High throughput RNA sequencing (RNA-Seq) can generate whole transcriptome information at the single transcript level providing a powerful tool with multiple interrelated applications including transcriptome reconstruction and quantification. The sequences of novel transcripts can be reconstructed from deep RNA-Seq data, but this is computationally challenging due to sequencing errors, uneven coverage of expressed transcripts, and the need to distinguish between highly similar transcripts produced by alternative splicing. Another challenge in transcriptomic analysis comes from the ambiguities in mapping reads to transcripts.http://alan.cs.gsu.edu/NGS/?q=maltaWe present MaLTA, a method for simultaneous transcriptome assembly and quantification from Ion Torrent RNA-Seq data. Our approach explores transcriptome structure and incorporates a maximum likelihood model into the assembly and quantification procedure. A new version of the IsoEM algorithm suitable for Ion Torrent RNA-Seq reads is used to accurately estimate transcript expression levels. The MaLTA-IsoEM tool is publicly available at: Experimental results on both synthetic and real datasets show that Ion Torrent RNA-Seq data can be successfully used for transcriptome analyses. Experimental results suggest increased transcriptome assembly and quantification accuracy of MaLTA-IsoEM solution compared to existing state-of-the-art approaches. Massively parallel whole transcriptome sequencing, commonly referred to as RNA-Seq, and its ability to generate full transcriptome data at the single transcript level, provides a powerful tool with multiple interrelated applications, including transcriptome assembly -4, gene RNA-Seq has become the technology of choice for performing transcriptome analysis, rapidly replacing array-based technologies . The Ionde novo or it can be assisted by existing genome and transcriptome annotations. Transcriptome quantification is the problem of estimating the expression level of each transcript. In the remainder of this section we give a brief description of the common protocols used for mRNA sequencing.Here, we focus on two main problems in transcriptome analysis, namely, transcriptome assembly and quantification. Transcriptome assembly, also known as novel transcript discovery or reconstruction, is the problem of assembling the full length transcript sequences from the RNA sequencing data. Assembly can be done RNA-Seq uses next generation sequencing technologies, such as SOLiD , 454 1919, IllumTranscriptome assembly and quantification from RNA-Seq data has been the focus of much research in recent years. The sequences of novel transcripts together with their expression levels can be inferred from deep RNA-Seq data, but this is computationally challenging due to the short length of the reads, high percentage of sequencing errors, uneven coverage of expressed transcripts, and the need to distinguish between highly similar transcripts produced by alternative splicing. A number of methods address the problem of transcriptome assembly and quantification from RNA sequencing data. Methods for transcriptome assembly fall into three categories: "genome-guided", "genome-independent" and "annotation-guided" methods . Genome-Many transcriptome reconstruction methods fall in the genome-guided category. They typically start by mapping sequencing reads onto the reference genome, using spliced alignment tools, such as TopHat  or Splicde novo assembly methods 32], two R2) between the (qRT-PCR/ERCC) and Fragment Per Kilobase of exon length per Million reads (FPKM) estimates was used as accuracy measure.Transcriptome quantification accuracy was evaluated by comparing RNA-Seq estimates with TaqMan qRT-PCR measurements  or ExterIn this section, we use sensitivity and PPV defined above to compare the MaLTA to other transcriptome assembly tools. The most recent versions of Cufflinks version 2.1.1)  and IsoL.1.1  andTable There is a strong correlation between the number of splicing events within the gene and the number of annotated transcripts. A high number of splicing events leads to increased number of candidate transcripts, which makes the selection process more difficult. To explore the behavior of the methods depending on number of transcripts per gene we divided all genes into categories according to the number of annotated transcripts and calculated the sensitivity and PPV within each such category.Figures Table For this study, we compared MaLTA and Cufflinks on 3 cancer datasets downloaded from the Ion Community website: GOG-382 (HepG2 - hepatocellular carcinoma), DID-416 (K562 - myelogenous leukemia) and DID-413 . Comparison with IsoLasso on the real datasets is omitted due to technical problems (IsoLasso results were consistently incomparable to other methods). Reads were mapped to the hg18 reference genome using TopHat2 (with default parameters) which is able to produce spliced alignment used by transcriptome assembly tools -(CT of g), and the average of the qPCR expression values in the four replicates was used as the ground truth. Mapping gene names to Ensembl gene IDs using the HUGO Gene Nomenclature Committee (HGNC) database resulted in TaqMan qPCR expression levels for 832 Ensembl genes. Tables To evaluate transcriptome quantification accuracy of the methods we ran IsoEM and Cufflinks on Ion Torrent RNA-Seq data generated from two commercially available reference RNA samples that have been well-characterized by quantitative real time PCR (qRT-PCR) as part of the MicroArray Quality Control Consortium (MAQC); namely the Ambion Human Brain Reference RNA, Catalog #6050), henceforth referred to as HBRR and the Stratagene Universal Human Reference RNA , henceforth referred to as UHRR. We used five HBRR datasets and five UHRR datasets for the comparison. To assess accuracy, gene expression estimates obtained from RNA-Seq data were compared against those obtained from TaqMan qRT-PCR measurements (GEO accession GPL4097) collected as part of the MAQC project. As described in , each TaR2 for IsoEM in both cases.We also compared IsoEM and Cufflinks on two of the cancer Ion Torrent datasets, GOG-382 and DID-413. Methods were evaluated by calculating correlation between estimated FPKMs for External RNA Controls Consortium (ERCC) spike-in controls [In this paper we described the MaLTA-IsoEM method for simultaneous transcriptome assembly and quantification from Ion Torrent RNA-Seq data. Our approach explores transcriptome structure and incorporates a maximum likelihood model into the assembly and quantification procedure. Results on real cancer and MAQC RNA-Seq datasets show that Ion Torrent RNA-Seq data can be successfully used for transcriptome analysis. Transcriptome assembly and quantification accuracy was confirmed by comparison to annotated transcripts and TaqMan qRT-PCR measurements and External RNA Controls Consortium RNA spike-in controls. Experimental results on both real and synthetic datasets generated with various sequencing parameters and distribution assumptions suggest increased transcriptome assembly and quantification accuracy of MaLTA-IsoEM compared to existing state-of-the-art approaches.DB is a member of the Ion Bioinformatics group at Life Technologies Corporation. The work of S.M., A.C., S.A.S., I.M. and A.Z. was supported in part by Life Technology Grants "Novel transcript reconstruction from Ion Torrent sequencing" and "Viral Metagenome Reconstruction Software for Ion Torrent PGM Sequencer". The authors recognize the presence of potential conflicts of interest and affirm that the results reported in this paper represent original and unbiased observations.S.M., D.B., I.M. and A.Z. conceived the idea. S.M. designed algorithms, developed software, performed analysis and experiments, wrote the paper. A.C. performed analysis and experiments, wrote the paper. S.A.S. developed software, performed analysis and experiments, wrote the paper. D.B. contributed to designing the algorithms and writing the paper. I.M. contributed to designing the algorithms and writing the paper. A.Z. designed the algorithms, wrote the paper and supervised the project. All authors have read and approved the final manuscript.


Escherichia coli mutator strains that have accumulated deleterious mutations during short- and long-term laboratory evolution. We identify strain-specific mutations that are deleterious and subject to buffering when introduced individually into the ancestral genotype. For DBRHs, we show that buffering requires helicase activity, implicating RNA structural remodelling in the buffering process. Our results suggest that RNA chaperones might play a fundamental role in RNA evolution and evolvability.Both proteins and RNAs can misfold into non-functional conformations. Protein chaperones promote native folding of nascent polypeptides and refolding of misfolded species, thereby buffering mutations that compromise protein structure and function. Here, we show that RNA chaperones can also act as mutation buffers that enhance organismal fitness. Using competition assays, we demonstrate that overexpression of select RNA chaperones, including three DEAD box RNA helicases (DBRHs)  and the cold shock protein CspA, improves fitness of two independently evolved DOI:http://dx.doi.org/10.7554/eLife.04745.001 Stretches of DNA known as genes contain the instructions to make the proteins and RNA molecules that are essential for life. The DNA sequence of the gene is first copied to make a strand of RNA, which may subsequently be ‘translated’ to make a protein. To carry out their tasks, proteins and many RNA molecules must fold into specific three-dimensional structures. Since folding can easily get derailed, proteins known as chaperones assist with this process.Mutations sometimes occur in the DNA that reduce the ability of the proteins or RNA molecules to fold correctly. Previously, scientists had found that some chaperones help incorrectly folded proteins adopt ‘normal’ shapes and thus mask the harmful effects of mutations. However, it was not known whether the chaperones that fold RNA similarly suppress harmful mutations.Escherichia coli bacteria that had been grown in the laboratory as part of long-term evolution experiments. During this time, they had accumulated mutations that reduced the fitness of later generations in comparison with their ancestors. Rudan et al. then found that increasing the levels of certain RNA chaperones—in particular, a group called DEAD box RNA helicases—in the evolved bacteria improved their fitness. This strongly suggests that RNA chaperones, like protein chaperones, can suppress harmful mutations. Compromised versions of the same RNA chaperones, which were unable to dismantle folded RNA structures, did not show any improvements in fitness, demonstrating that the capacity to unfold and refold RNA is critical.To address this question Rudan et al. studied the effects of several RNA chaperones in Rudan et al. suggest that different types of chaperones are likely to alleviate RNA mutations using different mechanisms. A future challenge will therefore be to work out how these mechanisms work together to mask different mutations and allow them to persist through evolution, their harmful effects rendered invisible to the forces of natural selection.DOI:http://dx.doi.org/10.7554/eLife.04745.002 Escherichia coli strains overexpressing GroEL  and theE. coli strain propagated under conditions of weak selection is expected to accumulate deleterious mutations and experience a concomitant decline in fitness compared with its ancestor. Similarly, strains adapting to a novel environment will accumulate not only beneficial but also deleterious mutations, which may hitchhike along with beneficial alleles and can be exposed as deleterious in the old environment , and two evolved mutS mutator strains that were sampled from a lineage after ∼20,000 (20k) and ∼40,000 (40k) generations of adaptation to a minimal glucose-limited medium  into each of the three genetic backgrounds . We then competed each transformed strain against a strain of the same genotype but bearing an empty control plasmid. Whereas overexpression in the ancestral and 20k backgrounds had limited effects on competitive fitness, overexpression of either DBRH enhanced fitness of the mutationally compromised 40k genotype  strain was sampled after a shorter period of laboratory evolution (∼500 generations) but also exhibited reduced fitness compared with its ancestor , we individually introduced each mutation located in a gene of known function  into the MG1655 genome. We then competed each of these strains, isogenic except for a single mutation, against MG1655. Only one strain, carrying a mutation in the uffering . Althougr uptake . LamB de glucose , suggestH strain  consisterplS—one synonymous (rplSsyn) and one non-synonymous  of buffering in the future.Elucidating in greater detail how different chaperones interact with wild-type and mutant RNAs will be critical to elucidate the molecular basis of buffering and how it relates to altered RNA secondary and tertiary structure. Here, we demonstrate that buffering by RNA chaperones occurs at the organismal level, establish that helicase and nucleic acid-binding activity are required for buffering by DBRHs and CspA, respectively, and identify individual mutations that are amenable to buffering. These mutations, located in lamB and rplS stand out amongst other mutations in their predicted effect on RNA structure. Several metrics that quantify mutational impact on RNA secondary structure do indeed suggest comparatively severe effects for rplSsyn  would be premature. Rather, competing mechanistic hypotheses will have to be confirmed or debunked through targeted follow-up experiments. For example, a logical first step to eliminate one of the hypotheses above would be to measure LamB levels . In designing insightful follow-up studies, a few issues deserve wider consideration.Provided deleterious effects arise at the level of RNA structure, RNA chaperone activity might be beneficial through stabilizing (CspA), destabilizing, or remodelling (DBRHs) affected structures in the focal transcript or through changing how these transcripts interact with other RNAs or RNA-binding proteins . Considemutation  introducFirst, buffering might be direct (involving interactions between the RNA chaperones and the mutant RNA) or indirect . In addition to dissecting specific chaperone–RNA interactions, it will therefore be important to establish system-level effects of RNA chaperone overexpression.lamB) should not be discarded a priori as unlikely candidates for buffering by RNA chaperones; deleterious consequences might arise at the RNA level even though the amino acid change is selectively neutral. Conversely, synonymous mutations can affect translation kinetics and protein folding . For strain construction and subsequent experiments, bacteria were grown in LB at 37°C. To distinguish competitors during competition assays, cells were plated onto TA solid medium  were prepared using the TruSeq DNA PCR-Free LT Sample Prep Kit , with median insert size of 487 bp (ancestor) and 495 bp (evolved ΔmutH), for subsequent sequencing on the Illumina HiSeq 2000 platform using the TruSeq v3 reagent kit. Sequencing yielded 7365202 and 6504588 read pairs for the ancestral and evolved strains, respectively. The genomes of the REL606-derived 20k (REL8602A) and 40k (REL10953) clones were previously sequenced on the Illumina Genome Analyzer platform using a single lane of single-end 36bp reads per genome.DNA was extracted from the MG1655 ancestor and the evolved ΔmutH deletion in the mutator strain. We found no other large deletions. Similarly, no duplications were detected after visually inspecting neighbouring regions with unusual differences in coverage (>1.5-fold). All candidate regions were found to be due to local drop-offs in neighbouring regions rather than excess coverage in focal windows. For REL606-derived strains, candidate genomic differences compared with the ancestral genome were identified using the PALOMA  were individually introduced into the ancestral MG1655 background using a recombineering approach . The primers used for sequencing are listed in All point mutations detected in the evolved Δapproach . Brieflyapproach . They wemutH strains overexpressing one of the four RNA chaperone proteins  were pelleted by centrifugation and resuspended in UTCDTT buffer containing 8 M urea, 2 M thiourea, 4% CHAPS and 10 mM DTT supplemented with a mixture of protease inhibitors containing aprotinin bestatin, leupeptin, pepstatin A, E-64 and AEBSFxHCL, EDTA-free (Life Technologies). Cells were incubated for 2 hr at room temperature followed by a 20-min centrifugation step at 12,000×g. Protein concentrations were determined using the Bradford assay . Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.eLife posts the editorial decision letter and author response on a selection of the published articles . An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent , a Reviewing editor, and two reviewers.Thank you for sending your work entitled “RNA chaperones buffer deleterious mutations in The Reviewing editor and the reviewers discussed their comments before we reached this decision, and the Reviewing editor has assembled the following comments.The conclusion of the paper is highly interesting: that RNA chaperones may buffer mutations that could impact on mRNA folding. The possible implication is that this way hidden genetic variability can be maintained in the genome and become effecting under certain (e.g. stress) conditions by compensating the function of RNA chaperones, e.g. by over-loading their functions. This role of chaperone was previously reported and extensively discussed in the context of protein mutations, but not in the context of RNA mutation.Stronger evidence that this is indeed the case, however, is required. The main issue raised is that the mutations described were not characterized in the context of RNA stability. Therefore, it is difficult to interpret the experiment. If you decide to submit a substantially revised version of the manuscript, a minimal set of experiments required to better support your conjecture has to include: (1) the identification of at least one mutation in the original strain predicted to affect RNA stability , (2) the demonstration that it has a detrimental effect on fitness when in isolation, and (3) evidence that it is being buffered by RNA chaperones.Additional comments:ΔmutH because it has such a small number of mutations, and all of the SNPs are in coding sequence. This could be done computationally.1) Given the overall conclusion of the work, that overexpression of RNA chaperone proteins confers a fitness benefit by assisting with RNA folding or structural rearrangements in the mutated strains, it seems important to evaluate whether any of the mutations in known or suspected structured RNAs. This question is especially intriguing for the 2) The data presentation does not allow a direct comparison, but the impression upon comparing parts B and C of 3) Fitness was determined by competitions: cultures were grown for 24 hours, as default. It's unclear to which growth phase the fitness effects relate to: is it most exponential growth, or stationary phase as well. Further, given the different growth rates of the different strains, it could may well be that some were subjected to growth only and other to growth + stationary phase survival. Many, I'd say, most mutations have conflicting effects on growth vs. survival in stationary phase. It could therefore be that the actual buffering effects would be different, possibly much larger, if competitions were perfumed under a defined phase—exponential growth on one hand, and survival at stationary phase on the other. This should be clarified by performing competition experiment in well-defined conditions (e.g. maintaining cells in log phase).4) “Relative fitness was calculated as the ratio of the competitors' growth rates during competition.” The method relates to counting the ratio of CFUs on tetrazolium plates, so how would growth rates be derived from that; especially, when no data is available as number of generations within the 24 hours growth ?5) “The arabinose-utilization marker, which is neutral under the conditions utilized”. Was this tested, or assumed? Many markers do affect growth rates.6) An empty vector control is missing.7) The supplementary tables listing the mutations are not very informative. The genes and positions of the mutations should be specified.8) The expression levels of the RNA chaperones are not reported. It is interesting that overexpression of CspA restores fitness to the same level as overexpression of the ATP-dependent DEAD-box proteins, but this result cannot be fully evaluated without knowing whether the different proteins were expressed at comparable levels.Neurospora crassa CYT-19 protein—facilitate the re-folding of misfolded species .” This reference seems to be incorrect here.9) The sentence: “RNA chaperones can promote orderly structural transitions towards and subsequently stabilize the native fold or—as exemplified by classic work on the 10) In the subsection headed “Mutant proteins”, the sentence: “Here, we used DBRH mutants in which the central glutamic acid residue has been recoded to yield lysine, a change known to abolish RhlB helicase activity .” In Vanzo, 1998, helicase activity was not measured. Instead, it was shown that RNaseE-dependent ATPase activity is compromised for this mutant. It is certainly expected that the mutation will also result in a loss of helicase activity, which depends on ATPase activity, but the connections to previous work should be clarified.E. coli” for further consideration at eLife. Your revised article has been favorably evaluated by Detlef Weigel (Senior editor), a Reviewing editor, and two reviewers. The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:Thank you for resubmitting your work entitled “RNA chaperones buffer deleterious mutations in As you can see from the reviews, both reviewers appreciated the additional data and discussion provided in the revised manuscript and agreed that the paper is improved. There is also an agreement that you nicely illustrated the core finding—that the slow growth phenotypes of these strains can be rescued with overexpression of DEAD-box proteins, known RNA-dependent ATPases (and one RNA-binding protein that lacks ATPase activity)—and that this finding is interesting.However, both reviewers also agreed that the mechanism(s) of rescue is not at all understood, and that you haven't convincingly proved that it has to do with buffering mRNA stability.What we would like to ask you is to relate to these points specifically in the manuscript. Currently, the Discussion is very short, and it can easily be extended to a real section where you can discuss and relate to the all the points raised by the reviewers (in particular the points raised by reviewer #1).It may also be interesting (but not necessary) to relate to protein chaperones. Would you expect mutations that would also be buffered this way at the protein level? What do you think would be more important?Reviewer #1:The authors have done a significant amount of work, and provided new data. The overall picture remains, however, enigmatic, and possibly even more confusing than it had been prior to the addition of these new data. Specifically, there is still no clear indication that the mutations in the evolved lines relate to RNA stability/folding, and are thereby buffered by RNA chaperones.The puzzling points that cast doubt on this interpretation are several:1) There is not even a single overlapping mutation between the two evolved lines. This may suggest a non-specific buffering mechanism, but then on the other hand, as with protein chaperones, RNA chaperones are likely to have some obligatory 'clients', and other RNAs that are highly dependent on chaperone action. However, none of the identified mutations is in known structural RNAs.lamB) exhibited both reduced fitness and evidence for buffering. But this mutation involves an amino acid exchange.2) Making isogenic strains is probably the most rigorous test possible, and the authors took this avenue. Sadly, however, the results are not supporting the hypothesis. Only one out 7 mutations tested  The synonymous mutation in Overall, as I said, I'm sorry to say that the additional data cast in my view more doubts on the paper's main conclusion.Reviewer #2:The authors have addressed my major concerns. The work demonstrates that mutations in the evolved strains are reduced in growth and this reduction can be 'buffered' by overexpression of proteins that function as ATP-dependent or ATP-independent RNA chaperones. It is interesting that both mutations that confer fitness defects individually are in coding regions. While one is a missense mutation, the other one is synonymous, suggesting a role of the chaperone proteins in regulating the lifetime or translatability of the mRNA. The work raises very interesting questions on the roles of RNA chaperones in the functions of mRNAs, which will presumably spur future work. The main issue raised is that the mutations described were not characterized in the context of RNA stability. Therefore, it is difficult to interpret the experiment. If you decide to submit a substantially revised version of the manuscript, a minimal set of experiments required to better support your conjecture has to include: (1) the identification of at least one mutation in the original strain predicted to affect RNA stability , (2) the demonstration that it has a detrimental effect on fitness when in isolation, and (3) evidence that it is being buffered by RNA chaperones.rplS (REL606) and the glycoporin-encoding gene lamB (MG1655), respectively. Interestingly, the mutation in rplS is synonymous, ruling out protein-level effects, and fitness defects are consistent with previous observations for rplA and rpsT in Salmonella typhimurium , we took advantage of the small number of point mutations in the evolved ΔmutH strain were located in coding regions of nonessential genes, where it is difficult to assess the biological significance of RNA structural change. Second, existing computational tools can model mutational effects on RNA secondary structure, but provide no insight into whether a given mutation can be rescued by RNA chaperones. Third, it is not obvious how to extrapolate from molecular fitness, i.e. how disruptive a mutation is at the RNA structural level, to organismal fitness. For example, a relatively small change in structure in a highly expressed gene might matter more than a strongly disruptive mutation in a gene that is not even expressed. Given the likely complex determinants of whether rescue can occur, we would have needed a much larger set of deleterious mutations amenable to buffering to train and validate a predictive algorithm. Thus, while we provide multiple metrics of RNA structural change , so we lacked obvious candidates for buffering. In fact, all mutations in the evolved Δl change , some ofAdditional comments:Given the overall conclusion of the work, that overexpression of RNA chaperone proteins confers a fitness benefit by assisting with RNA folding or structural rearrangements in the mutated strains, it seems important to evaluate whether any of the mutations in known or suspected structured RNAs. This question is especially intriguing for the ΔmutH because it has such a small number of mutations, and all of the SNPs are in coding sequence. This could be done computationally.1) As previously mentioned in the Methods section, neither strain harbours mutations in known structural RNAs . We now highlight this more prominently in the main text . Indeed, we took extra care with SNP calling in those regions, because a lack of unique read mapping might have concealed the presence of pertinent mutations. This was not the case (see Methods).ΔmutH strain were located in the coding regions of nonessential genes. This is problematic when it comes to computational predictions of phenotypic effect: all mRNAs will be predicted to exhibit a multitude of local structures but for the vast majority of individual structures, we have no idea whether they are functionally relevant. As elaborated earlier, we considered computational predictions here to be of limited utility and instead decided to systematically probe mutations experimentally for this genetic background.We further considered whether mutations might be located in experimentally described structured elements of known functional importance, such rho-independent terminators or REP elements. However, surprisingly, all point mutations in the evolved The data presentation does not allow a direct comparison, but the impression upon comparing parts B and C ofis that, upon over-expression of some DBRHs , the evolved 40k strain grows faster than the ancestral strain . Is that the case? If so, how can this be accounted for?2) It is not so much data presentation than the nature of the data that precludes a valid comparison. We think it is important to highlight that competition assays are designed to quantify the relative fitness of different strains competing in the same environment. They do not allow extrapolation of how strain A in one competition would perform against strain B in another experiment. This is exacerbated in the current case: in From a theoretical perspective, we think it is possible in principle  that a 40k overexpression strain outcompetes a REL606 overexpression strain (the experiment that would address the referee’s suspicion directly). For example, some evolved changes in the 40k genotype might incidentally have eliminated potential costs associated with overexpression in REL606. This theoretical possibility is another reason why experiments in Fitness was determined by competitions: cultures were grown for 24 hours, as default. It's unclear to which growth phase the fitness effects relate to: is it most exponential growth, or stationary phase as well. Further, given the different growth rates of the different strains, it could may well be that some were subjected to growth only and other to growth + stationary phase survival. Many, I'd say, most mutations have conflicting effects on growth vs. survival in stationary phase. It could therefore be that the actual buffering effects would be different, possibly much larger, if competitions were perfumed under a defined phase—exponential growth on one hand, and survival at stationary phase on the other. This should be clarified by performing competition experiment in well-defined conditions (e.g. maintaining cells in log phase).3) The 24h competitions were designed to assay fitness across a full growth cycle in part because we expected to find and wanted to capture small differences in fitness, which might only become evident after extended periods of competition. As a consequence, the fitness we report is a compound measure that subsumes the contributions of individual mutations and how these contributions vary across the course of the experiment (i.e. with growth phase). We agree that it would, in addition, be interesting to describe fitness effects in a more fine-grained fashion, establishing which mutations matter when. We therefore followed the referees’ suggestion and performed an additional set of competition experiments in which, instead of running the competition for 24 hours, we diluted and spread the cells on indicator TA plates after 2 hours, when cultures are in mid-exponential phase.ΔmutH strains exhibit lower fitness compared to their respective ancestor, whereas the 20k and de novo ΔmutH strains do not. This demonstrates that, in both evolved strains, there are mutations that have fitness-relevant effects during the early growth phases . Second, 40k/evolved ΔmutH strains that overexpress one of the RNA chaperones outcompete strains of the same genotype that carry an empty control plasmid, suggesting that mutations with early-growth-phase relevance are being buffered by chaperone overexpression. For most chaperone/genotype combinations there are quantitative differences in relative fitness after 2h and 24h, consistent with the possibility that differential survival during later growth can further exacerbate or ameliorate fitness differences. It is interesting to note that buffering in the evolved ΔmutH strain is consistently stronger after 24h whereas the picture is more variable in the 40k strain. We suggest (in the Results and Discussion section) this might reflect a larger number of contributory mutations in the 40k strain. This suggestion is further supported by the observation that competitions involving the isolated lamB mutation broadly replicate overall trends for the evolved ΔmutH strain , in line with this mutation being a strong contributor to overall fitness effects. In contrast, fitness effects of the rplS mutation introduced in isolation into the ancestor of the 40k strain does not track overall fitness patterns for this strain, again consistent with the presence of multiple fitness-relevant mutations in this strain.We make the following observations  “Relative fitness was calculated as the ratio of the competitors' growth rates during competition.” The method relates to counting the ratio of CFUs on tetrazolium plates, so how would growth rates be derived from that; especially, when no data is available as number of generations within the 24 hours growth ?Relative fitness was measured as:i and f represent initial and final densities, respectively.where A and B are the densities of the two competitors and This calculation reflects the differences between the two competitors in lag, growth, and stationary phases over the same serial-transfer cycle. This formula has been applied previously to measure relative fitness, notably in the long-term evolution experiment. We have revised the text and included the above formula (in the subsection headed “Competition assays”) to make this computation more explicit.5) “The arabinose-utilization marker, which is neutral under the conditions utilized”. Was this tested, or assumed? Many markers do affect growth rates.Neutrality of the arabinose-utilization marker was previously demonstrated by Lenski in the context of establishing the long-term evolution experiment . We have added this reference to support our original statement (in the subsection headed “Competition assays”).6) An empty vector control is missing.An empty vector control is already present wherever appropriate. Competition experiments have to be controlled internally and all strains bearing a chaperone overexpression vector are therefore competed against the same genotype bearing an empty vector. We highlight this in the text (in the subsection headed “Overexpression of DEAD box RNA helicases enhances fitness of a low-fitness strain”) and in all relevant figures and their legends.The supplementary tables listing the mutations are not very informative. The genes and positions of the mutations should be specified.7) We agree that the original tables were rather Spartan in information content. We have revised all supplementary files to provide salient annotations to help the reader place mutations in biological context. The revised tables  detail nThe expression levels of the RNA chaperones are not reported. It is interesting that overexpression of CspA restores fitness to the same level as overexpression of the ATP-dependent DEAD-box proteins, but this result cannot be fully evaluated without knowing whether the different proteins were expressed at comparable levels.8) mutH overexpression strains. Briefly, we expressed His-tagged versions of the four RNA chaperones , quantified expression levels from Western blots and normalized by the intensity of Coomassie-stained total protein (see Methods and Results). This allows an assessment of relative chaperone levels in the different overexpression strains. For example, as indicated in the new cspA suggest diverse mechanisms of buffering”), this might be related to known negative autoregulation of cspA .” This reference seems to be incorrect here.9) Thank you for catching this error. We have replaced the incorrect reference with the one we meant to use .” In Vanzo, 1998, helicase activity was not measured. Instead, it was shown that RNaseE-dependent ATPase activity is compromised for this mutant. It is certainly expected that the mutation will also result in a loss of helicase activity, which depends on ATPase activity, but the connections to previous work should be clarified.10) .We have clarified the link between DEAD motif mutants and ATPase/helicase activity (in the subsection headed “Mutant proteins”), We have clarified the link between DEAD motif mutants and ATPase/helicase activity (in the subsection headed “Mutant proteins”). As mentioned by the referee, mutating the DEAD motif directly affects ATPase activity. However, since helicase activity is dependent on ATPase activity, DEAD motif mutants also compromise helicase activityAs you can see from the reviews, both reviewers appreciated the additional data and discussion provided in the revised manuscript and agreed that the paper is improved. There is also an agreement that you nicely illustrated the core finding—that the slow growth phenotypes of these strains can be rescued with overexpression of DEAD-box proteins, known RNA-dependent ATPases (and one RNA-binding protein that lacks ATPase activity)—and that this finding is interesting.However, both reviewers also agreed that the mechanism(s) of rescue is not at all understood, and that you haven't convincingly proved that it has to do with buffering mRNA stability.What we would like to ask you is to relate to these points specifically in the manuscript. Currently, the Discussion is very short, and it can easily be extended to a real section where you can discuss and relate to the all the points raised by the reviewers (in particular the points raised by reviewer #1).We are gratified that the reviewers agree that our core finding—that deleterious mutations can be buffered by RNA chaperones—is strongly supported by the evidence. This is the key claim of the paper. We agree with the reviewers that the specific mechanism(s) of buffering remain enigmatic. In fact, we were very careful not to make mechanistic claims, although we naturally discussed the modus operandi of DEAD box RNA helicases and CspA.Although this study was focused on demonstrating buffering at the organismal level rather than on elucidating the molecular mechanism(s) of buffering, we appreciate the desire for a more extensive discussion of the latter. Since our experiments cannot directly inform on mechanism, we have focused on illustrating (rather than exhaustively enumerating) potential buffering pathways that are consistent with the data while highlighting more general considerations that should guide follow-up investigations. This includes considerations of the significance of synonymous and non-synonymous mutations and whether fitness effects are proximately mediated through protein defects. We also now highlight that the individual deleterious mutations we identify will provide an invaluable platform for the mechanistic dissection of buffering in the future.Reviewer #1:The authors have done a significant amount of work, and provided new data. The overall picture remains, however, enigmatic, and possibly even more confusing than it had been prior to the addition of these new data. Specifically, there is still no clear indication that the mutations in the evolved lines relate to RNA stability/folding, and are thereby buffered by RNA chaperones.It is important to discriminate two logically distinct claims here, one being that RNA chaperones buffer deleterious mutations (a claim about fitness that we make and provide strong evidence for), the other being that buffering occurs via a specific mechanism that involves certain changes in RNA stability and/or folding (a claim about mechanism that we are keenly aware we cannot make based on the data presented). The new experiments have indeed not established a specific mechanism (doing so will require further work), but their discovery has enabled mechanistic explorations to be carried out in the future, which is a significant advance in itself.The puzzling points that cast doubt on this interpretation are several:There is not even a single overlapping mutation between the two evolved lines. This may suggest a non-specific buffering mechanism, but then on the other hand, as with protein chaperones, RNA chaperones are likely to have some obligatory 'clients', and other RNAs that are highly dependent on chaperone action. However, none of the identified mutations is in known structural RNAs.1) Mutations in structural RNAs are expected to be exceedingly rare in evolving colonies as fitness effects tend to be severe enough for mutants to quickly get outcompeted. But finding mutations outside of known structural RNAs does not invalidate a model where buffering occurs through RNA structural remodeling. More fundamentally, buffering (regardless of mechanism) clearly does occur and is dependent on helicase activity/nucleic acid binding. The unanswered question is not if RNA chaperones buffer mutations but how.Making isogenic strains is probably the most rigorous test possible, and the authors took this avenue. Sadly, however, the results are not supporting the hypothesis. Only one out 7 mutations tested (in lamB) exhibited both reduced fitness and evidence for buffering. But this mutation involves an amino acid exchange.2) The mutations we find cannot and do not conclusively implicate a specific buffering mechanism as we readily acknowledge in the manuscript. However, it is important to highlight that finding a mutation to be non-synonymous does not automatically disqualify it from consideration as causing an issue at the level of RNA structure. Both synonymous and non-synonymous sites affect mRNA folding. It simply places a heavier burden of proof on future experiments to show that either the effect on protein sequence is irrelevant or that it is relevant but buffering nonetheless mediated at the RNA level . We discuss these issues in the revised manuscript.The synonymous mutation in rplS is more convincing, as it was shown to be deleterious and compensated by DRBH over-expresssion. However, then authors' assumption that fitness effects of synonymous mutations indicate the presence of selective constraints beyond the protein level is wrong. Synonymous mutations have been shown to affect the rate and outcome of protein folding, and thus the yield of soluble, function protein.3) We now briefly discuss synonymous and non-synonymous mutations and their potential effects at the RNA and protein level. We have also removed the ambiguous term “selective constraints beyond the protein level” and replaced it with the more concise “selective constraints unrelated to amino acid composition”.


We apply these algorithms to 45 graphs corresponding to all solved RNA structures up through 11 vertices (∼220 nucleotides). While we observe that the median cut divides a graph into two similar-sized subgraphs, the sign and gap cuts partition a graph into two topologically-distinct subgraphs. We find that the gap cut produces the best biologically-relevant partitioning for RNA because it divides RNAs at less stable connections while maintaining junctions intact. The iterative gap cuts suggest basic modules and assembly protocols to design large RNA structures. Our graph substructuring thus suggests a systematic approach to explore the modularity of biological networks. In our applications to RNA structures, subgraphs also suggest design strategies for novel RNA motifs.Graph representations have been widely used to analyze and design various economic, social, military, political, and biological networks. In systems biology, networks of cells and organs are useful for understanding disease and medical treatments and, in structural biology, structures of molecules can be described, including RNA structures. In our RNA-As-Graphs (RAG) framework, we represent RNA structures as tree graphs by translating unpaired regions into vertices and helices into edges. Here we explore the modularity of RNA structures by applying graph partitioning known in graph theory to divide an RNA graph into subgraphs. To our knowledge, this is the first application of graph partitioning to biology, and the results suggest a systematic approach for modular design in general. The graph partitioning algorithms utilize mathematical properties of the Laplacian eigenvector (µ2) corresponding to the second eigenvalues (λ2) associated with the topology matrix defining the graph: λ2 describes the overall topology, and the sum of µ2′s components is zero. The three types of algorithms, termed  Ribonucleotide Acid (RNA) has become a prominent subject in modern biology, due to recent discoveries of RNA’s vital roles in regulating gene expression, which come in addition to well-known roles in protein synthesis At the heart of RNA structure is its modularity http://www.biomath.nyu.edu/rna) to represent global RNA topologies as graphs  based on the second Laplacian eigenvector µ2, and the RNA data set that we use. We then present results for the topological aspects described by the second Laplacian eigenvector, partitioning results for RNA graphs, and iterative partitioning results. The final discussion provides future directions of the work.G using RAG tree representation (see We represent RNA as a graph tion see . In the an edge (−) represents a double-stranded helical stem with more than one base pair.a vertex (•) represents a single strand that occurs in segments connecting 2D structural elements such as bulges, loops, and junctions. Here, a bulge motif is considered to be an internal symmetric or asymmetric loop with more than one unmatched nucleotide or one unstable base pair.G =  where V is the set of labeled vertices and E is the set of edges, we define the associated Laplace matrix  L = (ijm) = A, where A is an adjacency matrix and D is a diagonal matrix, as the n×n matrix whered(i) is the total number of edges from vertex i and n is the total number of vertices (n = |V|). See For a graph L is symmetric, the eigenvalues of the Laplacian matrix  are non-negative with the smallest eigenvalue λ1 = 0. In the field of the spectral graph theory, the Laplacian eigenvalues and eigenvectors have been extensively studied since they describe combinatorial or topological properties of a graph. In particular, the second smallest eigenvalue, λ2, and its corresponding eigenvector, µ2, provide information about topological compactness and the partitioning properties of graphs, respectively. The second eigenvalue λ2 is positive for any connected graph and increases with the compactness of a graph . Note that a graph topology can correspond to multiple RNAs. See We apply the graph partitioning algorithms to 45 RAG tree graphs with 4 to 11 vertices that have corresponding known structures in the Protein Data Bank (PDB) database  and eigenvector (µ2) of the Laplacian matrix constructed from the adjacency and degree matrices of each graph describe overall graph connectivity. The eigenvalue λ2 measures the graph complexity. For example, The corresponding eigenvector µ2 describes the local topological aspect of a graph. Thus, symmetry produces same numerical components. For example, From These three partitioning methods applied to 45 RAG graphs from 4 through 11 vertices corresponding to existing RNAs  produce the results shown in For large RNAs, we apply the gap partitioning iteratively to analyze how basic modules are assembled to make larger ones. For example, we partition graph 10_19 corresponding to the glycine riboswitch , which hSuch an iterative partitioning of gap cuts also suggests hierarchical assembly procedures from the basic modules building up to a large structure by an inverse cutting procedure. For example, to build up an RNA structure of 10_19, we start with five graphs with vertex labels in the fourth row in http://www.biomath.nyu.edu/rna, N. Baba et al. in preparation) has suggested concrete substructuring as well as design procedures. Namely, sequences corresponding to these basic motifs can be combined in a build-up type optimization we have done for RNAs We have presented graph-partitioning approaches using three splitting values – median, sign, and gap – based on the Laplacian eigenvector µ2 for understanding modular features of RNA. Our application of these spectral algorithms to all 45 existing RNA graphs from 4 through 11 vertices  requires further development. It may also be possible to approach pseudoknot partitioning using modified tree graphs that have additional edges or graph elements accounting for pseudoknot interactions.Our work here is the first application of standard graph partitioning algorithms to the area of biology. The partitioning procedures can be extended to the design of novel RNA motifs by assembling modules corresponding to the subgraphs identified from partitioning, as we have done previously for ten specific motifs A generalization of RNA graph partitioning to other areas of biology can certainly be envisioned. Many biological networks, such as metabolic pathways associated with biochemical reactions and regulatory protein interaction networks, have been constructed by organizing building blocks. Thus, the application of graph partitioning to these systems could define a valuable tool to identify the modularity of many networks in biology, engineering, and medicine.


The CD curves of homochiral L-RNA structural motifs are always reversed, whereas CD curves of heterochiral duplexes present individual features dependent on the composition of chiral strands.This is the first report to provide comprehensive thermodynamic and structural data concerning duplex, hairpin, quadruplex and i-motif structures in β-L-RNA series. Herein we confirm that, within the limits of experimental error, the thermodynamic stability of enantiomeric structural motifs is the same as that of naturally occurring D-RNA counterparts. In addition, formation of D-RNA/L-RNA heterochiral duplexes is also observed; however, their thermodynamic stability is significantly reduced in reference to homochiral D-RNA duplexes. The presence of three locked nucleic acid (LNA) residues within the D-RNA strand diminishes the negative effect of the enantiomeric, complementary L-RNA strand in the formation of heterochiral RNA duplexes. Similar behavior is also observed for heterochiral LNA-2′-O-methyl-D-RNA/L-RNA duplexes. The formation of heterochiral duplexes was confirmed by  RNA interference (RNAi), antisense strategy and steric blocking [e.g. 2'-O-methyl-RNA [Oligonucleotides have a wide range of applications related with the regulation of the biological and structural functions of nucleic acids. These molecules can be considered to be potential therapeutics involved in the modulation of pathogenic features of RNAs or DNAs. Oligonucleotides constitute an interesting alternative to currently marketed drugs that target proteins, which represent a relatively narrow group of total cellular proteins. Oligonucleotide-based drugs that target mRNA  have the potential to be effective for diseases not treatable by currently used drugs. In general, oligonucleotide therapy may consist of three different approaches blocking . Howeverblocking . Naturalthyl-RNA , locked thyl-RNA , 2′-fluothyl-RNA , unlockethyl-RNA , 7, 2′-Othyl-RNA  or phospthyl-RNA . Recentlthyl-RNA . Recentlthyl-RNA , 12. Furthyl-RNA . A similthyl-RNA . Bindingthyl-RNA , 16. Recthyl-RNA . These r20 and poly(rA) in the presence of 5 mM MgCl2 [et al. and Kawakami et al. studied the influence of single L-nucleotide residue on the stability of DNA, RNA and DNA/RNA duplexes and demonstrated that the incorporation of an unnatural residue significantly destabilizes formed structures [Thermodynamic studies published so far suggest that L-modified oligonucleotides containing all four canonical nucleotides do not hybridize to natural DNA or RNA , 19. Howructures –23.In this paper, the common structural motifs formed by L-RNA were analyzed thermodynamically. Also, the quantitative effect of the inverted chirality of homopyrimidinic or homopurinic strands on the stability of heterochiral RNA duplexes was studied. The model structures were characterized by circular dichroism (CD) and nuclear magnetic resonance (NMR) spectra.-L-RNA phosphoramidite monomers were purchased from ChemGenes. All oligonucleotides were synthesized on a MerMade12 (BioAutomation) RNA/DNA synthesizer using standard phosphoramidite chemistry. Thin-layer chromatography (TLC) purification of the oligonucleotides of length 6–12 nt was carried out on Merck 60 F254 TLC plates with the 1-propanol/ammonia/water = 55:35:10 (v/v/v). The oligonucleotides of length 13–32 nt were purified by polyacrylamide gel electrophoresis. Details of the deprotection and purification of oligonucleotides have been previously described [The βescribed . The com2EDTA, pH 7.0 (hairpins); 1 M sodium chloride, 20 mM sodium cacodylate and 0.5 mM Na2EDTA, pH 7.0 ; 100 mM potassium chloride, 20 mM sodium cacodylate and 0.5 mM Na2EDTA, pH 7.0 (quadruplexes); 100 mM potassium chloride, 20 mM sodium cacodylate and 0.5 mM Na2EDTA, pH 3.4 (i-motif). Single-stranded oligonucleotide concentrations were calculated based on the absorbance measured above 80°C and extinction coefficients that were approximated with the nearest-neighbor model, using the ribotask.com website. LNA-modified and unmodified RNA and 2ʹ-O-methyl-RNA strands with identical sequences were assumed to have identical extinction coefficients [−4–10−6 M, except for heterochiral duplexes. Melting temperatures of heterochiral duplexes were determined for 20 μM-sample concentrations via the first derivative of experimental melting curves. The measurements were performed in triplicate and the Tm value was obtained as an arithmetic average.Oligonucleotides were dissolved in a buffer containing 100 mM sodium chloride, 20 mM sodium cacodylate and 0.5 mM Naficients . The sam−4 M concentration of oligonucleotide are denoted by TM, and melting points for any other concentration of oligonucleotide are denoted by Tm.Melting curves were obtained using the UV melting method at 260 nm , 265 nm (i-motifs), and 295 nm  in the CD spectra were recorded on a JASCO J-815 spectropolarimeter. The oligonucleotides were dissolved in the same buffer as for UV melting studies to achieve a sample concentration of 3.0 μM. All samples were denatured at 90°C for 2 min and then slowly cooled to room temperature prior to data collection . The spe2HPO4/NaH2PO4, 0,1 mM EDTA (pH 6.8), while the D1, D2, D3, D4, D5 and β-L-UCUUUCUCUUCU and β-D-AGAAAGAGAAGA strands were dissolved in 1 M NaCl, 5 mM Na2HPO4/NaH2PO4, 0,1 mM EDTA (pH 6.8). The solvent was H2O/D2O . The samples were annealed by heating at 90°C for 5 min and then slowly cooled down to room temperature. The 3 mm thin wall tubes were used with a final sample volume of 200 μl. The water signal was suppressed by excitation sculpting with a gradient pulse. Spectra were processed and prepared with TopSpin 3.0 Bruker Software.All NMR spectra were acquired on a Bruker AVANCE III 700 MHz spectrometer, equipped with a QCI CryoProbe. The β-D-GCAUGC and β-L-GCAUGC strands were dissolved in 150 mM NaCl, 10 mM Nai.e. enthalpy, entropy and Gibbs free energy, as naturally occurring D-RNA counterparts .We also studied the thermodynamic stability of other commonly known RNA structural motifs in L-RNA series Tables  and 2. Sof error . AdditioIt has been reported that heterochiral duplexes composed of mixed-sequence homooligomers with opposite chirality cannot be formed , 19. HowIn general, inversion of chirality within one strand causes a serious decrease in thermodynamic stability. The thermodynamic studies were performed with oligonucleotides containing 12 nucleotides in each strand. Heterochiral RNA duplexes , D1. InvCircular dichroism (CD) spectra of various enantiomeric, homochiral L-RNA structural motifs are shown in The CD spectra of heterochiral duplexes are presented in 1H NMR spectroscopy to evaluate the nature of hydrogen bond pairing in the homo- and heterochiral RNA duplexes. 1H NMR spectra of β-L-RNA and β-D-RNA homochiral duplexes in the presence of 150 mM NaCl. The 1H NMR spectra of (β-D-GCAUGC)2 and (β-L-GCAUGC)2 duplexes with the chemical shifts of imino protons characteristic of Watson-Crick base pairs are identical . The absence of imino resonances in  strands  shows th strands  and 4A. Here we have shown a comprehensive thermodynamic analysis of structural motifs formed by mirrored RNA. Homochiral L-RNA duplexes are characterized by the same thermodynamic parameters as their naturally occurring D-RNA counterparts. Therefore, parameters from the nearest-neighbor model that are conventionally applied for predictions of the stability of natural RNA duplexes can also be used for mirrored RNA. Thermodynamic stability of other structural motifs formed by L-RNA are also comparable, even if not always the same, to the stability of those formed by D-RNA. Heterochiral duplexes show strongly decreased thermal stability in reference to homochiral structures; however, the presence of LNA residues within D-RNA strand reduces the unfavorable energetic cost of hybridization between two enantiomeric strands. CD spectra of homochiral L-RNA structural motifs are mirror images of the curves obtained for the structures formed by D-RNA; nevertheless, the CD shape of heterochiral duplexes is similar to their homochiral D-RNA counterparts. Using NMR spectroscopy, we were able to monitor the formation of heterochiral β-D/β-L-RNA duplexes. However, in order to determine their conformational features additional study would be necessary. Unfortunately, the broadening of the majority of the resonances may hinder the use of NMR spectroscopy in further structural analyses.S1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table2, pH 3.4; italic– β-L-RNA.a–solution: 100 mM KCl, 20 mM sodium cacodylate, 0.5 mM EDTANa(DOCX)Click here for additional data file.


These functions include important roles in translation, building translational machinery and maturing messenger RNA. More recent discoveries include the miRNAs and bacterial sRNAs that regulate gene expression, the thermosensors, riboswitches and other cis-regulatory elements that help prokaryotes sense their environment and eukaryotic piRNAs that suppress transposition. However, there can be a long period between the initial discovery of a RNA and determining its function. We present a bioinformatic approach to characterize RNA motifs, which are critical components of many RNA structure–function relationships. These motifs can, in some instances, provide researchers with functional hypotheses for uncharacterized RNAs. Moreover, we introduce a new profile-based database of RNA motifs—RMfam—and illustrate some applications for investigating the evolution and functional characterization of RNA. All the data and scripts associated with this work are available from:  Characterizing functional RNAs is an extraordinarily difficult task. Even highly transcribed RNAs from model organisms have remained uncharacterized for decades after their discovery. A specific example is the 6S sRNA, which was discovered in 1971. The 6S sRNA is conserved across Bacteria and is highly expressed in stationary-phase cells . But theA new generation of tools for RNA discovery is now available thanks to powerful new sequencing technologies. Entire transcriptomes from species at different life stages, tissue types and conditions can be studied with RNA-seq –8. The tFor the purposes of this work we define a RNA motif as a functional RNA structure that recurs within or across different RNA families. A motif may be characterized by a blend of primary, secondary and tertiary structural features. The motifs that have been characterized to date are involved in a diverse number of functions, including increasing structural stability , facde novo discovery of RNA secondary structure motifs from RNA primary sequences . These have been used to predict ∼1900 conserved motifs in the Rfam (v11.0) alignments of RNA families (these are available in Rfam (v12.0)  ); many oThe Rfam database collects and curates ‘seed alignments’ of RNA families. These are non-coding RNAs, cis-regulatory elements and self-splicing introns. The alignments are manually constructed and annotated with consensus secondary structures, and used to seed probabilities for CMs for each family. The Rfam CMs are widely used for genome annotation projects to identify RNA loci (e.g. ). A requAs described in the Introduction, we define RNA motifs as functional RNA structures that recur within or across different RNA families. These are a blend of primary, secondary or tertiary structure. Fortunately, for the purposes of this work, the majority of internal RNA contacts are local (i.e. within 100 nucleotides) , therefoAn example RNA motif is the GNRA tetraloop . These serve as positive controls for two further tests. For the ‘Rfam sequences’ benchmark we randomly selected at least five sequences from each Rfam seed alignment . We generated 10 shuffled versions of each sequence; all had an identical di-nucleotide distribution to the native sequence. These sequences were all annotated with RMfam motifs, their CM scores were recorded and used to evaluate the accuracy of the annotations. Finally, for a ‘Rfam alignments’ benchmark, we evaluated the accuracy of RMfam annotations in an alignment context. Each Rfam alignment was filtered, removing sequences more than 90% identical. The remaining sequences were annotated with RMfam CMs, retaining only those that cover more than 10% of the seed sequences and more than two Rfam seed sequences. The summary statistic we use for this final benchmark is a ‘sum-bits’ score, this is the sum of the bit scores for each match in all the sequences in a seed.We constructed two further tests based upon Rfam (v11.0) families. We identified Rfam families where there exists good evidence (primarily based upon reviewing the RNA literature) that a motif is conserved in the family of related sequences  , sensitiThe CMs built from RNA motifs tend to be short and contain little sequence information. In RMfam the mean sequence length is just 34.3 nucleotides and the mean number of base pairs is 10.9. Therefore, a scan of a large sequence database with these models will result in a number of false-positives. We propose that annotating sequence alignments of ncRNAs has the potential to improve the specificity of our predictions. This assumes that evolutionarily conserved motifs are more likely to be correct. In theory, this approach could be extended to genome alignments of, e.g. transcribed regions.http://github.com/ppgardne/RMfam). These have been used to predict ∼2500 conserved motifs in the Rfam (v11.0) seed alignments; many of which are confirmed in the published literature. Furthermore, our permutation tests have shown that both the sensitivity and specificity of this approach is remarkably high given the short motifs we use . These In this work, we have shown that RMfam is surprisingly accurate. Despite the fact that the average RMfam motif consists of just 34.3 nucleotides and 10.9 base pairs, we show that the CMs are specific enough to distinguish between motif-hosting sequences and negative control sequences  can in theory be a large distance apart, and therefore outside the QDB window covered by CMs . In pracAs a discovery tool the RMfam resource has already made some useful predictions. We have predicted the existence of two new CsrA-binding ncRNAs, potentially the first of this class of regulatory molecules to be found outside of the Gammaproteobacteria. However, further work needs to be carried out to validate this claim.We have identified some future developments and applications for the RMfam resource. We plan to continue developing the accuracy of the motif annotation tools as well as increase the access to RMfam annotations via other databases, such as Rfam (v12.0) , and expThe Lysine riboswitch example raises the possibility that certain types of motif are preferentially exchanged during the evolution of ncRNAs. Do stable hairpin motifs, such as the GNRA and T-loops, replace each other more frequently than we expect by chance? This would blur the lines between our understanding of homologous and analogous structures . AnotherTypical RNA structure prediction methods to not incorporate information about RNA motifs. We propose that RMfam predictions can be used as constraints for existing RNA structure prediction software, thus improving the accuracy of structure prediction tools which can often be inaccurate . This apAnother application for RMfam CMs is as a pre-filter to accelerate the more complex methods, for example, the Bayesian network approach implemented in RMdetect .Increasing the access of motif annotations is another goal of the authors. We are active in the Rfam consortium which curates non-coding RNAs alignments –14. Our New technologies, such as the sequencing of cross-linked RNA and protein, are a potential source of new RNA-protein motifs. In the future we will mine these data sets –80 for nFinally, as previously mentioned, the specificity of the RMfam annotations is generally low. However, incorporating the genomic and taxonomic context of annotations into the predictions may result in performance gains. For example, Shine–Dalgarno and Rho-independent terminators are generally located in bacterial sequences and at the extremities of annotated genes. A probabilistic incorporation of contextual information will likely result in further performance gains.In summary, we have developed a resource for annotating diverse sets of RNA motifs in nucleotide sequences and alignments. We have proven the accuracy using benchmarks, and the utility of this resource for alignment curation, evolutionary analyses and shown that it has some promise for the prediction of RNA function.Supplementary Data are available at NAR Online.


Identifying similarities and differences in choice behavior across species is informative about how basic mechanisms give rise to more complex processes. In the present study, we compared pre- and post-choice latencies between rats and humans under two paradigms. In Experiment 1, we used a cued choice paradigm where subjects were presented with a cue that directed them as to which of two options to respond for rewards. In Experiment 2, subjects were free to choose between two options in order to procure rewards. In both Experiments rewards were delivered with distinct probabilities. The trial structure used in these experiments allowed the choice process to be decomposed into pre- and post-choice processes. Overall, post-choice latencies reflected the difference in reward probability between the two options, where latencies for the option with higher probability of reward were longer than those for the option with lower probability of reward. An interesting difference between rats and humans was observed: the choice behavior for humans, but not rats, was sensitive to the free-choice aspect of the tasks, such that in free-choice trials post-choice latencies no longer reflected the difference in reward probabilities between the two options. According to optimal foraging theoryThere are clear similarities between animal and human choice behavior, suggesting common underlying mechanisms. For example, animals and humans obey the Matching Law23467Previous assessments of choice behavior have mainly focused on pre-choice latencies, where an inverse relationship is revealed between reaction times and self-reported choice confidence10111213111213161718Outcome expectancy and choice confidence are usually assessed with self-report measures. This typically involves a series of questions after the experimental session, or interspersed sporadically throughout the session. Thus, self-reports may not reflect choice mechanisms on a trial-by-trial basis. We used a discrete-trial structure to parse a single choice into its constituent stages and focused on the temporal dynamics of behavior as manifestations of choice mechanisms. This allowed us to measure response latencies leading up to and following from the explicit choice act. These behavioral correlates of choice were compared between rats and humans, in addition to comparing differences in choice allocation. Our previous work found response latencies to be informative beyond choice allocation as to how environmental contingencies shape mechanisms underlying choice2021Here, we investigate if the systematic profiles in pre- and post-choice behavior generalize across species under two paradigms: firstly, when choices are cued (Experiment 1), and secondly, when choices are un-cued (Experiment 2). In the cued choice paradigm, a visual cue directed subjects to one of two options which were rewarded probabilistically. This cued-choice paradigm allowed precise temporal alignment of choice latencies, which facilitated interpretation of the differences and similarities between rat and human behavior. This provided a framework with which we could assess response latencies in an un-cued paradigm in Experiment 2, where subjects were free to choose between two options. Importantly, in the present study rats and humans were presented with tasks that were similar in trial structure.Experiment 1 compared choice behavior of rats and humans in a discrete-trial cued-choice paradigm. On every trial, visual cues directed subjects as to which of two reward options to select, and the two options were rewarded with distinct probabilities . We examined two behavioral aspects of choice; firstly the duration between stimulus presentation and response (cue response time), and secondly, the amount of time subjects were willing to wait for a reward . Half of the human participants were additionally presented with interleaved free-choice trials, which enabled us to directly assess choice selection and the response latencies associated with that choice.To examine response latencies, we focused on data from the second and third sessions (second and third quarters) as these periods were when behavior was stable and most sensitive to cue contingencies . The foc2324For rats, all groups followed the High cue on more than 90% of trials . Performp < 0.001, ηpDetection of the High versus Low cue probabilities for human participants was verified by examining their response to the interleaved free-choice trials . One-wayOverall, rats were faster to respond to the High cue than the Low cue and this was most pronounced for group 80–20 , left. Pp < 0001, z = 9.803, r = 0.19; group 70–30: U = 512041, p < 0.0001, z = 22.242, r = 0.42; group 80–20: U = 219042, p < 0.0001, z = 31.861, r = 0.63). Rats showed longer spout sampling durations relative to baseline when responding to High .The pattern observed in  to High  and thisp = 0.02, ηp 2 = 0.03; In contrast to what was observed for rats, human participants’ cue response times were not different for the two cues (data points lie around zero) and this lack of a difference between cue response times for High and Low was evident across all four quarters of the experimental session , left. Pp = 0.019, z = 2.34, r = 0.06) and group 80–20 , and effect sizes were larger for group 80–20 than 70–30.All groups had longer key press durations when presented with High compared to baseline while key press durations in response to Low were shorter than baseline , left. Tp = 0.04, z = 2.083, r = 0.17) and 80–20  chose Low than when they chose High  and 80–20  showed longer High key press durations than Low . In contrast to the similar cue response times for High and Low on regular trials, cue response times on interleaved free-choice trials were significantly longer when participants in groups 70–30 , thereby introducing variability to the relationship between latency variables. We found cue response times to be negatively correlated with spout sampling durations for all rat groups . Thus, we propose that participants selected the Low cue on free-choice trials when they felt it was more likely to rewarded, and this manifested as longer key press durations. Alternatively, it might be the case that participants viewed free-choice trials as an opportunity for task exploration. A related possibility might be that free-choice trials provided a means of testing the experienced probabilities on the 10 preceding trials. In both these possibilities, participants’ choices would be motivated by uncertainty resolution as distinct from outcome expectancy.Experiment 2 used an un-cued choice task to verify the effect of self-generated choice on post-choice latencies. Experiment 2 also aimed to verify that the same positive relationship between cue response times and key press durations would be evident when all trials involved a free-choice for human participants. To do this, we presented human participants with an un-cued version of the task in Experiment 1. For rats, this un-cued task is identical to that used in our previous work and we re-analyzed the data from that studyIn Experiment 2, we presented subjects with an un-cued version of the first experiment  and group 80–20 . Thus, choices which are self-generated result in an escalation of commitment. Experiment 2H is a further demonstration of escalation of commitment using an un-cued task where every trial consisted of free-choice between High and Low. In this second experiment, escalation of commitment was evident in equivalent key press durations for High and Low. The positive relationship between pre-choice latencies and key press durations seen in Experiment 1H was also replicated in 2H, where long choice execution latencies were associated with long key press durations. Thus, for human participants, key press durations did not reflect the differences in reward probabilities between High and Low, but instead correlated to the duration of choice execution.Choice proportions indicated that rats and humans detected the greater likelihood of reward from High compared to Low. This is particularly interesting for human participants, as we found no difference in key press durations between High and Low. Hence, although the differences in reward probabilities were reflected in choice allocation, they did not manifest in post-choice commitment. This can be seen to be in-line with what we observed in Experiment 1. From Experiment 1R and 2R showed consistent spout sampling duration patterns reflecting greater choice commitment for High than Low regardless of whether choices were cued or not. Thus, while the free-choice aspect changed the appearance of choice commitment in human participants, it did not alter the choice behavior of rats. For rats in Experiment 2R, the correlation between choice execution latencies and spout sampling durations was not consistent, suggesting that spout sampling durations reflected the differences in reward probabilities between High and Low, and were not related to the amount of time taken to execute a choice.By contrast, rats in Experiment 1The present study examined choice behavior of rats and humans using a discrete trial procedure where rewards were delivered probabilistically. In Experiment 1, rats and humans were cued as to which option to select, and we quantified post-choice behavior as spout sampling durations for rats, and key press durations for humans. For both rats and humans, pre-choice latencies were quantified as cue response times. Detection of reward contingencies was additionally assessed for half of the participants through free-choice trials interleaved throughout the session. These free-choice trials were unrewarded and allowed us to directly compare participants’ choices, cue response times and key press durations. We found that behavior on these free-choice trials differed from regular trials and this was examined further in a second experiment which employed an un-cued version of Experiment 1.R indicated that rats had learnt there was a greater probability of reward from High. Rats showed the same pattern of differential latencies between cued and un-cued choice. This was clearest for spout sampling durations, which were longer for High than Low regardless of whether choices were cued or not. For group 60–40 in Experiment 2R, choice proportions were at equivalence (0.5) but there was still evidence of differential spout sampling durations in some of sessions  did not vary with reward probability, while for rats in Experiment 1R, cue response times reflected the difference in reward probabilities. This is interesting as the knowledge that High was more frequently rewarded did manifest in key press durations of human participants. Another difference between rat and human behavior was the way free-choice altered choice behavior for humans, but not for rats. This was observed in two ways. Firstly, for humans, self-generated choice in Experiment 2 resulted in equivalent commitment levels for High and Low , but rats showed longer spout sampling durations for High than Low when choices were cued and un-cued. Secondly, the choice aspect of free-choice trials in Experiment 1H and the un-cued task in Experiment 2H resulted in a positive relationship between pre-choice latencies and key press durations for human participants, as indicated by the correlation analyses. One possibility is that for human participants, longer pre-choice latencies reflected the need for deliberation due to uncertainty as to which was the better option. In order to resolve this uncertainty, participants invested more time in determining outcomes by holding the response key down for durations that exceeded the maximum reward delay. By contrast, self-directed choice for rats resulted in more variable relationships between choice execution latencies and spout sampling durations. For rats in the cued choice task, there was a negative relationship between pre-choice latencies and post-choice latencies. This could reflect that the same process of learned value which shortens approach latencies, also increases expectation of reward and increases sampling durations. This supports previous research suggesting that response latencies can reflect choice confidence. It is important to note, however, that these correlations may also point to other explanations. Indeed, although the p values associated with the correlation analyses are highly significant , the r values themselves are small, pointing to small but robust effects that could indicate a relationship with a third variable (e.g. passage of time since commencement of the experiment).One difference between rat and human choice behavior was that cue response times for human participants in Experiment 1Most characterizations of confidence relate to self-report measures. Behavioral measures of choice confidence have recently been used in a variety of paradigms2627We were able to show that post-choice latencies can be dissociated from the explicit choice itself. Recent studies of the Perruchet effect have similarly shown that reaction times can be dissociated from self-reported outcome expectancy using human participants1617182021We conducted four experiments to compare choice behavior in rats with humans. For both humans and rats immediate post-choice behavior demonstrated greater commitment to the more frequently rewarded option. We identified that the element of free-choice resulted in an escalation of commitment for humans, which manifested as equivalent key-press durations for High and Low in the second experiment, despite the difference in reward probabilities. This was not observed for rats; instead the difference in High versus Low reward probabilities exerted control over post-choice latencies across both experiments, when choices were either cued or un-cued. Overall, rat and human choice behavior followed optimal foraging theory in that, suggesting a common mechanism which guides choice across species.It is important to note that the findings presented here are open to interpretation. Further research is needed to verify the underlying mechanisms proposed here, as well as to rule out the alternative explanations that have also been put forth.Subjects were 18 experimentally naïve, adult, male Wistar rats with initial weights of 266–575 g. Rats were housed in a climate controlled colony room on a 12 hour light-dark cycle (lights on at 7 am). All procedures were approved by, and carried out in accordance with the guidelines provided by, the Animal Care and Ethics Committee at the University of New South Wales. Rats were given one hour free access to food and water after each session and body weights were monitored to ensure they did not drop below 85% of their free-feeding weight.Rats were trained in a clear Plexiglass chamber measuring 30 cm (length) × 30 cm (width) × 50 cm (height) with a circular nose-poke port . The nose-poke port had optical sensors attached to the inside walls to detect head entries. To the right and left of the aperture were drinking spouts which allowed a 500 ms delivery of 5% sucrose solution via pumps located outside the testing chamber. These reward spouts were fitted with sensors to detect licking at a temporal precision of 1 ms. An infrared camera provided video monitoring of behavior. The experiment was controlled using programs written in Matlab. All training occurred in a dark room with background noise (~70 db) to mask any extraneous sounds. Visual stimuli  were presented directly above the left or right reward spouts depending on the cue allocation on every trial. Rats were placed in the experimental chamber for 10 minutes and reward was freely available from both reward spouts. The circle stimulus was presented simultaneously with every reward delivery. The nose-poke aperture was blocked at this stage. The nose-poke aperture was un-blocked and stimuli were presented only when rats had performed a nose-poke. Across four sessions, stimulus and reward delays were introduced and gradually increased: 0 ms (session 1), 75 ms, 100–200 ms, 100–400 ms (session 4). Training continued until rats were correctly following the stimulus location on more than 90% of trials. Rats were presented with the assigned reward probabilities for four sessions.Participants were 90 undergraduate students  enrolled at the University of New South Wales who volunteered to participate in the study in return for course credit. All participants provided informed consent prior to commencement of the experiment. All procedures were approved by, and carried out in accordance with the guidelines provided by, the University of New South Wales Human Research Ethics Committee. Participants were randomly allocated to one of three conditions.www.abdnfacelab.com/downloads). Gender of the photographs was associated with reward probability; for example, male photographs had a higher probability of reward compared to female photographs for some participants and this was counterbalanced across groups. We used a range of male and female faces to increase the level of engagement with the task. The computer task was controlled using programs written in Matlab .Participants were presented with five female and five male photographs (each presented 20 times) in random order over a total of 100 trials. Photographs were selected from the Glasgow Face Matching Test database (Participants were told to imagine they were employed by the customs department at the airport to search people’s luggage for contraband items (reward). Participants were also told that they were free to determine the duration of time spent searching a particular person’s luggage, however, not every person they encounter carries contraband items. This therefore presented participants with the task of having to determine the balance between searching long enough (so as not to miss finding contraband items) yet not too long (thereby delaying the entire task).Photographs were presented either on the left or right of the computer screen and participants responded by pressing and holding down the F1 key (labeled “left”) for photographs presented on the left, and the F10 key (labelled “right”) for photographs presented on the right. Release of either key terminated a search and the trial; the key had to be pressed down continuously for the duration of time that participants wanted to engage in the luggage search. The order of left/right location of stimuli was randomly determined and half of each stimulus type  were presented on the left and right. Participants were given feedback as to the number of total offenders they had successfully apprehended through a tally shown on the top of the computer screen, but this tally did not show the breakdown of contraband items that had been obtained from male versus female photographs.The experiment was a between subject-design, with three groups which differed in the probability of reward associated with male and female stimuli; 60 vs 40; 70 vs 30; and 80 vs 20 . The key press durations on unrewarded trials (when contraband items were not found) were the focus of our analyses and we do not consider key press durations when contraband items were found.Half of the participants in each probability group additionally experienced interleaved free-choice trials where a male and a female photograph appeared simultaneously , and a choice had to be made by pressing the corresponding response key. Free-choice trials occurred every 10 regular trials and were all unrewarded. The free-choice trials assessed whether participants were sensitive to the reward probability manipulations.Demographic information and written consent was obtained before the experiment commenced. Participants were given written instructions for the task and asked to complete the task with one hand only (dominant hand). We did not identify any systematic differences related to handedness of the participant. Participants were then given a full debriefing and the experiment concluded.R and 1H differ in terms of the stimulus property that predicted rewards. For rats in Experiment 1R, the stimulus location was associated with reward probability, thus there was a High side versus a Low side. By contrast, the gender of presented stimuli was associated with reward probability for human participants in Experiment 1H; rather than a High side/Low side, participants had to distinguish between male and female photographs. This difference did not alter the task structure between rats and humans, and The experimental task used for Experiment 1R, key press onset for Experiment 1H). Post-choice latencies on unrewarded trials were defined as the duration of the response; for Experiment 1R this was the duration between first and last lick at reward spouts (spout sampling duration), while for Experiment 1H, this was the duration with which participants held the response key down (key press duration).All analyses were carried out using Matlab, Graphpad and SPSS. Cue response times were defined as the duration of time between onset of stimulus and onset of response  enrolled at the University of New South Wales who volunteered to participate in the study in return for course credit. All participants provided informed consent prior to participating in the experiment. All procedures were approved by, and carried out in accordance with the guidelines provided by, the University of New South Wales Human Research Ethics Committee. Participants were randomly allocated to one of three conditions . Apparatus, stimuli, and procedure were similar to Experiment 1H. The key difference was that participants were instructed to make a choice between a left or right option (A or B) on every trial. The two options were rewarded according to the same probability pairs , and required the same key-press response as in Experiment 1H. A tally of rewards received was presented on the computer screen, but did not provide a breakdown of the number of rewards received from selecting A or B. This maintained the requirement for participants to acquire internal representations of which option was more frequently rewarded.Participants completed a computer task which closely resembled that described in Experiment 1How to cite this article: Fam, J. et al. Temporal dynamics of choice behavior in rats and humans: an examination of pre- and post-choice latencies. Sci. Rep.6, 20583; doi: 10.1038/srep20583 (2016).


Arabidopsis thaliana genetic studies have demonstrated that RDR2 and RDR6 are involved in the synthesis of double stranded RNA (dsRNA) from single stranded RNA (ssRNA) targeted by RNA silencing. The dsRNA is subsequently cleaved by the ribonuclease DICER-like into secondary small interfering RNAs (siRNAs) that reinforce and/or maintain the silenced state of the target RNA. Models of RNA silencing propose that RDRs could use primer-independent and primer-dependent initiation to generate dsRNA from a transcript targeted by primary siRNA or microRNA (miRNA). However, the biochemical activities of RDR proteins are still partly understood. Here, we obtained active recombinant RDR2 and RDR6 in a purified form. We demonstrate that RDR2 and RDR6 have primer-independent and primer-dependent RNA polymerase activities with different efficiencies. We further show that RDR2 and RDR6 can initiate dsRNA synthesis either by elongation of 21- to 24- nucleotides RNAs hybridized to complementary RNA template or by elongation of self-primed RNA template. These findings provide new insights into our understanding of the molecular mechanisms of RNA silencing in plants.Cellular RNA-dependent RNA polymerases (RDRs) are fundamental components of RNA silencing in plants and many other eukaryotes. In  RNA-dependent RNA polymerases (RDRs) are required in RNA silencing, a conserved RNA-based mechanism involved in stability, protection, inheritance and expression of eukaryotic genomes at transcriptional or post-transcriptional levels –6. RDRs Arabidopsis thaliana, six RDRs have been identified. RDR1, RDR2 and RDR6 are functional in several siRNAs pathways whereas the functions of RDR3a, RDR3b and RDR3c, which belong to a distinct phylogenetic clade, have yet to be identified , 7 M urea gels, using TTE buffer (89 mM Tris/HCl (pH 8.0), 28 mM Taurine and 0.5 mM EDTA). The gels were exposed and analysed using photo-stimulated plates and a Fluorescent Image Analyzer .RDR activity assays were performed according to Makeyev and Bamford  with somFour microliters of the polymerase reaction mixture supplemented with 5 units of RNaseT1 (Invitrogen) were incubated 30 min at 30°C and the reaction was placed on ice. Then, 1μl of 200 mM Dithiothreitol in addition to the formamide/EDTA loading buffer was added to the mixture and samples were heated 5 min at 65°C before loading on polyacrylamide/Urea gels.N. benthamiana. Full-length RDR2 and RDR6 coding sequences with a double HA tag at the 3’ end were cloned into the pEAQ-HT plasmid. pEAQ-HT contains a gene for the expression of the P19 inhibitor of RNA silencing in planta and the introduced coding sequences are flanked by the 5’-untranslated region (UTR) and the 3’-UTR from Cowpea mosaic virus (CPMV) RNA-2. These features allow for an extremely high-level production of proteins GTP, RDR2-HA was able to synthesize “higher product” . SimilarThis size heterogeneity observed for RDR2-HA products could arise from a more efficient transcription initiation of RNA templates with 3’ terminal nucleotide primed at different positions. Another nonexclusive hypothesis could be that RDR2-HA start transcription initiation of RNA templates with 3’ terminal nucleotide primed at a unique position but has reduced elongation processivity which gives rise to abortive products.We conclude that both RDR2-HA and RDR6-HA are able to perform elongation of self-primed ssRNA templates. However, contrary to RDR2-HA, RDR6-HA has elongated only one of the three RNA templates used, RNA38. This could be due to different nucleotide sequences or to the nature of the 3’ terminal nucleotide of the templates . In addiIn vivo studies have shown that AtRDR6 has primer elongation activity [in vitro with RDR6 alone [in vivo [in vivo hybridization of an siRNA or miRNA to a target RNA. Each RDR was incubated with the 5’[32P]-labeled partial dsRNA templates and cold NTPs  i.e. primary siRNA or miRNA hybridized to target ssRNA ,19. In vactivity , this acR6 alone  or with R6 alone  and that-labeled 38-nt ssRNA template (RNA38☢), cold NTPs and trace amounts of [α32-GTP]. (*) A single star indicates the position of the RNA38☢ template.(TIF)Click here for additional data file.S3 FigRNA37A template was hybridized to either 5‘[32P]-labeled RNA of 21- (RNA21☢), 22- (RNA22☢) or 24-nt (RNA24☢) sizes oligonucleotides.(TIF)Click here for additional data file.S4 FigPurified RDR2-HA and RDR6-HA were incubated with partial dsRNAs formed with RNA37A hybridized with 5’[32P]-labeled 22-nt ssRNA (RNA22☢). 5'[32P]-labeled RNA37A (RNA37A☢) and 5'[32P]-labeled RNA22 (RNA22☢) are used as a size markers. (*) Simple star indicate the position of the RNA22☢. (**) Double star indicates the position of the full-length elongated RNA template.(TIF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.