Current theories suggest that decision-making arises from multiple, competing action-selection systems. Rodent studies dissociate deliberation and procedural behavior, and find a transition from procedural to deliberative behavior with experience. However, it remains unknown how this transition from deliberative to procedural control evolves within single trials, or within blocks of repeated choices. We adapted for rats a two-step task which has been used to dissociate model-based from model-free decisions in humans. We found that amixture ofmodel-based andmodel-free algorithms was more likely to explain rat choice strategies on the task than either model-based or model-free algorithms alone. This task contained two choices per trial, which provides a more complex and non-discrete per-trial choice structure. This task structure enabled us to evaluate how deliberative and procedural behavior evolved within-trial and within blocks of repeated choice sequences. We found that vicarious trial and error (VTE), a behavioral correlate of deliberation in rodents, was correlated between the two choice points on a given lap. We also found that behavioral stereotypy, a correlate of procedural automation, increased with the number of repeated choices. While VTE at the first choice point decreased with the number of repeated choices, VTE at the second choice point did not, and only increased after unexpected transitions within the task. This suggests that deliberation at the beginning of trialsmay correspond to changes in choice patterns, while mid-trial deliberation may correspond to an interruption of a procedural process. It has long been known that multiple systems within the brain contribute to making decisions . Vicarious trial and error is a behavior where rats pause at choice points of a maze, and look back and forth down each path as if deliberating over which path to take . This is because the model-based algorithm stores information about the relation between states , while the model-free algorithm does not store information about relations between states .To illustrate this difference, suppose a subject chooses A at C1, experiences a rare transition and is presented with C3 (a choice between E and F), chooses E at the second choice, and receives a large reward Figure . A modelOur version of the two-step task for rats was a spatial maze with two left/right choices, which corresponded to the two choice stages in the human task Figure . The secNrats = 7, p = 0.0156, rat delays were 3.31 s lower on average than simulation delays). This indicates that rats were able to learn the task, by making decisions which led to lower-delay outcomes.Rat behavior on the spatial two-step task was collected from 7 rats for at least 48 sessions each  rewards than those which led to high-delay (high-cost) rewards, even if this reward occurred after a rare transition Figure . HoweverTo more rigorously evaluate model-based or model-free influences on rat choices, we fit model-based and model-free algorithms to rat choices on the two-step task using Stan, in order to perform Bayesian inference and compare models .Vicarious trial and error is a behavioral correlate of deliberation in rats Redish, . We usedp < 0.05).Similarly, path stereotypy is a behavioral correlate of procedural decision-making . Considered individually, two individual rats showed significant positive correlations, while no rats showed significant negative correlations . On the other hand, path stereotypy was positively correlated with the number of repeated choices . We found no significant correlation between VTE at the second choice point and the number of choice repeats .Previous rodent research has found that animals transition from displaying deliberative behavior to stereotyped behavior over the course of a session, or with experience on a task. If this shift toward stereotyped behavior is due to procedural learning, then a decrease in deliberative behavior and a corresponding increase in stereotyped behavior should also be apparent as a function of the number of repeated choices an animal makes. For the two-step task, we defined a “repeated choice” to be when a rat made the same choice at both the first and second choice points as on the previous lap. We found that VTE at the first choice point was negatively correlated with the number of repeated choices rats made on the two-step task  on the current and previous laps as fixed variables, and rat and session as random variables. There was a significant increase in the amount of VTE at the second choice point following a rare transition Table . VTE at To determine what may have been driving VTE at the first choice point, we fit a mixed model of VTE at the first choice point, with random effects of rat and session, and with fixed effects of the transition on the previous lap, whether the rat repeated its previous choice, and the delay on the previous lap. We found that VTE at the first choice point was driven by a complex interaction between these three factors Table . ConfirmThese results indicate that VTE at the first and second choice points may have been partially driven by different factors. VTE at the first choice point occurred more often when rats had just switched to a new choice pattern and interactions between task variables, but was not detectably affected by the transition on the previous lap alone. On the other hand, VTE at the second choice point occurred more often after an unexpected transition, but was not detectably affected by choice repetitions. We hypothesize that VTE at the first choice point arises more as a result of some deliberative process, which in theory also decreases with the number of repeated choices. Conversely, we hypothesize that VTE at the second choice point, when not being driven by a deliberative mode, arises more as a result of the interruption of a procedural process, which may lead to deliberation, because it is influenced more strongly by unexpected transitions in the middle of a lap than by a change in choice patterns.We found that rat choices on the two-step task were better explained by a mixture of the model-based and model-free systems than by either system alone. Furthermore, we were able to use the fact that each lap on the two-step task contained multiple decisions to evaluate how VTE changed over the course of a trial, and found that VTE at the two choice points were correlated within lap, suggesting rats likely entered deliberative modes for entire laps. We also observed that VTE at the first choice point was more strongly driven by changes in choice patterns and interactions between task variables, suggesting a deliberative process, while VTE at the second choice point was more strongly driven by unexpected mid-lap changes in state action outcomes, suggesting an interruption in a procedural process.The correlation between VTE at the two choice points may seem inconsistent with our interpretation that VTE at the second choice point is driven by an interruption of a procedural process. However, we do not believe that VTE at the second choice point is being driven entirely by such interruptions. Rather, we would hypothesize that VTE at the second choice point likely co-occurs with VTE at the first choice point when rats are in a deliberative mode, and that VTE at the second choice point is only primarily driven by rare transitions when rats are in a procedural mode and the unexpected transition interrupts their stereotyped behavior.Our findings are consistent with previous work in humans which finds that hybrid algorithms are more likely to explain behavior than model-based algorithms alone, and that the weights in these hybrid algorithms favor model-free decision-making , and attempting to fit multilevel models (models with rat as a mixed effect) only aggravated this problem. Furthermore, the fit learning rates of our reinforcement learning models were suspiciously low. We suspect that the complexity of our version of the two-step task for rodents, along with the use of delay to reward delivery as the cost, prevented the rats from learning the task well enough to employ solely the model-based system, and so relied also on the model-free system in order to make choices on the task. This may explain why we found that a mix of model-based and model-free strategies best explained rat choices on our task.We noticed that some rats preferred certain feeders over multiple days, regardless of delay (data not shown). It could be that Pavlovian decision-making or place preferences also played a role in some rats' choices. This might explain in part the relatively low values of the fit second-stage learning rates  on either side of the maze, at which point rats were required to wait a certain amount of time before they received two food pellets per lap. We used delay to food as a proxy for reward, instead of reward probability as was used in Daw et al.  and MillSeven male Brown Norway rats aged 6–15 months obtained from Harlan  were subjects for the experiment. Before behavioral training, rats were handled for 7 d, then acclimated for 7 d to eat the food pellets delivered during the task , and finally trained to run through the one-way doors on a separate maze for 7 d. Rats were housed on a 12-h light-dark cycle, and behavioral sessions were run at the same time daily. Rats were food restricted to encourage them to run the task, and maintained weight at >80% of their free-feeding weight. Water was always available in their home cage. All experimental and animal care procedures complied with US National Institutes of Health guidelines for animal care and were approved by the Institutional Animal Care and Use Committee at the University of Minnesota.Animal behavior on the task was captured with a video camera  placed above the maze. Custom Matlab (MathWorks) software determined animal position from the video on-line; controlled delays, pellet dispensers, and monitors; and recorded animal trajectory through the maze along with task events. Custom Matlab software was written to track animal head positions from video off-line.There were three phases of task training, each lasting 8 d. For the first, there was no delay to food delivery, no second-stage auditory or visual cues, and one option was blocked at each choice point, leaving only one possible path through the maze. Choices were blocked such that all four paths through the maze  were sampled equally. Eight pellets were dispensed at the two feeder sites per reward on the first day of training, and the number of pellets decreased by 1 pellet every 2 days for the duration of the training phase. A single pellet per lap was dispensed at the rear feeder site.For the second training phase, there were still no second-stage auditory or visual cues, and one of the first-stage options was blocked, but both second-stage choices were left open. Delay to food was set randomly between 1 and 10 s on the first day of second phase training, and the maximum delay increased by 2 s/day for the duration of the training phase. The delay values were allowed to change over the course of the session according to the same Gaussian random walk used in the full task . Four pellets were dispensed at each feeder site for the first 4 days of this training phase, and three pellets for the last 4 days.The third training phase was 8 d of the full task, with no choices blocked, a maximum delay of 30 s, and two pellets per feeder site.All analyses except the Bayesian modeling were performed in Matlab (MathWorks). The Bayesian reinforcement learning model fits were performed in Stan . At the second choice point, this occurred on 10 laps (0.038% of laps). We excluded these laps from our analysis.To measure path stereotypy, we used the inverse of the mean distance between the path on a given lap and all other paths during the same session of the same type , re-sampled in time. This resulted in a value which was larger when paths were more stereotyped (similar to the average path), and smaller for irregular paths through the maze. When a lap was the only lap of its type in a session, we could not calculate path stereotypy (with no similar paths for which to compute the mean distance), and so we excluded such laps from our analysis. These laps made up a very small proportion of the total data (0.66%).Q-value) of taking an action a, in any given state, s. Our model of the two-step task included only two possible actions in any state (“go left” or “go right”), and only three states: the first choice point , and the two possible second choice points  see diagram in Figure Each algorithm computed the expected value  of taking an action a, in any given state, s. The section after that (“Computing the Likelihood of Each Algorithm”) describes how the likelihood is computed for each algorithm from that algorithm's Q-values. This “likelihood” is the probability that the algorithm, with a given set of values for its parameters, would make the same choices we observed the rats make on the two-step task. The section after that (“Bayesian Algorithm Fitting Using Stan”) describes how the models are fit using these likelihoods. Finally, the section after that (“Algorithm Comparison”) describes how algorithms were compared to determine which one was most likely to explain our data.The next three subsections explain how each algorithm computes the expected value (or QMF) of taking a given action a, in any given state s, by updating the Q-values according to the delta rule:For the model-free algorithm, we used the SARSA(λ) temporal difference learning algorithm  used α1, and decisions at the second stage , and dt2,  is the delay experienced on trial t (and explicit delays only occurred after a choice at stage 2). This assumes that rats are aware of the maximum delay, which we believe is a valid assumption, because rats were trained extensively on the task before the experiment began. We also defined a third “virtual” state, where QMF = 0, because there is no further reward in a trial following food delivery. The algorithm updates the first-stage state-action value based on the eligibility trace parameter and second-stage reward prediction error at the end of each trial:where QMF occurs twice per trial: once after the first-stage choice (where the α1 learning rate is used), and again after the end of the trial according to the eligibility trace parameter, λ (where a learning rate of (α1λ) is used, as in the equation above).Note that the update for Q] in exactly the same way as the model-free system. However, for the first-stage state action values, instead of updating them according to the delta rule, the model-based algorithm takes into account the transition probabilities and the best option at either second stage, and computes the first-stage action values at decision time by:The model-based algorithm updates the second-stage state-action values , we used a softmax for each algorithm, in the same way as in Daw et al.  used β1, and decisions at the second stage , or to switch to the opposite action (p < 0), regardless of expected action values. rep(a) was a function which evaluated to 1 if the rat repeated its action, that is, performed action a at that stage on the previous lap , and 0 if it chose a different action. Therefore, if the p parameter was positive, the algorithm was more likely to repeat the previous choice, and if it was negative, the algorithm was more likely to switch .The Q-values to the mean reward value at the beginning of each session. The log probability of observing rat choices across all Ns sessions given an algorithm is then:We initialized all θ is the set of all parameters for a given algorithm, Ni is the number of choice stages in each trial t  d, and Nd is the total number of sessions across all rats.where http://mc-stan.org), in which models can be written using a simple modeling language, and Stan performs MCMC sampling resulting in model and parameter posterior probabilities. This allowed us to perform Bayesian inference as to the values of model parameters, and model comparison using DIC scores.We used Markov chain Monte Carlo (MCMC) in Stan   is computed by:where the effective number of parameters (D(θ), over all the MCMC samples of θ:θ:The deviance is computed by:p(data|θi)) is the algorithm likelihood, as computed above (in section Computing the Likelihood of Each Algorithm), given parameters θ for a MCMC sample. The deviance is technically D(θ) = −2log(p(data|θ))+C, but C is a constant which cancels out when comparing different models. Algorithms are compared based on their DIC scores, where models with lower DIC scores are more likely to explain the data.where log. We used an agent with learning rates set to 0 because this yielded an agent whose choice probabilities were 50% for any choice on the model of our task, since the Q-values were all initialized to the same value.The data used for the “chance” line in Figure 1, α2 = 0.5, β1, β2 = 3, p = 0.3, λ = 0.The model-free and model-based simulations in Figures To determine if levels of VTE were correlated between the two choice points, we fit a mixed model to LogIdPhi at the first and second choice points. Specifically, the model tried to predict LogIdPhi at the second choice point from LogIdPhi at the first choice point on that same lap. These models included subject and session as random effects; that is, they allowed levels of VTE to vary across subjects and sessions, but not in a totally independent way. Our model included a fixed intercept, a fixed effect of transition type on the current lap, a fixed effect of transition type on the previous lap, a per-subject random effect, and a per-session random effect.wherezLogIdPhii2,  is the z-scored LogIdPhi value at the second choice point on lap i,zLogIdPhii1,  is the z-scored LogIdPhi value at the first choice point on lap i,β0 is the fixed intercept of the model (baseline LogIdPhi),βVTE is the standardized coefficient (a parameter which captures the relationship between the amount of VTE at the two choice points),Rr is rat r's random effect (or adjustment coefficient), which accounts for the possibility that some rats have different baseline levels of LogIdPhi,SS is session s's random effect, which accounts for the possibility that rats have different baseline levels of LogIdPhi on different sessions,σr and σs are the standard deviations of per-rat (R) and per-session (S) random effects, respectively,σe is the standard deviation of the error, andβ0 was not reported in Table In order to determine if VTE and path stereotypy changed depending on whether there was a rare transition on the current or previous lap, we fit mixed models to VTE and path stereotypy. These models included subject and session as random effects; that is, they allowed levels of VTE or path stereotypy to vary across subjects and sessions, but not in a totally independent way. Our model included a fixed intercept, a fixed effect of transition type on the current lap, a fixed effect of transition type on the previous lap, a per-subject random effect, and a per-session random effect.whereYi is the LogIdPhi value at the first choice point on lap i ,β0 is the intercept of the model ,T is the parameter capturing the fixed effect of rare transitions on the current lap,ti is an indicator variable which is 0 when there was a common transition on lap i, and 1 when there was a rare transition on lap i,TP is the parameter capturing the fixed effect of a rare transition on the previous lap,ti−1 is an indicator variable which is 0 when there was a common transition on lap i−1, and 1 when there was a rare transition on lap i−1,Rr is rat r's random effect (or adjustment coefficient), which accounts for the possibility that some rats have different baseline levels of LogIdPhi or path stereotypy,SS is session s's random effect, which accounts for the possibility that rats have different baseline LogIdPhi or path stereotypy values on different sessions,σr and σs are the standard deviations of per-rat (R) and per-session (S) random effects, respectively,σe is the standard deviation of the error, andμ with standard deviation σ.Laps which were the first in a session were not used in this analysis, as the transition type of the previous (non-existent) lap was undefined. The degrees of freedom in the mixed model for path stereotypy were different from the degrees of freedom in the mixed models for VTE because on some laps path stereotypy could not be calculated . Also the degrees of freedom in the mixed models for VTE are different here than for the mixed model used between VTE at the two choice points, because this model does not include laps which were the first in a session (see above).In order to determine what was contributing to VTE at the first choice point, we fit a mixed model to VTE at the first choice point. This model included subject and session as random effects, a fixed intercept, a fixed effect of transition type on the previous lap, a fixed effect of delay experienced on the previous lap, and a fixed effect of choice repetition (whether the previous choice was repeated or not).whereYi is the LogIdPhi value at the first choice point on lap iβ0 is the intercept of the model ,TP is the parameter capturing the fixed effect of a rare transition on the previous lap,ti−1 is an indicator variable which is 0 when there was a common transition on lap i−1, and 1 when there was a rare transition on lap i−1,DP is the parameter capturing the fixed effect of the delay on the previous lap,di−1 is the delay in seconds on lap i−1,C is the parameter capturing the fixed effect of choice repetition,ci in an indicator variable which is 0 when the rat did not repeat its choice on lap i, and 1 when it did,Rr is rat r's random effect (or adjustment coefficient), which accounts for the possibility that some rats have different baseline levels of LogIdPhi or path stereotypy,SS is session s's random effect, which accounts for the possibility that rats have different baseline LogIdPhi or path stereotypy values on different sessions,σr and σs are the standard deviations of per-rat (R) and per-session (S) random effects, respectively,σe is the standard deviation of the error, andσ.BH and ADR conceived and designed the experiment and analyses, and wrote the manuscript. BH collected and analyzed the data.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.


N6-methyladenosine (m6A), A editing into I, and non-templated addition of uridines at their 3′-ends. Biological functions and enzymes involved in those sRNA and micro RNA epigenetic modifications, when known, are presented and challenged.Trans-encoded bacterial regulatory RNAs (sRNAs) are functional analogues of eukaryotic microRNAs (miRNAs). These RNA classes act by base-pairing complementarity with their RNA targets to modulate gene expression . Based on base-pairing, algorithms predict binding and the impact of small RNAs on targeted-RNAs expression and fate. However, other actors are involved such as RNA binding proteins and epigenetic modifications of the targeted and small RNAs. Post-transcriptional base modifications are widespread in all living organisms where they lower undesired RNA folds through conformation adjustments and influence RNA pairing and stability, especially if remodeling their ends. In bacteria, sRNAs possess RNA modifications either internally  or at their ends. Nicotinamide adenine dinucleotide were detected at 5′-ends, and polyadenylation can occur at 3′-ends. Eukaryotic miRNAs possess  All living organisms require regulation of gene expression to survive, grow, and spread in their dynamic and precarious environments, e.g., the survival of the fittest. A large portion of the eukaryotic genomes is expressed as regulatory RNAs , and bacIn bacteria, regulatory RNAs (sRNAs) are also essential players in many physiological and adaptive responses and are central components of networks controlling bacterial responses to stress . A largeEpitranscriptomics correspond to RNA chemical modifications and post-transcriptional nucleotide additions that are involved in gene expression regulation, including pre-mRNA splicing, degradation and translation. Post-transcriptional RNA modifications provide another layer of gene expression monitoring. More than 160 chemical modifications can be detected on RNAs  m6ACU and related variants , and almost 90% of all m6A peaks contain at least one of the motifs [6A is found in regions of high evolutionary conservation. m6A is found within long internal exons and is preferentially enriched within 3′ -UTRs and around stop codons.Predominant consensus motifs of me motifs . The loc6A writing [6A on RNAs. Even if the knock-down of these two proteins strongly affect the m6A abundance in mammalian cells, other proteins belongs to the ‘m6A writer complex’ such as RBM15 and WTAP proteins [The methyltransferase-like 3 (METTL3) plays a critical role in m writing . Howeverproteins . The exa6A motif [6A-modified RNAs and affect their functions. In mammals, there are five members in the YTH family, i.e., YTHDC1, YTHDC2, YTHDF1, YTHDF2, and YTHDF3. Interestingly, YTHDC1 is localized in the nucleus in contrast to the other in the cytoplasm. YTHDF1, YTHDF3, and YTHDC2 selectively promote the translation efficiency of m6A-containing mRNAs. In contrast, YTHDF2 decreases the mRNA stability by favoring deadenylations. Insulin-like growth factor-2 mRNA-binding proteins 1, 2, and 3 (IGF2BP1–3) also behave as m6A readers. IGF2BPs selectively recognize m6A-containing RNAs and promote their translation and stability [Several proteins termed Readers have been described to recognize the m6A motif . These ptability .6A on RNA (here considered as a demethylation). Even if these ALKBH5 and FTO erase the m6A motif on RNA, the FTO requires two additional intermediates to obtain an adenosine residue from a m6A. Importantly, FTO is involved in other RNA demethylation processes (3-methyluracil (3-meU) and N6,2′-O-dimethyladenosine (m6Am)) [AlkB homolog 5 (ALKBH5)  and the  (m6Am)) .6A is crucial for miRNA biogenesis and expression levels [6A-dependant production of miRNA is global and non-cell-type specific, suggesting that m6A provides additional global functions to the miRNAs.mn levels . Pri-miRSince miRNAs target mRNAs by binding at their 3′-UTRs in a complex with Ago proteins, this suggests a potential role of mRNA methylation in the miRNA-mediated expression inhibition of mRNA targets.6A site within their 3′-UTR also have at least one miRNA binding site in their 3′-UTRs [6A peaks and miRNA binding sites, the m6A peaks precede miRNA binding sites ~60% of the time. m6A peaks in mRNAs are abundant near the stop codon and generally decrease in frequency along 3′-UTRs. In the other hand, miRNA targeting miRNA binding sites located in the first hundred nucleotides in 3′-UTR (near the stop codon) seem to have a stronger effect than the other. Thus, it is tempting to speculate that m6A may influence the function or the binding of miRNAs in 3′-UTR. By this way, m6A could promote the mRNA stability at least by limiting the miRNA binding on these 3′-UTR. In accordance with this idea, m6A has been found enriched in ribosome-associated mRNA [It is noteworthy that two-third of the mRNAs which contain a m 3′-UTRs . Intriguted mRNA .6A is also detected in bacterial ribosomal RNAs, mRNAs and some sRNAs [6A profiling in E. coli and Pseudomonas aeruginosa revealed a conserved m6A pattern that is distinct from those in eukaryotes, with a unique GCCAU sequence consensus motif. RsmY and RsmZ sRNAs are enriched in the P. aeruginosa ‘methylome’. These two sRNAs sequester RNA-binding proteins and are central regulatory elements in signal transduction pathways controlling virulence and the selection of favorable carbon sources [6A RNA methylation of RsmY and RsmZ RNAs could modulate their binding affinity with their protein ligands.mme sRNAs . Transcr sources . m6A RNAE. coli. Some methyltransferases are conserved in many species of eubacteria, eukaryotes, and archaea [l-methionine to two adjacent adenosine bases in 16S rRNA. KsgA seems conserved in all three domains of life, a conservation that extends further into mitochondria and chloroplasts.In recent years, a variety of methyltransferases was identified in bacteria, which play important roles in various functional pathways. Some enzymes, as RlmF and RlmJ, methylate selected adenines in 16S  and 23S  archaea . As an eRNAs undergo multiple types of tailing, including adenylation, uridylation, and guanylation . Pioneer+ (nicotinamide adenine dinucleotide), resulting in a portion of cellular RNAs (up to 25% in specific RNAs) being ‘capped’ at their 5′ ends with NAD+ [Whereas miRNAs do not carry specific RNA modifications at their 5′-ends, some sRNAs do. Although many eukaryotic transcripts contain cap structures, it has been long thought that bacterial RNAs do not carry any special modifications on their 5′-ends. In bacteria, primary transcripts are produced by transcription initiated with a nucleoside triphosphate and are triphosphorylated on 5′-ends. In addition to mono- and triphosphorylated moieties, some mRNAs and sRNAs contain cap-like structures and diphosphates on their 5′-ends. Bacterial RNA polymerase can initiate transcription with adenosine-containing cofactor NADith NAD+ ,61, remiith NAD+ . 5′-NAD-RNA uridylation is a widespread posttranscriptional regulator of gene expression in eukaryotes  but is nMonouridylation can occur during miRNA biogenesis. Unlike prototypic pre-miRNAs (group 1), group 2 pre-miRNAs acquire a shorter (1nt) 3′ overhang from Drosha processing and therefore require a 3′ end mono-uridylation, for Dicer processing . TUT7/ZCThis mono-uridylation seems to be specific to some miRNAs. The majority of let-7 and miR-105 miRNA per cell seems to be mono-uridylated . Mono-urHowever, in vivo gene knockout studies coupled with deep sequencing have revealed that the TUT ZCCHC11 uridylates the 3′ ends of many mature miRNAs . The addAltogether, these data underline post-transcriptional uridylation as a mechanism to fine-tune miRNA levels.Several miRNAs are mono-uridylated but TUTases also target pre-miRNA. let-7 maturation is inhibited by an RNA-binding protein Lin28 ,71. Lin2E. coli that greatly accelerates their decay [E. coli [E. coli by blocking their RNase E-mediated decays, but it is currently unknown if this observation can be extended to other bacterial sRNAs, especially those expressed by Gram-positive bacteria. The transient addition of poly(A) tails to bacterial RNAs is essential for the 3′ exonucleolytic degradation of stem-loop structures in decay intermediates . This hoir decay . More ge[E. coli . Poly(A)Caenorhabditis elegans [sRNA and miRNA are thought to be involved in fine-tuning of gene expression. In eukaryotes, loss of function of miRNA genes is usually not associated to phenotypes, as for prokaryotic sRNAs. Less than 10% of knockout mice for evolutionarily-conserved miRNAs induce developmental or abnormal embryonic phenotypes , althoug elegans ,80 and i elegans . Added t elegans , these r elegans ,82. In tNevertheless, co-evolution of coding and miRNA genes have been described in almost all models studied, suggesting that small RNAs provide advantages for fitness and adaptation . Since iEpigenetic modifications on small RNAs can generate three outputs: (1) reduction of sRNA amounts by inhibiting their biogenesis or increasing their turnovers, (2) increase their stability, or (3) sequence modifications enhancing or reducing pairing with their canonical or alternative targets. Thus, epigenetics events induced by stresses will alter the magnitude of effects and repertoire of small RNAs expressed . ReciproNevertheless, the exact nature and number of small RNAs affected by epigenetic modifications is not yet established accurately, despite massive RNA sequencing experiments. Only subsets of miRNAs  and sRNAImportant questions remain unsolved: the delay between miRNA-target hybridization and mRNA decay . To dateCollectively, these epigenetics modifications provide survival advantages. Curiously, epigenetic modifications can differ when models are compared . Even ifE. coli culture after induction of Shiga toxin-converting bacteriophage Φ24B [E. coli enterohemorrhagic strains, giving physiological significance of a microRNA-size molecule in bacterial cells. Whether this recent example corresponds to the tip of the iceberg or an exception is unknown. In bacteria, the existence of miRNA analogues, beside sRNAs, is poorly studied. Small RNAs (15–28 nt long), considered as RNA fragments, were identified in some bacteria , but wheage Φ24B . Phages


Caenorhabditis elegans. Ever since, several new endogenous small RNA pathways have been described and characterized to different degrees. The very prominent secondary small interfering RNAs, also called 22G-RNAs, bear a 5′ triphosphate group after loading into an Argonaute protein. This creates a technical issue, since 5′PPP groups decrease cloning efficiency for small RNA sequencing. To increase cloning efficiency of these small RNA species, a common practice in the field is the treatment of RNA samples, prior to library preparation, with Tobacco Acid pyrophosphatase (TAP). Recently, TAP production and supply was discontinued, so an alternative must be devised. We turned to RNA 5′ pyrophosphohydrolase (RppH), a commercially available pyrophosphatase isolated from E. coli. Here we directly compare TAP and RppH in their use for small RNA library preparation. We show that RppH-treated samples faithfully recapitulate TAP-treated samples. Specifically, there is enrichment for 22G-RNAs and mapped small RNA reads show no small RNA transcriptome-wide differences between RppH and TAP treatment. We propose that RppH can be used as a small RNA pyrophosphatase to enrich for triphosphorylated small RNA species and show that RppH- and TAP-derived datasets can be used in direct comparison.RNA interference was first described in the nematode •We show that treatment of small RNA samples with RppH prior to sequencing library preparation increases the cloning efficiency of 5′ triphosphorylated small RNAs;•RppH treatment is a valid alternative to TAP treatment. Specifications TableCaenorhabditis elegans [C. elegans there are several endogenous small RNA species and 27 genomically-encoded Argonautes [RNA interference (RNAi), the process whereby small RNAs and their cognate Argonaute proteins regulate gene expression, was initially discovered in the nematode  elegans . In C. eThe 5′ triphosphate group decreases clonability of 22G-RNAs in standard small RNA library preparations. A common practice in the field to improve the yield of 22G-RNA reads in deep-sequencing experiments is to treat samples, prior to library preparation, with Tobacco Acid Pyrophosphatase (TAP) . TAP hydE. coli RNA 5′ Pyrophosphohydrolase (RppH). RppH primes mRNA degradation in E. coli by converting the 5′ triphosphate into a 5′ monophosphate [C. elegans with TAP and RppH and compared their performance in regard to 22G-RNA enrichment.Recently, TAP production was discontinued, so we sought a commercially available alternative. We chose hosphate . In pracCaenorhabditis elegans populations were grown according to standard conditions [E. coli OP50. For synchronization, gravid adult worm populations were bleached and eggs were allowed to hatch overnight in M9 buffer, at 20 °C. Synchronized L1 larvae were plated the next day on OP50-seeded plates and grown at 20 °C for 60–63 h until adulthood. Gravid adults were washed off plate with M9 buffer and lysed in 250 μL of Worm Lysis Buffer  with 150 μg Proteinase K (Sigma-Aldrich #P2308) for at least 30 min at 65 °C. 750 μL of Trizol LS  were added per sample and extraction followed manufacturer’s instructions, with the exception that phase-lock tubes were used to facilitate phase-separation. Total RNA extraction was followed by TURBO DNase treatment (Life Technologies #AM1907) according to manufacturer’s instructions. Small RNAs were isolated from DNase-treated total RNA using MirVana (Life Technologies #AM1561).nditions . Wild-tyA single RNA sample was divided into 6 technical replicates. Three of these technical replicates were treated with Tobacco Acid Phosphatase  and the other three, with RNA 5′ Pyrophosphohydrolase . For TAP-treated samples, 1 μg of total RNA was incubated for 2 h at 37 °C, with 5 units of TAP enzyme and 10X TAP buffer. For RppH treated samples, 1 μg of RNA was incubated for 1 h at 37 °C, with 5 units of RppH enzyme and 10X NEB Buffer 2. To stop the RppH treatment, 500 mM EDTA was added and samples were incubated for 5 min at 65 °C.Small RNAs (16–30 nt) were enriched by performing size selection of the RNA prior to library preparation. RNA samples were run on a 15% TBE-Urea polyacrylamide gel (BioRad), and purified with sodium chloride/isopropanol precipitation. Library preparation was performed using the NEBNext Multiplex Small RNA Library Prep Set for Illumina (New England BioLabs) as recommended by the manufacturer (protocol version v2.0 8/13), but replacing the RNA ligation adapters with adapters with 4 additional random bases at their 3′ ends to allow identification of PCR clonal artefacts (synthesized by Bioo Scientific). Fourteen PCR cycles were used for library amplification. The PCR-amplified cDNA was purified using AMPure XP beads (Beckman Coulter). Size selection of the small RNA library was done on LabChip XT instrument (Perkin Elmer) using DNA 300 assay kit extracting the 135–170 bp fraction.The 135–170 bp library fractions of the RppH- and TAP-treated libraries were pooled in equal molar ratio. The resulting 4 nM pool was denatured and diluted to 9 pM with 5% PhiX spike-in and sequenced as single-read on MiSeq (Illumina) for 68 cycles. Libraries for untreated samples, originating from a different biological replicate, were also prepared using the protocol above, but were sequenced as single-read on HiSeq 2500 (Illumina). Untreated samples were originally used in .C. elegans genome assembly WBcel235 with bowtie v0.12.8 (--tryhard --best --strata --chunkmbs 256 -p 8 -v 1 -M 1). For the analysis, only reads mapping to a single genomic location were used. Except where indicated, reads used for comparison are those whose sequence is exactly 22 nucleotides long and have a guanine at their 5′.The quality of raw sequenced reads was accessed with FastQC, illumina adatpers were then removed with cutadapt (-O 8 -m 26 -M 38), reads with low-quality calls were filtered out with fastq_quality_filter (-q 20 -p 100 -Q 33). Using information from unique molecule identifiers (UMIs) added during library preparation, reads with the same sequence (including UMIs) were collapsed to removed putative PCR duplicates using a custom script. Prior to mapping, UMIs were trimmed (seqtk trimfq) and library quality re-assessed with FastQC. Reads were aligned against the To determine the similarity between libraries, two approaches were used: (1) the genome was divided into equally sized bins and reads mapping to each bin were counted ; or (2) reads mapping to a set of published 22G-RNA targets . Using either of those counts, the correlation coefficient (spearman and Pearson) between samples was calculated and plotted .C. elegans genes . Read counts were then used in DESeq2 to determine differential expression between RppH and TAP treatments. Since the goal was to determine the experimental  differences between both treatments, we considered each technical replicate as a biological replicate to perform the analysis.We used featureCounts (v1.4.6) with options -s 2 --ignoreDup to count the number of 22G-RNAs mapping antisense to the annotated To identify putative 21U-RNA precursors we selected reads that mapped to annotated 21U-RNA loci (bedtools intersect -F 0.5). The length and 5′ nucleotide frequency of these reads was then summarized.The list of genes targeted by 22G-RNAs was compiled from previous publications . We thenTo address if RppH treatment enriches for 22G-RNAs as efficiently as TAP, we sequenced small RNAs of wild-type adult worms with prior treatment with TAP or RppH. To properly compare both treatments, we sequenced three technical replicates of TAP treatment and three technical replicates of RppH treatment, all derived from a unique biological sample.We first determined if RppH treatment would affect the quality of the sequenced reads. QC reports generated by FastQC shows that the per base sequence quality of RppH-treated libraries is at least as good as that of TAP-treated libraries . Other key indicators such as the average quality per read and sequence duplication levels are also similar in libraries treated with either method. Importantly, the three RppH-treated technical replicates show that these libraries are consistently of high quality.The goal of both Rpph- and TAP-treatment is to enrich the library for 22G-RNAs. This class of small RNAs has two features that can be readily used to determine if and how successful the enrichment was: (i) most sequences are 22 nucleotides in length and; (ii) high proportion of sequences with a guanine at their 5′ end. After adapter and UMI removal we plotted the distribution of read length in the sequenced libraries A. Reads C. elegans genome with equal efficiency, and similarly to untreated libraries (For both treatments, reads were mapped to the ibraries A. Despitibraries , it is pibraries C. The maEven though the overall genomic location of RNA recovered from RppH or TAP treatment is very similar , there mLastly, TAP and RppH phosphatase activity also results in 5′ cap removal. Since 21U-RNA precursors are 5′ capped, decapping may affect 21U-RNA cloning during library preparation. The number of 21U-RNAs and potential precursors sequenced in TAP- and RppH-treated samples are not significantly different (Supplementary C. elegans. Whilst no consensus alternative for TAP has been found, others have used other alternatives to TAP and RppH [The unavailability of TAP from commercial sources eliminated a common tool used in the field of small RNA research, where, amongst other uses, TAP was used to enrich samples for 22G-RNAs in and RppH ,22, howe


We simulated the A3G-NTD residues with high RNA-binding propensity, experimentally validated our prediction by testing A3G-NTD mutations, and identified structural determinants of A3G-RNA binding. In addition, we found a novel amino acid residue, I26 responsible for RNA interaction. The new structural insights provided here will facilitate the design of pharmaceuticals that inhibit A3G-Vif interactions without negatively impacting A3G-RNA interactions.APOBEC3G (A3G) is a cellular protein that inhibits HIV-1 infection through virion incorporation. The interaction of the A3G N-terminal domain (NTD) with RNA is essential for A3G incorporation in the HIV-1 virion. The interaction between A3G-NTD and RNA is not completely understood. The A3G-NTD is also recognized by HIV-1 Viral infectivity factor (Vif) and A3G-Vif binding leads to A3G degradation. Therefore, the A3G-Vif interaction is a target for the development of antiviral therapies that block HIV-1 replication. However, targeting the A3G-Vif interactions could disrupt the A3G-RNA interactions that are required for A3G's antiviral activity. To better understand A3G-RNA binding, we generated  APOBEC3G (A3G), a member of the cellular cytidine deaminase APOBEC3 superfamily, exhibits anti-HIV activity primarily by inducing G-to-A hypermutation in the viral cDNA . Interestingly, this soluble A3G-NTD mutant was not packaged into virus particles . Adherent HEK293T cells or non-adherent M8166 cells were cultured in 10% Fetal Calf Serum of Dulbecco's Modified Eagle's Medium or RPMI Medium, respectively -tagged human A3G, pcDNA3/HA-A3G, as previously described  was aligned to either the soluble form of human A3G-NTD (PDBID: 2mzz) , median, and quartiles of the all BP and CF. High values were defined as being greater than the third quartile, while low values were defined as being less than the first quartile.To visualize the encapsidation of EYFP-tagged A3G or its mutants in virions, pA3G-3xFLAG-EYFP was co-transfected with imCHΔVifΔEnv in HEK293T cells (3.5 million per 10 cm dish) with the PEI transfection reagent . The virus-containing supernatant was harvested at 24 h post-transfection, filtered through 0.45 μm pore size sterile cellulose membrane and concentrated up to 20-fold by ultracentrifugation through a 20% sucrose cushion at 25,000 rpm for 90 min at 4°C . The concentrated virus supernatant (0.5 μL) was briefly mixed with 200 μL Hank's Balanced Salt Solution (HBSS) (+) without Phenol Red (Wako) and mounted onto 8-well glass bottom chamber slides (Matsunami), then incubated overnight at 4°C. Images of virions were acquired with an A1R Confocal Microscope (Nikon). mCherry was used to identify virus particles and EYFP was used to detect incorporated A3G protein. Images were first exported using the NIS-Elements software (Nikon) and representative images were processed with Fiji, an image processing package in ImageJ (NIH). A binary image of the mCherry and EYFP signals was generated to count the positive puncta. A3G encapsidation efficiency was calculated as percentage of mCherry positive signals that co-localized with the EYFP signal within a 3-pixel range from the centroid of the mCherry signal by an in-house MATLAB program (MathWorks).Cells were lysed using RIPA buffer (Wako) supplemented with protease inhibitor cocktail (Sigma-Aldrich) and the supernatants were collected after centrifugation. Polyacrylamide gel electrophoresis and protein transfer to PVDF membranes  were followed by hybridization with primary antibodies. Blots were probed with rabbit anti-HA (Sigma-Aldrich), mouse anti-Vif (NIH AIDS Reagent Program), and mouse anti-GFP (Thermo Fisher Scientific) primary antibodies overnight at 4°C. HRP-conjugated anti-mouse or rabbit IgG antibodies  were used as secondary antibodies. Western blot images were obtained with x-ray films. EYFP fluorescence intensity in the cell lysates was measured with a plate reader .P-values were calculated relatively to the A3G WT.A3G and its derived mutants (Y22E and R122A) were immunoprecipitated with anti-HA antibody conjugated Protein A beads from cell lysates extracted from transfected HEK293T cells. After immunoprecipitation, A3G associated RNAs were purified with the High Pure RNA extraction kit (Roche). The concentration of purified RNA was measured by a NanoDrop ND-1000 Spectrophotometer and adjusted to the total RNA amount prior to the reverse transcriptase (RT) reaction. The RT product was obtained by TaKaRa PrimeScript 1st strand cDNA Synthesis Kit. The RNA denaturing process was performed at 65°C for 5 min, followed by 4°C for 10 min with 1 μL Random hexamers and 1 μL dNTP mix. The denatured RNAs were then reverse-transcribed with 4 μL 5xPrimeScript Buffer, 20 units RNase Inhibitor (40 U/μL), 200 units PrimeScript RTase, and 4.5 μL RNase free water. The RT reaction was performed at 30°C for 10 min, 42°C for 60 min, 95°C for 5 min and then 4°C overnight. A Real-Time PCR instrument, Thermal Cycler Dice Real Time System TP800 (TaKaRa) was used for monitoring PCR amplifications. All primers were synthesized by SIGMA. qRT-PCR was performed using SYBR green. Each reaction mixture contained 0.5 μL of forward and reverse primers (10 μM), 10 μL of THUNDERBIRD SYBR qPCR Mix (TOYOBO), 7 μL of RNase-free water, and 2 μL of template cDNA. The reactions were performed under the following conditions: 94.0°C for 10 s, followed by 40 cycles of 95°C for 5 s and 60°C for 30 s, for the final step. Single peaks in the melting-curve analysis indicated specific amplicons. Primer sequences used in these assays were previously published -inducible A3G expression vectors or the empty vector  in HEK293T cells as previously described  based on the previously published NMR solution structure snapshots of a soluble mutant A3G-NTD (PDBID: 2mzz) , the innate A3G-NTD RNA binding surface/bound RNA, and the potential steric hindrance due to amino acids orientation could all determine RNA binding. Therefore, we hypothesized that the potential impact of these factors is minimized on a local scale and RNA contact frequency (CF) can be modeled with the docking of short nucleotides. Recently, York et al. reported about the RNA binding specificity of A3G  to identify putative RNA-binding residues in the N-terminal domain (NTD) of A3G. Our model identified the previously reported RNA-associating hot-spot residues, S28, R30, R122, and Y124 and we confirmed their involvement in RNA binding in a series of mutagenesis experiments. Interestingly, our experimental data confirmed that the I26 residue is an RNA binding determinant as initially predicted in our model. Although I26 is a known determinant of A3G-Vif interactions  to A3G  in the A3G genes in a cohort of HIV-1 infected South African individuals (Matume et al., CF score , indicatin silico models to predict A3G-RNA interaction sites and experimentally confirmed these predictions. In addition, we utilized the models to identify a novel residue, I26 that contributes to RNA association. Together with previous alanine scanning experiments and structural modeling of Vif docking to A3G, the RNA interaction predictions described here could be used for in silico drug screening or design of selective inhibitors of A3G-Vif binding while maintaining the A3G-RNA association necessary for virion incorporation.In summary, we applied a novel approach using All datasets generated for this study are included in the manuscript and/or the HF, LS, AS, and TI performed the experiments. SL, KY, DS, and TI designed and analyzed the data. LS, JS, KS, AT-K, and TI wrote the manuscript. KS, AT-K, and TI contributed financial assistance.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.


High-quality RNA is an important precursor for high-throughput RNA sequencing (RNAseq) and subsequent analyses. However, the primary metric used to assess RNA quality, the RNA Integrity Number (RIN), was developed based on model bacterial and vertebrate organisms. Though the phenomenon is not widely recognized, invertebrate 28S ribosomal RNA (rRNA) is highly prone to a form of denaturation known as gap deletion, in which the subunit collapses into two smaller fragments. In many nonmodel invertebrates, this collapse of the 28S subunit appears as a single band similar in size to the 18S rRNA subunit. This phenomenon is hypothesized to be commonplace among arthropods and is often misinterpreted as a “degraded” rRNA profile. The limited characterization of gap deletion in arthropods, a highly diverse group, as well as other nonmodel invertebrates, often biases RNA quality assessments. To test whether the collapse of 28S is a general pattern or a methodological artifact, we sampled more than half of the major lineages within Arthropoda. We found that the 28S collapse is present in ∼90% of the species sampled. Nevertheless, RNA profiles exhibit considerable diversity with a range of banding patterns. High-throughput RNAseq and subsequent assembly of high-quality transcriptomes from select arthropod species exhibiting collapsed 28S subunits further illustrates the limitations of current RIN proxies in accurately characterizing RNA quality in nonmodel organisms. Furthermore, we show that this form of 28S denaturation, which is often mistaken for true “degradation,” can occur at relatively low temperatures.  Similar to traditional quantification methods ), it can now be performed in most modern laboratories with relative ease and accuracy  while yi studies , and evo studies ). Moreov studies , 6.RNAlater®; [Compared with DNA, total RNA extraction and sequencing poses nontrivial methodological challenges to maintain RNA stability. Due to its ephemeral nature, RNA is prone to cellular degradation  from theAlater®; ), when tHistorically, RNA was assumed to be of high quality if the ratio of large to small ribosomal subunits (28S:18S) was ∼2 or higher , though Although relevant ribosomal RNA (rRNA) profile standards exist for several model organisms , most brThere are few recent studies that have evaluated the universality and suitability of “standard” RNA quality metrics for nonmodel organisms . Early et al. [Hidden breaks in RNA are correlated with the presence of a cleavage site characterized as an Uracil (U) and Adenine (A) or UAAU-rich segment in the rRNA loop . High teet al.  hypothesde novo transcriptome assembly for two representative arthropod species exhibiting seemingly denatured RNA profiles provide further support for high-quality RNA despite evidence of 28S subunit collapse. Findings from this effort will aid future nucleic acid quality assessments for arthropods and other nonmodel invertebrates and will provide methodological insight for laboratories beginning to conduct RNA-based research.Here we document the range of variation among RNA extraction profiles for the major lineages within Arthropoda, with dense taxonomic sampling. Our primary motivation is to disentangle natural patterns of subunit denaturing from true degradation and methodological artifacts. Data available from prior studies are a composite of multiple extraction protocols from different laboratories that can further obscure these artifacts. This study was done using standardized reagents and conditions to improve comparisons among taxa. High-throughput RNAseq and n = total number of individuals sampled within each subphyla): Chelicerata (n = 4), Myriapoda (n = 11), Crustacea (n = 24), and Hexapoda (n = 20). Within Chelicerata, we sampled Arachnida  and Merostomata (horseshoe crabs); within Myriapoda, we sampled Chilopoda (centipedes) and Diplopoda (millipedes); within Crustacea, we sampled Branchiopoda (water fleas), Oligostraca , Multicrustacea , and Malacostraca ; and within Hexapoda, we sampled Insecta (insects) and Entognatha (springtails). Our lineage selection encompasses all current Arthropod subphyla, approximately two-thirds of the classes in both Chelicerata and Crustacea, all classes within Hexapoda and half of all classes in Myriapoda. To account for the large diversity within Hexapoda and Crustacea , we samstacea,”  and infrstacea,” . We wereRNAlater  with an abdominal incision and then placed at −80°C until RNA extraction. All specimens/or tissues are cataloged and deposited as vouchers (specimen or tissue) in the Florida International Crustacean Collection at FIU  in North Miami, FL, USA, and brought them back to the lab for preservation. Some specimens were ordered from a commercial vendor  or donated by colleagues (see Acknowledgements section). Smaller individuals (bee size and below) were flash frozen and preserved at −80°C. Larger individuals were placed in n at FIU .Asellus aquaticus (n = 3), Cubaris cf. murinus (n = 3), amphipod Niphargus hrabei (n = 3), cave shrimp Barbouria cubensis (n = 3), silverfish Lepisma saccharina (n = 2), the insects Brachymesia gravida (n = 2) and Panorpa debilis (n = 2), the millipede Anadenobolus monilicornis (n = 4), and centipede Hemiscolopendra marginata (n = 4). Though RNA quality tissue replicates were difficult to obtain for some species, all species were included in the study regardless as an effort to more thoroughly survey RNA profile diversity across Arthropoda.We extracted RNA from 40 species across the four Arthropod subphyla. RNA was extracted from replicates where possible, including: the isopods ® . We isolated total RNA from varied tissues or body parts depending on the specimen size . Approximately 10–12 sterile 2 mm zirconia/ceramic beads were used in a MiniBead Beater  for homogenization in a 2 mL screwcap tube, followed by the addition of chloroform (0.2 mL). Samples were mixed (shaken) vigorously by hand for 15  s and incubated at room temperature for 2–3 min. Phase separation was carried out by centrifuging samples (12 000g for 15 min) at 4°C, following manufacturer’s instructions, and carefully transferring only the top (clear) aqueous phase containing the RNA into new vials. RNA was then precipitated from the clear aqueous phase by mixing in (∼100%) isopropyl alcohol (0.5 mL) and glycogen (0.1 μL). Samples were then incubated at room temperature for 10 min and centrifuged (12 000g for 15 min) at 4°C. The supernatant was discarded, the RNA pellet washed twice with freshly made 75% ethanol (EtOH) and the supernatant was again discarded; 1 mL EtOH was added before centrifugation at 7500g for 5 min at 4°C (X2). Before elution, precipitated RNA was treated with DNases  to remove genomic DNA (15-min incubation at 37°C) following manufacturer’s guidelines. After repeating the isopropyl precipitation and ethanol washes (2×), total RNA was eluted in 50 μL of RNase-free water  and stored at −80°C.All stages of our RNA extraction protocol were carried out under a fume hood in a dedicated RNase-free room. Bleach (2% solution in distilled water) was applied to all surfaces, equipment, and reagent containers to remove RNases and secondarily sterilized with RNaseZAPsize see . For verRNA integrity was determined initially on a 1% agarose gel  or TAE) treated with a 1% bleach solution and ran in cold buffer at 4°C to prevent RNA degradation during electrophoresis . In ordA. aquaticus and amphipod N. hrabei, each exhibiting denatured RNA profiles indicative of 28S collapse . The resulting de novo transcriptome assembly was assessed using Transrate v1.0.3 [n = 1066) [RNA aliquots of the isopod ils, see ). These o et al. . Brieflyo et al.  and trimo et al.  using the v1.0.3  using Or = 1066)  to deterEscherichia coli, Life Technologies, USA) as a noncleaved 28S positive control. RNA aliquots were taken from the three samples prior to the DNase treatment, precipitated, eluted, and kept at 4°C. We prepared a heat treatment gradient as recommended by the Bioanalyzer manufacturer, heating all samples in a MJ Research PTC-200 thermocycler, incubating for 2 min at a designated temperature using a nonheated lid, prior to cooling at 4°C. RNA from the four samples (including the positive control) were aliquoted into eight different temperature treatment sets (0.2 mL PCR strip tubes). Each set was placed across a temperature gradient ranging from 20°C to 90°C, at increments of 10°C. This range encompasses “room temperature” and standard DNase treatment temperatures, as well as temperatures 20°C higher than the recommended denaturing step in an attempt to force denaturation for comparison. After the eight tube sets were incubated at their corresponding temperature on the thermal gradient, we kept the samples at 4°C and stored them permanently at −80°C until they were run on both a 1% TAE bleach gel in cool buffer and Bioanalyzer picochips as described previously.High temperature exposure is considered as one of the determining factors behind the 28S subunit collapse , 22. To Apis mellifera  and the millipede (A. monilicornis) maintained 28S subunit integrity throughout the 20–90°C gradient, exhibiting a two-band profile reminiscent of the vertebrate rRNA model reference. The blue centipede (H. marginata) showed a single band throughout the entire gradient  had at least a portion of their 28S subunit already collapsed at 20°C . Blue crgradient . Some lagradient  showed Rgradient .A. aquaticus and N. hrabei, exhibiting 28S denaturation  and 90.5% , respectively, of the single-copy orthologs employed for benchmarking in Arthropoda were present, indicating fairly complete, high-quality assemblies.Approximately 32 M paired-end reads were generated for the arthropods turation . De novoils, see ). CorresE. coli 2900 bp for 23S and 1700 bp for 16S; There is a growing body of evidence suggesting that the current vertebrate RNA profile references are not reliable proxies to assess the quality of invertebrate RNA with seemingly “degraded” RNA profile patterns , 27, 28.Daphnia water fleas, suggests a significant translocation of the UAAU loop with 400 bp separating the two 28S fragments. Likewise, the possibility exists that a second UAAU region, or underlying secondary structure, is creating an additional cleavage point. A four-banded profile in tiger centipedes also implies that complex secondary structures within the loop region are creating additional cleavage points, possibly making them more accessible to enzymes . Alternatively, a secondary UAAU-rich loop may be present, resulting in three 28S fragments  as tiger centipede RNA showed no signs of degradation . The third and fourth bands in tiger centipedes are also much smaller than bacterial rRNA subunits, rendering contamination from mitochondrial DNA or microorganisms unlikely. However, this four-banded pattern was not found in the blue centipede species (single band). This suggests that the four-banded pattern is not unanimous among Chilopoda. Similar to the RNA profiles observed in aphids [The diversity of RNA profiles, ranging from one to five bands , indicatn aphids , millipeCtenomys have a denatured 28S subunit [de novo transcriptome assemblies from RNAseq data for two arthropod species exhibiting denatured RNA profiles provides additional support for high-quality RNA despite 28S subunit collapse [A. aquaticus [The observed variation in RNA profiles and 28S denaturation found in this study is comparable to other systems. For example, a majority of mammals have a typical nondenatured two-banded vertebrate profile, but rodents in the genus  subunit . Thus, acollapse . Assemblquaticus  and othequaticus , as wellquaticus . Denaturquaticus . For reset al. [A prior study by McCarthy et al.  examininDNase treatments, which should be a standard practice in RNA extraction protocols, typically require heating samples to 37°C for enzyme activation. Therefore, 28S fragmentation will likely occur as part of the extraction protocol in all organisms with a UAAU-rich loop. Our findings also suggest that the large ribosomal subunit of some species may be resistant to denaturation as millipedes were able to maintain 28S integrity regardless of heat exposures to 90°C. This is possibly a result of a missing UAAU loop region similar to aphids . Our reHere, we show that both methods for evaluating RNA quality, denaturing gel electrophoresis and Bioanalyzer electropherograms, have their individual strengths while also being subjected to methodological artifacts. Though RIN values are unreliable in profiles where 28S is collapsed, electropherograms should still be used to assess RNA quality as they provide more accurate size estimates. Bleach agarose gels  can alsoAlthough comparing multiple RNA extraction methods are outside the scope of this study, we found one in particular that worked well for arthropods and downstream RNAseq studies. We ultimately selected solvent-based  over column-based methods , as it was cost-effective while also yielding higher concentrations of high-molecular weight nucleic acids as well as small and micro-RNAs. Column membranes sequestered too much material and often resulted in the isolation of ≪1 μg of total RNA per sample. Obtaining high RNA yields becomes particularly important when working with small-bodied organisms, a challenge faced by many invertebrate researchers. Lastly, solvent-based methods prove to be reliable across taxa and laboratories as the RNA profiles reported here are consistent with past arthropod RNA studies  remained constant among arthropods, making it a useful proxy for RNA integrity. Lastly, our study demonstrates that RNA is substantially labile and extraction protocol temperatures (>37°C) will denature 28S that contains the UAAU-rich loop region.Raw sequencing data are available on the National Center for Biotechnology Information (NCBI)’s Sequence Read Archive (SRA) database under Bioproject PRJNA476149. Supplementary Figure S1Click here for additional data file.


WO-Seq: a bisulfite-free and base-resolution sequencing method based on peroxotungstate oxidation is presented for the identification of hm5C sites in the transcriptome. Combining the peroxotungstate oxidation with TET enzyme oxidation, m5C can also be detected in a procedure termed TET-Assisted WO-Seq (TAWO-Seq). 5C) and 5-hydroxymethylcytidine (hm5C) have also been identified in RNA, their distribution and biological function in RNA remain largely unexplored, due to the lack of suitable sequencing methods. Here, we report a base-resolution sequencing method for hm5C in RNA. We applied the selective oxidation of hm5C to trihydroxylated-thymine (thT) mediated by peroxotungstate. thT was subsequently converted to T during cDNA synthesis using a thermostable group II intron reverse transcriptase (TGIRT). Base-resolution analysis of the hm5C sites in RNA was performed using Sanger sequencing. Furthermore, in combination with the TET enzyme oxidation of m5C to hm5C in RNA, we expand the use of peroxotungstate oxidation to detect m5C in RNA at base-resolution. By using this method, we confirmed three known m5C sites in human tRNA, demonstrating the applicability of our method in analyzing real RNA samples.5-Methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC), two of the best-studied DNA modifications, play crucial roles in normal development and disease in mammals. Although 5-methylcytidine (m M5C is abundant in noncoding RNA, and has the ability to stabilize tRNA secondary structure,5C has been shown to be enriched in Drosophila melanogaster mRNA, increase mRNA translation and play a central role in Drosophila brain development.5C also exists in mammalian RNA, albeit at low levels,5C in the mammalian transcriptome remain unknown due to the lack of sensitive and robust sequencing methods.Epitranscriptome, which refers to the multitude of RNA chemical modifications, has vital roles in post-transcriptional gene regulation.5C in RNA is to adopt bisulfite sequencing, which is widely used to sequence 5mC in DNA. Bisulfite treatment deaminates unmethylated cytosine to uracil in single-strand RNA, while leaving m5C unconverted. Therefore, bisulfite sequencing provides base-resolution information of m5C. Using bisulfite sequencing, widespread m5C sites were identified in both coding and non-coding RNAs.5C in RNA are immunoprecipitation-based that use m5C-specific antibodies or methyltransferases to pull down m5C-containing RNA.5C. Mapping hm5C in RNA is even more challenging. To date, there is no base-resolution sequencing method for hm5C. The only reported method is the antibody-based immunoprecipitation approach.5C and m5C based on peroxotungstate oxidation.The most common way to sequence mthT). thT is a thymine derivative, and will induce C-to-T transition in DNA after PCR. However, the peroxotungstate oxidation reaction requires single-strand DNA. The reaction is strongly inhibited in double-strand DNA with a conversion rate of less than 10%, which severely limits its application.5C in RNA, which is mostly single-stand . To enable us to monitor the efficiency of the hm5C-to-T conversion, one hm5C was positioned so that, upon successful hm5C-to-T conversion, a TaqαI restriction enzyme recognition site in the resulting RT-PCR product was destroyed 5C sites, while Superscript III and Bst 3.0 DNA polymerase induced truncations at the reacted hm5C sites   was also tested for the TAWO-Seq, which gives similar results with NgTET1  . The resAWO-Seq) . RestricAWO-Seq) . Sanger g. S7 ESI, 33.3% oAsp(GUC) in 293T cells. The tRNAAsp(GUC) contains three known m5C sites at structural positions 38, 47 and 48  was cloned and sequenced. As shown in Fig. S10b and c , we found that m5C at position 48 has the highest C-to-T conversion rate . According to the tRNAAsp(GUC) structure, this site is in a double-stranded CpG context, which is an ideal substrate of TET enzyme.5C-to-T conversion rates of three m5C sites are caused by the sequence preference of the TET proteins.To further demonstrate the utility of TAWO-Seq on real RNA sample, we applied it to the endogenous tRNAand c ESI, 35.2% o5C in RNA. We demonstrate the specific hm5C-to-T transition using peroxotungstate to oxidize the RNA followed by cDNA synthesis with the TGIRT enzyme, and Sanger sequencing results have proved the base-resolution sequencing ability of this method. We further demonstrate the ability of TAWO-Seq to detect m5C by combining WO-Seq with the prior NgTET1 or mTet1 oxidation of m5C to hm5C. The successful detection of m5C sites in human tRNA demonstrates our method is applicable for real RNA samples. Both WO-Seq and TAWO-Seq could potentially solve the false positive issue of bisulfite sequencing since they directly detect modified cytosine without affecting unmodified cytosine. Further improvement of both methods to increase the conversion rate and apply to mRNA samples using next-generation sequencing technology are underway in the lab. We believe this method could be highly useful for the identification of unexplored m5C/hm5C distribution and function in the transcriptome.In conclusion, we have described WO-Seq as an RNA friendly, chemical oxidation-based, base-resolution method to sequence hmWe would like to acknowledge P. Spingardi, G. Berridge and B. Kessler for helping with the HPLC-MS/MS; C. He for the mTet1; F. Howe for editing the manuscript. This work was supported by the Ludwig Institute for Cancer Research. Work in the C.-X. Song lab is also supported by Cancer Research UK (C63763/A26394 and C63763/A27122), NIHR Oxford Biomedical Research Centre, and Conrad N. Hilton Foundation. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. F. Yuan and Y. Bi are supported by China Scholarship Council.There are no conflicts to declare.Supplementary informationClick here for additional data file.


In China, the shortage of doctors leads to stressful clinical work and increasing turnover. Medical students undergoing postgraduate specialty training will be the country’s medical workforce in the coming decades, but are also subject to high workloads and academic pressure. This may have significant implications for burnout and career choice regret. Despite the importance of burnout and career choice regret, the status and relationship of these aspects in Chinese neurology postgraduates are largely unexplored, and associated factors remain unknown.This study investigated the prevalence of and factors influencing burnout and career choice regret among neurology postgraduates in China. We conducted a national cross-sectional study of Chinese neurology postgraduates. Data were collected using a self-administered questionnaire that covered demographic information, the Maslach Burnout Inventory, and additional item to assess career choice regret.P <  0.05). Career choice regret was the strongest risk factor for burnout . Multiple logistic regression showed postgraduates with shorter work or study hours per week  had a low risk for career choice regret, whereas married participants  had a high risk for career choice regret. No symptoms of burnout  was also associated with a low risk for career choice regret.Of 4902 neurology postgraduates, 2008 returned completed questionnaires (response rate 41%). After excluding incomplete questionnaires, data for 1814 participants were analyzed. In total, 83.6% of participants had experienced symptoms of burnout, and 46.6% reported career choice regret. Binary logistic regression analysis showed postgraduate entrance examination scores, marital status, and having children were associated with burnout  contains supplementary material, which is available to authorized users. Burnout is a prolonged physical and mental response to chronic emotional and interpersonal job-related stressors . It is cMedical postgraduates or interns often work long hours and have heavy workloads, which may increase the risk for burnout and career choice regret. The incidence of burnout was 20% among medical interns in Mexico , 52.8–76Previous surveys suggest that neurology is a specialty with high levels of job-related burnout and low levels of satisfaction , 24. TheDespite the widely acknowledged importance of burnout and career choice regret , 27, theThis survey was conducted by the China Neurologist Association from September 2014 to March 2015. The study design was adapted from our previous study [The cover letter for the questionnaire introduced the purpose of the survey, which was to explore the status of and factors potentially associated with burnout and career choice regret. The cover letter also stated that participation was voluntary and anonymous, and personal privacy would not be violated in the study processes. Consent was assumed for any participant who returned a completed survey. Ethics approval was granted by the Ethics Committee of Chongqing Medical University.The survey questionnaire contained four parts. Part 1 covered demographic variables, including gender, academic year, age, degree type, postgraduate entrance examination score, hours worked or studied per week, hours slept per day, marital status, whether the respondent had children, and work type (part-or full-time). Part 2 comprised the Maslach Burnout Inventory . This inP-value below 0.05 was considered statistically significant.All statistical analyses were performed with SPSS version 21.0 . Associations between variables were evaluated using univariate analysis with a chi-square test for categorical variables. Binary logistic regression analysis was used to verify the factors affecting burnout. We fitted three logistic regression models. Model 1 was adjusted for all demographic variables, Model 2 for career choice regret, and Model 3 for demographic variables and career choice regret. Multicollinearity analysis was used to test collinearity among variables when the tolerance was ≤0.1 or the variance inflation factor was ≥10. Three methods  were used to select significant independent variables in the logistic models. Multiple logistic regression analysis was used to identify factors potentially associated with career choice regret. For the dependent variable (career choice regret), the category ‘without career choice regret’ was set as the reference category. Demographic variables and burnout were independent variables. A In March 2015, 4902 questionnaires were distributed, and 2008 collected from 249 hospitals (response rate 41.0%). We excluded 194 invalid questionnaires because of missing data for the Maslach Burnout Inventory, leaving 1814 questionnaires for analysis.Table P <  0.05). In the univariate analysis (Table P <  0.05).Table is Table , career P <  0.05). Women were at higher risk for burnout than men . Married postgraduates  and those without children  were less likely to show symptoms of burnout. Model 2 (including career choice regret) showed career choice regret was independently associated with burnout (P <  0.05). In Model 3 (both demographic variables and career choice regret), being married and without children were protective factors. Career choice regret was the strongest risk factor for burnout .The multicollinearity analysis indicated there was no collinearity for burnout  had a low risk for career choice regret, whereas postgraduates who were married  had a high risk. Being without symptoms of burnout  was also associated with a low risk for career choice regret  through improving working conditions, rationally allocating workers, and setting up career development planning for staff. Hospitals could also ensure staff has sufficient rest time, and pay attention to any psychological problems among postgraduates. Third, it is important for postgraduates to understand burnout correctly, improve their physical and psychological status, and pay attention to the balance of work and rest.This study had a number of limitations. First, the survey did not use random sampling methods, which might have resulted in selection and response bias. However, the findings of this study may have some generalizability given the large and diverse sample. Second, only limited information was collected on demographic characteristics and other influencing factors, and burnout and career choice regret may be affected by other confounding variables. Third, the survey was cross-sectional, and we could not examine dynamic changes in burnout factors, which may be influenced by geography, professional factors, the local practice environment, and practice types. Further studies with more rigorous designs are needed to confirm these findings.Overall, symptoms of burnout and career choice regret are prevalent among Chinese neurology postgraduates. Career choice regret is the strongest predictor of burnout in this population. Our results provide a high-level overview to support interventions to prevent burnout and improve career choice regret. Active measures from the government, hospitals, and postgraduates are needed to change the current situation.Additional file 1:The initial and final logistic models of binary logistic regression analysis of burnout. (XLS 41 kb)