations that transformed tourism’, with 32 citations; and ‘A systematic review of research on innovation in hospitality and tourism’, with 57 citations."Consistently, Hjalager's proposals of innovations in the tourism domain apply and consolidate Schumpeter's innovations and introduce specific innovations in tourism.Forms of innovation include the following: product or service innovations, as changes or new meanings of products or destinations are perceived by tourists to be new tourism experiences; process innovations, related to backstage activities, which often increase efficiency and productivity through technological investment and generate new combinations of processes; and managerial innovation, which impacts the organisational model and human resources management in new ways to empower human resources and enhance productivity and workplace satisfaction.Management innovation occurs when new destination governance models, such as tourism boards or destination management organisations, are introduced to co-ordinate, integrate and manage diverse stakeholders in destination strategies and marketing."Institutional innovation has been interpreted as a new collaborative/organisational structure or legal framework that redirects or enhances local actors' actions and generates network forms that change the institutional logic and power relations. "Furthermore, in a more recent work, Hjalager considers 100 innovations that have transformed tourism and identifies the following diverse categories of innovation: changing the product/service elements that create tourists' experiences and that increase the social and physical efficacy of the process; increasing the productivity and efficacy of tourism firms; building new destinations; enhancing mobility to and within destinations; enhancing opportunities to transfer and share information; and changing the institutional logic and power relations. "Gomezelj's study proposes a systematic literature review of innovation in tourism by analysing 152 papers that adopt diverse criteria, such as location, point of view, level of analysis, and the method and forms of innovation.The innovations discussed were classified considering the process as follows: general, institutional, product/service, knowledge importance, environmental process, entrepreneurial characteristics, green innovation, and managerial and theoretical.Applying a bibliometric analysis, Gomezelj identifies nine clusters of papers, namely, fundamental studies, the resource-based view and competitive advantage, organisational studies, networking, innovation in service, innovation systems, knowledge, management of organisational innovation and technology.These fields of research are discussed at the following three levels of analysis: the micro-level or firm level, at which innovative ideas are developed by enterprises, clusters and networks and are analysed considering the ICT and knowledge role; the macro-level, at which the effects of innovation on society, regions and tourism destinations are discussed, including their determinants and barriers; and the general level, at which innovation systems, or the collaborative approach of different institutions, aim to improve destination or regional development or the interweaving of ideas developed in firm clusters and their implementation in destinations.Although innovation is an emerging topic in tourism research, it remains fragmented, with the word ‘innovation’ often used as a ‘catchy tag’ with several different definitions.The consolidated literature classifies the diverse typologies of innovation in the tourism domain by adopting the traditional Schumpeterian approach that characterises the manufacturing industry as mainly technology-driven, which describes innovation in the tourism industry.The distinction between the different types of innovation manifests limitations and grey areas in the tourism and hospitality domains that impede the capture of the complexity of local tourism contexts but open spaces for new avenues of research.Tourism destination is a complex domain in which numerous private and public actors interact influenced by the social, economic and political factors of the context, and they generate a holistic tourism experience embedded in a specific local context that involves tourists.This type of tourism complexity calls for an interdisciplinary perspective to propose an interpretation of innovation in tourism destinations as a pervasive and contextual phenomenon, considering how the value of the context can play a significant role in generating and sharing knowledge, which nurtures innovation.The richness of knowledge in context, as a public good that does not involve rivalry, influences local innovation and provides opportunities at the spatial, sectorial and network levels.This broader perspective considers the conjoint effect of local and sectorial influences, in which co-location and transversal networks drive the process of connectivity and knowledge sharing, which enhances innovation generation and dissemination.Accordingly, through the triple helix model of innovation, the local co-evolution of different actors generates a spiral of innovation and knowledge transfer among the networks of institutions, universities, firms and other actors through relations exchange."Spatial proximity and concentration enhance learning through interaction; transform local contexts, including tourism's local contexts, as specific learning systems in which collective innovativeness is nurtured by tacit and explicit knowledge; and create a dynamic spiral of knowledge conversion that leads to innovation.In the tourism domain, the literature unanimously argues that knowledge management plays a significant role in facilitating innovation and competitive advantage not only at the firm level but also at the network, cluster and destination levels.A conceptual framework of the innovation process in destinations has been proposed that describes the role of the knowledge management theoretical framework in enhancing the comprehension of the destination innovation phenomenon.This framework explicates the process of innovation creation and management in destinations, which supports the knowledge and learning of multiple tourism-related agents at the local and national levels to define the following five stages: the development and sharing of tacit knowledge; the integration between tacit and explicit knowledge; the creation of innovative knowledge; the development of policies and strategies to transform knowledge into an innovation type; and the transfer and implementation of innovation.The role of cognitive, social and relational factors embedded in context in tacit and codified knowledge generation, knowledge dissemination and knowledge sharing within firms, network and clusters is a significant determinant of knowledge-based innovation in destination.Buzz and intensive face-to-face interactions between public and private actors nurtures the tacit knowledge embedded in local contexts and the collective learning that enhances the iterative process and dynamic spiral of knowledge conversion in the collective innovative capacity that leads to innovation and a spill-over effect.The development and integration of explicit and tacit knowledge represent a driver of innovation in destinations.Knowledge creation and the development of policies and strategies transform the destination into an incubator for the innovation of new products, new companies and new businesses at the local and regional levels.The transfer of innovation to and the implementation of innovation at the destination require destination managers to develop core competences and dynamic capabilities, including the ability to manage technology and to co-ordinate diverse actors.Tourism destinations are ideal contexts for generating innovation through clusters and informal and formal networks in which heterogeneous private and public actors interact; this innovation combines individual and collective knowledge and activates the value co-creation process with tourists to enhance destination competitiveness.Such forms of location-specific innovation are not easily transferable between places and are thus unique, which creates conditions for the defensible competitive advantage of the destination.Such examples and authentic and creative tourism or responsible tourism represent possible innovative forms of tourism in which tacit knowledge and collective learning can differentiate the destination.The theoretical frameworks of knowledge-driven innovation in local contexts guide us to define the concept of the knowledge-based destination as “a social community that serves as an efficient vehicle for creating and transforming knowledge into economically rewarding products and services for its stakeholders in an innovative process that continually facilitates the growth of its regional economy”."The knowledge-based destination summarises Nonaka and Konno's ‘ba’ concept.It represents the context in which collective and shared knowledge, both tacit and explicit, emerges through the interaction of diverse destination actors.‘Ba’ provides the physical, virtual and cognitive spaces to create, develop, codify, share and disseminate collective knowledge and facilitates diverse forms of innovation in the destination.The synergies among knowledge, collective learning and innovation are embedded in a specific local context and are activated by public and private actors, which creates conditions for a local system of innovation in which diverse learning systems enhance the opportunities to nurture tacit and explicit knowledge and facilitate collective innovation capacity.Consequently, collective innovation in the destination becomes a social process that transforms valuable individual and common knowledge through a learning system involving diverse actors that is facilitated by platforms that enhance knowledge sharing and communication processes.This critical review paper integrates existing but separate theoretical frameworks that describe the six drivers of four emerging innovations in knowledge-based destinations to reduce the grey areas in this emerging field of research.To overcome the traditional approach of innovation typologies, this paper attempts to capture the complexity of the local tourism context by identifying four emerging destination innovations as a holistic result of the collective and pervasive knowledge generated by the interaction among four destination actors – political actors, destination management organisations, enterprises and local communities – which is facilitated by two platforms.The framework considers two platforms that facilitate innovation in knowledge-based destinations, namely, the ICT infrastructure, which the consolidated literature confirms as one of the drivers of innovation in tourism, and social capital, which is an underdeveloped field of research in innovation studies.The two platforms create destination conditions that facilitate interaction, define soft and hard connections, facilitate knowledge sharing among diverse public and private actors and drive innovation.The four emerging innovations that result from the interaction and synergies among the six internal drivers of innovation in knowledge-based destinations are experience co-creation, smart destinations, e-participative governance and social innovation.The emerging innovations are presented in the following paragraphs that define the key questions creating possible avenues of research.In tourism as a knowledge-intensive industry, ICT has played a re-engineering role that changes the paradigm by which organisations, destinations and tourists communicate, collaborate and interact.ICT infrastructures have activated a process of restructuring traditional tourism products in the management of complex tourism experiences.Technological applications in the tourism sector can be summarised by considering the diverse opportunities for development that they have contributed to the creation of new firms, including tourists sharing experiences on social media, decision support tools for firms, marketing intelligence sources, e-learning tools, automation tools, game changers, transformers of the tourism experience and co-creation platforms.In the tourism domain, ICT infrastructures represent the drivers of innovation; they support managerial decision making and enhance openness and participation through their capacity to find new intermediation forms and develop their interactive interfaces between organisations and tourists.Indeed, by removing the traditional barriers of communication and interaction, ICT has facilitated the recourse to new forms of creation, organisation and consumption.Different ICT-based tools used at the destination level generate pervasive knowledge and drive innovation through the presence of platform connections among political actors, destination management organisations, enterprises and local communities.Examples of ICT-based tools include destination management systems, virtual and augmented reality, location-based services, computer simulations, intelligent transport systems, etc.Finally, the transition to the Web of Thought fosters innovation processes and enhances opportunities for co-creating destination value through the digital engagement of diverse stakeholders in social communication and knowledge sharing.The following table systematises the studies of the main authors.Social capital identifies a social structure based on norms, values, beliefs, trust and forms of interaction that facilitates tacit and codified knowledge sharing and generates collective actions.The research on social capital has received increasing attention and has become an interdisciplinary topic that involves the social structure of societies, organisations, networks and local contexts, which creates opportunities to interpret its role in the destination.Social capital in destinations can be analysed using three dimensions, namely, the structural, cognitive and relational dimensions.The structural dimension of destination social capital describes the non-hierarchical and hierarchical connections among the diverse stakeholders and actors that enable the generation of interpersonal and inter-organisational interactions and that facilitate collective actions and co-ordination among community members.The cognitive dimension refers to the values, attitudes, norms, and beliefs that create obstacles to or opportunities for sharing knowledge about and collaborating in local development.The relational dimension is a critical aspect of social capital that identifies the trust among stakeholders.The hard and soft linkages of social capital constitute an infrastructure in knowledge-based destinations that allows tacit and codified knowledge sharing and enhances the collaboration and co-creation among diverse destination actors – i.e. local governments, small businesses, residents and other stakeholders – which stimulate changes and nurture either incremental or radical innovations.The following table systematises the studies of the main authors.The consolidated literature recognises the centrality of political and institutional actors in creating advantageous conditions for innovative tourism clusters and networks in destinations.Actors play the roles of co-ordinators, planners, legislators, regulators, stimulators, promoters and financers of innovations in tourist destinations.Other functions of political and institutional actors include sharing educational resources among public and private actors to facilitate knowledge spill-overs, promoting networks and incubating tourist clusters, thereby reducing risk-financing or opportunism and free-= riding, facilitating market access to all tourist actors and activating innovation co-creation and increasing productive entrepreneurial initiatives and technology transfers.In facilitating and guiding these processes, political actors attempt to search for the correct balance between innovation and community preservation in both planning and implementation.Political actors move towards polycentricity in effective policy formulation and implementation through a hybrid approach that reconciles the complex negotiation in a joint decision-making process in which policy agents, firms, residents, and other stakeholders participate in resolving common development problems.The following table systematises the studies of the main authors.Traditionally, DMOs have had the legitimacy and competence to plan and manage destination development, including the co-ordination of marketing processes, to facilitate place brand building and to engage stakeholders in destination decision making.The evolution of the DMO is changing the role played by stakeholders in destination management, shifting it towards the embedded governance model that reconciles the top-down and bottom-up perspectives and in which stakeholder co-ordination and integration results in participative models of destination management."Consequently, a DMO's legitimacy and institutional mechanisms, which are legitimised by political actors, are also derived from formal and informal interactions with diverse destination stakeholders based on destination social capital and knowledge sharing.In this redefined scenario, the DMO can play a new role and become a learning organisation that promotes the enhancement of trust and collaboration in social capital and the use of ICT infrastructures as intelligent platforms; this role enhances organisational, community and individual learning and knowledge sharing and guides stakeholders towards diverse forms of innovation.The following table systematises the studies of the main authors."The analysis of the possible influences of the local community on tourism development and destination competitiveness considers aspects strongly related to social capital, such as knowledge sharing, value and behavioural patterns, the quality of residents' lives, cultural identity and local community participation.Different levels of local community participation, which range from manipulative participation to citizen power, influence the effectiveness and pervasiveness of destination decision making.Although an active community role is becoming central in the academic debate, the manner in which it generates innovative processes in the destination remains an unexplored topic of research."Developing an innovative community requires creating conditions that encourage a shift from residents' passive to active roles in knowledge generation, knowledge sharing and open communication channels, as social networks among residents and other types of actors increase co-operation, co-ordination and integration and innovative proposals and actions.Community participation can be analysed in three forms: coercive, induced and spontaneous participation.In coercive participation, local actors do not influence destination decision making; they assume a passive disposition and manifest a low level of interaction with key actors, such as government authorities and a restricted number of private actors who define the future of the destination.Coercive participation limits the conditions for innovation, which is relegated to tokenism.In induced participation, the community does not control the decision-making process, but it has a consultative role, which manifests conditions for proposing or contributing to the destination innovation process.In spontaneous participation, local actors have a high ability to participate in decision making and to interact and co-ordinate with other actors, which presents opportunities for innovative processes.The following table systematises the studies of the main authors.Small and medium-sized enterprises in tourism destinations play a significant role in enacting creative destruction processes, and they contribute to dynamic knowledge regeneration and promote innovation.Drivers and forms of local company innovation can be diverse, such as new organisational forms, new marketing approaches and experiential services, ICT infrastructures that facilitate networking and collaboration in the tourism destination and promotion of social changes that impact the community and economic sectors.The entrepreneurial capability to innovate in a destination is determined by three factors.First, the geographical proximity effect allows for knowledge generation, i.e. the sharing and assimilation of new information, innovation and technologies by competitors, residents or policy agents, which reduces R&D investments and costs.The second factor involves whether the organisational structure can innovate when presented with financial resources for R&D, a high level of deconcentration, a strategic orientation and high quality standards.The third factor, human capital, represents the driving force that innovatively connects all organisational resources and creates synergies with networks and destinations, thereby reducing the risk of failure in the innovation processes.Indeed, the entrepreneurial propensity to innovate is influenced by the co-operative relationships with other firms and is embedded in diverse innovation systems, such as tourism destinations.The following table systematises the studies of the main authors.Our conceptual framework proposes the following four emerging innovations in knowledge-based destinations as the holistic result of six drivers: experience co-creation, smart destinations, e-participative governance, and social innovation."After Pine and Gilmore's seminal work, the experience economy became a pervasive subject and has come to involve diverse topics and fields of research, including the tourism domain, in which the paradigm of experience co-creation nurtures the process of innovation.Innovation in destinations, as a result of experience co-creation, emerges as the collective action of diverse actors and is facilitated and triggered by the elements of social capital – such as trust, openness, networking and collaboration – and technological tools.ICT, e-tourism, virtual communities and gamification have reshaped the destination models to transform social interactions among destination actors and tourists in which experiences are dynamically co-created through stakeholder contributions, which thus defines a participatory approach to destination development."These technologies enable knowledge-based processes in destinations that are powered by user participation, openness and stakeholder engagement, making it possible to re-invent tourist experiences and enhance the differentiations among destinations.These results require maintenance to keep the experience alive over time.Experiential tourism, supported by social capital and ICT, poses significant challenges to reinterpreting the role of destination actors in generating innovation.Diverse questions emerge and create the following avenues for future investigations:How can DMOs and political actors exploit the disruptive power of ICT and digital platforms to facilitate knowledge sharing, trust and collaboration in the local community to enhance experience co-creation?,How can social capital building and stakeholder engagement enhance the maintenance of experience innovation to support dynamic experience co-creation with tourists?,How can actors capitalise on experience co-creation to generate value for stakeholders and destinations?,A smart destination can be seen as part of the evolutionary concept of smart cities, in which interconnected technological tools – ICT infrastructures, the Internet of Things, cloud computing and end-user Internet service systems, and augmented and virtual reality – connect destination stakeholders, which enhances the opportunities to communicate, collaborate and nurture knowledge.A smart destination creates opportunities to engage stakeholders in using ICT infrastructures dynamically as a neural system to allow knowledge sharing and the dispersing of innovation so that tourists can be included in the co-creation experience.Combining human capital, social capital and innovations, a smart destination combines efficiency with experience co-creation and sustainability.Regarding experience co-creation, a smart destination constitutes a pervasive innovation that includes diverse actors and stakeholders in the process and requires social capital that has the ability to facilitate knowledge sharing and trust.Diverse questions relate to this innovation, which create the following avenues for future investigation:How can smart destinations enhance the interactions between hosts and guests in various phases to thus improve their satisfaction?,Can smart destinations create opportunities for new destination models in which technological and social platforms enhance the quality of life and sustainable development?,How can DMOs and political actors create an inclusive process of smart destination building?, "The prevalent literature supports the shift towards forms of destination governance in which destination stakeholders' engagement plays a significant role and creates opportunities for innovation.The evolutionary process of destinations, where top-down governance models have been succeeded by hybrid models in which stakeholder engagement plays a significant role, has been accelerated by ICTs and digital platforms.ICTs and digital platforms provide digital spaces to enhance stakeholder engagement in decision making, which reduces the boundaries among diverse actors.E-participative governance models represent an emerging destination innovation that creates new avenues for future research.Some possible key questions include the following:How can the power of ICTs and digital platforms be enhanced to facilitate stakeholder engagement in destination planning, co-ordination and collaboration?,How can social capital be nurtured to facilitate community participation?, "Alternatively, how can e-participative governance impact social capital, transform culture, values, and so on, and consequently change the destination's identity?",What roles exist for DMOs and local actors?,Social innovation has received increasing attention by diverse academic fields of research and in political-institutional debates as a pervasive topic that impacts both society and local firms.The recent literature reviews diverse streams of research and analyses phenomena from a multidisciplinary perspective to present diverse definitions and to identify the challenges and implications for social and local development.Interesting implications for research on destination innovation emerge from these streams of research."In particular, Schumpeter's theories of entrepreneurship, social entrepreneurship and social innovation are closely related concepts, and organisational innovation impacts social wellbeing, which causes positive spill-over effects for society.The multidisciplinary approach adopted in these studies allows for the consideration of social innovation as a new concept that produces social change and introduces new solutions − products, services, models, processes, etc – that influence social capital, local development and knowledge capabilities.Social innovation involves both changes in the social capital structure and a new way to solve social imbalances, and it represents a novel social technology that creates social value to transform the destination patterns.This innovation influences attitudes, behaviour and the multiple levels of interactions of diverse actors in tourism destinations that involve unusual key players − such as local communities, non-profit and non-governmental organisations, etc – in the exploitation and exploration of destination resources and opportunities of innovation.Such forms of social innovation can drive new destination models redefining the relationships among actors.New relationships among destination actors debunk the consolidated top-down process, and forms of soft power prevail, thereby upending the traditional relationships and roles in the destination architecture and power.The following diverse challenges for future research have emerged:How can governance nurture social capital and entrepreneurship to facilitate diffused and successful social innovation?,How do local community bottom-up processes activate social innovation to create new solutions and creative spaces?,How do the local community and entrepreneurs interact in these processes?,How can social innovation enhance opportunities to activate spontaneous stakeholder participation in the experience of co-creation and e-participative governance?,How can social innovation drive a novel social technology that creates social value and reduce social imbalances in tourism destination?,Although academics and policy makers around the world consider innovation to be one of the main drivers of destination development and competitiveness, the research on innovation in tourism destinations is fragmented, manifests grey areas in the tourism domain and lacks an integrative theoretical framework that can capture the complexity of the destinations in which diverse public and private actors interact.This conceptual paper cross-fertilises and discusses the relevant literature in the tourism and other theoretical domains and proposes an integrative theoretical framework that interprets destination innovation as a complex and evolutionary knowledge-driven phenomenon resulting from human-technology interactions.This framework considers emerging innovations in knowledge-based destinations as a holistic and pervasive result of the collective knowledge generated by the interaction among four destination actors and facilitated by two platforms in a specific local context.Although it mainly explores this emerging stream of research, this paper also presents some preliminary contributions to the theoretical debate on innovation in destinations.First, the paper designates borders and differences among innovation in the tourism domain, innovation in the tourism destination and innovation in the knowledge-based destination.Innovation in the tourism domain, as defined by the consolidated literature, classifies the diverse typologies of innovation by adopting the traditional Schumpeterian approach and the lens of the manufacturing industry.This approach captures the forms of innovation at the level of the single tourism organisation/networks and manifests certain grey areas in interpreting the tourism and hospitality domains.This paper calls for overcoming the generic term of tourism innovation by defining specific research areas of innovation investigation, such as hospitality, destination, etc, in which a specific theoretical framework can be developed and consolidated.Consequently, innovation in destinations can follow the application of the traditional tourism innovation approach in which forms of innovation such as ICT tools do not embrace the complexity of the knowledge-based destination."Innovation in knowledge-based destinations, such as Nonaka and Konno's ‘ba’ concept, overcomes the borders of the single actors and/or ICT platform and emerges as the result of collective and shared knowledge, both tacit and explicit; this approach represents a holistic and pervasive result of human-technology interactions.Second, this paper argues that specific local contexts matter in destination innovation.Contexts assume a repository role of spatial and cross-sectorial knowledge generation and dissemination, which drives the pervasive and emerging innovations of the destination."The destination represents a specific learning system based on the geographical dimensions and multiple tourism-related agents' interactions to generate a dynamic spiral of knowledge sharing, collective innovativeness and pervasive innovation. "The destination's capacity to reach a high level of innovativeness is subject to the value in the context of six drivers of innovation, namely, four local public and private actors and two platforms.The four actors can play diverse roles with varying amounts of authority in driving the four typologies of innovation to leverage social capital and ICT.This paper opens up new avenues of research through which to analyse the role of public and private actors in this dynamic spiral of knowledge sharing, collective innovativeness and pervasive innovation facilitated by technological platforms and social capital.The paper suggests the creation of local conditions to facilitate offline and online stakeholder engagement as a key element to enhance knowledge generation, sharing and transformation to thus activate innovation processes at the destination.Third, the integrative framework presented here overcomes the limited focus on technology-driven innovation at the destination and introduces to the theoretical debate the complementary role of social capital and ICT infrastructures in creating conditions that facilitate innovativeness, stakeholder engagement and bottom-up processes for pervasive and holistic destination innovation.The consolidated literature emphasises the disruptive role of ICT in the tourism innovation process but neglects the significant role of social capital.Social capital and ICT represent the structural, cognitive and technological platforms of the destination in which human/organisational and technological factors converge to facilitate interaction, collaboration, trust building and knowledge sharing among the four diverse actors and to trigger the emergence of diverse forms of destination innovation.Accordingly, with the new way of thinking and interpreting tourism and innovation, this paper suggests capitalising on the connections between technological and societal changes in local contexts.It opens up a new scenario for the role of institutions and local actors in building social capital that can nurture innovation acceptance and innovativeness in local contexts to enhance the effectiveness of innovative ICT tools.Fourth, this approach goes beyond the current innovation paradigms that analyse innovations in the tourism domain, which usually present traditional forms of innovation based on the manufacturing paradigm that are considered in a single and reductive way.The complexity of the tourism experience co-created by the interaction between tourists and local actors is associated with the complex dynamic spiral of knowledge sharing, collective innovativeness and pervasive innovation, which requires a new interpretation of innovation in destinations.This paper identifies four emerging innovations as the pervasive and holistic results of the collective knowledge generated by the interaction among four destination actors and facilitated by ICT infrastructures and social capital.Overcoming the traditional innovation paradigms, this integrated framework proposes advances in academic research that presents four destination innovations as the result of the convergence of diverse typologies of innovations that are transforming tourism and local contexts, specifically, experience co-creation, smart destinations, e-participative governance and social innovation.In these innovations, difficulties emerge in defining the borders between the diverse determinants and the emerging typologies of innovation because they are strongly interrelated, and a synergetic process intervenes between the determinants and the emerging innovation.All innovations are the intangible result of interdependences among the six determinants of destination innovations, and they simultaneously redefine the six determinants.Finally, the preliminary key questions related to these four emerging innovations create avenues for future research and identify the challenges for academics, policy makers and destination managers to understand and strengthen the possible role of destination actors and their synergies in destination innovation under the conditions of knowledge-driven innovation in destinations.Emerging innovations that influence behaviour and multiple levels of interactions of diverse actors create changes in the social capital structure and introduce new ways to co-create value in the context that drives new destination models.New destination models can be derived from emerging innovations and can be designed and analysed in future theoretical and empirical research.Emerging innovations, such as social innovation, can open up new scenarios in which unusual relationships among destination actors debunk the consolidated top-down process to create new patterns of relationships, influences and power beyond the six innovation drivers.This paper is not without limitations.First, this integrative literature review overlooks the phenomenon of innovation.As previous literature suggests, some papers adopt words such as ‘creativity’ or ‘change’ to debate innovation.Consequently, the paper underestimates the ‘soft’ forms of innovation, such as creative cities, which are transforming the consolidated paradigms in destinations.Second, the paper discusses four emerging innovations that represent a preliminary synthetic design of possible destination innovations to contribute to a research agenda for academics, policy makers and destination managers.This review does not aspire to be exhaustive, and other possible innovations can be identified, discussed and validated by the theoretical research and empirical analysis in future papers.Third, there are other external factors that influence the innovation process of destinations, including tourists, which are unexplored in this paper.Future research will overcome this limitation with a more holistic and comprehensive model in which tourist participation in knowledge generation and destination innovation processes can play a significant role."Because this is still a relatively young field of research, further research is needed to underpin this conceptual framework and other diverse and related streams of research through theoretical contributions, in-depth case studies and empirical analysis, which would overcome this paper's limitations.

Research on innovation in tourism is fragmented and confined to traditional paradigms. This critical review paper, which cross-fertilises and discusses the relevant literature in tourism and other theoretical domains, proposes an integrative theoretical framework of innovation in destinations. The paper identifies four emerging innovations – experience co-creation, smart destinations, e-participative governance and social innovation – as evolutionary, knowledge-driven phenomena that are generated by the interaction among four destination actors and facilitated by information and communication technologies (ICTs) and social capital. The discussion and conclusion present some theoretical advances as follows: local contexts matter in destination innovation when assuming a repository role of spatial and cross-sectorial knowledge; social capital and ICT infrastructures facilitate innovativeness and stakeholder engagement; and emerging innovations are pervasive and the holistic results of the collective knowledge of four destination actors and are facilitated by ICT and social capital. The paper offers avenues for future research and challenges that should be explored by academics, policy makers and destination managers.

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