The LeuT-fold superfamily includes secondary active transporters from different functional families, which share a common tertiary structure, despite having a remarkably low sequence similarity. By identifying the common structural and dynamical features upon principal component analysis of a comprehensive ensemble of 90 experimentally resolved structures and anisotropic network model evaluation of collective motions, we provide a unified point of view for understanding the reasons why this particular fold has been selected by evolution to accomplish such a broad spectrum of functions. The parallel identification of conserved sequence features, localized at specific sites of transmembrane helices, sheds light on the role of broken helices (TM1 and TM6 in LeuT) in promoting ion/substrate binding and allosteric interconversion between the outward- and inward-facing conformations of transporters. Finally, the determination of the dynamics landscape for the structural ensemble provides a promising framework for the classification of transporters based on their dynamics, and the characterization of the collective movements that favour multimerization.This article is part of a discussion meeting issue ‘Allostery and molecular machines’. Remarkably, several secondary active transporters, though belonging to genetically and functionally distant families, share a common architecture (or fold). Four common folds among transporters are the LeuT-, MFS-, GltaA-folds ,2. Protonsporter a, a membs (NSSs) ; a humans (NSSs) ; the arcrikoshii , which hhia coli .Figure An immediate question concerning the selection of a small pool of folds by a large number of transporters involved in different functions, and vastly differing in their sequence, is what is special about those folds that lend themselves to different functionalities. What are their structural and dynamic characteristics that are exploited, or how do they adapt to different functions? Differences can be at various levels, from sequence, to structural motifs, or quaternary organization (for multimeric transporters) while maintaining the tertiary fold. We will focus here on the LeuT-fold superfamily, which probably has the broadest representation in the Protein Data Bank (PDB) among the four folds. LeuT has served as a model for exploring the mechanism of action of monoamine transporters such as the dopamine transporter (DAT)  or the svice versa to resume the transport cycle. While this model has helped appreciate the mechanistic aspects of substrate transport, recent structural data integrated with biochemical and computational studies have improved our understanding of the complex machinery of transporters [+/proton antiporters or other Na+/dicarboxylate co-transporters [A classical model for the transport mechanism of secondary transporters is the alternating access model ,11: mainsporters ,12–15. Tsporters  or rock-sporters . In the sporters ,19 emergsporters . In addi+ ion (Na2) has been observed in silico [Ph [More importantly, couplings between global and local events ,21 or evn silico –29 and sn silico  to coopelico [Ph ; and tholico [Ph . Such colico [Ph ,34, althc for a few members of the LeuT-fold family. Their protomers retain their fold (and access to OFS and IFS) , suggesting that this modular fold is used to perform transport functions regardless of the oligomerization state. A similar observation was made in the trimeric GltPh: the individual protomers exhibit different levels of exposure to either the EC or IC region, without interference from the trimerization scaffold [Another interesting observation is that structurally homologous transporters function in various oligomerization states, as illustrated in figure 1scaffold . Recent scaffold ,36. Obsescaffold ; and a tscaffold . Thus, oIn the present study, we first examine the sequence and structure properties of LeuT superfamily members, and then proceed to their dynamics to determine a ‘signature’ mobility profile shared by sequentially and functionally dissimilar LeuT-fold transporters that allosterically engages all TM helices. We further examine the role of structural irregularities such as helical disruptions, and that of multimerization, in the differentiation or allosteric modulation of transport activities, and determine the dynamics landscape of a large ensemble of structures sharing the LeuT-fold, which indicates the collective motions that underlie the OFS ↔ IFS transition or the multimerization of LeuT-fold members. Our analysis sheds light into the ways transporters achieve functional differentiation, while efficiently recruiting the same fold whose modular dynamics is exploited.2.(a)We consider a set of 90 structures with LeuT-fold deposited in the PDB, which belong to five functional families, listed in the electronic supplementary material, table S1. The set includes crystallographic structures resolved for eight different transporters: LeuT in different conformational states, DAT, and MhsT from the NSS family; galactose transporter (vSGLT) from the sodium/solute symporter (SSS) family; betaine transporter (BetP) and carnitine/betaine antiporter (CaiT) in multiple states from the betaine/choline/carnitine transporters (BCCT); benzylhydantoin (BH) transporter Mhp1 from the nucleobase/cation symport-1 (NCS1) family; and arginine/agmatine antiporter AdiC from the amino acid/polyamine/organocation (APC) family.(b)a and electronic supplementary material, figure S1) is characterized by 10 TM helices, organized into two pseudo-symmetric inverted repeats, TM1–TM5 and TM6–TM10 [The LeuT-fold (figure 1TM6–TM10 . The eleb). Mainly, the structures resolved for the same protein (e.g. LeuT) in different conformations exhibit RMSDs of approximately 2.0 Å in general; those within the same family  differ by 3–4.5 Å; while across families  the RMSDs may exceed 6 Å. Thus, although all the transporters have the same fold, there is a hierarchy of structural differences, increasing with their functional differences.Structural alignments of the transporters listed in electronic supplementary material, table S1 reveal differences of up to 6.5 Å root-mean-square deviation (RMSD) between pairs of transporters b. MainlyPairwise alignments of LeuT-fold family sequences  confirm their low sequence identities. Pairs belonging to the same family, e.g. DAT–LeuT (NSS), BetP–CaiT (BCCT) or AdiC–ApcT (APC), exhibit sequence identities of 0.25 ± 0.03; across families, the identities drop to 0.15 ± 0.05 . If we focus on TM1 and TM6, the sequence identities are much higher within families  , whereas there is a major drop across families. For example, CaiT TM1 shows sequence identities of 0.06 ± 0.02 with respect to most transporters; see details in electronic supplementary material, tables S2 and S3. The strong conservation within families and low conservation across families strongly suggest that these helices play a role in defining the specificity of the LeuT-fold transporters.(c)a–c). Specific residues  reveal the recurrence of the helix-breaking motif GXG in the TM1 of LeuT (GLG), MhsT (GLG), BetP (GIG), AdiC (GSG) and GadC (GSG), and the TM6 of LeuT (GFG), dDAT (GPGFG), SERT (GPGFG), MhsT (GMG) and ApcT (GFG). The broken regions of these TM helices harbour binding sites for the substrates and ions a–c. Spec in AdiC , Asp79 i in AdiC ,40; figud–f and 1c). These residues reorient to firmly hold the substrate in place to prevent its escape during OFS → IFS reconfiguration. Furthermore, they provide a framework for robustly transmitting the local reconfigurations that are triggered upon substrate binding [f) [Another design principle that apparently further enhances the effectiveness of the broken helices to propagate structural changes induced upon ligand binding is the tight coordination of the bound substrate by bulky/aromatic residues Trp, Phe and Tyr  .This analysis thus reveals the three-fold significance of helical disruptions: (i) presenting a high-avidity site for substrate binding originating from the need to satisfy the hydrogen-bond-forming groups; (ii) the high potential to undergo a spatial reorientation change owing to the high flexibility of the GXG motif; and (iii) efficient propagation of structural perturbations to the EC and IC ends by virtue of the tight packing at the hinge centre and rigidity of the two helical arms. Thus, an energetically ‘frustrated’ region which also serves as a hinge-bending centre for reorientation of compact/rigid structural elements  on both sides appears as a highly versatile allosteric mechanism for substrate binding-induced reorganization of the tertiary structure. Support for such an allosteric effect is further provided by the change in cross-correlations upon substrate binding. Electronic supplementary material, figure S3 shows how substrate binding induces an increase in the cross-correlation between the movements of the two arms, TM1a and TM1b, of the broken helix TM1.(d)intrinsic dynamics uniquely encoded by its overall architecture, or fold, which often facilitates its functional interactions; and the intrinsic dynamics may be analytically evaluated using elastic network models coupled with normal mode analysis [Previous studies have demonstrated that each protein has its own analysis ,43. Hereanalysis .a. The black line therein is obtained for LeuT, rescaled based on X-ray crystallographic B-factors (purple line); and the red line (and light red shading) represents the average behaviour (and standard deviation) over the entire set. The profiles for the individual transporters can be seen in electronic supplementary material, figure S4. A strong tendency to exhibit the same ‘signature’ profile among all homologues (monomers and protomers) is seen, with small-to-moderate deviations from the mean.First, we examined the root-mean-square fluctuation (RMSF) profile of residues for a subset of 11 representative transporters in both OFS and IFS, indicated in electronic supplementary material, table S1. The results are presented in figure 3soft modes, energetically favoured by the architecture, which often provide paths for cooperative reorganization of the overall structure and enable allosteric effects. A few classical examples of successful representations of allosteric transitions by soft modes predicted by the ANM are the transition of haemoglobin between its T and R states [green line in figure 3b), and comparison with ANM soft modes (red line). The comparison reveals that residues’ motions during OFS ↔IFS transition can be traced back to the global modes, or the signature profile, uniquely defined by the LeuT-fold.The next question is to what extent this signature profile is used to enable the global transition of the transporters. To address this question, we determined so-called R states , the cooR states  and the R states ; other eR states , for exa9Pro of MhsT [A closer inspection shows differences at certain regions, such as TM1, the IC loop between TM4 and TM5, and the EC loop EL4 between TM7 and TM8. The latter participates in regulating EC gating and substrate access , a role  of MhsT  and an e of MhsT  have bee(e)a demonstrates that LeuT-fold monomers or protomers belonging to different functional families exhibit shared dynamics regardless of their conformational (OFS/IFS) or multimerization (monomer/dimer/trimer) states. Yet, they stabilize the OFS, IFS or intermediate/occluded state and sample a spectrum of conformational changes, during the transport cycle. How are those different conformers compatible with the same fold and signature fluctuation profile?Figure 3reference structure) permitted us to identify a core region  and RMSDs from the mean that varied from approximately 1.5 Å for LeuT monomers/protomers to approximately 5 Å for vSGLT, BetP and Mhp1 . Comparison of the results from PCA with ANM predictions  showed that the softest ANM mode (ANM1) computed for the reference structure (the closest to the average structure of the ensemble in terms of RMSD) yields a cumulative overlap of 0.55 with the principal components 2 and 6 (PC2 and PC6).To gain a mechanistic understanding of the conformational spectrum accessible to LeuT superfamily members, we performed a principal component analysis (PCA) of the ensemble of PDB structures listed in electronic supplementary material, table S1. Optimal superposition of 104 monomers and protomers in this set onto the LeuT OFS structure  tend to cluster together, we note that within each family a certain degree of segregation between IF (upward triangle) and outward-facing  states takes place, for instance in the case of BetP (in orange) and Mhp1 (in purple), consistent with the analogous separation for LeuT (blue). Such observation points to the fact that a common gating mechanism might be shared among members of the superfamily, and is well captured by the softest mode favoured by the common fold.a provides an overview of the ‘dynamics landscape’ of the LeuT-fold transporters. Therein, all 100+ monomer/protomer structures are projected onto the subspace spanned by ANM1, ANM2 and ANM3, allowing visualization of the different classes of proteins based on their collective motions. Notably, proteins belonging to the same functional family tend to cluster, highlighting the relevance of soft modes to transporter function.Figure 5b. In this representation, a clear-cut separation can be drawn between trimeric transporters (BetP and CaiT) and monomeric/dimeric transporters, while secondary cuts (dashed lines) further subdivide both groups into OF and IF conformations. This clear separation may be an effect of the structural constraints imposed by the trimeric organization to the monomers of BetP and CaiT transporters, which share a similar quaternary structure (c). The trimers, indeed, feature a different interface compared with dimers, involving the rearrangement of helix H7 in BetP, corresponding to EL3 in LeuT (figure 5b inset). Such a rearrangement is well reproduced by ANM2 of LeuT  as well as ANM3 , suggesting an intrinsic predisposition  of H7 to adopt the correct positioning for trimeric interface formation.Another interesting fact emerges by focusing on the projection of structures onto the first two ANM modes, shown in figure 5tructure c. The tr(f)a,b we compare how well the structural changes observed between OFS and IFS are reproduced by the low-energy modes computed for two models: ANM based on the isolated LeuT monomer (blue bars) and ANM for the full dimer (red bars). The experimental deformation vectors have been calculated as the difference between the coordinates of LeuT in OFS  and IFS , after structurally aligning them in both monomeric and dimeric form. This alignment procedure allows a direct comparison of the respective monomeric/dimeric ANMs.In figure 6bar plots in figure 6a,b clearly show that the dimeric configuration boosts the displacement propensity of the protomer along the direction of the deformation observed in crystal structures. ANM3 in the dimer, in particular, yields an overlap (cosine correlation) as high as approximately 0.6 (in red), compared with a maximum overlap of approximately 0.4 for ANM2 of the isolated monomer (in blue). This result is even more notable after considering that ANM3 in the dimer is the first genuine mode describing internal conformational changes of the protomer, because modes 1 and 2 basically involve quasi-rigid rotations of the two protomers around a central axis at the dimer interface, illustrated in electronic supplementary material, figure S8.The c,d clarify that the main effect of dimerization is to suppress the mobility of the helices involved in the dimer interface , in agreement with what is observed in the crystal structures. Interestingly, neither monomeric nor dimeric main ANM modes could reproduce the opening of TM1 (in blue on the structure), corresponding to the highest peak in the far-left portion of the deformation vectors profile. Such large displacements of TM1, however, are reproduced by using the IF conformation of LeuT, as explained above.Figure 6e,f reveal a better reciprocal overlap between the two sets of soft modes accessible to OFS and IFS in the case of the dimeric model, compared with the monomeric one, suggesting that a reversible interconversion between the two gating states is favoured in the dimer.Figure 63.The present study focused on a superfamily of structural homologues, LeuT superfamily, that encompasses members from five families of transporters with different functions and low sequence identity. We first characterized their shared structural and dynamic characteristics, and then proceeded to elucidate which features on a local or global scale, structural or dynamic, differentiate them to lead to different functions. The first task helped us identify a signature residue–fluctuation profile  intrinsiChemical specificity can be detected at the dissimilar sequence patterns among superfamily members that belong to different functional families, while those transporters within a given family exhibit distinctively higher sequence identities. The difference becomes even more pronounced upon focusing on TM helices involved in substrate/ion binding: similarities among the same functional family members are enhanced, while dissimilarities across different functional families become even more pronounced .Physical flexibility, on the other hand, is manifested by structural differences between family members on both a global ; and ANM3 together with ANM2 helps the transition between OFS and IFS, as evidenced by electronic supplementary material, figure S6. This analysis shows that the adaptation of the shared fold to different conformational or oligomerization states is mainly accomplished by the soft paths of reconfiguration intrinsically encoded by the LeuT-fold.A rigorous examination of the distribution of LeuT superfamily members in the conformational space accessible to them figures  and 5 defigure 5+-independent antiporters or H+-coupled symporters. Specific amino acids apparently serve the same functional roles as the co-transported ions, suggesting common principles among ion-coupled or -uncoupled transporters [−-dependent activity in a LeuT mutant (E290S) [+-independent CaiT and ApcT is occupied by the positively charged Arg262 in CaiT [On a broader scale, this study provides an example of adaptability of structures to different functions by virtue of their intrinsic flexibility, as recently reviewed , and poisporters ,3,56, e. (E290S) ; a methiPh [The dynamics landscape generated here for the ensemble of LeuT-fold transporters  allows aPh . The neePh .Oligomerization of NSSs has been suggested to be a determinant of transporter trafficking to the plasma membrane in addition to enabling efficient substrate transport ,61. Whil4.We used several modules in ProDy  for perf


Individuals often face choices that have uncertain outcomes and have important consequences. As a model of this environment, laboratory experiments often offer a choice between an uncertain, large reward that varies in its probability of delivery against a certain but smaller reward as a measure of an individual’s risk aversion. An important factor generally lacking from these procedures are gambling related cues that may moderate risk preferences. The present experiment offered pigeons choices between unreliable and certain rewards but, for the Signaled group on winning choices, presented a ‘jackpot’ signal prior to reward delivery. The Unsignaled group received an ambiguous stimulus not informative of choice outcomes. For the Signaled group, presenting win signals effectively blocked value discounting for the large, uncertain outcome as the probability of a loss increased, whereas the Unsignaled group showed regular preference changes similar to previous research lacking gambling related cues. These maladaptive choices were further shown to be unaffected by more salient loss signals and resistant to response cost increases. The results suggest an important role of an individual’s sensitivity to outcome-correlated cues in influencing risky choices that may moderate gambling behaviors in humans, particularly in casino and other gambling-specific environments. Indeed, individual differences in risk tolerance are an important factor in risky decision making as these measures have shown clinical relevance through associations with gambling5, smoking7, and internet gaming8 behaviors, as well as obesity9. Additionally, as the DSM-V has categorized gambling as an addictive disorder10, and a high prevalence of negative outcomes (monetary or otherwise) are associated with it13, determining the underlying processes involved in risky decision making may aid in understanding these maladaptive behaviors.Individuals are often faced with choices involving uncertain outcomes that can have critical consequences such as predation in the wild or large financial losses. In the laboratory, risky environments are often modeled by offering a choice between an uncertain large reward (UL) against a certain but smaller reward (CS), where the odds against receiving the UL reward are systematically increased to determine how the value of the UL changes. Under this probability discounting (PD) procedure, the rate at which individuals discount the value of the UL with increased odds against its receipt16 and optimal foraging theory17. Evidence suggests, however, that optimal choice does not always occur ] receiving it (Θ) multiplied by h, a free parameter that reflects the degree of UL value discounting.In a PD procedure, optimal decision makers should maximize their expected reward as described by normative theories such as expected value19, the manner in which the probabilities are presented20, and asymmetries between decisions of gains and losses22. Another potential but less explored factor is the influence of gambling related cues23. Two animal models of risky choice have shown cues to be efficacious moderators of risky decisions. For example, using a rodent version of the Iowa Gambling Task, Barrus and Winstanley24 found that the addition of audio-visual cues simultaneously presented with winning outcomes biased choice towards a UL-like alternative that provided less reinforcement overall relative to a condition without cues.The rate at which individuals will discount the probability of an outcome has been shown to be influenced by factors such as the magnitude of the reward25 used with pigeons and starlings found that signaling the outcome before actual receipt of the reward greatly promoted gambling-like choices28 that provided as little as 10% percent of the reward of the non-gambling alternative31. In this suboptimal choice procedure, choice of a gambling-like alternative is followed either by a signal indicating that a win or loss will follow, while the alternative choice generally results in an ambiguous, uninformative cue that provides greater overall reward26. It has been hypothesized that under these conditions pigeons over-weight the infrequent signal for wins29, and show more optimal preferences if these signals are ambiguous33; conversely, pigeons also appear to under-weight the signal for losses35, and show little change in choice when the salience of the loss is manipulated35. Similar effects involving signaled outcomes may also be relevant to human risk taking, as Molet et al.36 found using an analogous procedure. Specifically, individuals who engaged in commercial gambling behaviors chose the suboptimal gambling-like alternative significantly more than those who did not.Another procedure31, the effect is robust and has yet to be employed for a variety of risky choice procedures such as PD. The suboptimal choice and PD procedures are similar, but there are two notable differences. First, strong suboptimal preferences have generally been found when the gambling-like alternative is compared to an alternative with an ambiguous signal not predictive of the outcome28. In PD, the UL is often compared to the CS, or the certain small alternative that has no uncertainty as to its outcome. When similar conditions that lack uncertainty are used with suboptimal choice procedures, indifference or relatively weaker preferences are often found with 10 s signal durations39. Second, PD procedures usually employ magnitude discriminations of either differential amounts of money for humans19 or food rewards for animals41. Many current theories as to why suboptimal choice occurs, however, are based upon procedures that primarily assess one dimension42 of how the signals could be operating: its predictive utility for the presence or absence of the forthcoming reward31. Furthermore, while procedures have parametrically assessed preferences for a predictive ‘jackpot’ signal against an ambiguous signal across a range of probabilities31 and differential magnitudes at single choice probabilities43, the interaction of the two has not been studied.Although various mechanisms have been proposed for why the signals for wins promote suboptimal decision making to such an extent41. For the Signaled group, however, choice of the UL only resulted in a stimulus when the outcome was a win, or a ‘jackpot’ signal. If differential magnitudes of reinforcement, rather than predictive utility of the UL and CS signals, can produce a suboptimal choice effect, then the pigeons in the Signaled group should discount the UL less  than the Unsignaled group; however, if the suboptimal choice effect requires that the UL win signal has greater predictive utility for reward than the CS, the Signaled and Unsignaled groups should discount at similar rates.Therefore, the purpose of the present experiments was to extend the suboptimal choice research by testing how differential magnitudes of reinforcement interact with probabilistic outcomes within the framework of PD. To assess the effects of the signaled outcomes, the probability of receiving the UL reward (4 pellets) was gradually decreased over blocks of trials from 100% to 6.25% against a CS choice of a certain 1 pellet  confirmed significant differences in discounting rates between the Signaled  and Unsignaled  groups, F = 12.98, p = 0.001, and further indicated that the h parameter for the Signaled group was not significantly different from zero, p > 0.999. These results effectively show that discounting of the UL outcome, while similar in appearance to previous studies with the Unsignaled group . If the large differences in preferences were indeed a product of the signal for wins and not a procedural artifact, the pigeons previously in the Unsignaled condition should now show attenuated discounting.A potential limitation of the reduced discounting is the use of a visual/spatial discrimination and presenting probabilities of reinforcement in a single decreasing order. Spatial discriminations of choices can confound spatial preferences  with a choice alternative preferenceA parameter , in which the Unsignaled group  had a significantly steeper slope than the Signaled group , F = 37.28, p < 0.001. Furthermore, the slope parameter for the Signaled group was also not significantly different from zero, p = 0.201, again indicating a lack of discounting of the UL alternative’s value for the signaled group.Figure 17 and as such, they should prefer choice alternatives that produce higher probabilities of primary reinforcement45. This was the case in the Unsignaled conditions, where pigeons showed preference changes and indifference points that generally followed the scheduled reinforcement rates and tracked their relative expected value  the reward earned in the Unsignaled conditions , clearly exemplifying suboptimal choice for the signaled conditions. Furthermore, Fig. r2 = 0.95, p < 0.001, suggesting that discounting within the range observed here advantageously led to increased reward.Animals are thought to have been pressured by their environments to behave optimally in order to survivesee Fig. . To conf35, translational procedures with rats have suggested that, if there is inhibition to loss signals, suboptimal choice does not occur46, and humans may show differential discounting of wins and losses22. Although the lack of the ‘jackpot’ signal appearing on loss trials likely serves as a signal for a loss, we introduced a more salient signal for loss outcomes in the Signaled group to determine if salient losses would influence discount rates .In the previous phases of this experiment there was a signal for winning outcomes but no signal for losses. Although there is a growing body of evidence to suggest that losses minimally influence preferences by pigeonsA parameter  revealed no significant changes after introducing a loss on h parameters for either the Signaled  or Unsignaled  group, ps ≥ 0.136, indicating the signal for losses had no effect on discounting. The Signaled group continued, however, to discount at a significantly reduced rate relative to the Unsignaled group, F = 54.51, p < 0.001, and discounting was not significantly different from zero, p = 0.136.Figure 28, decreasing the duration of the win signal prior to reinforcement28, and decreasing the salience of the win signal31 can all decrease the effectiveness of signaled win outcomes. An alternative method is to increase the ‘cost’ or effort required to choose the UL.As signaling losses did not reduce the Signaled group’s preference for the UL, we next asked if there were conditions under which the UL can be devalued for the Signaled group. Previous research has shown that altering the delay to reinforcement such that the UL has a longer delay relative to the CSelastic48. We therefore predicted that the Signaled group would show less elastic preference for the UL with increasing cost, relative to the Unsignaled group.To assess the effect of changing the cost on choice, we systematically increased the number of pecks required to choose the UL from 1 to 2, 4, 8, and 16 across session blocks while the cost of the CS remained at one peck . If an alternative has greater relative value, as the signaled UL appears to in the present experiment, its preference should decrease at a relatively slower rate, often described as being less A and h as a function of UL cost are also shown in the bottom row of Fig. A parameter to vary for both groups also confirmed these effects as indicated by a significant Group × Cost interaction on discounting (h parameter), F = 14.24, p = 0.002, and main effects of group, F = 6.16, p = 0.0138, and cost, F = 31.01, p < 0.001, on the intercept (A parameter).UL choice proportions at response costs of 1 and 16 as a function of the odds against receiving its reward are shown in the top row of Fig. 48:Q0 indicates the percent choice of the UL at the lowest cost (one response required), α indicates the rate at which UL preferences decreased (elasticity), and k is a scaling parameter. Illustrated in Fig. Q0 = 2.00, SEM = 0.03) than the Unsignaled group  at the lowest cost of 1, F = 32.09, p < 0.001, consistent with the effects discussed above. Importantly, as the cost of UL choices increased, the Signaled group also showed less elasticity  than the Unsignaled group , by continuing to choose the UL despite the increase in cost, F = 6.87, p = 0.013. Collectively, the above analysis suggests that when wins were signaled, demand for the UL choice was greater than when that same choice was unsignaled.As predicted, the Unsignaled group showed a faster increase in discounting rates with increased cost. Both groups also showed decreased intercepts, indicating that when the cost was high enough, four pellets at 100% probability lost value relative to one pellet at a cost of one response. To better illustrate the changes in preference, the data were restructured as the average percent UL choice across the last five sessions of each UL peck requirement and fit with Equation 43, signaling uncertain choice outcomes prior to reward delivery greatly increased risk preferences. Previous research showing strong suboptimal preferences has generally occurred, however, when the predictive value of the signal following the UL is greater than the CS37. In the present experiments, the predictive value between the UL and CS signals were equal, which can lead to indifference or relatively weaker preferences39. With the addition of a magnitude difference, strong suboptimal choice was found even when the UL and CS signals were equally predictive.Although components of the present results have been reported in previous experiments, the current work advances our understanding of suboptimal choice by collectively encompassing past and predicted results within one model. Similar to previous work38 and suboptimal choice has been found with magnitude differences43, their combination had not been tested and led to the blocking of PD. The choice behavior of the Unsignaled group with uninformative signals is also in stark contrast to the Signaled group. The Unsignaled group served as a control for how risky choice tasks are often modeled without signals49 and more optimally discounted the UL leading to nearly twice as much reward as the Signaled group. Reversing the conditions27 and providing a more salient loss signal35 further revealed that the difference between the two groups was not due to procedural artifacts and is consistent with previous research. Finally, a novel finding was that when the cost of UL choices was increased, demand for the UL was found to be more inelastic for the Signaled group.Within the framework of PD, the interaction of an increased reward magnitude and predictive value of the UL ‘jackpot’ signal blocked discounting of the UL’s value which, we believe, is the first demonstration of such an effect in the literature. While pigeons and starlings have been previously shown to be insensitive to signaled probabilities of reinforcement31, such as its predictive utility for reward, the duration of its appearance prior to reward, and its overall conditioned reinforcement value, leading to different hypotheses. One hypothesis stems from the value of information provided by the win signal31. That is, the appearance of a signal for reward reduces the time spent in uncertainty of the reward. In the present experiment, however, the signaled condition had equally informative cues between the UL and CS alternatives. The fact that suboptimal preferences still emerged may therefore challenge this interpretation.Why signaling win outcomes reduced loss aversion to such an extent, however, is still unclear. For example, we have interpreted the group effects as the ‘jackpot’ signal reducing the effect of discounting , but it may also be that presenting a probabilistic cue that does not produce food  produces increased discounting. In either case, while the discounting Equation 29. The stimulus value hypothesis29, based on the contextual choice model42, posits that the multidimensional conditioned reinforcement strength37 of the win signal  drives suboptimal choice  across multiple trials prior to reinforcement. Greater UL devaluation is therefore consistent with the UL discounting seen in the unsignaled conditions and predicts increased CS preference as the probability of UL reinforcement declines. For the signaled conditions, the CS signal is also always followed by reinforcement, but the UL signal only appears when reinforcement will follow. Thus, even across diminishing UL reinforcement probabilities, both the CS and UL signals are equally subjected to 10 s of devaluation and individuals should be indifferent between them. While individuals in the signaled condition were indeed unaffected by diminishing UL reinforcement probabilities, they showed a strong preference for the UL rather than being indifferent between the UL and CS. In order to account for the present findings, a small addition of a magnitude term would need to be added50. Upon doing so, the initial value for the UL and CS changes to 4 and 1, respectively. Thus, the hyperbolic decay model is consistent with the present findings and predicts the current group differences are due to the combined effects of a signal occurring only when reinforcement follows and the magnitude of the UL being greater than the CS. Additionally, it may be possible for the hyperbolic decay model to account for the cost manipulation conducted here by accounting for the increased time it takes to complete the response requirement50.The hyperbolic decay model has also been applied to risky choice26 and signal for good news (SiGN)27 hypotheses suggest that it is the change from a state of uncertainty (when making a probabilistic choice) to a state of certainty  that produces the suboptimal choice effect. As the outcome of the CS in the present experiments can be predicted at the time of choice (a certain one pellet), this alternative produces no contrast and, in the case for the SiGN hypothesis, would not serve as a conditioned reinforcer. The UL, however, cannot be predicted at the time of choice and, upon the appearance of the ‘jackpot’ signal, generates contrast or an increase in reinforcement value that leads to suboptimal preference. In their present form, the contrast and SiGN hypotheses both would predict suboptimal preferences in the present experiment. The SiGN hypothesis also states that, because the UL win signal appears temporally closer to reinforcement than the CS choice stimulus, the appearance of the UL win signal reduces the delay to reinforcement. With this added component, the SiGN hypothesis has been able to account for changes in suboptimal preferences based on changing delays to reinforcement51 and the cost manipulation conducted here (as it increases the UL’s delay to reinforcement) that the contrast explanation currently cannot. Neither hypothesis, however, makes any assertion as to the role of differential magnitudes of reinforcement, although it follows to reason that signals predicting greater magnitudes of reinforcement could be conceptualized to produce greater contrast and/or reinforcement value than signals predicting smaller magnitudes. Still, the present results require that these models better formulate their predictions of how other dimensions of reinforcement may interact.Finally, the contrast54, implicating an important role of cues on an individual’s risky decision making. Laboratory measures of risk taking such in humans, however, do not often assess the role that cues may have on risk.Although the present experiments cannot clearly distinguish between these different models, the results presented here better support hypotheses stemming from the reinforcing value of the ‘jackpot’ signal rather than its information. The general finding that ‘jackpot’ cues have following a risky choice is a robust phenomenon in animal models2, these measures should also take into account the individual’s sensitivity to ‘jackpot’ signals. While evidence exists that human gamblers show increased physical arousal or gambling intentions56 and regional fMRI brain activation to gambling-related scenarios or stimuli58, fewer experiments have examined the role of outcome-correlated cues modulating gambling behavior23, although one study, using a reinforcement learning model, indicated that cues can effect choices when reinforcement rates were equivalent59.If laboratory measures such as PD are to inform other risky decisions such as gambling in humans61. This has led to the suggestion that, similar to substance abusers, gamblers seek highly rewarding events to compensate for a hypoactive reward system. Additionally, there is evidence that, relative to controls, gamblers show increased brain activity during anticipation of an expected win following risky choices62 and both humans and animals have shown increased neuronal activity during uncertainty prior to receiving a reward63. These findings suggest that the period after choice but prior to the outcome are an important factor in biasing risk preferences. Indeed, a procedure analogous to the signaled outcomes used here showed that individuals who are self-described gamblers increased choice of gambling-like alternatives36. These results suggest that outcome-correlated cues may indeed modulate human risk sensitivities relevant to certain behavior (e.g. gambling), but this needs to be verified in future research. Additionally, the effect of outcome-correlated cues may be different depending on whether they precede or occur simultaneously with the outcome, and future research should take this point into consideration.Human gamblers have also shown reduced fMRI reward pathway activation to risky choice outcomes relative to healthy controlsThe present experiments show that signaling a win prior to receipt of its outcome effectively increases risk taking and can block PD in pigeons. Furthermore, signaling losses do not attenuate the effect, and the value added by these signaled wins is resistant to increases in cost. Collectively, the results suggest that, when making risky decisions, stimuli correlated with win outcomes can increase risk to the point of suboptimality. Indeed, numerous examples of signaling stimuli prior to gambling outcomes occur in casinos, such as the images on the reels of a slot machine, the ball on a roulette wheel, and matching numbers on lottery and Powerball tickets. That pigeons in the Signaled group were also willing to pay an increased cost for the chance to obtain the ‘jackpot’ reward may also be an indicator of why some individuals can expend increasing resources gambling. Future gambling research and laboratory measures of an individual’s risk sensitivity should therefore assess the effect of such cues by controlling for their presence (and absence) to further determine their influence on decision-making.NIH Guide for the Care and Use of Laboratory Animals (8th edition).Ten White Carneau pigeons approximately 8–12 years old originally purchased from the Palmetto Pigeon Plant  with previous experience in suboptimal choice tasks and no systematic differences in experience were used in the experiment. Subjects were housed in individual cages measuring 28 × 38 × 30.5 cm and maintained at 80–85% their free feeding weight on a 12:12 light-dark cycle (lights off at 7 pm) with free access to grit and water. All research was approved by the University of Kentucky Institutional Animal Care and Use committee (Protocol 01029L2006) and was conducted according to the 2010 The experiment was conducted in a Med Associates  modular operant chamber (ENV-008) measuring 30.5 × 25.5 × 33 cm inside a noise attenuating box. The pigeons responded to three circular keys approximately 21.5 cm above the floor, 2.5 cm across, and 5 cm apart. A 12-stimulus inline projector  behind each key projected one of four stimuli  onto the left and right response keys and a white light onto the center key. Reinforcement was delivered to a magazine tray at the base of the response panel in the form of a 45-mg pellet from a dispenser  behind the response keys. The chamber was illuminated by a 28 V, 0.1 A house light centered over the chamber. White noise was generated from outside the chamber and a computer in an adjacent room controlled the experiment using Med-PC IV.Subjects were first trained using an autoshaping procedure in which one of four stimuli were illuminated randomly onto either the left or right response keys; the white light was only presented on the center response key. Following either 30 s or a peck to the stimulus, whichever came first, the house light illuminated and a single pellet delivered into the magazine. The house light remained illuminated for 5 s and then offset for 5 s resulting in a 10-s intertrial interval (ITI). This procedure for reinforcement and the ITI remained consistent throughout the experiment.Following pretraining, subjects were trained on a visual/spatial one versus four pellet magnitude discrimination. All trials began with a white orienting stimulus on the center key. A response to the center key turned off the orienting stimulus and began either a forced or free choice trial. On free choice trials, concurrently available initial link stimuli of three horizontal or vertical white lines on a black background on each side key appeared. Choice of the uncertain large (UL) alternative led to a terminal link stimulus (red or green) for 10 s after which four pellets were delivered to the magazine. Choice of the certain small (CS) alternative led to a different terminal link stimulus (red or green) for 10 s after which a single pellet was delivered to the magazine. Forced choice trials were identical to free choice trials except that only one alternative appeared on either the left or right key. Sessions consisted of 65 trials, 25 free and 40 forced, divided into five 13-trial blocks. The first eight trials of each block were forced and the last five were free choice. All initial and terminal link stimuli  were counterbalanced across subjects. Magnitude training continued until all subjects chose the UL alternative at least 95% of the time for two consecutive sessionsh) was not statistically different from zero in both groups for five sessions, totaling 30 sessions.Subjects were then randomly assigned to the Signaled and Unsignaled groups and trained on a PD procedure structured similar to magnitude training. Each block began with eight forced trials followed by five free choice trials. The first block of trials of each session was the same as magnitude training. In subsequent blocks, the probability of receiving the UL reward when chosen decreased from 100% to 50%, 25%, 12.5%, and 6.25%. For the Signaled group, choice of the UL in these subsequent blocks led either to the predictive terminal link stimulus  for 10 s followed by four pellets or a blackout period for 10 s. For the Unsignaled group, choice of the UL was always followed by the nonpredictive terminal link stimulus for 10 s that was followed by the four-pellet reward according to the probabilities of reinforcement associated with that block. Training continued until a line fit to the slope estimates . As h estimates appeared non-linear in form, correlations including this measure used the ranked Spearman correlation.Data were analyzed using nonlinear mixed effects (nlme) modeling using Equation All data presented in the main document can be found as an online supplementary file.


GBR) as a model system. We focus on grazing herbivores that directly access algal primary productivity in the epilithic algal matrix (EAM). By assessing abundance, biomass, and potential fish productivity, we explore the potential of the reef flat to support key ecosystem processes and its ability to maintain fisheries yields. On the GBR, the reef flat is, by far, the most important habitat for turf‐grazing fishes, supporting an estimated 79% of individuals and 58% of the total biomass of grazing surgeonfishes, parrotfishes, and rabbitfishes. Approximately 59% of all (reef‐wide) turf algal productivity is removed by reef flat grazers. The flat also supports approximately 75% of all grazer biomass growth. Our results highlight the evolutionary and ecological benefits of occupying shallow‐water habitats (permitting a ninefold population increase). The acquisition of key locomotor and feeding traits has enabled fishes to access the trophic benefits of the reef flat, outweighing the costs imposed by water movement, predation, and sediments. Benthic assemblages on reefs in the future may increasingly resemble those seen on reef flats today, with low coral cover, limited topographic complexity, and extensive EAM. Reef flat grazing fishes may therefore play an increasingly important role in key ecosystem processes and in sustaining future fisheries yields.The reef flat is one of the largest and most distinctive habitats on coral reefs, yet its role in reef trophodynamics is poorly understood. Evolutionary evidence suggests that reef flat colonization by grazing fishes was a major innovation that permitted the exploitation of new space and trophic resources. However, the reef flat is hydrodynamically challenging, subject to high predation risks and covered with sediments that inhibit feeding by grazers. To explore these opposing influences, we examine the Great Barrier Reef ( These zones are well characterized in terms of their respective structural features , and leeward by the back reef  an evolutionary breakthrough into an area of high primary productivity, or (2) an undesirable high‐sediment, high‐energy, high‐risk location, which is detrimental to fish populations. The goal of this study, therefore, is to reconcile these two conflicting characterizations of the reef flat by quantifying the relative importance of the reef flat in modern coral reef ecosystems. By focusing on grazing reef fishes, we specifically examine the reef flat's role in reef trophodynamics within the context of the previously posited challenging environmental characteristics.2To quantify fish abundance in a relatively undisturbed reef system, we surveyed two cross‐shelf transects on the Great Barrier Reef (GBR) in the northern (2004) and central (2005) sectors, before recent major cyclones  on hard substrata. By excluding species that feed on other benthic resources, we focus on the direct link between algal (EAM) primary productivity and fish biomass. We, therefore, only included those species in the Acanthuridae (7 species) Figure b, Labrid−2). All censuses were conducted at mid to high tide, at least 1 m above chart datum. This ensured that counts were undertaken when the shallowest areas were all available for feeding (the reef flat and crest may become unavailable at low tides (<0.3 m) with a lack of water forcing fish to feed elsewhere). It is estimated that the flat would be covered by less than 0.3 m of water for approximately 0.4% of daylight hours  in a 5‐m‐wide transect and the other fish <10 cm TL in a 1‐m‐wide transect. Both divers placed fishes into TL size categories (5 cm for fishes >10 cm and 2.5 cm for fishes <10 cm). These transects were combined into a single area‐standardized abundance metric . Daily biomass growth of grazing fishes (g 100 m−2 day−1) was estimated by applying the von Bertalanffy growth model (VBGM) to censused fishes following the procedure described by Depczynski, Fulton, Marnane, and Bellwood (−1) was calculated based on the equation γ = 27.78χ + 2.79  and χ is the algal yield to grazing fishes (gC/day m2). It should be noted that this equation does not address size‐specific consumption or metabolic rates of grazing fishes, just an overall relationship for grazing fish assemblages .To calculate biomass, we used Bayesian length‐weight regression parameters for each species from Fishbase . The effects of tides on algal consumption was examined by assuming cessation of feeding by reef flat individuals at 0.3 m of water and relocation of flat individuals equally to the slope, crest, and back. Displaced fishes from the flat often school, without feeding, in the reef crest area. However, to examine the potential effect of tides, we assumed that all fishes feed at the same rates but in the new locations.Grazer abundance, biomass, algal productivity consumed, and grazing fish biomass growth were also calculated after standardizing for the relative area of each reef zone  of ten haphazardly selected mid‐ and outer‐shelf reefs from the central/northern GBR. Reefs were selected where satellite resolution and reef configurations permitted clear habitat delineation (where possible these reefs included the reefs where fish were censused). Areas of nonhorizontal reef zones  were calculated using trigonometry . In the images, the reef crest was identified as the pale outer margin on the windward edge of each reef. The crest was separated from the slope by a distinct darkening in color as the water deepened, and in a leeward direction from the flat as the substratum darkened in color. Where waves were present in the photo the initiation of the wave break was also used to help identify the reef crest  were examined using generalized linear mixed‐effects models (GLMMs) with a negative binomial distribution (abundance data) or gamma distribution  with a log link function . In all cases, shelf position (mid and outer) and reef habitat  were treated as fixed effects, while reef was treated as a random factor nested within shelf position. Models were simplified to the most parsimonious solution using the corrected Akaike information criterion . Statis3p < .001 in all cases: Table The high wave energy reef flat and crest zones supported 2.4 to 4.8 times more individuals and 1.5 to 1.7 times more grazing fish biomass per unit area than either of the two low energy zones, that is, the back reef and slope Figure . Howeverp < .001 in all cases: Table The importance of the reef flat for coral reef ecosystems is also clearly seen in terms of the dynamic processes operating in these shallow high‐energy locations, that is, algal productivity consumption and daily biomass growth of grazing fishes. When standardized by habitat area, the reef flat alone accounts for up to 64.1% of all algal productivity consumed (gC/day) by grazing fishes Figure a. If the42 densities of grazing herbivorous fishes. These findings are in broad agreement with previous studies that reported relatively high abundances of nominally herbivorous fishes in the shallowest areas of the reef, usually the crest, on both GBR , it is only when total available habitat area is taken into consideration that the remarkable role of the reef flat in coral reef trophodynamics becomes apparent. The reef flat zone alone supports an estimated 79% of individuals and 75% of biomass growth in this important group of GBR coral reef algal consumers.The reef flat is by far the most important area on GBR coral reefs for EAM‐grazing fishes, supporting approximately 79% of individuals, 58% of fish biomass, 59% of turf algal productivity consumption, and 75% of fish biomass growth. The wave‐swept reef flat and/or crest also supports the highest mBR Russ,  and CariBR Russ, , 1988. HThe reef flat is the largest coral reef habitat by area (excluding the sediment apron and off‐reef mesophotic zones). It supports the highest grazing fish biomass and, presumably, the highest total productivity to maintain this grazing activity Hatcher, . High EA4.1This high concentration of EAM‐grazing fishes on coral reef flats agrees well with earlier evidence. Steneck  noted thThis study only considers primary productivity from algal turfs within the EAM. In some areas, the reef flat may also support a significant biomass of macroalgae  and those that eat the whole EAM  appear to be most deterred by sediments . However, there are two distinct reef types on the GBR: exposed reefs where sediments are largely biogenic and coastal reefs where inorganic (siliceous) terrigenous inputs can have a major influence (Tebbett, Goatley, & Bellwood, Thalassoma. These fishes possess some of the highest pectoral fin aspect ratios seen among coral reef fishes (Fulton & Bellwood, The evolutionary and ecological evidence both suggest that strong water movement may restrict occupation of the reef flat and that the benefits of living there may be increased by modifications to the locomotor system. In extant fishes, it appears that one of the key adaptations that underpinned effective colonization of the reef flat was labriform locomotion using high‐aspect‐ratio pectoral fins (Bellwood & Wainwright, Caranx ignobilis are leading contenders, with reports of large C. ignobilis patrolling shallow reef areas during the day (Khan et al., Evolutionary and ecological evidence also suggests that the reef flat can be, and probably always was, a dangerous place (Bellwood et al., The reef flat, therefore, appears to be a location with high‐stakes tradeoffs. Predation and locomotion challenges are evolutionary and ecological problems that persist to this day. There is therefore no contradiction in the proposed advantages and disadvantages of the reef flat. It appears that the flat is indeed a high‐sediment, hydrodynamically challenging, and dangerous place. However, the trophic rewards make the reef flat worth occupying and over evolutionary time adaptations have allowed fishes to take advantage of this challenging environment.4.3Our observations have important ramifications for the future of coral reefs and the services they provide to humans, particularly fisheries yields. With increasing anthropogenic pressure, reefs are changing rapidly (Hughes et al., As coral reefs lose coral cover, the relative importance of the reef flat, and species that can live in exposed locations, is likely to increase. Thus reef flats may hold the key to the future sustainability of reef fishery yields. Furthermore, the sustainability of reef flat species is likely to be enhanced by the resilience of the dominant shallow‐water algal grazers, parrotfishes, and rabbitfishes, to heavy fishing pressure (Bellwood, Hoey, & Hughes, In conclusion, the reef flat appears to be the single most important area for grazing fishes on coral reefs in terms of abundance, biomass, consumption of algal productivity, and grazing fish biomass production. The suggestion that a move on to the reef flat was a substantial improvement for fishes in the Miocene is supported by observations on modern reefs. Of the major challenges presented by the reef flat, food availability seems to have been relatively easily overcome, while water movement and predation appear to have been the primary constraints on reef flat access from the Miocene to this day. Yet, under future climate‐change scenarios, this reef habitat may become an increasingly important area for both coral reef ecosystems and reef fisheries yields.None declared.DRB, SBT, and OB conceived the ideas; SBT and RM analyzed the data; DRB, SBT, OB, MM, RM, RPS, and CJF interpreted the data and wrote the article. All authors contributed critically to the drafts, gave final approval for publication, and agree to be accountable for all aspects of the work.Data will be uploaded to the Tropical Data Hub, James Cook University. Click here for additional data file.


We used CRAC  in growing cells to identify transcriptome-wide interactions of RNAs with the major nuclear exosome-cofactor Mtr4 and with individual exosome subunits  along the threaded RNA path. We compared exosome complexes lacking Rrp44 exonuclease activity, carrying a mutation in the Rrp44 S1 RNA-binding domain predicted to disfavor direct access, or with multiple mutations in Rrp41 reported to impede RNA access to the central channel in vitro. Preferential use of channel-threading was seen for mRNAs, 5S rRNA, scR1 (SRP) and aborted tRNAs transcripts. Conversely, pre-tRNAs preferentially accessed Rrp44 directly. Both routes participated in degradation and maturation of RNAPI transcripts, with hand-over during processing. Rrp41 mutations blocked substrate passage through the channel to Rrp44 only for cytoplasmic mRNAs, supporting the predicted widening of the lumen in the Rrp6-associated, nuclear complex. Many exosome substrates exhibited clear preferences for a specific path to Rrp44. Other targets showed redundancy, possibly allowing the efficient handling of highly diverse RNA-protein complexes and RNA structures. Both threading and direct access routes involve the RNA helicase Mtr4. mRNAs that are predominately nuclear or cytoplasmic exosome substrates can be distinguished in vivo.The RNA exosome complex functions in both the accurate processing and rapid degradation of many classes of RNA. Functional and structural analyses indicate that RNA can either be threaded through the central channel of the exosome or more directly access the active sites of the ribonucleases Rrp44 and Rrp6, but it was unclear how many substrates follow each pathway  in vivo.In all organisms, diverse classes of RNA perform a wide range of functions, including programing of protein synthesis, transcriptional and post-transcriptional regulation of gene expression and forming key structural and functional components for pre-mRNA splicing, ribosome synthesis and translation. In consequence, control of the quantity and quality of RNA synthesis and maturation is of key importance. In Eukaryotes, the RNA exosome complex plays a major role in both degradation of aberrant RNA molecules and maturation of functional RNAs. The exosome has a barrel-like structure, with a central channel through which RNAs are threaded to the exonuclease active site of Rrp44. However, in a different exosome conformation, RNAs can also directly access the Rrp44 active site. Here we report the interaction of RNAs with different exosome components and its major cofactor Mtr4 in wild type and mutant strains of yeast. We identified numerous RNAs with clear channel specificities, whereas channel-threaded and direct access routes cooperated for many other substrates. The pathway used by RNA to access Rrp44 reflects the nature and structure of the RNA and whether it is addressed to the exosome for accurate processing or complete degradation, helping us to understand these crucial but highly complex pathways  In Eukaryotes, the exosome is the major RNA degradation complex responsible for quality control of most transcripts in both the nucleus and cytoplasm, processing of stable RNA precursors and turnover of pre-mRNAs, mRNAs and large numbers of non-coding RNAs (ncRNAs). A puzzling aspect of exosome substrate targeting is the basis of the distinction between precise 3’ processing of stable RNA species and the rapid, complete degradation of “constitutive” degradation substrates or aberrant RNAs and RNA-protein complexes (reviewed in ). ProcesThe exosome core has a barrel-like overall structure  and in tRrp44 is composed of an N-terminal PIN domain responsible for endonuclease activity, two continuous RNA-binding cold-shock domains (CSD domains), an RNB domain carrying the exonuclease active site, and an RNA-binding S1 domain . RNA subiMet and truncated 5S ribosomal RNA (rRNA) . RN. RN37]. + reads are substantially better correlated with RNAPIII than were total Rrp44-exo reads. This is consistent with the model that Rrp44 is involved in 2 events: A cotranscriptional (pre-) tRNA degradation pathway, in which TRAMP and the exosome channel play major roles , Rrp44-exo and Rrp44-exo-S1 were determined by calculating Pearson coefficients . The totor roles , and a por roles .Clustering analysis was also performed for RNAPII transcripts, allowing us to identify four clusters . RecoverThe relative association of exosome channel components Csl4 and Rrp41 with the different RNAPII clusters correlated with higher relative binding to Rrp44-exo-S1, consistent with increased threading . Rrp6 anThe box C/D and box H/ACA classes of snoRNA were present in all four clusters . AlignmeExamples of individual box C/D snoRNAs, U14 and U3, are shown in In the case of the U3 snoRNA, previous analyses had shown that rapid cotranscriptional cleavage by Rnt1 (RNase III) is followed by binding of the Lsm2-8 complex and Lhp1 (La) to 3’ oligo(U) tracts –43. Rrp4Overall snoRNA recovery was not strongly affected by the S1 mutation . The 3’ INO1, which encodes Inositol-3-phosphate synthase [INO1. The INO1 gene has a 5’ UTR region of 437 nt, which is exceptionally long for yeast. This highly unusual structure suggests that the region is involved in regulation, and we speculate that direct access to Rrp44 might contribute to the regulation of INO1 mRNA levels.Binding of the top 1000 expressed mRNAs to Rrp44-exo and Rrp44-exo-S1 was compared . Their dsynthase . Rrp44-ein vitro [The Rrp44-exo exosome complex protects some 33 nt of RNA in vitro . Howeverin vitro . In an aIn the crystal structure of an Rrp6-associated nuclear exosome complex, the channel was seen to be widened relative to the complex lacking Rrp6 . We ther+ transcripts showed no reduction in the Rrp41-channel mutant , whereas Rrp44 alone was robustly bound, strongly indicating cytoplasmic turnover  and SUT (Stable Unannotated Transcripts) classes of ncRNAs, we determined the ratio of Rrp44 binding in Rrp41 and Rrp41-channel strains as a measure of sensitivity to channel narrowing . Compariturnover . In cont nucleus .NRD1 expression is auto-regulated through transcription termination and degradation by the exosome, induced by Nrd1 binding. The peak of Rrp44 binding on NRD1 corresponded well with the locations of Nrd1-binding sites [Based on this insight, mRNAs were analyzed to identify species for which Rrp44 binding was insensitive to the Rrp41-channel mutation, indicating predominant nuclear degradation Figs . This idng sites  and consng sites  and was The CUT class of ncRNAs are well characterized as nuclear exosome substrates and showed little sensitivity to the Rrp41-channel mutation . In contWe conclude that the Rrp41-channel mutation offers a tool to distinguish nuclear and cytoplasmic sites of degradation for major exosome substrates.Here we have attempted to understand how the hugely complex and diverse population of exosome substrates interact with the complex in living cells. This has allowed us to infer clear preferences in pathway to the exonuclease active site in Rrp44.A difficulty in interpretation of the data is that a single RNA species may form multiple different interactions with the exosome and its cofactors, either during the course of processing/degradation or under different circumstances. As an example, pre-tRNAs and tRNAs showed evidence for at least three types of interaction: 1) Direct-access to Rrp44, involving the TRAMP complex, during degradation or processing of the pre-tRNA 3’ trailer. 2) Channel threading to Rrp44 for truncated TSS proximal pre-tRNA fragments, possibly resulting from premature transcription termination. Analysis of the distribution of the Rpo31 subunit of RNAPIII on tRNA genes showed strong peaks of occupancy, most notably over the box A internal promoter region, which we interpreted as reflecting slowed transcription elongation . The cloIn our data the 5S rRNA, as well as the truncated 5S* rRNA, appear to be threaded, whereas a recent report  proposedOn RNAPI substrates, exchange takes place between threading and direct access pathways to Rrp44 on the 5’ ETS, whereas exchange from threading to Rrp6-mediated degradation occurs during 5.8S rRNA 3’ processing. Notably, these transitions require ~30 nt of the substrate to be removed via the “top” of the exosome channel and then to be delivered to the direct access channel of Rrp44 at the “bottom” of the complex, or to Rrp6 at the top. On pre-rRNAs and other substrates we saw peaks of exosome crosslinking at putative sites of handover, which may reflect the time required for this process. The mechanism(s) involved in RNA extraction and retargeting remain unclear, but the helicase activity of Mtr4 is a plausible candidate, and it appears that re-adenylation by TRAMP occurs during handover see . Degradain vitro [in vivo. It was reported that the channel undergoes widening in Rrp6-associated exosome complexes in vitro [in vivo and is a distinctive feature of the nuclear exosome.A striking feature of the data was the effect and apparent specificity of Rrp41-channel mutants, carrying four charge reversal amino acid substitutions that were reported to impede binding of RNA to the central channel in vitro . Unexpecin vitro . Our datPublished structural data revealed that Mtr4 binds an interface between Rrp6 and Rrp47, while Rrp6 is anchored to the exosome core via a C-terminal extension that binds Csl4 and stabilises the latter in the threaded RNA binding pathway . This lo+ reads, we infer that cytoplasmic Rrp44 shows a clear 5’ peak in mRNA binding. This is in marked contrast to the peak of Ski2 binding previously observed over the 3’ UTR regions of mRNAs [Based on the analyses of nuclear oligo(A)of mRNAs . We specof mRNAs , and subin vivo relative to the cytoplasmic complex, presumably reflecting allosteric changes induced by Rrp6 binding [in vitro. Many RNA species showed clear preferences for either the threaded or direct-access pathway. However, these generally appeared not to be absolute requirements. We speculate that this redundancy reflects the outcome of selective pressure. The exosome degrades and/or processes thousands of different substrates, including large numbers of ncRNAs that tend to change rapidly during evolution. The system may therefore have been selected for versatility and redundancy to allow the efficient handling of highly diverse RNA-protein complexes and RNA structures.The central channel of the nuclear exosome is widened  binding . In cons binding  inhibiteStrains used in this work are listed in CRAC was performed as described  on yeast600 ~0.5 and complexes were purified on IgG sepharose in buffer TMN150 . To extract the coprecipitated RNA, immobilized protein-RNA complexes were treated with GTC:Phenol:Chloroform for 10 minutes at 65°C. RNA was then separated by acrylamide gel electrophoresis, analyzed by Northern blotting using 5’ end labelled oligonucleotide or random primed labelled probes listed in Supplementary To allow purification of exosome complexes and associated RNA substrates, plasmid-expressed wild type or mutant Rrp44 proteins carried C-terminal fusions with a tag containing the streptavidin-binding peptide, TEV cleavage site and two copies of the z-domain of protein A (szz-tag) or the C-terminal HTP tag used for CRAC analysis (see above and Saccharomyces cerevisiae genome (SGD v64) using Novoalign (Novocraft) with genome annotation from Ensembl (EF4.74) [Sequencing data were quality filtered and adapters were trimmed using Flexbar  with par(EF4.74) , supplem(EF4.74) , with paTo differentiate pre-tRNAs from mature tRNAs , processDownstream analyses were performed using pyCRAC software .pyReadCounters (pyCRAC) was used to count overlaps with genes and reads per millions per kilobase (RPKM). For each transcript, we averaged numbers of reads  between replicates to reduce the influence of experimental variation upon clustering analysis. For mRNAs, counts between transcription start site and polyadenylation site were counted. For others classes (except tRNAs), a flank of 50 nucleotides around the ends was added to each gene. To account for differences in transcripts abundance and to reflect the relative binding of a transcript to either Rrp44-exo or Rrp44-exo-S1, we normalized the data for each gene. Transcripts were then clustered using Cluster3.0 . The distribution of reads along transcripts of different classes was performed using homemade scripts using pyPileup on each individual transcript to count hits at each position. Each row represents a transcript and each column represents the absolute position from the 5’ end or 3’ end. The plot sums up binding at each position allowing the display of a binding profile aligned either at the 5’ end or 3’ end.Pre-processed reads containing non-encoded oligo-(A) tails were identified using a pipeline developed by Grzegorz Kudla , 60 and pyBinCollector (pyCRAC) has been used on tRNAs. Each tRNA was divided in 2 bins, and hits in each bin were calculated for each dataset either for all reads or reads containing non-encoded oligo-(A) tails for Rrp44 samples. Binding across tRNA halves (5’ or 3’) was calculated as a fraction of total binding across individual tRNAs (set to 1). Averages between two biological repeats were used to calculate Pearson correlation between samples.All sequence data are available from GEO under accession number GSE90647.S1 Figrrp44Δ strains expressing the indicated HTP-tagged Rrp44 constructs were grown at 30°C in SD -Leu medium for plasmid maintenance to exponential phase, diluted to OD600 0.1 and grown either in liquid media (A) or serially diluted (1:10) and plated (B) in the same medium. (C) rrp41Δ strains expressing the indicated Rrp41 constructs were grown and plated on YPD media supplemented with nourseothricin antibiotic (Nat) for plasmid maintenance.(A-B) (TIF)Click here for additional data file.S2 FigS and 5.8SL. (C) Distribution of reads across 7S pre-rRNA, recovered with Mtr4, Rrp6, Csl4 and Rrp41 in the Rrp44-exo background, and Rrp44-exo and Rrp44-exo-S1. Normalized to hits per million mapped reads. Scale is linear. The cartoon shows the 7S pre-rRNA, consisting of the 5.8S rRNA and the ITS2 spacer to cleavage site C1. The 3’ end positions of the 5.8S+30 and 6S pre-rRNAs and mature 5.8S (site E) are indicated together with the probe location use in panel B.(A) Distribution of reads and deletions across the 200 first nucleotides of 5’ ETS of pre-rRNA, recovered with Mtr4 (Rrp44-exo), Rrp44-exo and Rrp44-exo-S1. Normalized to hits per million mapped reads. Region from +80 to +90 nt is shown in grey. Scale is linear. (B) Northern analysis of pre-rRNAs coprecipitated with active Rrp44-HTP (WT), Rrp44-exo-HTP (exo) or Rrp44-exo-S1-HTP (exo-S1) and 2% of the input RNA, with probe #020 indicated at the bottom of panel C. Sybr safe staining for 5.8S and 5S rRNA is shown as a loading control. Alternative processing pathways operating in the ITS2 region give rise to long and short forms of the mature 5.8S rRNA, designated 5.8S(TIF)Click here for additional data file.S3 Fig(A-C) Distribution of reads across RPR1 (RNase P) (A), U6 snRNA (B) and 5S rRNA (C) recovered with Rpo31 (RNAPIII subunit), Mtr4, Rrp6, Csl4 and Rrp41 in the Rrp44-exo background, and Rrp44-exo and Rrp44-exo-S1. Normalized to hits per million mapped reads. Scale is linear. (D) Pairwise Pearson coefficient of binding across tRNAs. Each tRNA was divided in two bins  and the number of hits in each bin was calculated for Rpo31, Rrp44-exo and Rrp44-exo-S1. For Rrp44, separate analyses were performed for all reads or only on reads containing non-encoded oligo-(A) tails. Binding across each bin was calculated as a fraction of total binding across individual tRNAs (set to 1). Averages between two biological replicate for each protein were used to calculate Pearson correlations.(TIF)Click here for additional data file.S4 Fig(A) RPKMs for each snoRNA species were averaged between two replicates of either Rrp44-exo or Rrp44-exo-S1 datasets and displayed on a 2D scatter plot. Box C/D and box H/ACA snoRNAs are represented in red and blue respectively. (B) Metagene analyses of all snoRNAs aligned by the 3’ end of the mature snoRNA region. Mtr4 (light blue), Csl4 (red), Rrp41 (green) in Rrp44-exo background, Rrp44-exo (blue) are shown. An average of two experiments was used for each sample, except for Rrp41 in which fewer reads were recovered and only the largest dataset is shown.(TIF)Click here for additional data file.S5 FigNRD1, normalized by millions of mapped reads. Scale is linear. Locations of the consensus Nrd1-binding motifs (UGAUG) are also indicated.(A) RPKMs for each mRNA species were averaged between two replicates of either Rrp44-exo or Rrp44-exo-S1 datasets and displayed on a 2D scatter plot. (B) Metagene analyses of binding to top 1000 mRNAs aligned by TSS for Rrp44-exo (blue) and Rrp44-exo-S1 (yellow). Two independent experiments are shown for each analysis, normalized per million mapped reads. (C) Distribution of reads recovered with Rrp44-exo and Rrp44-exo-S1 across the INO1 gene, normalized by millions of mapped reads. Scale is linear. (D-E) Metagene analyses of binding of top 200 mRNA aligned by the TSS (D) or poly(A) site (E) for Rrp44 (blue) and Rrp44 (Rrp41-channel) (green). Data from two biological repeats were averaged for each strain background and represented as a fraction of total binding of Rrp44 across mRNAs for each strain. (F) Distribution of reads recovered with Rrp44, Rrp44 (Rrp41-channel) and Nrd1  on NRD1,(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 TableRPMs (reads per millions mapped reads) for each RNA species were averaged between two replicates of either the Rrp44-exo or Rrp44-exo-S1 construct and arranged by k-medians clustering. Distributions of transcripts among clusters displayed in (XLSX)Click here for additional data file.S4 TableRPMs (reads per millions mapped reads) for each RNA species were averaged between two replicates of either the Rrp44-exo or Rrp44-exo-S1 construct and arranged by k-medians clustering. Distributions of transcripts among clusters displayed in (XLSX)Click here for additional data file.S5 TableRPKMs for each of the 200 most expressed mRNAs are displayed for the two replicates of Rrp44 in strains also expressing either wild type Rrp41 or the Rrp41-channel mutant, and the ratio between average binding of Rrp44 and Rrp44 (Rrp41-channel) was calculated. mRNAs with higher relative binding to Rrp44 in combination with Rrp41-channel are predicted to be strongly subject to nuclear degradation and are listed at the top. See also 2D scatter plot for the top 200 mRNAs .(XLSX)Click here for additional data file.S6 TableRPKMs for each of the 200 most expressed SUTs are displayed for the two replicates of Rrp44 in strains also expressing either wild type Rrp41 or the Rrp41-channel mutant, and the ratio between average binding of Rrp44 and Rrp44 (Rrp41-channel) was calculated. SUTs with higher relative binding to Rrp44 in combination with Rrp41-channel are predicted to be strongly subject to nuclear degradation and are listed at the top. See also 2D scatter plot for top 200 SUTs .(XLSX)Click here for additional data file.


We have developed a highly accurate, speedy and general-purpose tool for RNA editing detection using RNA-seq data. With the availability of RED-ML, it is now possible to conveniently make RNA editing a routine analysis of RNA-seq. We believe this can greatly benefit the RNA editing research community and has profound impact to accelerate our understanding of this intriguing posttranscriptional modification process.With the advancement of second generation sequencing techniques, our ability to detect and quantify RNA editing on a global scale has been vastly improved. As a result, RNA editing is now being studied under a growing number of biological conditions so that its biochemical mechanisms and functional roles can be further understood. However, a major barrier that prevents RNA editing from being a routine RNA-seq analysis, similar to gene expression and splicing analysis, for example, is the lack of user-friendly and effective computational tools. Based on years of experience of analyzing RNA editing using diverse RNA-seq datasets, we have developed a software tool, RED-ML: RNA Editing Detection based on Machine learning (pronounced as “red ML”). The input to RED-ML can be as simple as a single BAM file, while it can also take advantage of matched genomic variant information when available. The output not only contains detected RNA editing sites, but also a confidence score to facilitate downstream filtering. We have carefully designed validation experiments and performed extensive comparison and analysis to show the efficiency and effectiveness of RED-ML under different conditions, and it can accurately detect novel RNA editing sites without relying on curated RNA editing databases. We have also made this tool freely available via GitHub < RNA editing provides a dynamic and flexible means to alter the sequence of RNA transcripts during development and in a cell-type specific manner. Since discovered almost 30 years ago , 2, the In the last few years, large-scale, genome-wide analyses of RNA editing finally became feasible with the availability of high-throughput RNA sequencing , 18. EveEnvisioning that deep, high-throughput RNA sequencing will keep acting as a driving force of RNA editing research, we have developed a fast, high performance, and user-friendly RED tool based on machine learning (ML) to better serve the community and advance the field. Our new tool RED-ML (RNA Editing Detection based on Machine Learning) can perform genome-wide RED based on human RNA-seq data alone, can take advantage of matching DNA-seq data if available, and integrates well with other common RNA-seq data analysis steps. By adopting ML principles , our newA flow chart of our RED pipeline using RED-ML is shown in Fig. There are three broad classes of features used by RED-ML, based on insights obtained from previous hard filtering approaches, our own experience of tuning these filters, and current understanding of RNA editing mechanism. The first class is basic read features, including the number of supporting reads of a candidate site and the putative editing frequency. The second class of features is related to possible sequencing artifacts and misalignments, including mapping qualities of the supporting reads, the relative position of the candidate site in the mapped reads, indication of strand bias, whether the candidate site falls into simple repeat regions, etc. The third class is based on known properties of RNA editing, such as the editing type (whether it is A-to-I), whether the candidate site is in an Alu region, and its sequence context. Note that while the first two classes of features could be directly used in hard filtering, the third class cannot, since it is inappropriate to make hard decisions based on them, that is, they cannot be used as criteria to directly filter out non-RNA editing sites. However, they still provide valuable information to ML-based approaches where different sources of evidence can be combined to make soft decisions. In total, we extracted 28 features for every possible editing site, and full details of each feature are provided in Table S4.To construct a classifier by supervised machine learning, it is imperative to have a high quality, adequate-sized training set on RNA editing. Unfortunately, the lack of a gold standard dataset is a well-known challenge in the field . Here, wThe three computational methods considered include the original one developed with the publication of the data by Peng et al. , a seconTo further validate these predicted RNA editing sites, we carried out high-throughput Ion Proton sequencing   that are further validated by Ion Proton sequencing with a minimum coverage of 15, which results in 1334 sites (the slightly reduced coverage threshold is to obtain a large enough positive set). In addition, we also selected sites detected by both Peng et al. and Ramaswami et al., but not our own method, that are validated by Ion Proton sequencing . This gives us an additional 141 validated RNA editing sites and results in a total of 1475 data points in the positive set. To construct the negative set, we first selected seven highly informative features used by our hard filtering method that are also shared by RED-ML, and randomly sampled 150 sites each that failed the corresponding hard filtering criterion, which results in 1050 data points. We also sampled 300 sites that were aligned by TopHat2 but filtered out by BWA, and not validated by Ion Proton sequencing. We further randomly sampled 1200 SNPs from dbSNP 138 so that the classifier can be trained to distinguish between typical SNPs and RNA editing sites. Finally, we added those RNA editing sites that are detected by only one or two of the three methods but not validated by Ion Proton sequencing even when the coverage is adequate (20x or more), which results in an additional 375 data points. This gives us 2925 negative samples overall and a total of 4400 data points in the training set (full details in SM).F0.5 score, which is defined as We tried several popular ML techniques to build classifiers for RNA editing detection and settled on logistic regression due to its simplicity, efficiency of implementation, and relatively good performance (further discussions later). The LR classifier was trained and tested using the scikit-learn Python package (version 0.17.1), with a slightly higher weight (2.0) given to positive points to minimize the The set of 4400 data points just described would be very challenging for hard filtering based approaches. To test the performance of our ML based approach, we randomly partitioned these data points into training (80%) and test (the remaining 20%) sets. Performance on test data, which is not used when training the model, is shown in Fig. We further took advantage of such an ability to do pair-wise comparison with the other three methods used in building our model. For the method of Peng et al. and RES-scanner, we adjusted the threshold of RED-ML to roughly match the total number of detected RNA editing sites and compared the validation rates by Ion Proton sequencing. For the method of Ramaswami et al., we adjusted the threshold to match the number of detected RNA editing sites in non-Alu regions only, since Ramaswami et al. applied a very loose filter in the Alu region and included many low frequency sites that are not able to be detected by RED-ML (more details in SM). These results are shown in Fig. It should be emphasized that evaluating RED-ML on the YH dataset is not truly unbiased, since a very small portion of the YH dataset has been used in training our model. Moreover, other methods have been more or less tuned on the YH dataset as well. Most importantly, however, is that a critical goal of adopting ML principles for RED is to build a tool that can generalize well, that is, by learning the intrinsic, underlying characteristics of RNA editing, it can reach high performance beyond a specific dataset, experimental setup or tissue type, etc. To fully test the real-world performance of RED-ML, we carried out independent RNA-seq experiments on two prostate tumor samples (CH24T and CH62T) and a HeLa sample to detect RNA editing with RED-ML, and further performed Ion Proton validation experiments on these samples. RED-ML detected ∼30 000–50 000 RNA editing sites using the default threshold of 0.5  and plotted the results as Venn diagrams , with no more than 5GB RAM usage. Most of the computation time was on variant pileup, while the ML step is extremely fast . Compared to our previously published RES-scanner, the improvement on speed is very substantial, achieving ∼6x–10x speedup (Table S7.2). This is mainly due to the removal of a time-consuming realignment step by BLAT, as well as some optimization of variant pileup.In conclusion, a highly effective and widely applicable RED tool based on ML has been developed. We have also adopted careful software design to make this tool easy to use and it comes as an all-in-one software package. In addition, by adopting ML principles in building our model, further improvement can be easily made when improved knowledge of RNA editing becomes available. For example, when more accurate, large-scale RNA editing validation results are available, we can retrain our model with a better training set. When more characteristics of the RNA editing mechanism are discovered, we can design more features to reflect our improved knowledge.One limitation of RED-ML is that it only detects RNA editing sites with relatively high editing levels. The lowest level in our training set is 0.1, and RED-ML rarely detects sites with levels lower than 0.1 in reality. This limitation is mostly by design, since we aim to detect functional RNA editing sites, which are unlikely to be of very low frequency, and it also helps to reduce the impact of sequencing errors and artifacts. However, if the accuracy in sequencing experiments and alignment tools could be substantially improved, such a limitation can be readily lifted when building our model. The speed of RED-ML can also be further improved if multithreading is supported in the variant pileup and feature extraction stage, and we plan to do so in the future. Meanwhile, a user could process the BAM files of each chromosome in parallel to speed up the pipeline.Although RED-ML can accept BAM files produced by different alignment tools, the current version has been specifically optimized for BWA and TopHat2 due to the construction of model, and we find that the choice of alignment tools and the associated parameters could have a large impact on RED. To help users with proper alignment strategies, we have detailed some recommendations in the SM. We have also tested some alignment tools other than those used in building our model. For example, when we tried the BAM file produced by STAR  on the CThe current version of RED-ML is designed for human RED since we used various features specific to human RNA editing as well as human data when building our ML model. With the increased RNA editing data available in other species as well as the growing interest of studying them, we could build future versions to support more species, as our previous method RES-scanner did. As a test, we have run RED-ML on ant BAM files from RNA-seq data in Li et al  by disabA simple ML technique, namely LR, has been adopted in the current version of RED-ML. We also tried other methods, including decision trees, random forests, and SVMs, but the gain in performance by more sophisticated techniques is very minor (data not shown). As a result, LR was picked since it runs very fast and can be easily incorporated into our existing RNA-seq pipeline. However, when the need is warrantied, more sophisticated ML techniques, including deep learning , could bProject name: RED-MLhttps://github.com/BGIRED/RED-MLProject home page: Operating system(s): Linux_x86_64Programming language: Perl & C++Other requirements: SAMtools package and the following Perl modules: FindBin, Getopt::Long, File::Basename.License: GNU General Public License version 3.0 (GPLv3)Any restrictions to use by non-academics: NoneData further supporting this work can be found in the GigaScience repository, GigaDB . More inFigureS1.pdfFigureS2.pdfFigureS3.pdfFigureS4.pdfFigureS5.pdfFigureS6.pdfFigureS7.pdfFigureS8.pdfTableS1-10.xlsxTableS11.xlsxThe authors declare that they have no competing interests.LJL and DL designed the study; HX, DL, LJL, QL, and CW contributed to the software programming and pipeline construction; SR, LW, and LX prepared the samples; LW, LX, WL, MX, LL, and HL performed the experiment; HX and ML analyzed the data; YH, SZ, XL, YS, JW, HY, KW, and XX supervised the project; LJL, HX, and DL wrote the manuscript; all authors read and approved the final manuscript.Additional file: Fig. S1: validation of RNA editing sites identified by Peng et al. [Additional file: Fig. S2: overlapping detected RNA editing sites among three methods  The numbers of editing sites identified by RED-ML are larger than those of RES-Scanner (Tophat2) in CH24T, CH62T, and Hela. (b) The Ion Proton validation rates of editing sites identified by RED-ML are higher than those by RES-Scanner (Tophat2) in CH24T, CH62T, and Hela. RED-ML has a greater advantage since TopHat2 has been used in constructing our LR classifier while RES-Scanner has only been optimized for BWA.Additional file: Fig. S4: comparison of RED-ML and RES-Scanner (Tophat2). To ensure a fair comparison, we have compared the RED-ML and RES-Scanner based on same alignment file  (a) Thea, b, and c show the CH62T and Hela samples, respectively.Additional file: Fig. S5: the number of RNA editing sites and Ion Proton validation rate under different thresholds. With the threshold increasing, the numbers of detected editing sites decrease and the Ion Proton validation rates increase. a–c) The overlap of detected sites with two curated RNA editing databases (DARNED and RADAR) in CH24T, CH62T, and Hela samples were shown as Venn diagrams. Significant portions of RED-ML detected sites are in neither of the existing databases , probably because these are not normal tissues.Additional file: Fig. S6: comparison with known RNA editing database.  in the three samples. The number of Ion Proton validated sites in each class is also indicated on the top of each bar. There are no significant differences on the validation rates among the categories in all three samples, which demonstrate that RED-ML performed quite consistently independent of existing RNA editing databases.Additional file: Fig. S8: SNP evaluation. The percentage of genomic variants in detected RNA editing sites as quantified by matching DNA sequencing data. And the percentage of genomic variants in RED-ML detected RNA editing sites is quite low (no more than 2%).Additional file: Table S4. The definitions of 28 selected features.Additional file: Table S11. The RNA editing sites of CH24T, CH62T, Hela and YH detected by RED-ML.GIGA-D-16-00159_Original_Submission.pdfClick here for additional data file.Reviewer_1_Report_.pdfClick here for additional data file.Reviewer_2_Report_.pdfClick here for additional data file.FigureS1.pdfClick here for additional data file.FigureS2.pdfClick here for additional data file.FigureS3.pdfClick here for additional data file.FigureS4.pdfClick here for additional data file.FigureS5.pdfClick here for additional data file.FigureS6.pdfClick here for additional data file.FigureS7.pdfClick here for additional data file.FigureS8.pdfClick here for additional data file.TableS1-10.xlsxClick here for additional data file.TableS11.xlsxClick here for additional data file.