During perceptual decisions the activity of sensory neurons covaries with choice, a covariation often quantified as “choice-probability”. Moreover, choices are influenced by a subject's previous choice  and neuronal activity often shows temporal correlations on long (seconds) timescales. Here, we test whether these findings are linked. Using generalized linear models, we analyze simultaneous measurements of behavior and V2 neural activity in macaques performing a visual discrimination task. Both, decisions and spiking activity show substantial temporal correlations and cross-correlations but seem to reflect two mostly separate processes. Indeed, removing history effects using semipartial correlation analysis leaves choice probabilities largely unchanged. The serial dependencies in choices and neural activity therefore cannot explain the observed choice probability. Rather, serial dependencies in choices and spiking activity reflect two predominantly separate but parallel processes, which are coupled on each trial by covariations between choices and activity. These findings provide important constraints for computational models of perceptual decision-making that include feedback signals.SIGNIFICANCE STATEMENT Correlations, unexplained by the sensory input, between the activity of sensory neurons and an animal's perceptual choice (“choice probabilities”) have received attention from both a systems and computational neuroscience perspective. Conversely, whereas temporal correlations for both spiking activity (“non-stationarities”) and for a subject's choices in perceptual tasks  have long been established, they have typically been ignored when measuring choice probabilities. Some accounts of choice probabilities incorporating feedback predict that these observations are linked. Here, we explore the extent to which this is the case. We find that, contrasting with these predictions, choice probabilities are largely independent of serial dependencies, which adds new constraints to accounts of choice probabilities that include feedback. During perceptual decisions humans and animals rely on the sensory evidence but also leverage the behavioral context, including their previous decisions . Such seConversely, spiking activity of sensory neurons shows fluctuations on slow timescales up to several seconds. Such slow fluctuations of spiking activity are observed under anesthesia , in awakMoreover, this view also has important implications for the observation that the activity of sensory neurons shows correlations with perceptual decisions that are not explained by the sensory stimulus . These ta). Finally, we use semipartial correlation analysis to examine the role of choice history on CPs. In contrast with the above prediction, we find that the serial dependencies of choices and spiking activity cannot explain CPs. Rather, they reflect two largely independent parallel temporal processes. This suggests that the feedback contribution to CPs is less pronounced or reflects a more complex process than previously thought.Here, we test this prediction in single-unit recordings from macaque visual area V2 while two animals performed a disparity discrimination task. First, we explore the effect of choice history on behavior and on neural activity using generalized linear models (GLMs). Consistent with previous studies  we find Fig. 1We performed novel analyses of previously published data . The detMacaca mulatta) performed a coarse disparity discrimination task. For each session the signal disparities  were tailored to the tuning preference of the simultaneously recorded neuron such that one disparity was close to the neuron's preferred disparity and the other at a trough of the neuron's tuning curve.All procedures were performed in accordance with the U.S. Public Health Service policy on the humane care and use of laboratory animals, and all protocols were approved by the National Eye Institute Animal Care and Use Committee. Extracellular activity from disparity-selective V2 single units was recorded while two male monkeys  surrounded by an annulus at zero disparity (1–2° wide). The center disparity changed randomly on each video frame (96 Hz frame rate) chosen from an evenly spaced set of disparity centered around 0° disparity, encompassing the tuning preferences of the recorded neuron. For “no-signal trials” (randomly interleaved) all disparities were drawn from a uniform distribution of probabilities. On other trials  we increased the probability of occurrence  of one disparity , whereas for the remaining video frames the disparities were drawn from the same uniform distribution as used for the no-signal trials. For each recording session we used two signal disparities, one near disparity and one far disparity, which approximated the neuron's preferred and null disparities.n = 76 neurons as used by To avoid that neural variability on no-signal trials  reflected systematic choice-dependent stimulus differences, we corrected spike counts on no-signal trials for stimulus-induced variability as described previously . The anaWe converted choice prediction performance ; cf. xycorr = Based on the results from r/τ)2). The value for its hyper-parameter τ was chosen such that the cross-validated prediction performance across the population was maximized . We did not use a sparsity penalty when predicting spike counts, because including it did not lead to an improvement in prediction performance. To ensure that estimates of choice- and spike-prediction performance were free of bias from overfitting, we cross-validated the model fits (We fit GLMs  to predidel fits . The modnC = 1): for example, when predicting choice from the current stimulus nS and the current choice spike count, nm = βo + βnCCn + βnS Sn and Prob(nC = 1) = normcdf(nm). We quantified GLM performance on cross-validated data by fitting the model on signal trials and computing the area under the ROC of the output of the GLM on no-signal trials. The experimental covariates (z-scored before fitting) for GLM fits predicting behavior were as follows:nC-1, choice on the preceding trialnT-1, target on the preceding trialnW-1, whether the preceding trial was rewarded (win)nI, stimulus (image) on the current trialnS, spike count on the current trialnS-1, spike count on the preceding trial (the stimulus-induced effect is regressed out).To predict behavioral choices, we used a generalized linear classification model with a probit function as link function . This menC-1,nT-1,nW-1 together as choice history, abbreviated as nH-1. We quantified the decrease in psychophysical performance attributable to choice history using the approximation derived by 2 is the choice history-induced variance of the behavior. To control for the effect of the stimulus on the previous trial, nI-1, on the previous spike count, Sn-1, we regressed out the stimulus-induced effect. To this end we used the residual spike count obtained after linearly regressing nS-1 on nI-1 .We refer to the covariates, nC, choice on the current trialnC-1, choice on the preceding trialnT-1, target on the preceding trialnW-1, whether the preceding trial was rewarded (win)nI, stimulus (image) on the current trialnS-1, spike count on the preceding trial (the stimulus-induced effect is regressed out)SF, slow fluctuation across multiple trials.The performance of the GLM-fits predicting spike counts was evaluated by computing the Pearson correlation coefficient between the predicted and measured spike counts on no-signal trials. The covariates for GLM fits predicting spike counts were as follows:In addition, for each unit, we fit a constant intercept term which controls the firing rate of the unit.nC, nC-1, nT-1, and nW-1. To find PSTH basis functions we performed PCA on the PSTHs after correcting for response latency, across all units. Two basis functions were sufficient to describe >99% of the variance of the PSTHs of all units across all stimuli. These two basis functions (minus the mean PSTH of the respective unit) were included in the GLM fit for each unit. The covariates for GLM fits predicting time-resolved spike counts were as follows:nC, choice on the current trial (4 predictors)nC-1, choice on the preceding trial (4 predictors)nT-1, target on the preceding trial (4 predictors)nW-1, whether the preceding trial was rewarded (4 predictors)nS-1, spike count on the preceding trial (the stimulus-induced effect is regressed out)nI, stimulus (image) on the current trialPSTH, PSTH basis functions (2 predictors)Intercept term (per unit).For the Poisson GLMs predicting time-resolved neural activity additional covariates were used. To account for the temporal structure of firing within trials, we introduced peristimulus time histogram (PSTH) basis functions, as well as time-varying features for Each trial was split into 20 bins of 100 ms each (which corresponds to the resolution of the PSTH basis functions included). For choice and choice history covariates, we included four predictors each into the design matrix of the full model: those four predictors were chosen to distinctly account for effects between 0 and 500, 500 and 1000, 1000 and 1500, and 1500 and 2000 ms of trials. The GLMs were fit on signal trials and the resulting weights used to predict the time-varying spike counts (100 ms resolution) for each no-signal trial.b–c). For this analysis, one session was excluded for having less than four near and four far choices on no-signal trials due to random sampling.To verify that the measured behavioral strategies by the animals were not confounded by the weak intertrial dependence in stimulus induced by pseudorandomization, we also analyzed the responses of a model without choice history (independent observer model). We first fitted psychometric functions with a cumulative Gaussian to match the model's performance for the stimulus on the current trial to the psychophysical performance of the animals. Next, we generated model responses based on the stimulus on the current trial assuming a binomial distribution defined by the model parameters of the psychometric functions. Therefore, in this simulation, the stimulus sequence and the association between current stimulus and response were exactly as in the actual experiment, but choices were not influenced by experimental history. When we performed our analysis on these data, it did not identify any significant or systematic components in the behavioral strategies see b–c. For nS on nH-1, and the animals' choices . In contrast to the above GLM-analysis, no cross-validation was used when calculating residuals for the correlation analysis. The semipartial correlation is then defined to be the correlation between the choices and the spike-count residuals.We computed correlations between the residuals of the spike counts obtained after linearly regressing c into a predicted part ĉ and a choice residual c̃, such that c = ĉ + c̃. Similarly, for spike counts, s = ŝ + s̃. The absolute value of the semipartial correlation corr is a lower bound to the partial correlation corr, i.e., |corr|≤|corr|: this can be seen to hold true writing the inequality as follows:ĉ,s̃) = 0.The absolute value of a semipartial correlation constitutes a lower bound  on the aWe performed simulations to test the statistical power of our analyses. Specifically, we defined two autoregressive models that are compatible and two models that are incompatible with our conclusions. In the compatible models (CMs) CPs arise from instantaneous feedforward (CM 1) or postdecision feedback (CM 2) correlations, whereas in the incompatible models (ICMs) they result from the effect of preceding choices on current neural activity (ICM 1) or of preceding neural activity on current choices (ICM 2), respectively.tc and ts the choice and spike count on trial t, respectively. We note that CM 1 can be easily extended to populations of  correlated neurons: in that case, the term ωts instead consists of the average activity of a population of neurons, and the weights and the strength of the “decision noise” σ1 would have to be adjusted such that the model remains consistent with the observable single-neuron statistics. However, as our analyses are based on  correlations between single-neuron spiking and choices, our results hold regardless of the population size and the size of these noise correlations.CM 1:ts−1 is replaced by a population of weakly correlated neurons, and our results are robust to population size and the size of these noise correlations within empirical ranges.CM 21, σ2, or α, β, ω, σ1, σ2, respectively) using empirically observed statistics: CPs, auto-covariance between choices on subsequent trials, and auto-covariance between spike counts on subsequent trials. Models were fit using the CMA-ES algorithm . For each repetition we then computed the semipartial CP (regressing out spike history and choice history) and performed a one-sample b, the fixating monkeys were presented with a dynamic random dot pattern positioned inside the receptive field of the simultaneously recorded neurons for a fixed 2 s duration. The stimulus was a circular random dot stereogram defining a central circular disk surrounded by an annulus. The monkeys' task was to judge whether they perceived the central disk as protruding (near) or receding (far) relative to the surround by making a saccade to one of two choice targets. Correct choices were rewarded. The disparity of the stimulus center was spatially uniform on each video frame but changed probabilistically between frames to control the difficulty of each trial . On a subset of trials, defined as no-signal trials, disparities were drawn from a uniform distribution centered on 0 degree disparity. On these trials there was no correct answer and the monkeys were rewarded randomly on 50% of the trials. For discrimination tasks, the dependence of an observer's choices on the stimulus can be captured by a sigmoidal function (psychometric function). For example, for the disparity discrimination task used here, the psychometric function maps the probability of a far choice as a function of the far signal in the stimulus (c). Note that for each session we define the sign of the stimulus based on the tuning preference of the simultaneously recorded neuron: positive and negative values correspond to signal strength at the neuron's preferred and nonpreferred disparity, respectively. Typically, the psychometric function includes a stimulus independent term to account for an observer's bias. As shown in Figure 1c, this psychophysical bias differed between trials following a choice to the preferred (positive) and nonpreferred (negative) disparity target. To examine any systematic effect of previous choices on the decision in the current trial in more detail, we used a statistical model that, in addition to the stimulus, took into account the influence of previous choices on the animal's present choice (a). In this model, the psychophysical bias-term was different for each trial, and depended linearly on the recent experimental history  and the previous choice (nC-1). Different strategies can be identified by plotting the weights of the model for nC-1 against those for nT-1  quantifies in which direction, on average, the monkeys are influenced by the previous choice: positive values indicate that they tend to repeat their previous choice , whereas negative values indicate that they tend switch their answer (a near choice following a far choice or vice versa). In contrast, the weight for the previous target  measures how strongly this overall choice strategy depends on whether the previous choice was rewarded. That is, a “win stay, lose switch” strategy would be reflected by positive weights for nT-1 and weights for nC-1 close to 0, whereas a “win switch, lose stay” strategy would be captured by negative weights for nT-1, and weights for nC-1 close to 0 . Together, nC-1 and nT-1 therefore quantify the behavioral strategies . We examined the weights for nT-1 and nC-1 for individual sessions in the two monkeys and found that both monkeys were more likely to switch their choices, i.e., that weights for nC-1 were negative . This tendency was slightly stronger after errors, corresponding to positive weights for nT-1 . Intriguingly, the monkeys' highly consistent behavioral strategy to switch their choices between consecutive trials likely reflects a learned strategy to adapt to the statistics of the task. Indeed, because we presented a fixed number of stimulus presentations in a random sequence, alternating trial types were slightly more likely (∼52% rather than 50%). Note that this did not affect the 50% probability with which no-signal trials were rewarded. In Figure 3a probabilities for stimulus sign alternations (left) and choice alternations (middle) across all sessions are shown. For Monkey 2 the choice alternation rate (solid black line) closely matched that of the stimulus, whereas Monkey 1 overshot (dashed black line). For Monkey 2 the strategy was therefore beneficial on (weak) signal trials. Across sessions, there was no systematic relationship between the animals' choice alternation rate and that for the stimulus sign . We note that the monkeys' switching strategy contrasts with the finding in human subjects that perceptual judgments are biased toward the preceding stimulus . For such an observer the weights in Figure 2b would not exhibit a systematic pattern, exactly as found in our control analysis. In a subset of n = 50 sessions for which we had a sufficient number of segments with at least three consecutive successfully completed trials, we also explored the effect of trials going further back to trial n-2. We found that the additional effect was small and not significantly larger than that of choice history only including trial n-1 (d). We therefore restrict our analysis presented here to the effect of trial n-1.In control analyses using a model observer that is not influenced by trial history, we verified that our results here could not be explained by the statistics of the trial sequence b,c. For rial n-1 d. We theWe next compared the predictive effect of choice history with that of other experimental covariates. First, we compared it to the choice-predictive effect of the spike counts of single disparity selective neurons , 2009. Cb compares choice prediction performance for five models. For the spike count alone, the mean choice prediction performance was 0.56 , very similar to the values obtained when quantifying neuron-behavior correlations as CP  but is smaller than that of the previous choice . This indicates that on no-signal trials, choice history explains a substantially larger proportion of the monkeys' choices than the single-neuron spike-count on the current trial alone. Indeed, when measuring the cumulative contribution of different covariates  to choice prediction performance, choice history had the largest contribution to choice-prediction (d). For this comparison, we converted choice prediction performance into Pearson correlation coefficients and termed these “choice correlations” , versus the gain in choice correlation when it is included as a last covariate]. We note that since we cross-validated the model , including more parameters does not necessarily improve performance .Figure 2ns as CP  directlyns as CP . This efediction d. For thlations” . In thisa). To additionally explore the effect of slow fluctuations (SFs) across multiple trials, we also used a Gaussian Process latent modulator . We quantified prediction performance as the correlation coefficient between the predicted and measured spike count on all no-signal trials . When fit as the only covariate, the preceding spike count had a substantial predictive effect , as expected for known non-stationarities in neuronal spiking activity. Indeed, for slow fluctuations across several trials the prediction performance improved further . These substantial temporal correlations of spiking activity combined with the serial dependencies of the animals' choices during this task may therefore contribute to the trial-by-trial correlations between the animal's choices and spiking activity.Spiking activity of sensory neurons is known to fluctuate on long timescales independently of the stimulus . Given tFig. 4ator cf.  within tnC-1), the preceding target (nT-1), whether the preceding trial was rewarded, i.e., a win (nW-1) and the current choice (nC) using a GLM predicting the spike counts (nS).Before more directly investigating whether decision-related activity during this task results from the serial dependencies in choices and spiking activity, we examined the predictive effect of behavioral covariates on spiking activity. Specifically, we explored the effect of the animal's preceding choice . When including covariates cumulatively (d) we found that only Wn-1, but not Tn-1, yielded an improvement in prediction that was independent of nC-1 . Conversely, nC-1, did not improve prediction performance over nW-1 . Indeed, choice history excluding past wins  was less predictive of the spike count than the choice on the current trial. Conversely, when including past wins , it was more predictive of the spike count than the choice on the current trial. The predictive effect of choice history was therefore of sufficiently large magnitude to fully account for the effect of choice in principle. However, we find that a component of the predictive contribution of nC was statistically independent of choice and spiking history: even when included as the last predictor it provided a small but significant improvement in prediction performance .Choice history had a small but significant predictive effect . Interestingly, this effect is comparable to the combined predictive information in spiking-activity and behavior on the previous trial . This is consistent with the view that these slow fluctuations reflect meaningful signals, such as those related to choice history, as previously proposed  is most pronounced at the beginning of each trial, while the other predictors show little variation in prediction performance throughout the trial.Note also the substantial component in variability that can be predicted by slow fluctuations alone d. Intereproposed . We alsoproposed . We findnC or nS) to be necessary if the predictive effect of the covariate cannot be explained by alternative covariates. For interactions that are not needed  we remove the dashed connection. Note that this analysis, while related, differs from that of causal interactions in directed graphical models (see Discussion).We systematically varied the order with which we included covariates in GLMs predicting choices or spike counts , to invep < 10−9) in prediction when including choice history after the preceding and current spike count  supports a significant statistical interaction between choice history and choice, independent of nS-1 and nS (a). Similarly, adding the covariate n-1S to a model that includes choice history and the current choice (nC) substantially improves the prediction of the spike count on the current trial , establishing an interaction between nS-1 and nS, independent of choice history and nC. Additionally, the statistically significant interaction between nS and nC  can be inferred from the results in Figures 2d and 4d: adding nS or nC, respectively, to the model that includes choice history and nS-1, improved the prediction of nC (d) and nS (d), respectively. The substantial improvement  and Sn d, respecnS-1) after choice history and the current spike count (b). The interaction between nS-1 and nC independent of choice history and nS therefore lacks statistical support. Moreover, choice history provides an improvement in predicting nS, independent of nS-1 and nC resulting in a significant statistical interaction between choice history and nS (c). We note however that the incremental improvement in prediction is small and only owed to the predictive effect of nC-1 and nW-1. Interestingly, our time-resolved analysis reveals that this weak improvement in prediction is only present early in the trial (b). Although weak, such an effect over time is compatible with a framework of perceptual inference . The predictive effect of nW-1 may therefore reflect a transient boost in arousal following a reward, leading to a modulation of the neuronal response independent of nS-1 and nC. Nonetheless, this effect is small, although statistically significant, the statistical interactions along the diagonal are therefore weak . This suggests that the serial dependencies in choices and spiking activity result from two largely separate processes. This should imply that trial-by-trial correlations between choices and neural activity are largely unaffected by these serial dependencies: CPs are a consequence of interactions on the single trial, rather than a consequence of correlations that are carried over from previous trials.In contrast, prediction performance is not significantly increased when including the preceding spike count . Because semipartial correlations mathematically provide a lower bound to the absolute value of the partial correlations .To test the prediction that CPs are largely independent of serial dependencies directly, we measured the residual CPs after removing the component of the spike count that could be explained by history. As choices are binary, computing and interpreting “choice residuals” by regressing out history effects is difficult; however, we circumvent these difficulties by only regressing out experimental history from spike-counts. As a result, we obtain semipartial correlation between spike counts and choice, rather than partial correlation coefficients. We found that the residual CPs were largely unchanged compared with the raw CPs  or incompatible  with our conclusions. In the compatible models CPs reflect instantaneous, i.e., trial-by-trial, feedforward  or feedback  correlations between choices and spike counts. Conversely, in the incompatible models, CPs result from influences of preceding choices on current spiking activity  or of preceding spiking activity on the current choice . We fit each model to the data from each unit while matching the number of no-signal trials to those in our dataset . Based on these model fits we then generated artificial model responses for no-signal trials and computed the semipartial CPs (regressing out choice history and spiking history). For the compatible models, the comparison of these semipartial CPs with the empirical CPs mirrors the animals' data . Indeed, as in our data, CPs and semipartial CPs are highly significantly correlated . In contrast, for the incompatible models, choice probability is largely explained by history . As a consequence, CPs and semipartial CPs when removing history are not correlated . Moreover, semipartial CPs when removing history are 0.5 on average . In contrast, in compatible models we reject the null hypothesis that semipartial CPs when removing history are 0.5 in close to all cases . This is similar to our empirical results for which this hypothesis is rejected with p < 10−6 (t tests).Given that CPs are overall small one might wonder whether our inability to identify an appreciable contribution of serial dependencies merely results from a lack of statistical power of our analysis. To determine the sensitivity of our analysis in our dataset, we therefore repeated the semipartial correlation analysis on models that are compatible a,b or inmpatible c,d with rd CM 1; a or feedck CM 2; b correlay ICM 1; c or of pe ICM 2; c. We fit compare b, 8e,f.  history g,h. As a average k,l. In cll cases i,j. ThisTogether, these analyses support the conclusion that, despite the fact that serial dependencies in choices and spiking activity are both strong, they do not have a strong impact on CPs.Here, we explored the effect of serial dependencies in behavior and spiking activity on decision-related activity, quantified as choice probability, of visual neurons in V2. Although the serial dependencies of both choices and spiking activity were substantial, and past choices and neural firing were correlated with both choices and spiking activity, these reflected two largely separate processes in our data: there was no statistical support for a direct interaction between the preceding spike count and the current choice, whereas the interaction between choice history and the current spike count was weak. This latter interaction resulted largely from “wins”, i.e., whether or not the preceding trial was successful and thus yielded a reward, compatible with a transient reward-dependent boost in arousal that modulated spiking activity, as well as with fluctuations in arousal that affect both firing rates and the probability of success. Importantly, when removing the effect of choice history, CPs remained essentially unchanged. The serial dependencies of choices and spiking activity therefore reflect two largely parallel processes that are correlated through instantaneous covariations between choices and spiking activity.Our analysis based on using GLMs to determine which statistical interactions are necessary to account for the structure of the data is reminiscent of the analysis of  causal interactions in directed graphical models . Note hoThat choice probability is independent of choice history is surprising for several reasons: first, it seems to contrast with a recent study using fMRI in humans performing a visual task that identified a signature of past choices in BOLD signals from V1 . HoweverSecond, if CPs reflect only the effect of correlated noise in the sensory representation (feedforward account), it suggests that the effect of this noise on choice is independent from trial to trial. This is interesting given the substantial slow fluctuations of the neuronal activity across trials. However, if, for example, the decision variable is computed as a difference between two pools with similar temporal correlations, then the temporal correlations could cancel when computing the decision variable, reducing or abolishing temporal correlations in the decision variable. If this is the case, it is surprising that the brain removes temporal correlations at the level of the readout of the relevant sensory information, but not temporal correlations at the decision stage.Finally, contrasting with this pure feedforward account, CPs are increasingly thought to reflect, in part, a feedback component ; i.e., a


E. coli genes display non-uniform mRNA stoichiometries despite being coded from the same operon. We further show that these altered operon stoichiometries are shaped post-transcriptionally by differential mRNA decay, which is regulated by RNA structures that protect specific regions in the transcript from degradation. These protective RNA structures are generally coded within the protein-coding regions of the regulated genes and are frequently evolutionarily conserved. Furthermore, we provide evidence that differences in ribosome densities across polycistronic transcript segments, together with the conserved structural RNA elements, play a major role in the differential decay process. Our results highlight a major role for differential mRNA decay in shaping bacterial transcriptomes.Bacterial operons synchronize the expression of multiple genes by placing them under the control of a shared promoter. It was previously shown that polycistronic transcripts can undergo differential RNA decay, leaving some genes within the polycistron more stable than others, but the extent of regulation by differential mRNA decay or its evolutionary conservation remains unknown. Here, we find that a substantial fraction of  Bacteria utilize operonic transcription to synchronize the expression of multiple consecutive genes. However, this strategy lacks the ability to fine-tune the expression of specific operon members, which is often biologically important. In this report, we integrate multiple transcriptome-wide RNA-sequencing methods to show that bacteria commonly employ differential mRNA decay rates for genes residing within the same operon, generating differential transcript abundances for equally transcribed operon members, at steady state. By comparing the transcriptomes of different bacteria, we show that differential decay not only regulates the expression levels of hundreds of genes but also often evolutionarily conserved, providing support for its biological importance. By mapping the RNA termini positions at steady-state, we show that stabilized operon segments are protected from different RNases through a combination of protective RNA structures, which surprisingly, are often encoded within protein-coding regions and are evolutionarily conserved. In addition, we provide evidence that differential ribosome densities over the regulated operons guide the initial events in the differential decay mechanism. Our results highlight differential mRNA decay as a major shaping force of bacterial transcriptomes and gene regulatory programs. One of the defining features of bacterial gene expression is the co-transcription of multiple genes within polycistronic mRNAs . Co-tranA major limitation of polycistronic transcription is that it theoretically generates equal amounts of mRNA for all genes sharing an operon. This can be disadvantageous in the common scenario where one of the proteins encoded in the operon is needed in higher amounts than the others. In many such cases this inconsistency is resolved by differential translation efficiencies of individual genes encoded on the same transcript ,3, or byE. coli malEFG operon encoding the maltose ABC-transporter complex, where MalF and MalG form the transmembrane channel and MalE acts as the substrate-binding protein, which is needed in higher quantities [malFG) is rapidly degraded by RNases whereas the 5’ portion  is stabilized, leading to accumulation of the malE mRNA and to a substantial relative increase of the MalE protein as compared to MalF and MalG [More than three decades ago it was shown that mRNA degradation can lead to differential mRNA stoichiometries for genes encoded on a single polycistron ,8. This and MalG –17, the E. coli. We find that regulation by differential decay is widespread and reshapes the stoichiometries of ~12% of polycistronic mRNAs in this model organism. This process is highly conserved also in the Salmonella and Enterobacter transcriptomes, and depends on conserved RNase-resistant RNA structures that guide the RNA decay dynamics. Furthermore, we find that differential decay is often dependent on translation and that stabilized operon segments are characterized by relatively higher ribosome densities than non-stabilized segments, suggesting that variation in translation efficiency guides endonuclease cleavage to ribosome depleted sections. Taken together, our data support differential operon decay as a common and frequently conserved mode of regulation in bacteria.In this study, we combine a set of multi-layered high-resolution RNA-seq approaches to extensively map and characterize differentially decaying operons in E. coli operons annotated in the EcoCyc database [E. coli bacteria using a previously established approach [E. coli BW25113 genome . However, we detected 47 operons , encompassing 177 genes, in which two consecutive genes displayed correlated differential decay rates and differential steady-state mRNA levels  . Indeed,malEFG [malFG and the malE segments to be 1 and 3.5 minutes, respectively, consistent with the relative stabilities measured for these genes in previous reports and validating our measurements. In addition to the malEFG operon we identified four other ABC transport systems, including those transporting polyamines, arginine, histidine and methionine  and arginine (artI) transporters occur at the end or the middle of their operons, respectively malEFG [Fthionine . In all ectively . StabilitatABC operon, which encodes the evolutionarily conserved twin-arginine protein export system [Another transport system subject to decay-based regulation was the t system  Fig 1D)tatABC opt system ,26, and rpoZ-spoT containing operon, such that the segment encoding rpoZ is stabilized compared with that containing spoT. The rpoZ gene encodes the RNAP omega subunit, which binds the regulatory alarmone ppGpp [rpoZ gene products, it is likely that RpoZ protein expression levels need to be higher than that of SpoT. Conceptually similar, we also identified differential decay in operons encoding 4 different two-component systems, where in each operon the transcription factor component was consistently more stable than the histidine-kinase enzyme [E. coli  of the decay-regulated E. coli operons shared between the two species present similar patterns of sub-operon differential expression in S. typhimurium  and RNase II enzymes [malE gene, which is the 5’-most gene in the operon [Our analyses discover many new cases where the steady-state levels of individual genes within polycistronic mRNAs are controlled via selective stabilization  . However enzymes ,31, thuse operon ,32  and were not followed by the uridine tract required for Rho-independent termination [The set of differentially decaying operons we found included 34 gene-pairs (70%) in which the 5’-most gene is stabilized as compared with the downstream region, akin to the case of malEFG . In 76% malEFG ; Methodsmination , supportSalmonella and Enterobacter genomes revealed that these protective RNA structures are conserved in 75% and 91% of the differential decay instances shared between E. coli and Salmonella, or E. coli and Enterobacter, respectively , the structural RNA elements presumed to protect the upstream gene from degradation were embedded within the protein-coding sequence of the downstream gene, positioned on average 107 +/- 56 nt into the coding sequence . The seqectively  Tables. s operon .E. coli [papBA operon of uropathogenic E. coli, encoding a transcription factor, PapB, and the major pilus protein, PapA [papBA mRNA at the intercistronic region separating the papB and papA genes, at a site located upstream of an RNA hairpin structure [papB segment, which is rapidly degraded by 3’-5’ exonucleases, and the papA mRNA, which is protected by the 5’ RNA-structure from additional RNase E cleavage, as well as an RNase resistant terminator structure at its 3’ end [In 30% of the decay-regulated operons we detected, the stabilized gene of the operon occurred in the middle of the transcript or was the most downstream one e.g., . In thesin, PapA . It was tructure . Consequs 3’ end  Fig 3A)E. coli [papBA-like protection mechanism in these cases , implying that ribosome densities play a common role in shaping the differential decay process, possibly by decreasing endonucleolytic cleavage rates in operon regions populated by more ribosomes.Ribosome densities were previously found to positively correlate with mRNA stability, presumably by physically restricting access to RNase cleavage sites –42. Re-aE. coli mRNA half-lives were measured following a brief pre-exposure to kasugamycin, a translation initiation inhibitor [To directly examine whether differential ribosome density is involved in guiding differential decay, we analyzed recently published data in which nhibitor . In thisE. coli , many bacterial lineages harbor different RNase combinations and properties, for example 5’-3’ processive exonucleases in Firmicutes [Whereas our proposed models for differential decay likely hold for other organisms that share similar RNA decay machineries  under aerobic conditions at 37°C with shaking to an optical density (OD600) of 0.5. Prior to sample collection, 1:10 ice-cold stop solution (90% ethanol and 10% saturated phenol) was added and the cultures were immediately placed on ice to stop all cellular processes [20 and incubated for 5min at 50°C. All RNA samples were treated with TURBO deoxyribonuclease (DNase) .rocesses ,47. BactE. coli, S. typhimurium and E. aerogenes were mapped to the CP009273, NC_016810.1 and NC_015663.1 RefSeq genomes, respectively, using NovoAlign (Novocraft) V3.02.02 with default parameters, discarding reads that were non-uniquely mapped as previously described [RNA-seq, term-seq and 5’-sequencing libraries were prepared and sequenced as previously described ,48. Sequescribed . For 5′-escribed ,49. TSSsescribed .E. coli or E. aerogenes overnight cultures were diluted 1:100 into 25ml fresh LB media and incubated at 37°C until reaching an optical density (OD600) of 0.5. The culture was then placed in a preheated 37°C shallow water bath to preserve the experiment temperature and 125μl Rifampicin  were immediately added to the culture to inhibit RNA synthesis. Selected time points were sampled by collecting 1.4ml from the culture into a pre-chilled tube containing 170ul of ice-cold stop solution (90% ethanol and 10% saturated phenol) to deactivate cellular processes and RNA-decay. The sample was quickly vortexed and then placed on ice until all time points were collected. The samples were centrifuged for 5min at 4000rpm to collect cell pellets and were flash frozen. During RNA extraction, after tri-reagent mediated lysis, each sample was spiked with 5fmol of the ERCC RNA  to allow normalization of RNA abundance between samples. RNA-seq libraries were prepared and sequenced as described above. Gene-expression was calculated as the median coverage per nucleotide (reads/nt) normalized by the number of reads that mapped to all ERCC spike-in RNA, an estimate which we found is more robust than the total number of reads or average coverage in cases of non-uniform decay patterns, where overlapping operon regions, or small sub-ORF regions can persist long after the full transcript has been eliminated. Transcript half-lives were calculated by fitting the decay time-course abundance measurements per gene with a delayed exponential-decay function as previously described [1/2 = ln(2)/ λ.escribed . The preescribed , which pE. coli treated with the Rho inhibitor Bicyclomycin [Operon gene annotations were extracted from EcoCyc  S2 Tabl. To idenclomycin . v) The Gene-pairs in which one gene was at least 2-fold more stable and 2-fold more abundant than its consecutive neighbor gene were classified as putatively decay-regulated . We manuTerm-seq libraries were prepared as previously described  and sequS. typhimurium and E. aerogenes protein-coding sequences were retrieved from NCBI and blasted against the E. coli protein database, with E-value set to 10−5. Gene and operon orthologues were classified as the Best Bi-directional Hits (BBHs). Gene-pairs were compared if they occurred consecutively as in E. coli and were substantially expressed, as described above (The ed above  Tables.E. aerogenes and S. typhimurium strains in this study) were identified using blast (Genes containing a protective structure embedded at least 50 bases into the coding region were selected (n = 23) and their orthologues from up to 21 different bacterial strains belonging to the Enterobacteriaceae family  were retrieved from a recently published dataset . The ribE. coli bacteria were pre-exposed to 1mg/ml kasugamycin for 15min before being subjected to a transcriptome-wide rifampicin-based RNA decay assay [Estimated decay rate values were retrieved from a recently published dataset, in which ay assay . The decS1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.S8 Table(XLSX)Click here for additional data file.S9 Table(XLSX)Click here for additional data file.S1 FigComparison of decay rates calculated in this study (x-axis) and recently published values (y-axis) . The sca(EPS)Click here for additional data file.S2 FigE. coli and E. aerogenes depicted by normalized RNA-seq coverage in steady state  or at two time points (green and red) following rifampicin treatment. RNA-seq coverage was normalized by the number of uniquely mapped reads in each library. RNA 3’ ends detected by term-seq are shown as black arrows, with the height of the arrow representing the total number of supporting reads. (A), The ptsHI-crr operon shows a conserved decay signature and 3’ end processing patterns in E. coli and E. aerogenes. (B-C), Conserved RNA decay patterns detected in two additional operons encoding the hfq-hflX and bepA-yfgD gene-pairs.Comparative RNA-decay analysis in (TIF)Click here for additional data file.S3 FigE. coli (blue) and Salmonella typhimurium (green) as measured by RNA-seq. Average of three biological replicates is shown with error bars representing standard deviation. (B-C), Examples of differential mRNA abundance in conserved E. coli and S. typhimurium operons with the median RNA-seq coverage/nt, calculated from a representative replicate, shown above the genes in red.(A), Ratio of steady-state mRNA abundance of consecutive decay-regulated gene-pairs in (TIF)Click here for additional data file.S4 Figp < 10−14).The 45 nucleotide sequence occurring immediately upstream of the recorded 3’ ends present at the sites of decay, as detected by term-seq , as well as randomly selected intergenic genomic positions , were folded using RNAfold  and the (EPS)Click here for additional data file.S5 FigEnhanced sequence conservation at the wobble codon positions of genes encoding a protective RNA structures (n = 23). The nucleotide sequence of genes encoding a protective structure (n = 23) was compared among up to 21 different bacteria ; Methods(EPS)Click here for additional data file.S6 FigMultiple sequence alignments generated for three representative genes that contain a protective structure. Rows show sequences from different organisms as noted and the Integrated Microbial Genomes (IMG) genome identification number for each organism is listed. A black box shows the region containing the protective RNA structure and the predicted RNA fold is shown above. Red stars represent positions with 100% sequence conservation.(EPS)Click here for additional data file.


As an alternative to DNA mutagenesis, RNA mutagenesis can potentially become a powerful gene-regulation method for fundamental research and applied life sciences. Adenosine-to-inosine (A-to-I) RNA editing alters genetic information at the transcript level and is an important biological process that is commonly conserved in metazoans. Therefore, a versatile RNA-mutagenesis method can be achieved by utilising the intracellular RNA-editing mechanism. Here, we report novel guide RNAs capable of inducing A-to-I mutations by guiding the editing enzyme, human adenosine deaminase acting on RNA (ADAR). These guide RNAs successfully introduced A-to-I mutations into the target-site, which was determined by the reprogrammable antisense region. In ADAR2-over expressing cells, site-directed RNA editing could also be performed by simply introducing the guide RNA. Our guide RNA framework provides basic insights into establishing a generally applicable RNA-mutagenesis method. Genetic engineering technologies for regulating intracellular target-gene functions and/or expression have been widely used in basic research and medicinal and therapeutic applications1234In contrast to DNA, messenger RNA (mRNA) is a transient cellular molecule. mRNAs possess genetic information that determines the functions and expression levels of the encoded proteins. Hence, RNA-targeted genetic manipulation is capable of controlling target-protein functions, similar to genome editing, without the risk of damaging the original genomic information. Small-interfering RNAs (siRNAs)Adenosine-to-inosine (A-to-I) RNA editing, in which a specific adenosine is converted to inosine by an adenosine deaminase acting on RNA (ADAR), is a widely conserved post-transcriptional modification mechanism in metazoans1112Intracellular ribosomal RNA (rRNA) modifications, such as pseudouridylation1718Human ADAR2 (hADAR2) is mainly expressed in neurons222324Here, we report ADAR-guiding RNA (AD-gRNA), which directly induces A-to-I mutations by guiding hADAR2 to the target-site . The ediOur objectives were to develop a gRNA design to induce A-to-I RNA editing activity of native hADAR2 and assess its capacity for site-directed RNA mutagenesis. Essentially, gRNAs commonly have 2 modules: a protein-recruiting region and an antisense region that mediates target-RNA recognition through base paring. Importantly, proteins bound to gRNAs are positioned to effectively react with the target site after the antisense gRNA hybridizes to a target RNA. To construct human AD-gRNA with such functions, we focused on the secondary structure of the natural substrate RNA. dsRBDs mainly control substrate RNA binding, and the deaminase domain catalyses the hydrolytic deamination of adenosine . AccordiAnthurium coerulescens. A200 of GFP mRNA was the target adenosine, and a 160-nt short fragment of GFP mRNA (sGFP RNA) template (including A200) was constructed as the target RNA. AD-gRNA targeting A200 (ADg-GFP_A200) was designed with a 19-nt ASR and a 49-nt ARR (in vitro transcription. Specific complex formation of ADg-GFP_A200 with sGFP RNA (depending on the antisense sequence) was confirmed in gel-mobility shift assays, followed by an annealing reaction  mRNA from 9-nt ARR , as descreaction . After creaction  was addetrans-type substrate structure, composed of the gRNA and target RNA, would not involve a phosphodiester bond between the strand containing a target-site and the ARR. Hence, the relative distance and direction of the ARR from the target-site should be flexible. We hypothesized that AD-gRNA possessing an ASR at the 5′-end of the ARR could induce a cognate editing substrate structure when the ASR hybridizes to the target RNA. To demonstrate the utility of this 5′-AS AD-gRNA design, ADg-rGFP_A200 was generated by introducing the ASR into the 5′-end of the ARR to target A200 in sGFP RNA . Both ADg-GFP_A200 and ADg-rGFP_A200 efficiently induced A-to-I editing in A200 . In the The AD-gRNA reported here was constructed using a basic framework, wherein the dividing line between both RNA components was fixed 3 nt downstream from the target-editing site . The ediTo clarify the dsRBD-dependence of the ARR on AD-gRNA, we performed editing assays using dsRBD-deleted or -mutated hADAR2 . We prepAG) to Trp (UGG), Tyr (UAC) to Cys (UGC), and Ser (AGC) to Gly (GGC) in the open-reading frame of enhanced-fluorescent proteinsin vitro assay system for this study, in which the in vitro-editing and in vitro-translation reactions were performed sequentially, without using a cellular system. A modified Renilla luciferase mRNA (Rluc-W104X), in which G311 was changed to A311 to alter Trp104 (UGG) into an amber stop codon (UAG), was used as a reporter . In the sequencing chromatograms of GFP mRNA obtained from p-AD-gRNA-transfected cells, editing of the A200 target site depended on AD-gRNA expression . The ediperiment . After tperiment , indicatperiment . The ediWe next verified the frequency of off-target intracellular editing on AcGFP mRNA induced by AD-gRNA expression. Analysis of all adenosines in the obtained sequencing chromatograms showed that distinct off-target editing did not occur at any adenosine in the GFP sequence .Because AD-gRNA can induce wild-type hADAR2, it potentially interfered with intracellular editing homeostasis. We analysed the effect of AD-gRNA expression on intracellular editing in Tet-ADAR2 cells by monitoring changes in the editing efficiencies with an endogenous editing substrate after p-AD-gRNA transfection. Thus, we determined the editing efficiencies at the Q/R site in FLNA and the Y/C site in BLCAP mRNA . With boFinally, we performed an intracellular codon-repair experiment to demonstrate the potential of AD-gRNA in regulating target-protein expression. To visualize intracellular functional-protein expression caused by AD-gRNA-induced codon repair, GFP-W58X mRNA26A versatile method for targeting intracellular A-to-I RNA editing would be a significant breakthrough toward the establishment of practical site-directed RNA mutagenesis. Previously, artificial gRNAs were used to direct the RNA-modification activities of natural riboproteins1718AD-gRNAs could be simply designed, based on the secondary structure of natural or artificial editing substrates containing the editing site for hADAR2. In the case of GluR2 RNA, the binding region for hADAR2 dsRBDs was solved by solution-structure analysisin vitro and in cells. These results clearly showed that the ARR of our AD-gRNAs promoted efficient editing. Although the editing efficiency was relatively lower, dsRBD-dependent editing was also observed with the ASR alone, which clearly indicated that antisense region-induced editing was not responsible for only deaminase domain. In addition, the efficiency of ASR-induced editing appeared highly dependent on the target sequence  clearly showed that AD-gRNA could be used to change codons, thereby altering protein expression. In this intracellular codon-repair experiment, the efficiency of AD-gRNA-induced GFP expression was low , were not perfectly replicated in cells. Additional studies are needed to understand such differences in activity.With our method, the intracellular editing-induction efficiency could be highly affected by many factors, including the expression and intracellular stabilities of hADAR2 and AD-gRNA. In addition, intracellular localization might also be key for efficient editing induction because hADAR2 is localized in the nuclear compartment, especially in the nucleolusRNA editing serves a critical role in cellular homeostasis. Hence, deregulation of ADAR activities caused by mutation or changes in its expression level have been related to wide variety of human diseases424447A-to-I RNA editing can potentially regulate various critical protein functions within cells, such as enzyme catalysis and signal transduction, by changing the codonIn summary, we developed a novel gRNA that can induce A-to-I mutations by guiding hADAR2 to the target site. AD-gRNA specifically introduced A-to-I mutations into target sites that were programed into the antisense region. Moreover, site-directed RNA editing could be achieved by only introducing gRNA into cells that over-expressed hADAR2. Our gRNA strategy has the potential to provide a basic framework for establishing a generally applicable RNA-mutagenesis approach.DNA oligonucleotides and synthetic RNAs were purchased from Hokkaido System Science Co., Ltd.  and Sigma Genosys . The sequences of all DNA oligonucleotides and synthetic RNAs used in this study are listed in in vitro transcription. First, template dsDNA for in vitro transcription was synthesized using synthetic oligonucleotides. In general, 1 μM of the forward DNA oligonucleotide containing the T7 promoter sequence and 1 μM of a reverse DNA oligonucleotide were mixed in annealing buffer (50 mM Tris-HCl [pH 7.6] and 50 mM NaCl), after which the annealing reaction was performed by denaturing the mixture at 95 °C for 3 min, followed by cooling to room temperature for 15 min. To generate dsDNA templates, the annealing product was elongated using Klenow polymerase (New England BioLabs). The obtained dsDNA fragment was purified by phenol/chloroform extraction and ethanol precipitation. Using purified dsDNA as a template, in vitro transcription reactions were performed using the AmpliScribe T7 Kit (Epicentre Biotechnologies), according to the manufacturer’s protocol. Reaction solutions were subjected to phenol/chloroform extraction and ethanol precipitation, and finally AD-gRNAs were purified from a denaturing 8% polyacrylamide gel containing 8 M urea. In the case of target RNAs, dsDNA templates were initially prepared by PCR, using standard PCR conditions . Subsequently, in vitro transcription and purification were performed as described above. The sequences of all DNA oligonucleotide and RNA samples used in this study are shown in AD-gRNAs were synthesized by Recombinant hADAR2 was prepared as described previously. Briefly, the coding region for human ADAR2 was PCR-amplified from a cDNA clone  and then cloned into the yeast expression vector pYES2/NT A (Invitrogen) that expresses N-terminally His-Express-tagged fusion proteins. The obtained plasmid was transformed into INVSc1 yeast cells (Invitrogen) using the Frozen-EZ Yeast Transformation II Kit (Zymo Research). Transformants were cultured in liquid medium, and recombinant hADAR2 was purified using a HisTrap HP column . Fractions containing hADAR2 were collected and dialyzed against storage buffer  using a 50-kDa molecular weight cut-off Float-A-Lyzer G2 (Spectra/Por). Purified hADAR2 was quantified using the DC Protein Assay Kit (BioRad), according to the manufacturer’s instructions.To prepare dsRBD-binding defective hADAR2 mutants, dsRBD-deleted and -mutated hADAR2 mutants were designed with references to previous reports492, 0.5 mM DTT, 0.01% TritonX-100, 5% glycerol, 1 U/μL Murine RNase Inhibitor (New England BioLabs) and then incubated at 37 °C for 2 h. Subsequently, the reacted RNA was purified by phenol/chloroform extraction and ethanol precipitation, after which the RNA pellet was dissolved in 5 μL TE buffer. To generate cDNA from the reacted RNA, the purified RNA was reverse transcribed using the PrimeScript II Reverse Transcription Kit (Takara Bio). cDNA was PCR-amplified using PrimeSTAR GXL DNA polymerase (Takara Bio), following a standard protocol. The editing efficiency at each site was analysed by direct sequencing, as follows. Ten nanograms of gel-purified PCR product was sequenced using a reverse primer and the BigDye Terminator Cycle Sequencing Kit (Applied Biosystems). Next, sequence chromatograms were obtained using a 3500 Genetic Analyzer (Applied Biosystems). The editing ratio at each site was calculated using the following equation: A/[A + G], where A and G correspond to adenosine and guanosine peak heights measured using Sequence Scanner software ver. 1.0 (Applied Biosystems), respectively. The fitting curves were calculated using the single-exponential equation: Ft = F0 + F1 (1 − ekt−), where F0 and F1 represent the editing fraction at time 0 and at the reaction end point, respectively, and where k is the first-order editing rate constant.To generate gRNAs and target-RNA complexes, 900 nM AD-gRNA and 300 nM target-RNA were annealed by heating at 80 °C for 3 min with subsequent slow cooling to 25 °C at 1 °C/10 s in annealing buffer (10 mM Tris-HCl [pH 7.6] and 150 mM NaCl). The editing reaction was performed as follows: 5 nM complex and various concentrations of purified hADAR2 were mixed in 20 μL of reaction buffer . The reporter mRNA, Rluc-W104X, was prepared by mutating G311 to A311 in codon Trp104 (UGG) in Rluc-WT, using the QuikChange Site-Directed Mutagenesis Kit (Stratagene). With this Rluc-W104X reporter, the full-length, wild-type Rluc transcript was synthesized when A311 was edited to I311. Sequential in vitro editing and translation reactions were performed as follows. First, 5 μM Rluc-W104X and 15 μM sADg-rRluc_A311 were annealed by heating them together at 80 °C for 3 min, with subsequent slow cooling to 25 °C at 1 °C/10 s in annealing buffer. Then, 0.5 μM of the reporter-gRNA complex was subjected to an editing reaction with 1.25 μM hADAR2 in the reaction buffer at 37 °C for 2 h. In parallel, a control sample (lacking gRNA) was also tested using the same process. The edited RNA sample was recovered by phenol/chloroform extraction and ethanol precipitation. To assess whether active Rluc could be translated from the edited reporter mRNA, an in vitro translation reaction was performed in 20 μl rabbit reticulocyte lysate (Promega), using 1 μg of reporter RNA obtained from the editing reaction. After translation at 37 °C for 1 h, a chemiluminescence reaction was performed using the Dual-Luciferase Reporter Assay System (Promega), according to the manufacturer’s protocol, and luminescence was measured with a spectrophotometer.The reporter for the Bgl II and Hind III restriction sites into the 5′ and 3′ ends, respectively. The DNA inserts were generated using synthetic oligonucleotides, which are shown in An AD-gRNA expression plasmid was constructed using pSUPER.neo (Oligoengine), which is used for expressing short cellular RNAs such as short hairpin RNAs and miRNAs, based on the H1 RNA polymerase III promoter. While preparing DNA inserts encoding AD-gRNAs, tetra uridine sequences were inserted into the 3′-end of the AD-gRNA sequence to terminate pol III transcription at a defined position. Cloning into the vector was facilitated by introducing 2. Tet-ADAR2 cells, in which hADAR2 and AcGFP were simultaneously expressed from a bi-directional promoter under the control of the Tet-on system, were previously established in our laboratory. Tet-ADAR2 cells were cultured as monolayers in DMEM supplemented with 10% Tet system-approved foetal bovine serum (Clontech), 1 μg/mL puromycin (Sigma), and 100 μg/mL G418 (Sigma) at 37 °C in 5% (v/v) CO2. To induce ADAR2 and AcGFP expression, tet-ADAR2 cells were cultured in the above medium supplemented with 5 μg/mL of Dox.HEK293 cells were harvested in Dulbecco’s modified Eagle’s medium  supplemented with 10% (w/v) foetal bovine serum (Biosera) at 37 °C with 5% (v/v) CO5) were added to 35-mm dishes in medium containing 5 μg/mL Dox. After culturing for 24 h, they were transfected with 2 μg of the AD-gRNA expression plasmid, using the X-tremeGENE HP DNA Transfection Reagent (Roche). At 72 h post-transfection, the editing efficiency at A200 in AcGFP RNA, which was simultaneously expressed with hADAR2 in Tet-ADAR2 cells, was analysed as follows. Total RNA was extracted from the transfected cells using Sepasol RNA I Super G , according to the manufacturer’s protocol. Then, RNA samples (30 μg) were treated with 10 U DNase I (Takara Bio) for 1 h at 37 °C, followed by phenol/chloroform extraction and ethanol precipitation. Purified RNA (0.5 μg) RNA was reverse transcribed using the adapter-linked oligo(dT) 17 primer and the Transcriptor High Fidelity cDNA Synthesis Kit (Roche), according to the manufacturer’s protocol. Using the obtained total cDNA as a template, AcGFP cDNA was amplified by PCR with AcGFP-specific primers (AcGFP_F and AcGFP_R). The efficiency of A-to-I RNA editing at the A200 site was analysed by direct sequencing, followed by quantification of the relative heights of the A and G peaks, after which each editing ratio was calculated on the basis of the peak height of G divided by that of A + G.Tet-ADAR2 cells , in which G173 was changed to A173 to mutate codon W58 into a stop codon, was designed to determine whether AD-gRNA could regulate functional gene expression in cells. The AcGFP-W58X expression plasmid was constructed based on pcDNA3.1 expression vector (Invitrogen). In addition, hADAR2 expression plasmid was constructed by cloning PCR-amplified hADAR2 cDNA into pcDNA3.1. Seven hundred nanograms each of the hADAR2, AcGFP-W58X, and AD-gRNA expression vectors were co-transfected into sub-confluent HEK293 cells cultured in 35-mm glass bottom dishes , using the X-tremeGENE HP DNA Transfection Reagent (Roche). The editing efficiency was analysed at 72 h post-transfection, and the recovery of intracellular GFP fluorescence was analysed by fluorescence microscopy.In the How to cite this article: Fukuda, M. et al. Construction of a guide-RNA for site-directed RNA mutagenesis utilising intracellular A-to-I RNA editing. Sci. Rep.7, 41478; doi: 10.1038/srep41478 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Rousettus leschenaulti bats by using pan-coronavirus RT-PCR and next-generation sequencing. Although the virus is similar to Rousettus bat coronavirus HKU9 (Ro-BatCoV HKU9) in genome characteristics, it is sufficiently distinct to be classified as a new species according to the criteria defined by the International Committee of Taxonomy of Viruses (ICTV). More striking was that Ro-BatCoV GCCDC1 contained a unique gene integrated into the 3’-end of the genome that has no homologs in any known coronavirus, but which sequence and phylogeny analyses indicated most likely originated from the p10 gene of a bat orthoreovirus. Subgenomic mRNA and cellular-level observations demonstrated that the p10 gene is functional and induces the formation of cell syncytia. Therefore, here we report a putative heterologous inter-family recombination event between a single-stranded, positive-sense RNA virus and a double-stranded segmented RNA virus, providing insights into the fundamental mechanisms of viral evolution.The emergence of severe acute respiratory syndrome coronavirus (SARS-CoV) in 2002 and Middle East respiratory syndrome coronavirus (MERS-CoV) in 2012 has generated enormous interest in the biodiversity, genomics and cross-species transmission potential of coronaviruses, especially those from bats, the second most speciose order of mammals. Herein, we identified a novel coronavirus, provisionally designated Rousettus bat coronavirus GCCDC1 (Ro-BatCoV GCCDC1), in the rectal swab samples of  Recombination is commonly reported in coronaviruses, and is an important mechanism by which these viruses generate genetic diversity. To date, however, most such recombination events involve homologous sequences among related viruses. We discovered a novel bat coronavirus that possesses a divergent but functional p10 gene that likely originated from, or shared the ancestry with, an ancestral non-enveloped orthoreovirus, thereby representing the outcome of heterologous recombination. We report herein a fusion-associated small transmembrane (FAST) protein encoded in an enveloped virus that arose through a putative inter-family recombination between a single-stranded, positive-sense RNA virus and a double-stranded segmented RNA virus. These findings shed important new light on the mechanisms of viral evolution and particularly the importance and scope of heterologous recombination. Coronaviridae are now classified into four genera: alpha-, beta-, gamma- and deltacoronavirus , 2524, 2To confirm the expression of p10 by the Ro-BatCoV GCCDC1 virus, we performed Western blotting (WB) to detect the presence of p10 protein in the bat feces and concentrated rectal swab specimens. The results revealed the expression of p10 by Ro-BatCoV GCCDC1 itself .Rousettus leschenaulti, that belongs to group D of the genus betacoronavirus and which is related to Ro-BatCoV HKU9 [We have identified a novel coronavirus, Ro-BatCoV GCCDC1, from CoV HKU9 . Accordibetacoronavirus group A possess an HE gene [Homologous recombination events frequently occur during the viral RNA replication of coronaviruses, and are important for their evolution , 27–29.  HE gene , 31 whic HE gene , 31, and HE gene . Uncommo HE gene , bandico HE gene , and rec HE gene , 36. In The biggest difference between fusogenic and nonfusogenic orthoreoviruses is the presence/absence of a small protein encoded by the segment S1 of the genome, termed the fusion-associated small transmembrane (FAST) protein. The FAST proteins are the only known nonenveloped reovirus fusogens that can mediate cell-to-cell, but not virus-cell, membrane fusion to induce the formation of syncytia , and whibetacoronavirus group A [Unfortunately, the isolation of Ro-BatCoV GCCDC1 failed on cell culture in the present study, so it is difficult to determine the role of p10 gene during the life cycle of Ro-BatCoV GCCDC1. However, functional analysis showed that the coronavirus p10 gene could induce syncytium formation in the transfected cells, in the same manner as orthoreoviruses, which might be beneficial for cell-to-cell virus spread. It is therefore possible that the p10 protein enhances the transmission potential of Ro-BatCoV GCCDC1. Previous studies of the potential recombination between coronavirus and influenza C virus revealed the pivotal role of the shared HE gene for the pathogenesis of  group A . Interes group A .Compared to nonfusogenic orthoreoviurses, fusogenic orthoreoviruses can cause severe pneumonia when infecting humans , 41, furIn the protein sequence of ORF1ab encoded replicase of Ro-BatCoV GCCDC1, the regular P1 position at two 3CLpro cleavage sites, NSP9/NSP10 and NSP10/NSP12, contains a Q to H mutation which may impair the proteolytic efficacy and the release of NSP9, NSP10 and NSP12. As NSP12 is a typical RNA polymerase and the core of replication-transcription complexes (RTC) and NSP10 usually serves as a molecular switch that can interact with multiple NSPs to form complexes, the replication ability of Ro-BatCoV GCCDC1 might be suppressed by the decrease of release of these vital elements. It is also interesting to note that a similar situation may be observed at the NSP13/NSP14 cleavage sites of replicase polyprotein of human coronavirus HKU1 and human coronavirus NL63. Clearly, further investigation will need to focus on the isolation of the virus, construction of infectious clones, and the virulence and replication ability of Ro-BatCoV GCCDC1 influenced by knockout of p10 gene and/or reverse mutation of cleavage sites.Phylogenetically distinct virus species or lineages have been reported co-circulating in certain bat populations , 46. UndThe protocol in this study was approved by the Committee on the Ethics of Animal Care and Use of the Chinese Center for Disease Control and Prevention (Permit 20140509015). The study was conducted in accordance with the Guide for the Care and Use of Wild Mammals in Research of the People's Republic of China.Myotis Davidii (IKMD) was a generous gift of Dr. Zhengli Shi, Wuhan Institute of Virology, Chinese Academy of Sciences. Cells were grown in Eagle’s minimum essential medium (EMEM)  or in Dulbecco’s modified EMEM (DMEM)  supplemented with 10% or 20% (CaCo-2) fetal bovine serum in a humidified chamber containing 5% CO2 at 37°C.African green monkey kidney cells (Vero E6), human epithelial colorectal adenocarcinoma cells (CaCo-2), human epithelial type 2 HeLa derivative cells (HEp-2) and human lung carcinoma cells (A549) were purchased from the China Center for Type Culture Collection, while baby hamster kidney (BHK-21) and Madin-Darby canine kidney (MDCK) cells were obtained from the Cell Resource Center of the Shanghai Institute for Biological Sciences, Chinese Academy of Sciences. The immortalized kidney cell line of All the bats analyzed here were captured at a roosting site with the assistance of villagers and staff of local the CDC office in Xishuangbanna, Yunnan Province, China. The rectal swab samples were collected and placed in the cryotube with viral transport medium (VTM) containing Earle's balanced salt solution , 5% bovine albumin, 50,000 μg/ml vancomycin, 50,000 μg/ml amikacin, 10,000 units/ml nystatin . All samTotal RNA was extracted from 100 μL of VTM suspension of each swab with the RNeasy Mini Kit  according to the manufacturer's protocol. The RNA was eluted in 60 μL AVE buffer, of which 8 μL RNA was used as the template for RT-PCR immediately, or stored at −80°C until use.Total RNA extracted from the rectal swab suspension was screened for the presence of coronavirus RNA using pan-coronavirus RT-PCR with universal degenerate primers. The primers were designed from a highly conserved region of the RdRp (primer sequences are presented in Fresh RNA was extracted from sample numbers 346 and 356 which were confirmed as coronavirus positive. The RNA were subjected to Next Generation Sequencing (NGS) using the Ion Proton platform. The original NGS data were filtered, refined and mapped to the reference sequence of Ro-BatCoV HKU9 (GenBank accession number NC_009021) using SOAP (Short Oligonucleotide Alignment Program) . Any remAs the amplification of 5’-end of the genome of Ro-BatCoV GCCDC1 strain 346 was unsuccessful, we focused our genome analyses on the complete genome of Ro-BatCoV GCCDC1 strain 356. This genome was compared to those of eight complete genomes of Ro-BatCoV HKU9  to annotate the 1ab, S, NS3, E, M and N ORFs, respectively. As the origin of the ORFs at the 3’-end of the genome were uncertain they were also blasted (tblastx) against the GenBank database. The amino acid sequence of ORF1ab was aligned with the reference sequences of SARS-CoV, human coronavirus HKU1, infectious bronchitis virus, turkey coronavirus, bovine coronavirus, mouse hepatitis virus and porcine epidemic diarrhea virus  to determine the cleavage and recognition patterns of the C-like proteinase and papain-like proteinase of the 16 nonstructural proteins. In addition, the sequences of the 5’ untranslated region (5’-UTR) and 3’ untranslated region (3’-UTR) were defined, and the leader sequence, the leader and body TRSs were illustrated, based on comparison with SARS-CoV.To eliminate the possibility of false amplification of DNA polymerase or inaccurate assembly of NGS data, the raw NGS data were further scrutinized and reads extracted for mapping to check the continuity of the p10 sequence, especially the upstream junction site between N and p10 genes and the downstream junction site between the p10 and NS7a genes. In addition, two sets of specific primers were designed to confirm the integrity and continuity of sequence surrounding the p10 gene  among the 28 so-called ‘absolutely conserved’ amino acids that characterize members of the FAST family . Hence, To determine whether the putative p10 protein could play the same role as homologous proteins of avian and bat orthoreoviruses, the p10 gene was cloned into the pCAGGS vector and the recombinant plasmid  was thenThe next step is to confirm that p10 can be transcribed or translated during the replication cycle of Ro-BatCoV GCCDC1. We confirmed that the p10 gene could be transcribed from the genome during the replication cycle of Ro-BatCoV GCCDC1, with the p10 subgenomic mRNA representing a distinct signal. Further, we cloned the deduced p10 subgenome into a pcDNA3.0-derived vector to construct an artificial plasmid , which cAs the p10 protein of Ro-BatCoV GCCDC1 is the first reported in an enveloped virus, we first tried to define the key amino acids for the p10 protein in the Ro-BatCoV GCCDC1 as described previously for p10 protein of reoviruses , 25. ForThe original specimens of bat rectal swabs and feces were used to test the expression of p10. BHK-21 cells with transient expression plasmid of p10 gene (pCAGGS-p10) were used as a positive control. Briefly, bat specimens and BHK-21 cell lysates were subjected to SDS-PAGE and transferred to a PVDF membrane. The membranes were blocked with a 5% non-fat dry milk solution and incubated with p10 antibody overnight at 4°C followed by peroxidase-conjugated affinipure goat anti-mouse IgG (H+L) . After washing with TBS-T buffer, the membrane was treated with ImmobilonTM Western Chemiluminescent HRP Substrate  and pictures were taken with Chemiluminescence System MicroChemi 4.2 .The complete genome sequences of Ro-BatCoV GCCDC1 strains 346 and 356 have been deposited in the GenBank database and assigned accession numbers KU762337 and KU762338, respectively. We also deposited the sequences of the p10 genes from the rectal swabs of 24 bats in GenBank. All these accession numbers are listed in S1 FigThe READs were extracted from the raw NGS data and then mapped to the complete genome of Ro-BatCoV GCCDC1 using Geneious R9 (Biomatters Limited) to confirm the integrity and continuity of context sequence surrounding the p10 gene, especially the upstream junction site between N and p10 genes, and downstream junction site between p10 and NS7a genes.(TIF)Click here for additional data file.S2 FigThe TRSs of NS7a or ORFx gene just downstream of the N gene are marked with red arrows. The length of intergenic spacer between N gene and NS7a or ORFx gene is indicated with numbers. GenBank accession numbers of the coronaviruses used in this analysis: Ro-BatCoV HKU9: Rousettus bat coronavirus HKU9 ; BatCoV philippines: Bat coronavirus Philippines/Diliman1525G2/2008 (AB543561); Ei-BatCoV Kenya: Eidolon bat coronavirus/Kenya/KY24/2006 (HQ728482); Ro-BatCoV Kenya: Rousettus bat coronavirus/Kenya/KY06/2006 (HQ728483).(TIF)Click here for additional data file.S3 FigAmplicon of each subgenomic mRNA, including S, NS3, E, M, N, p10, NS7a, NS7b and NS7c, was sequenced and illustrated with sequencing peak pattern. The leader sequence, TRS and CDS are marked with blue, red and yellow arrow respectively. The bias of TRS of p10 gene is marked with a yellow block.(TIF)Click here for additional data file.S4 FigFirst row: cells were stained with DAPI. Second row: the merged image. . (B) Transient expression of p10 gene and syncytium formation with recombinant subgenomic p10 plasmid. First row: cells were stained with DAPI. Second row: the merged image. .(A) Transient expression of p10 gene and syncytium formation. (TIF)Click here for additional data file.S5 Fig(A) Schematic representation of p10 protein and mutant constructs. The substituted and inserted amino acids were marked with yellow and green blocks respectively. Motifs presented in the ectodomain , endodomain  and the central transmembrane domain (TMD) are depicted with yellow rectangles. The four conserved cysteine residues (C) are shown. The two cysteines in the ectodomain form an intra-molecular disulfide bond. (B) Transient expression and the observation of syncytium formation on the monolayer BHK-21 cells transfected with recombinant plasmid of wild type of Ro-BatCoV GCCDC1 p10 gene, mutant constructs and empty pCAGGS vector. .(TIF)Click here for additional data file.S6 FigThe expression of p10 protein are observed in fecal samples revealed by Western blotting. Rectal Swab 118: Representative of the samples which are negative for the Ro-BatCoV GCCDC1 RNA test. BHK21-p10: BHK-21 cells transfected with transient expression plasmid of p10 gene (pCAGGS-p10) as positive control. Feces 341 and 348: Feces samples of representatives of the feces samples whose corresponding rectal swabs are positive for the Ro-BatCoV GCCDC1 RNA test. Con. Swabs: The concentrated sample of 47 swabs which are positive for the Ro-BatCoV GCCDC1 RNA test.(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.S7 Table(DOCX)Click here for additional data file.S8 Table(DOCX)Click here for additional data file.S1 File(RAR)Click here for additional data file.S2 File(RAR)Click here for additional data file.


C9orf72 gene is the most common genetic cause of familial and sporadic Amyotrophic lateral sclerosis (ALS), a devastating degenerative disease of motor neurons, and of Frontotemporal Dementia (FTD), the second most common form of presenile dementia after Alzheimer’s disease. C9orf72-associated ALS/FTD is a multifaceted disease both in terms of its clinical presentation and the misregulated cellular pathways contributing to disease progression. Among the numerous pathways misregulated in C9orf72-associated ALS/FTD, altered RNA processing has consistently appeared at the forefront of C9orf72 research. This includes bidirectional transcription of the repeat sequence, accumulation of repeat RNA into nuclear foci sequestering specific RNA-binding proteins (RBPs) and translation of RNA repeats into dipeptide repeat proteins (DPRs) by repeat-associated non-AUG (RAN)-initiated translation. Over the past few years the true extent of RNA misprocessing in C9orf72-associated ALS/FTD has begun to emerge and disruptions have been identified in almost all aspects of the life of an RNA molecule, including release from RNA polymerase II, translation in the cytoplasm and degradation. Furthermore, several alterations have been identified in the processing of the C9orf72 RNA itself, in terms of its transcription, splicing and localization. This review article aims to consolidate our current knowledge on the consequence of the C9orf72 repeat expansion on RNA processing and draws attention to the mechanisms by which several aspects of C9orf72 molecular pathology converge to perturb every stage of RNA metabolism.A large GGGGCC hexanucleotide repeat expansion in the first intron or promoter region of the  Frontotemporal Dementia (FTD), the second most common form of dementia after Alzheimer’s disease, is characterized by behavioral and language deficits and manifests pathologically by neuronal atrophy in the frontal and anterior temporal lobes in the brain. A hexanucleotide GC9orf72 DNA. These encode two protein isoforms consisting of a long isoform (isoform A) of approximately 54 kDa derived from variants 2 (NM_018325.4) and 3 (NM_001256054.2), and a short isoform (isoform B) of approximately 24 kDa derived from variant 1  in mice has failed to recapitulate pathology  for Rab GTPases to regulate vesicular trafficking and autophagy , possessing two RNA recognition motif (RRM) domains. TDP-43 regulates splicing, RNA turnover and transport and therefore interacts with a myriad of proteins involved in RNA metabolism  and long noncoding RNA (lncRNA). TDP-43 associates with both microprocessor complexes and its depletion results in reduced binding efficiency of both complexes to a subset of miRNAs  RNA molecules which serve key regulatory roles and have been implicated in numerous cellular pathways. In cases of sporadic frontotemporal lobar degeneration with TDP-43 pathology, TDP-43 exhibits significantly increased binding to Drosophila motor neurons, mouse cortical neurons and motor neurons derived from human induced pluripotent stem cells (iPSCs), TDP-43 associates with bidirectionally transported messenger ribonucleoprotein complex (mRNP) granules. In neuronal cells harboring ALS-causing TDP-43 mutations, the anterograde transport of mRNP granules is selectively impaired n and the antisense (G2C4)n DNA strand  have identified a panel of RBPs binding to G4C2 repeat RNA, including hnRNP A1, hnRNP A3, hnRNP H, ADARB2, Pur-α, ASF/SF2, ALYREF and nucleolin  (Box 4C2).(C4G2)n results in the synthesis of five dipeptide repeat protein (DPR) species; poly-GA, poly-GP, poly-GR, from the sense transcript and poly-GP, poly-PR and poly-PA, from the antisense transcript. All five DPR species form ubiquitinated inclusions in cerebellum, hippocampus and other brain regions in expansion carriers 50 dramatically decreases survival of primary cortical and motor neurons, presumably through a pathway of translational dysregulation  Box . RAN traBox 14C2 repeats into dipeptide repeat proteins (DPRs) is a mature mRNA. First G4C2 repeats have been shown to cause overt neurotoxicity in Drosophila only if there are in the context of an mRNA  cap  containing the G4C2 repeat domain -mediated translation initiation  selection of a large number of transcripts have been identified in the cerebellum of individuals with Mature mRNA transcripts are transported to the cytoplasm from the nucleus through large proteinaceous assemblies embedded in the nuclear envelope, termed NPCs. The mRNA transcript, together with proteins involved in pre-mRNA processing, exist as a large mRNP which interacts with the NPC through nuclear transport receptors. RNA processing reactions generate signals in the form of proteins docked onto the mature mRNA which interact with the nuclear export machinery, ensuring that only fully matured mRNAs are transported to the cytoplasm for translation 30 repeats identified RanGAP as a potent suppressor of the rough eye phenotype 30, in patient-derived iPSNs and in the motor cortex from c9ALS patients. A significant correlation between the nuclear to cytoplasmic ratios of Ran and TDP-43 has been established 58 specifically identified disruptions in the protein and mRNA nuclear export pathways 58 toxicity is exacerbated by disruptions to the CRM1-mediated nuclear export pathway, the pathway responsible for the nuclear export for the majority of proteins and RNA transcripts. Depletion of the nuclear pore components Nup107 or Nup160 which function in RNA nuclear export exacerbates degeneration caused by (G4C2)58. Furthermore, downregulation of Ref1, the Drosophila ortholog of ALYREF, was identified as the strongest suppressor of toxicity  family of splicing factors 50 toxicity in yeast.To elucidate the contribution of RNA and DPR toxicity to nucleocytoplasmic defects, a yeast genetic screen was used to generate poly-PR employing codon-optimized constructs without using the G4C2 repeat toxicity. ASOs targeting the C9orf72 transcript have already been shown to normalize Ran and TDP-43 localization in neurons derived from c9ALS iPSCs, presumably through the reduction of G4C2 RNA foci 30Drosophila model 31. An RNA pulldown assay using (G4C2)31 revealed binding proteins enriched for functions in translational control, such as EF1α, eIF2α, eIF2β and Pur-α, indicating that protein sequestration by the repeat expansion may also contribute to impaired translation 20 and poly-(GR)20 were shown to aggregate in the presence of mRNA transcripts, obstructing access of the eIF4E and eIF4G translation initiation factors and disrupting proteins translation 31-expressing cells and was strongly associated with relocalization of cytoplasmic poly(A)-binding protein (PABPc) to the nucleus. PABPc directly binds G4C2 RNA and accumulates into RNA foci in vitro transcription assay using G4C2 repeats that form G-quadruplex structures demonstrated a repeat-length dependent accumulation of transcripts truncated within the repeat expansion region. RNase H treatment, which specifically digests the RNA transcript within DNA-RNA duplexes and resolves R-loop structures, reduces abortive transcripts  Figure , taking C9orf72-linked disease R-loop formation has been characterized in several microsatellite disorders, such as Fragile X Syndrome (FXS) and Friedrich’s Ataxia (FRDA) (Groh et al., FXN gene (Groh et al., C9orf72 toxicity makes it an imperative area of future research and a potential attractive therapeutic target applicable to a range of microsatellite repeat disorders. Small molecules able to intercalate within G-quadruplex or hairpin structures have demonstrated promising suppression of R-loops arising from trinucleotide expansions (Colak et al., In addition to C9orf72 expansion carriers. Given the importance of RNA processing in cellular function, RNA metabolism can be considered an essential target for therapeutic intervention to improve the lives of those living not only with c9ALS/FTD but for a range of neurodegenerative disorders.It is becoming clear that impaired RNA processing is a central feature of c9ALS/FTD pathogenesis which is mediated via several interconnected mechanisms, including sequestration of RBPs, RAN translation and TDP-43 mislocalization. It is likely that these mechanisms all contribute to RNA processing abnormalities to different extents, generating numerous small defects at the level of RNA metabolism. Initially the cell may be able to compensate for these defects, however over time these abnormalities may accumulate beyond a certain threshold at which point the aging cell loses viability. The idea of c9ALS/FTD as a disorder mediated by an accumulation of several minor disruptions, rather than a few isolated RNA misprocessing events as has been described for myotonic dystrophy type 1 (Timchenko et al., HVB wrote the manuscript. MN, Y-BL and CES reviewed the manuscript. J-MG reviewed and edited the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Spermatogenesis comprises highly complex differentiation processes. Nuclear envelope (NE) proteins have been associated with these processes, including lamins, lamina-associated polypeptide (LAP) 2 and the lamin B-receptor. LAP1 is an important NE protein whose function has not been fully elucidated, but several binding partners allow predicting putative LAP1 functions. To date, LAP1 had not been associated with spermatogenesis. In this study, LAP1 expression and cellular/subcellular localization during spermatogenesis in human and mouse testes is established for the first time. The fact that LAP1 is expressed during nuclear elongation in spermiogenesis and is located at the spermatids’ centriolar pole is singularly important. LAP1 binds to members of the protein phosphatase 1 (PP1) family. Similar localization of LAP1 and PP1γ2, a testis-specific PP1 isoform, suggests a shared function for both proteins during spermiogenesis. Furthermore, this study suggests an involvement of LAP1 in manchette development and chromatin regulation possibly via interaction with acetylated α-tubulin and lamins, respectively. Taken together, the present results indicate that, by moving to the posterior pole in spermatids, LAP1 can contribute to the achievement of non-random, sperm-specific chromatin distribution, as well as modulate cellular remodeling during spermiogenesis. In addition, LAP1 seems to be associated with dynamic microtubule changes related to manchette formation and flagella development. Spermatogenesis is a highly complex and unique differentiation process, involving germ cell proliferation and renewal, meiosis and spermiogenesis, under the regulation of Sertoli cells in the seminiferous tubules, as well as Leydig and peritubular cells in the interstitium. During spermatogenesis, undifferentiated diploid spermatogonia undergo intricate morphological and biochemical transformations, leading to the formation of highly specialized cells, the haploid spermatozoa . The morTOR1AIP1 gene, that binds directly to lamins and indirectly to chromosomes -propanesulfonic acid (CHAPS)) containing a protease inhibitor cocktail were added to tissues, which were then homogenized in a variable speed motor drive homogenizer  with a pestle tissue grinder. Subsequently, homogenized tissues were transferred to a sterile tube and centrifuged at 1000 relative centrifugal force (rcf) and 4 °C for 2 min. The supernatant was collected and sonicated on ice for 10 s .®). Immunoblotting was performed by blocking the membranes in 5% BSA in Tris-buffered saline containing 0.1% Tween-20 (TBS-T). Membranes were then incubated with anti-LAP1 primary antibody in 3% BSA in TBS-T overnight, at 4 °C . Samples were separated on SDS-PAGE (5%–20% gradient gel) and electrophoretically transferred onto a nitrocellulose membrane (0.2-μm pore size; Whatman at 4 °C . After wThroughout the years, researchers have established that the NE undergoes significant changes in the course of spermiogenesis. Therefore, nuclear reshaping and condensation processes seem to have a highly important role in the development of healthy sperm cells. This study contributes to the identification of LAP1 as a putative functional player during this differentiation process. Initially, by moving to the posterior pole, LAP1 can contribute to the achievement of non-random, sperm-specific chromatin distribution, as well as modulate cellular reshaping features. Furthermore, this NE protein might be associated with the dynamic MT-based morphologic changes associated with manchette formation and flagellar development.