Neurons recorded in behaving animals often do not discernibly respond to sensory input and are not overtly task-modulated. These non-classically responsive neurons are difficult to interpret and are typically neglected from analysis, confounding attempts to connect neural activity to perception and behavior. Here, we describe a trial-by-trial, spike-timing-based algorithm to reveal the coding capacities of these neurons in auditory and frontal cortex of behaving rats. Classically responsive and non-classically responsive cells contained significant information about sensory stimuli and behavioral decisions. Stimulus category was more accurately represented in frontal cortex than auditory cortex, via ensembles of non-classically responsive cells coordinating the behavioral meaning of spike timings on correct but not error trials. This unbiased approach allows the contribution of all recorded neurons – particularly those without obvious task-related, trial-averaged firing rate modulation – to be assessed for behavioral relevance on single trials. Neurons encode information in the form of electrical signals called spikes. Certain neurons increase the rate at which they produce spikes under specific circumstances, e.g., whenever an animal hears a particular sound. These neurons are said to be 'classically responsive'. But not all neurons behave in this way. Others produce spikes at a variable rate that does not obviously relate to the animal's behavior. These neurons are said to be 'non-classically responsive'. They are often omitted from analyses, despite typically outnumbering their classically responsive counterparts. So, what are these neurons doing?To find out, Insanally et al. trained rats to respond to sounds. The animals learned to poke their nose into a window whenever they heard a specific tone, and to avoid responding whenever they heard any other tone. As the rats performed the task, Insanally et al. recorded from neurons in two areas of the brain, the frontal cortex and the auditory cortex. A computer then analyzed the activity of individual neurons during each trial.As expected, the firing rate of non-classically responsive cells did not relate to the animals' behavior. But the timing of this firing did. The interval between spikes contained information about which tone the animals had heard and/or how they had responded. The cells worked together in groups to encode this information. Over the course of each trial, every neuron in the group varied the interval between its spikes. Eventually, the group reached a consensus, with all neurons using the same interval to represent information relevant to the task. Groups of neurons in the frontal cortex encoded more information about the category of the tone than those in the auditory cortex.By including all neurons – both classically and non-classically responsive – this model offers a more comprehensive view of how neural activity relates to behavior. This may in turn help us understand the variable and complex neural activity seen in people with sensory and cognitive disorders. Spike trains recorded from the cerebral cortex of behaving animals can be complex, highly variable from trial-to-trial, and therefore challenging to interpret. A fraction of recorded cells typically exhibit trial-averaged firing rates with obvious task-related features and can be considered ‘classically responsive’, such as neurons with tonal frequency tuning in the auditory cortex or orientation tuning in the visual cortex. Another population of responsive cells are modulated by multiple task parameters (‘mixed selectivity cells’) and have recently been shown to have computational advantages necessary for flexible behavior . HoweverHow do these non-classically responsive cells relate to behavioral task variables on single trials? While there are sophisticated approaches for dissecting the precise correlations between classically responsive cells and task structure , there iWe trained 15 rats on an audiomotor frequency recognition go/no-go task  that reqTo correctly perform this task, animals must first recognize the stimulus and then execute an appropriate motor response. We hypothesized that two brain regions important for this behavior are the auditory cortex (AC) and frontal cortical area 2 (FR2). Many but not all auditory cortical neurons respond to pure tones with reliable, short-latency phasic responses . These nWe first asked if activity in AC or FR2 is required for animals to successfully perform this audiomotor task. We implanted cannulas into AC or FR2 , and infOnce animals reached behavioral criteria , they were implanted with tetrode arrays in either AC or FR2 . After rGiven that the majority of our recordings were from non-classically responsive cells, we developed a general method for interpreting neural responses even when trial-averaged responses were not obviously task-modulated which allowed us to compare coding schemes across different brain regions . The algorithm is agnostic to the putative function of neurons as well as the task variable of interest .Our algorithm empirically estimates the interspike interval (ISI) distribution of individual neurons to decode the stimulus category (target or non-target) or behavioral choice (go or no-go) on each trial via Bayesian inference. The ISI was chosen because its distribution could vary between task conditions even without changes in the firing rate – building on previous work demonstrating that the ISI distribution contains complementary information to the firing rate . The disFor each recorded neuron, we built a library of ISIs observed during target trials and a library for non-target trials from a set of ‘training trials’. Two different cells from AC are shown in Importantly, while the probabilities of observing particular ISIs on target and non-target trials were similar , small dThe algorithm uses the statistical prevalence of certain ISI values under particular task conditions , to infer the task condition for each trial. Each trial begins with equally uncertain probabilities about the stimulus categories (i.e. p(target)=p(non-target)=50%). As each ISI is observed sequentially within the trial, the algorithm applies Bayes’ rule to update p(target|ISI) and p(non-target|ISI) using the likelihood of the ISI under each stimulus category (p(ISI|target) and p(ISI|non-target) . As thesCan we uncover task information from non-classically responsive cells? We found that non-classically responsive cells in both AC and FR2 provided significant spike-timing-based information about each task variable . The abiWe also observed that task information was distributed across both AC and FR2, and neural spike trains from individual units were multiplexed in that they often encoded information about both stimulus category and choice simultaneously , Table 1AC = 0.016, pstim = 0.0013, Mann-Whitney U test, two-sided). Both of these observations would not have been detected at the level of the PSTH, as most cells in AC were non-classically responsive for behavioral choice , yet our decoder revealed that these same cells were as informative as choice classically responsive cells . Moreover, comparing single trial decoding outcomes demonstrated weak to no correlations between the ISI-based decoder and the conventional rate decoder, further underscoring that these two methods rely on different features of the spike train to decode  Poisson decoder , which aWe hypothesize that ISI-based decoding is biologically plausible. Short-term synaptic plasticity and synaptic integration provide powerful mechanisms for differential and specific spike-timing-based coding. We illustrated this capacity by making whole-cell recordings from AC neurons in vivo and in brain slices , as wellMoreover, we note that this type of coding scheme requires few assumptions about implementation, and does not require additional separate integrative processes to compute rates or form generative models. Thus, ISI-based decoding coding could be generally applicable across brain areas, as demonstrated here for AC and FR2.AC = 5 × 10−6, pPFC <0.0002, Mann-Whitney U test, two-sided). This surprising result demonstrates that our algorithm generalizes to novel datasets, and may be used to uncover coding for cognitive variables beyond those apparent from conventional trial-averaged, rate-based analyses. Furthermore, these results indicate that as task complexity increases non-classically responsive cells are differentially recruited for successful task execution.To further demonstrate the generalizability and utility of our approach, we applied our decoding algorithm to neurons that were found to be non-classically responsive in a previously published study . In thisAC stim=0.04, pFR2 stim=1×10−5, pAC = 0.29, pFR2 choice=7×10−5, Mann-Whitney U test, two-sided). This was not a trivial consequence of using more neurons, as the information provided by individual ISIs on single trials can be contradictory  and p(go | ISI)) on each trial . Analyzi−5, for four-member ensembles: p=0.03, Mann-Whitney U test, two-sided). Interestingly, decoding with an increasing number of non-classically responsive cells improved error prediction in both AC and FR2 .Can our decoding method predict errors on a trial-by-trial basis? In general, trial-averaged PSTHs did not reveal systematic differences between correct and error trials . HoweverWhile improvements were seen in decoding performance with increasing ensemble size, the ISI distributions/ISI-based likelihood functions were highly variable across individual ensemble members. Thus, we wondered if there was task-related structure in the timing of population activity that evolved over the course of the trial to instantiate behavior. To answer this question, we examined whether local ensembles share the same representation of task variables over the course of the trial. Do they ‘reach consensus’ on how to represent task variables using the ISI ? WithoutWe examined how ensembles coordinate their activity moment-to-moment over the course of the trial by quantifying the similarity of the LLRs across cells in a sliding window. Similarity was assessed by summing the LLRs of ensemble members, calculating the total area underneath the resulting curve, and normalizing this value by the sum of the areas of each individual LLR. We refer to this quantified similarity as ‘consensus’; a high consensus value indicates that ensemble members have similar LLRs and therefore have a similar representation of task variables . We shout = 0 to 0.42 s, pSNR = 3.9 × 10−4 Wilcoxon test with Bonferroni correction, two-sided). Sensory classically responsive ensembles in AC increased consensus beyond stimulus presentation, reaching a maximum ~750 ms after tone onset on correct trials . For choice-related activity, choice non-classically responsive ensembles in both regions as well as choice classically responsive ensembles in FR2 each reached consensus within 500 ms of the behavioral response . Importantly, this temporally precise pattern of consensus building is not present on error trials. On error trials, stimulus consensus dynamics decreased over the course of the trial whereas choice dynamics did not display a systematic increase with the exception of choice non-classically responsive ensembles in AC which remained systematically lower than correct trials . The observed increases in ensemble consensus on correct trials  suggests that achieving a shared ISI representation of task variables may be relevant for successful task execution.While the conventional trial-averaged PSTH of non-classically responsive ensembles recorded in AC and FR2 showed no task-related modulation, our analysis revealed structured temporal dynamics of the LLRs . On correct trials, we observe a trajectory of increasing consensus at specific moments during the trial signifying a dynamically created, shared ISI representation of task variables. In FR2, sensory non-classically responsive ensembles (ensembles in which at least two out of three cells were not tone-modulated) encode stimulus information using temporally-precise stimulus-related dynamics on correct trials. The stimulus representation of sensory non-classically responsive ensembles reached consensus rapidly after stimulus onset followed by divergence  the LLRs of each cell within an ensemble are completely dissimilar or (2) they are ‘out of phase’ with one another – the LLRs partition the ISIs the same way . For exat = 0 to 0.89 s, p=1.7 × 10−5 Wilcoxon test, two-sided). Non-classically responsive ensembles in AC and FR2 also increased their unsigned consensus immediately before behavioral response . This pattern of consensus-building was only present on correct trials. On error trials unsigned consensus values did not systematically increase  suggesting that behavioral errors might result from a general lack of consensus between ensemble members. In summary, we have shown that cells which appear unmodulated during behavior do not encode task information independently, but do so by synchronizing their representation of behavioral variables dynamically during the trial.Using this metric, we found that the unsigned consensus pattern for non-classically responsive ensembles  were shared between AC and FR2 – increasing until ~750 ms after tone onset on correct trials  while this information is reflected in a more complex and distributed manner throughout AC.We have identified task-informative non-classically responsive neurons recorded while animals performed a frequency recognition task or a task-switching paradigm. This does not preclude the possibility that these cells are driven by other acoustic stimuli or in other behavioral contexts; however, determining the significance of non-classically responsive activity must ultimately be considered in the specific behavioral context in question, as their role may be dynamic and context dependent.The finding that the ISI-based approach of our algorithm is not reducible to rate despite their close mathematical relationship raises the question of how downstream regions could respond preferentially to specific ISIs. Our whole-cell recordings from both AC and FR2 demonstrate that different postsynaptic cells can respond differently to the same input pattern with a fixed overall rate, emphasizing the importance of considering a code sensitive to precise spike-timing perhaps via mechanisms of differential short-term plasticity such as depression and facilitation . FurtherOur consensus results reveal dynamic changes in the relationship between the LLRs of ensemble members. How might such a downstream resonator interpret a given ISI in the context of these dynamics? Our consensus analysis provides one possible answer: downstream neurons may be attuned to the ISIs specified by the consensus LLR of an ensemble. In such a model, an ensemble would have the strongest influence on downstream activity when they reach high consensus. We additionally hypothesize that mechanisms of long-term synaptic plasticity such as spike-timing-dependent plasticity can redistribute synaptic efficacy, essentially changing the dynamics of short-term plasticity independent from overall changes in amplitudes . Thus, aIt is still unclear what the relevant timescales of decoding might be in relation to phenomena such as membrane time constants, periods of oscillatory activity, and behavioral timescales. Given that our ISI-based decoder and conventional rate-modulated decoders reveal distinct information, future approaches might hybridize these rate-based and temporal-based decoding methods to span multiple timescales. Other recent studies have also contributed to our understanding of non-classically responsive activity, by evaluating firing rates or responses from calcium imaging to demonstrate how correlations with classically responsive activity may contribute to the linear separability of ensemble responses .We have shown that underlying the task-relevant information encoded by each ensemble is a rich set of consensus-building dynamics that is invisible at the level of the PSTH. Ensembles in both FR2 and AC underwent stimulus and choice-related consensus building that was only observed when the animal correctly executed the task. Moreover, non-classically responsive cells demonstrated temporal dynamics synchronized across regions which were distinct from classically responsive ensembles. These results underscore the importance of measuring neural activity in behaving animals and using unbiased and generally applicable analytical methods, as the response properties of cortical neurons in a behavioral context become complex in ways that challenge our conventional assumptions .All animal procedures were performed in accordance with National Institutes of Health standards and were conducted under a protocol approved by the New York University School of Medicine Institutional Animal Care and Use Committee. We used 23 adult Sprague-Dawley male and female rats (Charles River) in the behavioral studies. Animals were food restricted and kept at 85% of their initial body weight, and maintained at a 12 hr light/12 hr dark cycle.Animals were trained on a go/no-go audiomotor task . OperantAnimals were rewarded with food for nose poking within 2.5 s of presentation of the target tone (4 kHz) and given a short 7 s time-out for incorrectly responding to non-target tones . Incorrect responses include either failure to enter the nose port after target tone presentation  or entering the nose port after non-target tone presentation . Tones were 100 ms in duration and sound intensity was set to 70 dB SPL. Tones were presented randomly with equal probability such that each stimulus category was presented. The inter-trial interval delays used were 5, 6, 7, or 8 s.For experiments involving muscimol, we implanted bilateral cannulas in either FR2  of seven animals or AC  of three animals. We infused 1 μL of muscimol per side into FR2 or infused 2 μL of muscimol per side into AC, at a concentration of 1 mg/mL. For saline controls, equivalent volumes of saline were infused in each region. Behavioral testing was performed 30-60 min after infusions. Power analysis was performed to determine sample size for statistical significance with a power of β: 0.8; these studies required at least three animals, satisfied in the experiments of Figure 1-figure supplement 3B,E. For motor control study, animals could freely nose poke for food reward without presentation of auditory stimuli after muscimol and saline infusion.Animals were implanted with microdrive arrays  in either AC  or FR2  after reaching behavioral criteria of d’≥1.0. For surgery, animals were anesthetized with ketamine (40 mg/kg) and dexmedetomidine (0.125 mg/kg). Stainless steel screws and dental cement were used to secure the microdrive to the skull, and one screw was used as ground. Each drive consisted of eight independently adjustable tetrodes. The tetrodes were made by twisting and fusing four polyimide-coated nichrome wires . The tip of each tetrode was gold-plated to an impedance of 300–400 kOhms at 1 kHz .ratio sorting quality metrics. To be initially included for analysis, cells had to have >3 spikes per trial for 80% of trials to ensure that there were enough ISIs to reliably estimate the ISI probability density functions.Recordings in behaving rats were performed as previously described . After tWe used two positive statistical tests for non-classical responsiveness: one to establish a lack of tone-modulation, the other to establish a lack of ramping activity. To accommodate the possibility of tone onset and offset responses, we performed our tone-modulation test on a 100 ms long tone presentation window as well as the 100 ms window immediately after tone presentation. The test compared the number of spikes during each of these windows to inter-trial baseline activity as measured by three sequential 100 ms windows preceding tone onset. Three windows were chosen to account for variability in spontaneous spike counts. Given that spike counts are discrete, bounded, and non-normal, we used subsampled bootstrapping to evaluate whether the mean change in spikes during tone presentation was sufficiently close to zero (in our case 0.1 spikes). We subsampled 90% of the spike count changes from baseline, calculated the mean of these values, and repeated this process 5000 times to construct a distribution of means. If 95% of the subsampled means values were between −0.1 and 0.1, we considered the cell sensory non-classically responsive (p<0.05). The range of mean values from −0.1 to 0.1 were included to account for both tone-evoked (increases in spike count) and tone-suppressed (decreases in spike count) activity. The value of 0.1 spikes was chosen to be conservative as it is equivalent to an expected change of 1 spike every 10 trials. This is a conservative, rigorous method for establishing sensory non-classical responsiveness that is commensurate with more standard approaches for establishing tone responsiveness such as the z-score.31. First, the trial averaged firing rate was determined in 50 ms bins leading up to the behavioral response. We then calculated the slope of a linear regression in a 500 ms long sliding window beginning 850 ms before behavioral response. The maximum value of these slopes was used as the ‘ramp index’ for each cell. Cells were classified as choice non-classically responsive if the ramp index did not indicate an appreciable change in the firing rate (less than 50% change) established via subsampled bootstrapping. Cells that were shown to be both sensory and choice non-classically responsive were considered non-classically responsive overall  were delivered in pseudo-random sequence. Primary AC location was determined by mapping multiunit responses 500–700 µm below the surface using tungsten electrodes. In vivo whole-cell voltage-clamp recordings were then obtained from neurons located 400–1100 µm below the pial surface. Recordings were made with an AxoClamp 2B (Molecular Devices). Whole-cell pipettes (5–9 MΩ) contained (in mM): 125 Cs-gluconate, 5 TEACl, 4 MgATP, 0.3 GTP, 10 phosphocreatine, 10 HEPES, 0.5 EGTA, 3.5 QX-314, 2 CsCl, pH 7.2. Data were filtered at 2 kHz, digitized at 10 kHz, and analyzed with Clampfit 10 (Molecular Devices). Tone-evoked excitatory postsynaptic currents were recorded at –70 mV.−1) with oxygenated ACSF at 33°C. Somatic whole-cell current-clamp recordings were made from layer five pyramidal cells with a Multiclamp 700B amplifier (Molecular Devices) using IR-DIC video microscopy (Olympus). Patch pipettes (3–8 MΩ) were filled with intracellular solution containing (in mM): 120 K-gluconate, 5 NaCl, 10 HEPES, 5 MgATP, 10 phosphocreatine, and 0.3 GTP. Data were filtered at 2 kHz, digitized at 10 kHz, and analyzed with Clampfit 10 (Molecular Devices). Focal extracellular stimulation was applied with a bipolar glass electrode . Spike trains recorded from AC and FR2 units during behavior were then divided into 150–1000 msec fragments, and used as extracellular input patterns for these recordings.Acute brain slices of AC or FR2 were prepared from 2 to 5 month old Sprague-Dawley rats. Animals were deeply anesthetized with a 1:1 ketamine/xylazine cocktail and decapitated. The brain was rapidly placed in ice-cold dissection buffer containing (in mM): 87 NaCl, 75 sucrose, 2.5 KCl, 1.25 NaH2PO4, 0.5 CaCl2, 7 MgCl2, 25 NaHCO3, 1.3 ascorbic acid, and 10 dextrose, bubbled with 95%/5% O2/CO2 (pH 7.4). Slices (300–400 µm thick) were prepared with a vibratome (Leica), placed in warm dissection buffer (32–35°C) for 10 min, then transferred to a holding chamber containing artificial cerebrospinal fluid at room temperature . Slices were kept at room temperature (22–24°C) for at least 30 min before use. For experiments, slices were transferred to the recording chamber and perfused  parameterized distribution. We used 10-fold cross validation to estimate the bandwidth of the Gaussian kernel in a data-driven manner. Finally, the use of the ISI was also motivated by previous work demonstrating that the ISI can encode sensory information  and thatIndividual trials were defined as the time from stimulus onset to the response time of the animal . Trials were divided into four categories corresponding to each of the four possible variable combinations . Approximately 90% of each category was set aside as a training set in order to determine the statistical relationship between the ISI and the two task variables .Each ISI observed was sorted into libraries according to the stimulus category and behavioral choice of the trial. The continuous probability distribution of finding a particular ISI given the task condition of interest  was then inferred using nonparametric Kernel Density Estimation with a Gaussian kernel of bandwidth set using a 10-fold cross-validation . BecauseThe remaining 10% of trials in the test set are then decoded using the ISI likelihood function described in the previous section. Each trial begins with agnostic beliefs about the stimulus category and the upcoming behavioral choice (p(target)=p(non-target)=50%). Each time an ISI was observed, beliefs were updated according to Bayes’ rule with the four probability distributions obtained in the previous section serving as the likelihood function. To update beliefs in the probability of the target tone when a particular ISI has been observed, we used the following relationship:On the left hand side are the updated beliefs about the probability of a target. When the next ISI is observed this value would be inserted as p on the right side of the equation and updated once more. Using the probability normalization, p can be determined,Similarly, for choice,As the likelihood functions were estimated in 1 s long sliding windows recalculated every 100 ms, Each ISI was assessed using the likelihood function that placed the final spike closest to the center of the sliding window.Continuing this process over the course of the trial, we obtain four probabilities – one for each of the variable outcomes – as a function of time during the trial: p, p, p, and p. At each moment, the total probability of both stimuli and both choices are 1. The prediction for the entire trial was assessed at the end of the trial, using the overall likelihood function. Given our independence assumption, the overall likelihood for a spike train is simply equal to product of the likelihoods for each ISI observed over the course of the trial,We used 10-fold cross-validation, meaning the trials in the four stimulus categories were randomly divided into ten parts and each part took a turn acting as the test set with the remaining 90% of trials acting as a training set. To estimate the statistical certainty of these results we used bootstrapping with 124 repetitions (except in the case of the null hypotheses where 1240 repetitions were used).t from neuron j with a likelihood pj:Ensemble decoding proceeded very similarly to the single-unit case. The ISI probability distributions for each neuron in the ensemble were calculated independently as described above. However, while decoding a given trial, the spike trains of all neurons in the ensemble were used to simultaneously update the beliefs about stimulus category and behavioral choice. In other words, p and p were shared for the entire ensemble but each neuron updated them independently using Bayes’ rule whenever a new ISI was encountered. Correlations between neurons were ignored and each of the ISIs from each cell were assumed to were assumed to be independent. For example, if an ISI is observed at time This process is repeated every time a new ISI is encountered from any cell in the ensemble.j is the likelihood of observing a given set of ISIs from neuron j.The joint likelihood of observing a set of ISIs during a trial is then the product of the likelihoods of each neuron independently. For example, for a two neuron ensemble, the combined likelihood, To test the null hypothesis that the ISI-based single-trial Bayesian decoder performance was indistinguishable from chance, synthetic spike trains were constructed for each trial of a given unit by randomly sampling with replacement from the set of all observed ISIs regardless of the original task variable values  and the exponential term represents the probability of silence in the periods between spikes. For comparison with our method, we can reformulate this equation using interspike intervals, if we first break up the exponential integral into domains that span the observed interspike intervals.To decode using the trial-averaged firing rate, we implemented a standard method  which us process . Just asiΔt is the time difference between spikes it and i+1t. The interpretation of each term in the product is straightforward: it is the infinitesimal probability of observing a spike a time Δt after a spike at time t multiplied by the probability of observing no spikes in the intervening time. In other words, it is simply t, as predicted by the assumption of a rate-modulated Poisson process. We can easily verify that this term is normalized which allows us to write,Collecting the first and last terms relating to trial start and trial end asWith the exception of the terms relating to trial start and end, we can then view the likelihood of a spike train as resulting from the likelihood of the individual ISIs (just as with our ISI-decoder),t, so we multiply by the probability of observing a spike at time t, To compare the ISI distribution inferred using non-parametric methods to one predicted by a rate-modulated Poisson process, we use the relationship above to calculate the predicted probability of observing an ISI of given length within the 1 s window used for our non-parametric estimates. The formula above assumes a spike has already occurred at time t and t + ISI with silence in between.In other words, the probability of observing an ISI beginning at time t is simply the probability of observing spikes at times any time within a time window spanning iw to fw is simply the integral of this ISI probability as a function of time across the window. To ensure the final spike occurs before fw the integral spans iw to (fw - ISI),C is a normalization constant which ensures p integrates to 1,The probability of observing an ISI at and choice decoding probabilities were primarily a result of true behavioral choice.For each cell, we fit a Logit model for both the stimulus and choice decoding probabilities on individual trials with the true stimulus category and behavioral choice as regressors. We then calculated the extent to which the stimulus decoding probability was determined by true stimulus category by subtracting the regression coefficient for stimulus from that of choice  was calculated by first calculating the conditional ISI probabilities and then taking the difference of the logarithm of these distributions. For stimulus,The weighted LLR weights the LLR according to the prevalence of a given ISI. For stimulus,The consensus value evaluates the extent to which the LLR (or weighted LLR) is shared across an ensemble. It is the norm of the sum of the LLRs (W. LLRs) divided by the sum of the norms. In principle, the functional norm can be anything but in this case we used the The for an n-member ensemble, the consensus is thenunsigned consensus, we first generate every permutation of the LLRs used and their inverses, -LLR, up to an overall sign. For example, for a pair of LLRs there are only two options,For the The consensus is then calculated over each these sets and the maximum value is taken to be the value of the unsigned consensus.scipy.interpolate.InterpolatedUnivariateSpline .To generate the consensus curves in Using our novel ISI-based decoding algorithm, we analyzed cells found to be non-classically responsive in a previously published study . BrieflyWe established that cells were non-classically responsive for the stimulus location or pitch using our own positive statistical criteria for non-classical responsiveness (described above) by comparing the average spiking activity in the 250 ms stimulus period and the 250 ms following stimulus to inter-trial baseline activity. Cells were also determined to be non-classically responsive for ramping using the same criteria as with our own data. We confirmed that cells were non-classically responsive for the selection rule by comparing their average spiking activity in the 100 ms immediately preceding stimulus onset across contexts.To determine whether non-classically responsive cells also encoded task information , we decoded each variable on single-trials using our ISI-based decoding algorithm. Selection rule information was only assessed in the pre-stimulus hold period, whereas stimulus and choice information was assessed in the period after stimulus onset prior to behavioral response (as with our own data). Cells shown in All statistical analyses were performed in Python, MATLAB, or GraphPad Prism 6. Datasets were tested for normality, and appropriate statistical tests applied as described in the text .https://github.com/badralbanna/Insanally2017 . In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Timothy Behrens as the Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "Nominally non-responsive frontal and sensory cortical cells encode task-relevant variables via ensemble consensus" for consideration by The reviewers found the work interesting, making a substantive and impactful contribution to the field. They have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Summary:Insanally et al. ask how information about auditory stimuli and behavioral choices, are reflected within the spiking patterns of neurons in the auditory cortex (AC), and frontal cortical area 2 (FR2) of rat. The subjects were presented with octave spaced tones, and required to select go/no-go to the presence of a target tone. The work focuses on neurons whose firing rates (PSTH) show no change in response to the auditory stimulus, and/or no ramping activity preceding the decision. The main finding is that timing information in the ISIs of these neurons can be decoded to determine the stimulus and/or choice. The work demonstrates this using a nonparametric decoding method. Specifically, the authors develop a novel method for quantifying the difference in ISI distributions corresponding to different task-conditions. They use this method to provide a posterior probability of task-condition given the ISIs in a single trial. The median decoding performance is moderate but statistically significant, and superior to rate/PSTH-based metrics.Essential revisions:1) The paper refers to neurons with no stimulus-dependent firing rate modulation as "nominally non-responsive", however, these neurons clearly do respond to the stimulus (by changing their ISI distributions). Indeed, this is the basis for the decoding analysis. Consequently, it seems like the main result is that the spike timing contains information that is missed if one only looks for stimulus dependence in the firing rates. This is an important point and worth making, but is lost by labeling these neurons as non-responsive. The paper would be much clearer if the authors would be more upfront in the title, Abstract, Results, etc., about what their data show: timing information is there even when the PSTH looks uninformative.2) The neural network model in Figure 7 lacks some relevance to the data. A rate-based model is used, in which some neurons have stimulus-locked rate modulations, and some don't, and show that inactivating these "non-responsive" neurons changes task performance (presumably due to the effects of those inactivations on the rest of the network through the recurrent connectivity). In the experimental data, however, the authors show that, even when the neurons' rates don't have obvious stimulus dependence, the timing information still does, and any effects of inactivating subsets of neurons are left untested. To make the model relevant, it would need to use spiking neuron models, and recapitulate the basic phenomenon from the experiments: spike timing information is present even when there's no rate information. Alternatively, the authors could do inactivation experiments  to test the main prediction of the current model, which would make it relevant. This experiment would probably be hard to do, it is suggested instead that the authors revise or remove the computational model.3) Relatedly, the conjecture in the last paragraph of the subsection “Ensemble consensus-building dynamics underlie hidden task information”, and again somewhat in the last paragraph of the Discussion, that the NR cells are a separate functional network from the responsive ones contradicts the modeling result. If they are separate networks, silencing the NR cells wouldn't affect the responsive ones. To retain the claim about separate functional networks, it seems necessary to do optogenetics, or some other manipulation of activity, and show that changing NR cells' activity doesn't affect responsive ones. Otherwise, the authors should remove this unsupported claim.4) The decoder is quite nice, but it makes several independence assumptions that are not borne out in real neural populations. This point should be made much more upfront in the Materials and methods and the Results: stating that this decoder approximates Bayesian inference, by ignoring all forms of correlation. Doing this will ensure that readers don't mistakenly think these simple expressions are exact. Alternatively, the decoder could be updated to include the correlations discussed below.Correlations: in the subsection “Decoding” the first, third and sixth equations, and the equation in the subsection “Ensemble decoding”, assume that each ISI is independent for a given neuron, which is not generally true, due to AHP currents among other things. The paper also assumes that ISIs are independent between neurons, which is generally not true in simultaneous neural recordings, due to noise correlations, etc.5) Two other recent works, listed below, showed a stronger form of the claim advanced in the title and Abstract: that neurons with no stimulus dependence can still contribute to the neural code. There, the non-responsive neurons individually contain no stimulus information, although through correlations with other cells, they still contribute to population-wide neural coding.The current paper's results are distinct from those, since they consider timing information, whereas the other papers consider rates. However, it would be worth making that distinction somewhere in the paper.a) Leavitt et al., 2017.b) Zylberberg, 2017.6) From the Materials and methods (subsection “Training probabilistic model”), it looks like the ISI distributions used for the decoder are recalculated for different time points during the integration period. This is not clear at all from the text in the Results section, but is a crucial point, as it puts severe limits on how a downstream neural circuit could read out this information: the downstream circuit would need to continually change its readout "decoder" somehow. The decoder and analysis still have a lot of merit. They show that the stimulus information is present in the timing, and show how it could be extracted . But the time dependence needs to be made clear in the Results section, and the biological plausibility or implausibility should be discussed.7) Please expand the Materials and methods and Results sections to clarify the following points:a) Temporal resolution. It seems like the ISI distributions are estimated in a 1s window (subsection “Training probabilistic model”). Sliding by what amount? How is it decided which 1s windows each ISI contributes? Only those that contain the 2 spikes enclosing the ISI? Are predictions are evaluated at the end of each of these 1s windows ?b) Clarify where the formula for the likelihood of the full spike train  is used. From reading the manuscript, it seems that the formulas for updating the posterior only consider likelihoods for a given ISI.c) Ensemble decoding. Please be more explicit and include a mathematical formula for how the updating of the posterior is done in this case.d) Subsection “Ensemble consensus-building dynamics underlie hidden task information”, second paragraph. Sliding window for consensus. Is this the same 1s sliding window above? Probably not, given the time-varying nature of the curves and the fact that they start at zero. Please add explanatory text about the time windows used in the consensus section in the methods. Please also describe in more detail how the curves in Figure 8E-H were calculated?e) Furthermore, is it correct that the Poisson process estimator takes into account the full duration of the stimulus-response interval? There is some asymmetry in the information available with the ISI approach (which takes into account the 1+ seconds following stimulus onset) and the PSTH . Were there any firing rate differences over the longer interval that would predict responses?8) Please add a careful discussion of whether the animal's choice can be cleanly dissected from stimulus encoding, particularly given the very high performance of these animals on the task, the long duration between stimulus and response, and the late build-up of stimulus prediction/consensus. There is plenty of literature on the effects of attention, behavior, and working memory on cortical synchronization and timing, albeit mostly in hippocampus. It is possible that the AC responses simply reflect the choice/memory already decided. The manuscript attempts to disambiguate the choice from the stimulus with a log linear regression, but this is dependent upon prediction information from the decoder analysis that itself may already contain mixed information.9) Title: Please consider revising your title to either omit or make more intuitive what "ensemble consensus" means and to clarify "nominally non-responsive", given the concerns of some of the reviewers about this terminology. One suggested revision "Response timing in frontal and sensory cortical neurons encodes task-relevant variables even when average responses are uninformative". Essential revisions:1) The paper refers to neurons with no stimulus-dependent firing rate modulation as "nominally non-responsive", however, these neurons clearly do respond to the stimulus (by changing their ISI distributions). Indeed, this is the basis for the decoding analysis. Consequently, it seems like the main result is that the spike timing contains information that is missed if one only looks for stimulus dependence in the firing rates. This is an important point and worth making, but is lost by labeling these neurons as non-responsive. The paper would be much clearer if the authors would be more upfront in the title, Abstract, Results, etc., about what their data show: timing information is there even when the PSTH looks uninformative.To avoid possible confusion, we have taken the reviewer’s suggestion and we have modified the manuscript  to better describe these neurons. We have changed all references to “nominally non-responsive” neurons to “non-classically responsive” neurons. Our title now reads: “Spike-timing-dependent ensemble encoding by non-classically responsive cortical neurons.” We have also modified the text to emphasize that spike timing reveals task-related information in non-classically responsive neurons even when the PSTH does not .2) The neural network model in Figure 7 lacks some relevance to the data. A rate-based model is used, in which some neurons have stimulus-locked rate modulations, and some don't, and show that inactivating these "non-responsive" neurons changes task performance (presumably due to the effects of those inactivations on the rest of the network through the recurrent connectivity). In the experimental data, however, the authors show that, even when the neurons' rates don't have obvious stimulus dependence, the timing information still does, and any effects of inactivating subsets of neurons are left untested. To make the model relevant, it would need to use spiking neuron models, and recapitulate the basic phenomenon from the experiments: spike timing information is present even when there's no rate information. Alternatively, the authors could do inactivation experiments  to test the main prediction of the current model, which would make it relevant. This experiment would probably be hard to do, it is suggested instead that the authors revise or remove the computational model.We agree with the reviewer and, following their suggestion, we have removed the neural network model from this manuscript. The suggestion to make the model more relevant by using spiking neurons has inspired us to instead include it in a separate, follow-up study where we can more extensively investigate network dynamics.3) Relatedly, the conjecture in the last paragraph of the subsection “Ensemble consensus-building dynamics underlie hidden task information”, and again somewhat in the last paragraph of the Discussion, that the NR cells are a separate functional network from the responsive ones contradicts the modeling result. If they are separate networks, silencing the NR cells wouldn't affect the responsive ones. To retain the claim about separate functional networks, it seems necessary to do optogenetics, or some other manipulation of activity, and show that changing NR cells' activity doesn't affect responsive ones. Otherwise, the authors should remove this unsupported claim.We agree that this point requires further clarification and substantiation and have removed this claim. To clarify, we did not intend the term “separate” to indicate that nominally non-responsive ensembles are independent from responsive ensembles, only that they may play a differential role during behavior .4) The decoder is quite nice, but it makes several independence assumptions that are not borne out in real neural populations. This point should be made much more upfront in the Materials and methods and the Results: stating that this decoder approximates Bayesian inference, by ignoring all forms of correlation. Doing this will ensure that readers don't mistakenly think these simple expressions are exact. Alternatively, the decoder could be updated to include the correlations discussed below.Correlations: in the subsection “Decoding” the first, third and sixth equations, and the equation in the subsection “Ensemble decoding”, assume that each ISI is independent for a given neuron, which is not generally true, due to AHP currents among other things. The paper also assumes that ISIs are independent between neurons, which is generally not true in simultaneous neural recordings, due to noise correlations, etc.We have added text to the Results section and Materials and methods (subsection “Training probabilistic model”) to clarify this point. The Results section now reads, “These ISI likelihood functions consider each ISI to be independent of previous ISIs and therefore ignore correlations between ISIs”. The decision to exclude correlations was made on practical grounds: we had initially attempted to include correlations between an ISI and the preceding ISI in the likelihood function, but in order to properly estimate this conditional likelihood function using non-parametric methods  would require a much larger number of trials than what our dataset allows. The same issue applies when estimating the joint probability of ISIs between neurons. In earlier implementations of our ensemble decoder, we instead tried including relative spike timing between cells  but this factor did not result in significantly better decoding performance overall.5) Two other recent works, listed below, showed a stronger form of the claim advanced in the title and Abstract: that neurons with no stimulus dependence can still contribute to the neural code. There, the non-responsive neurons individually contain no stimulus information, although through correlations with other cells, they still contribute to population-wide neural coding.The current paper's results are distinct from those, since they consider timing information, whereas the other papers consider rates. However, it would be worth making that distinction somewhere in the paper.a) Leavitt et al., 2017.b) Zylberberg, 2018.We appreciate the suggestion to put our work in context of these two studies; we now cite both of these papers. In the Discussion section, we have added text on the complementarity of their results with our own which reads, “Other recent studies have also contributed to our understanding of non-classically responsive activity, by evaluating firing rates or responses from calcium imaging to demonstrate how correlations with classically responsive activity may contribute to the linear separability of ensemble responses .”6) From the Materials and methods (subsection “Training probabilistic model”), it looks like the ISI distributions used for the decoder are recalculated for different time points during the integration period. This is not clear at all from the text in the Results section, but is a crucial point, as it puts severe limits on how a downstream neural circuit could read out this information: the downstream circuit would need to continually change its readout "decoder" somehow. The decoder and analysis still have a lot of merit. They show that the stimulus information is present in the timing, and show how it could be extracted . But the time dependence needs to be made clear in the Results section, and the biological plausibility or implausibility should be discussed.We thank the reviewer for pointing this out, as this is an important detail that should be made clear. We included a moving window to take into account non-stationarity in the ISI distributions over the course of the trial and interestingly this decision did improve decoding performance in practice. The observed dynamics of the ISI distributions both in our decoding results and our consensus building results raise the question of how a downstream cell would interpret the ISIs of these cells. Assuming this downstream cell was attuned to properly interpret one set of ISI distributions, which set should it choose? Our consensus results suggest an answer: it may be that downstream cells interpret upstream activity in terms of the consensus LLRs – that way when ensemble members disagree on a representation their activity is unlikely to drive activity in the downstream cell, but when their representation is aligned with each other and the downstream cell they can reliably influence downstream activity.We hypothesize that long-term synaptic plasticity can adapt spike timings during the initial phases of behavioral training, leading to stable representations and ISI distributions across cortical networks. One of the major predictions of our algorithm is that forms of short-term synaptic plasticity  are important aspects of downstream decoding of complex spike trains. This is supported by past theoretical and experimental work . Furthermore, mechanisms of long-term synaptic plasticity such as spike-timing-dependent plasticity can redistribute synaptic efficacy, essentially changing the dynamics of short-term plasticity independent from overall changes in amplitudes . Thus we wouldn’t necessarily expect that the downstream circuit needs to continually change the readout mechanism – rather, the upstream and downstream components might be modified together over the course of behavioral training, to set the ISI distributions appropriate for firing of task-relevant downstream neurons, which would ensure that ensemble consensus is reached for correct sensory processing in highly-trained animals.We have clarified the time dependence of the ISI distributions in the Results section by adding text that reads, “To accommodate any non-stationarity, these ISI distributions were calculated in 1 second long sliding windows over the course of the trial.” We have also added text regarding the biological plausibility in the Discussion section. It now reads: “Our whole-cell recordings from both AC and FR2 demonstrate that different postsynaptic cells can respond differently to the same input pattern with a fixed overall rate, emphasizing the importance of considering a code sensitive to precise spike-timing perhaps via mechanisms of differential short-term plasticity such as depression and facilitation”, and “Our consensus results reveal dynamic changes in the relationship between the LLRs of ensemble members. […] This would set the ISI distributions appropriate for firing of task-relevant downstream neurons, which would ensure that ensemble consensus is reached for correct sensory processing in highly-trained animals.”7) Please expand the Materials and methods and Results sections to clarify the following points:a) Temporal resolution. It seems like the ISI distributions are estimated in a 1s window (subsection “Training probabilistic model”). Sliding by what amount? How is it decided which 1s windows each ISI contributes? Only those that contain the 2 spikes enclosing the ISI? Are predictions are evaluated at the end of each of these 1s windows ?The algorithm calculated the ISI distributions in 1 second windows recalculated every 100 ms. Every time an ISI was encountered, the algorithm consulted the distribution generated in a window as close to centered around the final spike as possible and evaluated the prediction update at the time of the final spike. As mentioned above to point #6, we have clarified this point in the Results section, “To accommodate any non-stationarity, these ISI distributions were calculated in 1 second long sliding windows recalculated every 100 ms over the course of the trial.” And in the Materials and methods section “These distributions were estimated in a 1 second long sliding window .”b) Clarify where the formula for the likelihood of the full spike train  is used. From reading the manuscript, it seems that the formulas for updating the posterior only consider likelihoods for a given ISI.Our method updates sequentially every time an ISI is encountered. The formula for the overall likelihood function was included for conceptual clarity: to calculate the final prediction for the entire trial  using the likelihood function for the entire trial is sufficient. We have added text to the Materials and methods to clarify this point: “The prediction for the entire trial was assessed at the end of the trial, using the overall likelihood function.”c) Ensemble decoding. Please be more explicit and include a mathematical formula for how the updating of the posterior is done in this case.t from neuron j with a likelihood pj:We have clarified this by adding text and including a mathematical formula in the Materials and methods section, "For example, if an ISI is observed at time This process is repeated every time a new ISI is encountered from any cell in the ensemble.”.d) Subsection “Ensemble consensus-building dynamics underlie hidden task information”, second paragraph. Sliding window for consensus. Is this the same 1s sliding window above? Probably not, given the time-varying nature of the curves and the fact that they start at zero. Please add explanatory text about the time windows used in the consensus section in the methods. Please also describe in more detail how the curves in Figure 8E-H were calculated?scipy.interpolate.InterpolatedUnivariateSpline .”Yes, the intuition is correct, the sliding window used was not the same as for the above. To achieve improved temporal precision, we used a 750 ms sliding window to calculate the LLRs and the consensus values every 100 ms. The consensus values calculated were assigned to the middle of the window and the series of values were interpolated with a third-degree univariate spline for visual clarity . Figure 8E-H represent the means and standard deviations of these curves for the relevant region and ensemble type. We have clarified these points in the Materials and methods section, “To generate the consensus curves in Figure 8,LLRs are calculated using a 750 ms sliding window recalculated every 100 ms. […] For visual clarity, these values were interpolated by a third-degree univariate spline calculated using the python package e) Furthermore, is it correct that the Poisson process estimator takes into account the full duration of the stimulus-response interval? There is some asymmetry in the information available with the ISI approach (which takes into account the 1+ seconds following stimulus onset) and the PSTH . Were there any firing rate differences over the longer interval that would predict responses?Yes, both the Poisson process estimator and the ISI-based decoder took into account the full trial activity . We clarified this in the Materials and methods: “Just as with this ISI-based decoder, we decoded activity from the entire trial.”8) Please add a careful discussion of whether the animal's choice can be cleanly dissected from stimulus encoding, particularly given the very high performance of these animals on the task, the long duration between stimulus and response, and the late build-up of stimulus prediction/consensus. There is plenty of literature on the effects of attention, behavior, and working memory on cortical synchronization and timing, albeit mostly in hippocampus. It is possible that the AC responses simply reflect the choice/memory already decided. The manuscript attempts to disambiguate the choice from the stimulus with a log linear regression, but this is dependent upon prediction information from the decoder analysis that itself may already contain mixed information.assumption of this approach was that the decoding results may contain mixed information, and the objective was to evaluate the extent to which a decoder’s predictions  were correlated with not only the relevant behavioral variable  but also the other variable . While the results from this analysis demonstrated that the decoder’s output correlated with both variables, it was more strongly predicted by the relevant variable for multiplexed cells. The question of whether the choice information we observe is actively processed in auditory cortex or is a reflection of activity from another region is an interesting open question that goes beyond the scope of this study. We now say in the results: “Given the strong correlation between stimulus and choice variables in the task design, it is difficult to fully separate information about one variable from information about the other. […] This analysis establishes that a certain degree of separability is possible and demonstrates that the multiplexing observed in our decoding results is unlikely to be a trivial byproduct of correlations in the task variables.”We thank the reviewer for the opportunity to address this point. For the reasons the reviewer mentions, a clean dissection of stimulus and choice variables is a challenging endeavor. This is less of a concern for uniplexed neurons which only contain information about one of the two variables, which is why our multiple regression analysis focused on establishing that the multiplexed neurons were not simply a trivial byproduct of correlations between the behavioral variables. The 9) Title: Please consider revising your title to either omit or make more intuitive what "ensemble consensus" means and to clarify "nominally non-responsive", given the concerns of some of the reviewers about this terminology. One suggested revision "Response timing in frontal and sensory cortical neurons encodes task-relevant variables even when average responses are uninformative".We have revised our title according to the reviewer’s suggestion. It now reads: “Spike-timing-dependent ensemble encoding by non-classically responsive cortical neurons.”References:Abbott LF1, Varela JA, Sen K, Nelson SB. Synaptic depression and cortical gain control. Science. 1997 Jan 10;275(5297):220-4. DOI: 10.1126/science.275.5297.221


Vascular inflammation and endothelial dysfunction are thought to contribute to arterial stiffening and hypertension. This study aims to test this hypothesis with longitudinal data in the context of type 1 diabetes.1c, serum creatinine, total cholesterol, urinary AER, insulin treatment dose and mean arterial pressure.We investigated, in an inception cohort of 277 individuals with type 1 diabetes, the course, tracking and temporal inter-relationships of BP, specifically pulse pressure  and hypertension, and the following biomarkers of systemic and vascular inflammation/endothelial dysfunction: C-reactive protein (CRP), soluble intracellular adhesion molecule-1 (sICAM-1), soluble vascular cellular adhesion molecule-1 (sVCAM-1) and soluble E-selectin (sE-selectin). These biomarkers and other risk factors were measured at baseline and repeatedly up to 20 years after the onset of type 1 diabetes. Data were analysed with generalised estimating equations including adjustments for age, sex, smoking status, BMI, HbAIncreases were noted in all biomarkers except sE-selectin, which decreased over time. Levels differed from baseline at 2–4 years and preceded the increase in pulse pressure, which occurred at 8–10 years after the onset of type 1 diabetes. Higher levels of sICAM-1 and sVCAM-1, but not CRP or sE-selectin, at baseline and throughout the 20 year follow-up, were significantly associated with higher (changes in) pulse pressure at subsequent time points. Higher levels of sVCAM-1 at baseline and during follow-up were also significantly associated with the prevalence  and incidence  of hypertension. We also investigated the longitudinal associations between BP or hypertension as determinants of subsequent (changes in) levels of CRP, sICAM-1, sVCAM-1 and sE-selectin, but did not find evidence to support a reverse causality hypothesis.These findings support the involvement of vascular endothelial dysfunction and inflammation in the development of premature arterial stiffening and hypertension in type 1 diabetes.The online version of this article (10.1007/s00125-017-4470-5) contains peer-reviewed but unedited supplementary material, which is available to authorised users. Individuals with type 1 diabetes are characterised by accelerated arterial ageing , a mechaThe pathobiological mechanisms underlying the increases in pulse pressure in individuals with diabetes are, however, not clear. Diabetes is characterised by systemic and vascular inflammation and endothelial dysfunction –9, mechaWe have therefore investigated the longitudinal course of BP and markers of systemic and vascular inflammation and endothelial dysfunction  and their temporal inter-relationships, in a cohort of individuals with type 1 diabetes who were followed over 20 years, since the onset of disease.n = 286), which has been described in detail previously ); whenever more than one reading was available, median values were calculated when aggregating the data.All clinical data obtained from 6 months after the onset of diabetes  up to 21.5 years thereafter (the maximum follow-up duration) were aggregated within 2 year time intervals. The number of readings available on the participants’ records varied per variable within each 2 year time interval  were imputed conditional on all information available at the same and immediately adjacent t. We used a window of t ± 4 years and specified study entry and exit times for each participant so that imputations were not done outside these , which would assume an immortal cohort. Instead, in our study the target of inference was the mortal cohort (where only the surviving participants were included at each wave), hence the method chosen to impute the missing data ) was regressed on the entire subsequent levels of the same variable during follow-up  and 1 (perfect correlation).To analyse tracking of biomarkers and BP over the 20 year period, we used a model in which the initial value of each of these variables . Finally, we investigated the extent to which inter-individual differences in main determinants at t predicted subsequent intra-individual changes in outcomes (i.e. between t + 2 and t + 4) using a time-lagged (changes) GEE model  and BP (main outcomes), all modelling the levels of main determinants at time intervals prior to those of the outcomes, to minimise the possibility of overlap of the determinants and outcomes assessment times and enable inferences of causality. First, we examined the extent to which individual differences in biomarkers at baseline predicted individual differences in BP and the prevalence and incidence of hypertension over the entire follow-up period . In analyses with pulse pressure as main outcome, we additionally adjusted for MAP to ascertain if any observed associations could be attributed to arterial stiffness independently of peripheral resistance [e transformed because of their right-skewed distribution. In analyses with these variables as main determinants of BP, longitudinal linear regression coefficients or ORs are therefore expressed as differences in BP (in mmHg) or odds of hypertension per doubling of each biomarker. In analyses of BP variables as main determinants of biomarkers , longitudinal linear regression coefficients are expressed as per cent difference in biomarker per 10 mmHg increase in BP variable.All analytical models were first adjusted for time, and age at onset of diabetes, sex and smoking history (as time-independent covariates) and, second, for HbAsistance . BiomarkCharacteristics of the study participants at baseline are shown in Table p < 0.001). The estimated prevalence of hypertension over the 20 year follow-up is shown in Fig. BP increased significantly over the 20 year longitudinal period Fig. a. When ap < 0.05).Markers of low-grade inflammation and endothelial dysfunction also changed significantly over time Fig. a–d. WhenThe increases in levels of biomarkers (except sE-selectin) were observed early; levels already differed from baseline at 2–4 years after onset of disease and preceded the increases in BP, which differed significantly from baseline at 8–10 years after the onset of type 1 diabetes  and 0.36 (SBP and MAP)  and for the baseline levels of the other risk factors (model 2), higher levels of sICAM-1 and sVCAM-1, but not of CRP or sE-selectin, at baseline were significantly associated with higher pulse pressure during the 20 years of follow-up  but also the main exposures (biomarkers), and after adjustments for age, sex and smoking status (model 1), CRP, sICAM-1 and sVCAM-1 were all significantly associated with higher subsequent pulse pressure: 0.75 mmHg , 3.93 mmHg  and 5.13 mmHg  per doubling in each biomarker, respectively , higher levels of sICAM-1 and sVCAM-1, but not of CRP or sE-selectin, also predicted subsequent changes in pulse pressure: 2.47 mmHg  and 2.54 mmHg  per doubling in sICAM-1 and sVCAM-1, respectively  levels of CRP, sICAM-1, sVCAM-1 and sE-selectin, but did not find evidence to support a reverse causality hypothesis (ESM Table 2) observed in the course of follow-up  was the single common determinant of subsequent pulse pressure , prevalence of hypertension , incidence of hypertension  and subsequent levels of CRP .Given that CRP and BP were associated with each other but seemingly not in a causal fashion, we examined an alternative hypothesis: that both CRP and BP were, instead, determined by (a) common factor(s). After adjustments for the time-independent and time-dependent covariates, we found that (changes in) BMI albuminuria status or time revealed no consistent effect modification by any of these factors.We examined the longitudinal course of BP and biomarkers of endothelial dysfunction and inflammation, and their inter-relationships, in a cohort of individuals with type 1 diabetes who had been followed for 20 years since the onset of disease. Our main findings were as follows: (1) increases in levels of CRP, sICAM-1 and sVCAM-1 occurred early in the course of disease and preceded the increases in BP; (2) all variables tracked considerably over time, particularly sICAM-1, sVCAM-1 and sE-selectin; (3) higher sICAM-1 and sVCAM-1, at baseline and during follow-up, predicted the subsequent (changes in) levels of pulse pressure; higher levels of sVCAM-1, at baseline and during follow-up, predicted the prevalence and incidence of hypertension; (4) notably, these associations were independent of age, sex, smoking history and other relevant risk factors and (5) we found no evidence for a reverse causation hypothesis (i.e. that higher levels of BP or hypertension determined subsequent increases in these biomarkers). The unique characteristics of this study were the repeated assessment of biomarkers, BP and important covariates over the natural course of disease during a period of 20 years. This allowed us to examine, for the first time with a truly longitudinal design, the temporal relationships between biomarkers and BP  in individuals with type 1 diabetes. As such, the present study provides the strongest evidence regarding the involvement (or lack thereof) of CRP, sICAM-1, sVCAM-1 and sE-selectin in the pathogenesis of elevated pulse pressure and of hypertension, over the course of disease in individuals with type 1 diabetes.Tracking of BP and CRP levels over the 20 year follow-up period was moderate but was very high for sICAM-1, sVCAM-1 and sE-selectin. Interpretation of the tracking coefficients reported here requires consideration of a number of factors . First, Epidemiological evidence to support the concept of arterial stiffness/widened pulse pressure and hypertension as a consequence of vascular/systemic inflammation has been controversial , 26–29. Our findings suggest that, in contrast to CRP (and sE-selectin), both CAMs may causally underlie arterial stiffening. This contention holds inasmuch as arterial stiffness can be depicted by pulse pressure and may be too strong given that stroke volume (not measured) is also likely to explain a part of the variance in pulse pressure, particularly among young individuals. Our findings also suggest that sVCAM-1 seems to causally underlie the development of hypertension. This observation is in striking agreement with the only prospective study thus far that has examined CAMs and pro-inflammatory cytokines as predictors of incident hypertension among individuals with type 1 diabetes . Indeed,We used brachial, not central, pulse pressure as a crude estimate of arterial stiffness, reflecting the data that was accessible from clinical records. The technology enabling non-invasive measurement of central pulse pressure and aortic pulse wave velocity, that would allow better characterisation of the aetiology of arterial stiffening, was not available during the 20 year period covered by the present study. Nevertheless, brachial pulse pressure still provides valuable risk prediction information: in a meta-analysis , central pulse pressure tended to be more strongly associated with incident CVD and mortality than brachial pulse pressure but the added value of central pulse pressure in risk prediction was only marginal ; a similThere are some additional limitations to our study. Findings were confined to individuals with type 1 diabetes and therefore may not generalise to the background population. Measurement errors around BP measurements are likely to have led to an underestimation of the associations estimates reported in the present manuscript. We only measured CRP, sICAM-1, sVCAM-1 and sE-selectin, which reflect just a part of the complex and multifaceted process of arterial remodelling induced by endothelial dysfunction and inflammation . Still, In conclusion, in individuals with type 1 diabetes, increases in sICAM-1 and sVCAM-1 precede, and are associated with, subsequent increases in pulse pressure and hypertension throughout the course of the disease, supporting the involvement of endothelial dysfunction/inflammation in the development of premature arterial stiffening. The lack of support for a causal link between CRP and BP, and the observation that both derive from a common antecedent (BMI), suggests that weight gain should be monitored during treatment of individuals with type 1 diabetes. Targeting endothelial dysfunction/inflammation in the early stages of diabetes may slow down the accelerated arterial ageing characteristic of this disease and prevent related cardiovascular sequelae. Given that both CAMs tracked very highly, measuring their levels and changes soon after the onset of type 1 diabetes may enable identification of individuals at a high risk and who may need intensified/tailored treatment.ESM(PDF 209 kb)


Dpt) that combined the podophyllotoxin (Ptox) structural unit (etoposide) with the dithiocarbamate unit (iron chelator) through the hybridization strategy. The resulting PtoxDpt inherited characteristics from parent structural units, acting as both the p53 inducer and topoisomerase II inhibitor. In addition, the PtoxDpt exhibited significant inhibition in migration and invasion, which correlated with downregulation of matrix metalloproteinase (MMP). More importantly, PtoxDpt could inhibit EMT in the absence or presence of TGF-β1, concomitant to the ROS production, and the additional evidence revealed that PtoxDpt downregulated AKT/mTOR through upregulation of p53, indicating that PtoxDpt induced EMT inhibition through the p53/PI3K/AKT/mTOR pathway.Epithelial-mesenchymal transition (EMT) involves metastasis and drug resistance; thus, a new EMT reversing agent is required. It has shown that wild-type p53 can reverse EMT back to epithelial characteristics, and iron chelator acting as a p53 inducer has been demonstrated. Moreover, recent study revealed that etoposide could also inhibit EMT. Therefore, combination of etoposide with iron chelator might achieve better inhibition of EMT. To this end, we prepared di-2-pyridineketone hydrazone dithiocarbamate S-propionate podophyllotoxin ester (Ptox Metastasis is a hallmark of cancer and one of the urgent tasks to be solved in a clinical practice. During metastasis, the malignant cells spread from the primary tumor to distant sites, which cause failure of vital organs, consequently leading to the death of patients. In addition, concomitant to the metastasis, the cells acquire an ability to resist conventional treatments . Therefop53 is well characterized as a tumor suppressor gene ; the wilDpt), exhibited significant Topo II inhibition and acted as a p53 inducer. A growth inhibition assay against hepatocellular carcinoma cells in vitro revealed that PtoxDpt displayed a better antiproliferative effect than the parent compounds, 4′-demethylepipodophyllotoxin and etoposide. Moreover, PtoxDpt exhibited a significant antimetastatic effect, which likely correlated with matrix metalloproteinase (MMP) inhibition and concomitant to the mTOR downregulation. As expected, PtoxDpt could also reverse TGF-β1-induced EMT in hepatocellular carcinoma cells. In addition, ROS are involved in the EMT process [β1 or combination with PtoxDpt, revealing that both EMT and EMT reversion involved ROS production. Further study demonstrated that PtoxDpt-induced EMT reversal was through p53-mediated PI3K/AKT/mTOR pathways; this feature is first reported for an etoposide derivative.Since EMT is associated with metastasis and drug resistance , EMT inh process , but theDpt was prepared through a four-step reaction  was obtained from the RCSB Protein Data Bank. To ensure the accuracy of our docking protocol, etoposide was redocked into the Topo-DNA complex based on the recommended procedure. The conformation of etoposide in the Topo-DNA complex derived from molecular docking could be almost fully superimposed on the native cocrystallized structure, indicating that the protocol was appropriate. As shown in Fig. Dpt was docked into the Topo II complex following similar protocol . However, a slight differential effect on the cell lines was observed; similar growth inhibition was achieved at a lower concentration (IC50 ≤ 3μM) for Bel-7402 and HepG2 cells  and ~84% inhibition at 1.50 μM PtoxDpt treatment were observed based on quantitative analysis ; a quantitative analysis is presented in Dpt on the migration of HCCLM3 cells was determined. As shown in Figures Dpt in a dose-dependent manner. Furthermore, matrix metalloproteinases (MMPs) as key players are involved in tumor invasion and metastasis [Dpt-induced migration and invasion inhibition might correlate with MMP inhibition; thus, the Western blotting and gelatin zymography analyses were further conducted. As shown in Dpt treatment significantly reduced both MMP-2 and MMP-9 expression  and mesenchymal cells (vimentin) were investigated. The immunofluorescence technique is widely used to visualize the alteration in membrane proteins; thus, the vimentin (in red) and E-cadherin (in green) were labeled individually  or combination of TGF-β1 and PtoxDpt  with PtoxDpt, implying that PtoxDpt owned a powerful ability in EMT inhibition.TGF-ell line . To veriablished . Next, tal state , support Figures  and acco Figures  compared PtoxDpt . There w6 folds, ; those igulation , which aDpt-induced migration and invasion inhibition might involve mTOR inhibition or stem from the alteration of the PI3K/AKT/mTOR pathway [Dpt treatment, hinting that downregulation of p-AKT may stem from the downregulated AKT. A similar trend for mTOR, a downstream target of AKT, was also observed, indicating that the metastasis and invasion inhibition correlated with downregulation of mTOR that led to lower abundances of mTORC1 and mTORC2 complexes. The quantitative analysis of the proteins is shown in Dpt-induced downregulation of both AKT and mTOR had significant statistical significance (p < 0.05 or 0.01). On the other hand, in addition to migration and invasion inhibition, PtoxDpt also inhibited EMT, whether the PI3K/AKT/mTOR pathway was similarly involved in the EMT inhibition. To this end, the level of markers of the epithelium and mesenchymal cells, as well as AKT/mTOR, in the absence or presence of AKT inhibitor, LY294002, was further determined by Western blotting. As shown in Dpt and LY294002 downregulated vimentin (snail and slug) and contrarily upregulated E-cadherin, indicating that they acted in a similar way in EMT inhibition. Moreover, PtoxDpt also downregulated AKT and mTOR as LY294002 did  involves the metastatic process of cancer; targeting EMT is one of the options in cancer therapy; thus, the novel EMT reversal agents are required. It has been demonstrated that the alterations in abundance of epithelial-mesenchymal proteins, such as E-cadherin, N-cadherin, and vimentin determine EMT status. On the other hand, to keep cancer cells thriving, higher abundances of DNA topoisomerases (Topo) and iron are needed; thus, either downregulation (or inhibition) of topoisomerase or depletion of iron can slow down the proliferation of cancer cells. Recent studies demonstrated that some Topo II inhibitors, including etoposide, could inhibit EMT and attenuate metastasis , 25, whitoposide . A theorlex Fig. , supporthibition  and migrhibition , in accoDpt induced a downregulation of MMPs, it might inhibit EMT. To test the hypothesis, the alterations in epithelial-mesenchymal markers both in immunofluorescence and in the protein level were investigated. Immunofluorescence analyses in Dpt inhibited EMT through upregulation of E-cadherin and downregulation of vimentin; additional evidence from Western blotting  is associated with increase of matrix metalloproteases (MMPs) ; downregblotting  supporteoduction ; a similoduction , 47; how Figures ; the res Figures , B4. To ures Dpt , B4, indβ1, almost no change for ROS after 24 h . Fetal bovine serum was purchased from Every Green Zhejiang Tianhang Technology Co. Ltd. . Antibodies of vimentin, slug, snail, and p53 were purchased from Boster . Antibodies of AKT, p-AKT, mTOR, p-mTOR, E-cadherin, and Gapdh were purchased from EnoGene . 4′-Demethylpodophyllotoxin was purchased from Shanghai PureOne Biotechnology .MTT, PFT-Dpt was prepared by using a four-step reaction: preparation of 2,2′-dipyridineketone hydrazone dithiocarbamate S-propionic acid : 14.98, 8.87, 8.63, 8.01, 7.54 , 7.00, 6.95, 6.76, 6.56, 6.33, 6.01, 5.64,4.75, 4.52, 4.36, 4.16, 3.69, 2.93. 13CNMR:199.77, 179.25, 175.24, 169.82, 155.02, 151.93, 149.24, 148.94, 147.66, 146.79, 146.73, 145.23,140.66, 138.35, 137.91, 133.26, 130.81, 128.12, 126.94, 125.85, 124.97, 124.02, 109.49, 106.48, 104.92, 101.32, 68.00, 65.95, 65.39, 60.23, 56.47, 56.26, 45.46, 43.15, 33.20, 28.97. ESI-MS (C36H32N4O9S2K): m/z: 767.1268 .The PtoxDpt on DNA Topo II activity was conducted based on the protocol described previously [μg) was added to the Topo reaction buffer (10 mM Tris-HCl (pH 7.5), 1 mM EDTA, 1 mM ATP, 150 mM NaCl, 0.1% BSA, and 5% glycerol) that contained 0.4 μg supercoiled pUC18 plasmid DNA and 1-3 μL of PtoxDpt (1 mM in 8% DMSO) in a final volume of 20 μL. Following an additional incubation at 37°C for 30 min., 5 μL of stopping buffer  was added to terminate the reaction. The resulting products were separated by electrophoresis using a 1% agarose gel in a TBE buffer  containing 0.1% SDS and ethidium bromide (0.5 μg/mL) at 45 V for 3 h. The bands were visualized on a Tocan 360 gel scanner (excited at ~340 nm) . The assay was performed in duplicate.The assay of inhibition of Ptoxeviously . To initDpt and Topo II, the PDB file of the structure of human type II Topo (3QX3) was downloaded from the RCSB Protein Data Bank. The structure of PtoxDpt was generated by ChemDraw. Then, the 3QX3 and PtoxDpt in PDB were transformed in PDBQT format by the AutoDock Tool using the default parameters. PyMOL and LigPlot were used to display the conformation and interactions [To simulate the potent interaction between Ptoxractions , 52.Dpt was set to 22, 24, and 28 for the x-, y-, and z-axes, respectively.Molecular docking studies were performed by using AutoDock Vina . To opti3/mL) were seeded into a 96-well plate, and the varied concentrations of PtoxDpt were added to the wells after the cells were adhered. Following 48 h incubation at 37°C in a humidified atmosphere of 5% CO2, 10 μL of MTT solution (5 mg/mL) was added into each well, and a further incubation was conducted. Finally, 100 μL DMSO was added to each well to dissolve the formazan crystals after removing the cell culture. The measurement of the solution absorbance was performed on a microplate reader  at 570 nm. The percent absorbance inhibition that correlates with percent growth inhibition was obtained. The same assay was performed in triplicate.The proliferative inhibition of the agent was determined by the MTT method as described previously . BrieflyDpt at dose of 1/40 or 1/20 IC50 was added. Fourteen days later, colonies were fixed in 3.7% paraformaldehyde, stained with 0.1% crystal violet. Colonies containing 50 cells at least were counted under an inverse microscope , and the clone numbers were analyzed subsequently.The HepG2 cells in the exponential phase were trypsinized and seeded in 6-well plates at the density of 500 cells/well. PtoxDpt was determined using a wound-healing migration assay [Dpt was then added. The cells were photographed (time 0). After 24 h incubation at 37°C, the cells were photographed again (24 h).The inhibition of tumor cell migration by Ptoxon assay . Brieflyμm pore membranes coated with Matrigel were used to perform the invasion assay. Briefly, after overnight pretreatment with PtoxDpt in a 6-well plate, the HCCLM3 cells were starved for 12 h in serum-free medium. Following this, the cells were collected and resuspended as a single cell suspension. In total, 3 × 104 cells in 100 μL of serum-free medium were added to the upper chamber, and 600 μL of complete medium was added to the lower chamber. Following incubation for 18 h at 37°C, the invading cells were fixed in 4% paraformaldehyde, stained with 0.1% crystal violet, and photographed under a microscope . The percentage of inhibition was expressed using control wells as 100% , followed by a 24 h incubation with developing buffer . The gel was then stained with 0.25% Coomassie blue R-250 for 1 hour and destained with 10% methanol with 5% acetic acid. Clear bands against a dark blue background indicated where the protease had digested the gelatin and were taken to be indicative of protease activity.Gelatin zymography was performed as previously described . ConditiDpt treatment for additional 24 h, cells were first fixed with 4% paraformaldehyde in PBS for 15 min at 37°C and then permeabilized with 0.5 % Triton X-100 in PBS for 20 min. After blocking with 3% BSA in PBS for 60 min, the cells were incubated with combined vimentin with E-cadherin (EnoGene) primary antibody based on protocol recommended by the company; at 4°C, the plate was shaken overnight. Next, removing the primary antibodies and washing with PBS, the cells were further incubated with fluorescence-labeled secondary antibody for 3 h at room temperature. After removing the secondary antibody, the cells were further counterstained with DAPI. Finally, a confocal laser scanning microscope  was used to visualize the cells; the representative cells were selected and photographed.HepG2 cells were first cultured in a 24-well plate with cover glass overnight. Following PtoxDpt or other agent for 24 h. Next, the cells were treated by trypsin digestion and collected. Following washing, the cells were stained with H2DCF-DA . The intracellular ROS assay was conducted on a flow cytometry .HepG2 cells were placed in a six-well plate, once the cells were adhered and subjected to treatment with PtoxDpt were scraped in lysis buffer (RIPA lysis buffer), and the cell suspension in the EP tube was incubated on ice for 30 min and mixed by turning upside down occasionally, followed by centrifugation at 14,000 ×g. The clear supernatant was stored at -80°C. Protein concentration was determined using a colorimetric BCA assay. Proteins (20 μg) were loaded on a 13% sodium dodecyl sulfate-polyacrylamide gel at 120 V (20 mA) for 2 h. The separated proteins were subsequently transferred onto a PVDF membrane at 120 V (200 mA) for 90 min. The membrane was washed with Tris-buffered saline (TBS) and then blocked for 2 h in TBS containing 0.1% Tween-20 and 5% nonfat skimmed milk. The membrane was incubated at 4°C overnight with the appropriate primary antibody used at a dilution recommended by the company. The membrane was then washed several times with TBST and subsequently incubated with the appropriate HRP-conjugated secondary antibody for 2 h at room temperature. Finally, the protein bands were detected using a super sensitive ECL solution  and visualized on a Syngene G:BOX imager . Quantification of protein band intensities was performed using ImageJ software.The protein extracts were prepared based on the company recommended protocol (Solarbio). Briefly, the HepG2 cells treated with or without Ptoxt-test. Comparisons between multiple groups were performed by one-way ANOVA with Dunnett post hoc correction. All statistical tests were conducted by using IBM SPSS Statistics (version 19 software). p < 0.05 was accepted as significant.Results are presented as the mean ± SEM. Comparisons between two groups were carried out using two-tailed Student's


Moreover, the ROS production correlated with ferritin degradation. The upregulation of LC3-II and NCOA4 from immunofluorescence and Western blotting analysis revealed that the occurrence of ferritinophagy contributed to ROS production. Furthermore, DpdtpA could induce an alteration both in morphology and in epithelial-mesenchymal markers, displaying significant EMT inhibition. The correlation analysis revealed that DpdtpA-induced ferritinophagy contributed to the EMT inhibition, implying that NCOA4 involved EMT process, which was firstly reported. To reinforce this concept, the ferritinophagic flux (NCOA4/ferritin) in either treated by TGF-β1 or combined with DpdtpA was determined. The results indicated that activating ferritinophagic flux would enhance ROS production which accordingly suppressed EMT or implementing the EMT suppression seemed to be through “fighting fire with fire” strategy. Taken together, our data demonstrated that ferritinophagic flux was a dominating driving force in EMT proceeding, and the new finding definitely will enrich our knowledge of ferritinophagy in EMT process.Epithelial-mesenchymal transition (EMT) contributes to metastasis and drug resistance; inhibition of EMT may attenuate metastasis and drug resistance. It has been demonstrated that ferritinophagy involves the process of many diseases; however, the relationship between EMT and ferritinophagy was not fully established. Some iron chelators show the ability to inhibit EMT, but whether ferritinophagy plays a role in EMT is largely unknown. To this end, we investigated the effect of a novel iron chelator, DpdtpA , on EMT in the CT26 cell line. The DpdtpA displayed excellent antitumor (IC Epithelial-to-mesenchymal transition (EMT) is a cellular process allowing epithelial cells to undergo several biochemical alterations that permit a polarized epithelium switches to a highly invasive mesenchymal phenotype , accordiEMT is considered as a driving force in tumor progression, while compelling evidence reveals reactive oxygen species (ROS) as crucial conspirators in EMT engagement . ROS areSince redox regulates epithelial-mesenchymal transition of tumor cell, scavenging ROS favors restoration of MET , which may efficiently slow dissemination of tumor cells , 19. A lLabile iron pool (LIP) is regulated by ferritin, a highly conserved iron storage protein which is composed of two subunits, H-ferritin and L-ferritin, and the twelve pairs of subunits binding head to foot form the 24 subunit ferritin cages . When th50: 1.5 ± 0.2 μM). Next, the cellular ROS level was measured by flow cytometry as described previously [2,2 ′-Di-pyridineketone hydrazone dithiocarbamate propionic acid DpdtpA,  displayeeviously . As showp < 0.05 or 0.01); however, the apoptosis induction could be attenuated by addition of NAC  staining, which measures externalization of phosphatidylserine on the cell surface of apoptotic cells specifically. n of NAC , A4, indAs described above, DpdtpA-induced growth inhibition involved ROS production; thus, the potential sites of ROS production were further explored. As well documented, ferritin degradation triggers Fenton reaction that is an important contributor in ROS production; therefore, the level of ferritin was determined; Next, we determined the site of ferritin degradation; it may occur in proteasomes. To this end, the levels of ferritin either in the condition of DpdtpA treatment alone or its combination with a proteasomes inhibitor, MG132, were determined. Beyond our expectation, the addition of MG132 did not attenuate the ferritin downregulation induced by DpdtpA , indicatDpdtpA displayed significant growth inhibition for CT26 cells; the effect of it on cellular morphology was further determined. As shown in Figures β1 is the most powerful EMT inducer; thus, the CT26 cells were pretreated with TGF-β1 for 48 h, which resulted in obviously morphological alteration. As shown in β1. Immunofluorescence analysis revealed that TGF-β1 markedly reduced the level of E-cadherin in the CT26 cells  through lowering ferritinophagic flux, contrarily DpdtpA inhibited EMT  through elevating ferritinophagic flux . Currently, NCOA4 function differently depends on the cancer context, while robust evidence for the role of ferritinophagy in tumorigenesis is lacking [It was interesting that the EMT inhibition was accompanied by upregulation of LC3 and NCOA4 and downregulation of ferritin , which h lacking ; here, wβ1 or combined with DpdtpA was investigated. TGF-β1 indeed induced rising of ROS in the CT26 cells, in accordance with reported previously [β1, seeming to support that DpdtpA induced EMT inhibition through a “fighting fire with fire” strategy , indicating that NCOA4 played an important role in EMT process. In addition, DpdtpA induced EMT inhibition through producing massive ROS that were due to occurrence of ferritinophagy. However, the effect of ferritinophagic flux on EMT in different cell lines 2DCF-DA), desferoxamine (DFO), 4 ′,6-diamidino-2-phenylindole (DAPI), Roswell Park Memorial Institute (RPMI) 1640, and other chemicals were purchased from Sigma-Aldrich. Antibodies of vimentin , LC3 (14600-1-AP), NCOA4 (E11-17114C), N-cadherin (22018-1-AP), and Gadph (E12-052) for Western blotting were obtained from Proteintech Group Inc. . Antibodies of E-cadherin (3195), ferritin , secondary antibodies , and cathepsin D were purchased from Cell Signaling Technology (USA). Ferritin antibody (SC-376594) for immunofluorescence was obtained from Santa Cruz Biotechnology . NCOA4 antibody (HPA0512) for immunofluorescence was purchased from Atlas Antibody (Sweden). Secondary antibodies for Western blotting were obtained from EarthOx, LLC .All chemicals used were analytical reagents (AR) grade. 3--2,5-diphenyltetrazolium bromide (MTT), monodansylcadaverine (MDC), 3-methyladenine (3-MA), chloroquine, dichlorofluorescein  was added and further incubated for 4 h; next, 100 μl DMSO was added in each well to dissolve the formazan crystals after removing cell culture. The measurement of absorbance of the solution was performed on a microplate reader  at 570 nm. Percent growth inhibition was defined as percent absorbance inhibition within appropriate absorbance in each cell line. The same assay was performed in triplet.The stock solution of DpdtpA (10 mM) was prepared in 70% DMSO and diluted to the required concentration with 70% DMSO. The CT26 cells were cultured in RPMI 1640 medium supplemented with 10% fetal calf serum (FCS) and antibiotics. The cells in the exponential phase were collected and seeded equivalently into a 96-well plate; next, the varied DpdtpA (or in the presence of NAC) was added after the cells adhered. Following 48 h incubation at 37°C in a humidified atmosphere of 5% COμM DpdtpA) for 24 h. Then, the cell culture was removed, following PBS washing, trypsin digestion; finally, the annexin V and propidium iodide  were added as recommended by the company. The stained cells were subjected to flow cytometer analysis . Similar to apoptosis assay, the CT26 cells were resuspended in H2DCF-DA containing serum-free culture medium and incubated for 30 min. Next, after removing the H2DCF-DA contained medium by centrifugation, washing with PBS, finally resuspended the cells in PBS. The intracellular ROS assay was performed on a flow cytometer .Cells were seeded into a 6-well plate and treated as described in the section of cytotoxicity assay. The cells were treated with different concentrations of the agent , or combined with LC3 (or NCOA4 (Atlas Antibodies)), or vimentin combined with E-cadherin  primary antibody based on the protocol recommended by the company; at 4°C, the plate was shaken overnight. Next, removing the primary antibodies and washing with PBS, the cells were further incubated with fluorescence-labeled secondary antibody for 3 h at room temperature. After removing the secondary antibody, the cells were further counterstained with DAPI. Finally, a confocal laser scanning microscope  was used to visualize the cells; the representative cells were selected and photographed.The alteration of LMP was assayed as previously described . For det7 CT26 cells that treated with or without DpdtpA were scraped in lysis buffer  on ice for 30 min., followed by centrifugation at 14,000 × g. The clear supernatant was stored at -80°C. Protein concentration was determined using a colorimetric Bio-Rad DC protein assay using the MK3 microplate reader at 570 nm. Proteins (30 μg) were separated on a 13~15% sodium dodecyl sulfate-polyacrylamide gel at 200 V for 3 h. The separated proteins were subsequently transferred onto a PVDF membrane at 60 V for 2 h. The membrane was washed with Tris-buffered saline (TBS) and then blocked for 2 h in TBS containing 0.1% Tween-20 and 5% nonfat skimmed milk. The membrane was incubated at 4°C overnight with the appropriate primary antibody. The membrane was then washed several times with TBST and subsequently incubated with the appropriate HRP-conjugated secondary antibody  for 1 h at room temperature. Following washing with TBST, the protein bands were detected using a super sensitive ECL solution  and visualized using a Syngene G:BOX imager . Quantifications of protein band intensities and fluorescence intensity were performed using ImageJ software.The protocol for Western blotting was followed as described previously ; brieflyt-test. Results are presented as the mean ± SEM. A p value < 0.05 was considered statistically significant.Data were analyzed with Prism 5.0 . Comparisons were made using a one-way analysis of variance or a two-tailed