Integration of RNA-seq expression data with knowledge on chromatin accessibility, histone modifications, DNA methylation, and transcription factor binding has been instrumental for the unveiling of cell-specific local and long-range regulatory patterns, facilitating further investigation on the underlying rules of transcription regulation at an individual and allele-specific level. However, full genome transcriptome characterization has been partially limited by the complexity and increased time-requirements of available RNA-seq library construction protocols.Use of the SX-8G IP-Star® Compact System significantly reduces the hands-on time for RNA-seq library synthesis, adenylation, and adaptor ligation providing with high quality RNA-seq libraries tailored for Illumina high-throughput next-generation sequencing. Generated data exhibits high technical reproducibility compared to data from RNA-seq libraries synthesized manually for the same samples. Obtained results are consistent regardless the researcher, day of the experiment, and experimental run.Overall, the SX-8G IP-Star® Compact System proves an efficient, fast and reliable tool for the construction of next-generation RNA-seq libraries especially for trancriptome-based annotation of larger genomes. C. elegans and D. melanogaster modENCODE [Deciphering the underlying determinants of transcriptional regulation in relation to cell differentiation, functional diversification, environmental signaling, and disease development remains a central question in biology today. Integration of expression data with knowledge on chromatin accessibility, histone modifications, DNA methylation, and transcription factor binding, has been instrumental for the unveiling of cell-specific local and long-range regulatory patterns, facilitating further investigation on the underlying rules of transcription regulation at an individual and allele-specific level. Current interest by large collaborative projects, such as the ENCODE , the NIHodENCODE , has beeMus musculus (mouse) samples. Use of the SX-8G IP-Star® Compact System significantly reduced the hands-on time for RNA-seq library synthesis, adenylation, and adaptor ligation providing with high quality RNA-seq libraries tailored for Illumina high-throughput next-generation sequencing. Generated data exhibited high technical reproducibility compared to data from RNA-seq libraries synthesized manually for the same samples. Obtained results are consistent regardless the researcher, day of the experiment, and experimental run. Overall, the SX-8G IP-Star® Compact System proves an efficient and reliable tool for the construction of next-generation RNA-seq libraries especially for trancriptome-based annotation of larger genomes.However, full genome transcriptional gene characterization has been partially limited by the complexity and increased time-requirements of available RNA-seq library construction protocols. Here we report the successful application of the SX-8G IP-Star® Compact System (Diagenode) for the easy, rapid, and reproducible RNA-seq library construction of five A schematic step-wise representation of the two tested protocols is presented in Figure Mus musculus GRCm38/mm10 genome using TopHat 2.0.7 [2, linear regression model) based on FPKM values of each sample and corresponding technical replicate were generated using the statistical language R. Data visualization, density distribution of FPKM values and cluster analysis were performed using the CummeRbund 2.7.1 R package (http://compbio.mit.edu/cummeRbund/).RNA-seq data generated using the manual and automated protocols were aligned against the at 2.0.7 . Followiat 2.0.7 ,7 to estApplication of the SX-8G IP-Star® Compact System for the RNA-seq library construction of five mouse samples, significantly reduced the amount of hands-on time required for the most time-demanding stages of library synthesis, adenylation, and adaptor ligation including all related clean up steps. Specifically, manual library construction with the protocol routinely used in our laboratory typically takes an average of four hours of hands-on time whereas Diagenode automated library construction with the same reagents and samples required only 30 minutes. This corresponds to a 8-fold decrease in the amount of time the researcher has to be directly involved with the procedure, offering substantial flexibility for experimental multitasking.2 obtained using the linear regression model in R for the five mouse samples and corresponding technical replicates ranged from 0.97-0.98, confirming that the SX-8G IP-Star® Compact System can be reliably used for the efficient and accurate construction of RNA-seq libraries (Figure Notably, generated data with the automated protocol exhibited high technical reproducibility compared to data from RNA-seq libraries synthesized manually for the same samples regardless operator and experimental run. Specifically, density distributions of FPKM values demonstrated high data concordance among samples and technical replicates Figure . Correlas Figure . Clusters Figure .Figure 2Overall, the SX-8G IP-Star® Compact System proves an efficient, reliable and accurate tool for the construction of next-generation RNA-seq libraries, especially for trancriptome-based annotation of larger genomes. We foresee that incorporation of this technology in Next-Generation Sequencing Cores or Genomics Laboratories will prove an indispensable tool for high-throughput RNA-seq library construction, significantly saving on-hands experimentation time, related costs and error-prone manual steps. Added benefits of the automated protocol include ease of operation and generation of consistent data regardless of human variability and experimental run. Adaptation of this technology should support the unveiling of the mechanisms governing differential gene expression and transcription processing genome-wide, leading to a better understanding of genetic and epigenetic regulation and inheritance in a time-efficient manner.


This article provides a detailed introduction to fundamental RNA-seq molecular biology and informatics concepts. We make available open-access RNA-seq tutorials that cover cloud computing, tool installation, relevant file formats, reference genomes, transcriptome annotations, quality-control strategies, expression, differential expression, and alternative splicing analysis methods. These tutorials and additional training resources are accompanied by complete analysis pipelines and test datasets made available without encumbrance at  This is part of the PLOS Computational Biology Education collection.Gene expression is a widely studied process and a major area of focus for functional genomics . Gene exThe RNA-seq method typically consists of identification of suitable biological samples (and replicates), isolation of total RNA, enrichment of nonribosomal RNAs, conversion of RNA to cDNA, construction of a fragment library, sequencing on a high-throughput sequencing platform, generation of single or paired-end reads of 30–300 base pairs in length, alignment or assembly of these reads, and downstream analysis  5,6]. T,6. T5,6]www.rnaseq.wiki, and the videos of these lectures made available at http://bioinformatics.ca/workshops/. Finally, we make available open-access tutorials that cover cloud computing for RNA-seq analysis, tool installation, relevant file formats, reference genomes, transcriptome annotations, quality control, and complete pipelines for expression, differential expression, and alternative splicing analysis . The tutorials represent an example RNA-seq workflow based on the “tuxedo” suite [www.rnaseq.wiki).In this educational piece, we explore molecular biology concepts of RNA-seq that influence RNA-seq analysis workflows and data interpretation. We also provide a detailed introduction to fundamental RNA-seq informatics concepts and common analysis questions. These concepts are covered here, in the Supplementary Materials and lectures made available at o” suite  and othewww.rnaseq.wiki). Since most RNA-seq experiments involve comparisons between and across conditions, it is desirable that these factors be consistent across all samples and replicates within an experiment. In addition to classic sources of batch effects , each of these design parameters can introduce systematic biases. Since there is currently a large amount of diversity across published datasets with respect to these and other factors, meta-analyses that combine publicly available data should be pursued with caution. It is likely that RNA-seq will become increasingly standardized with respect to experimental design, data generation, and analysis strategy. Several efforts aimed at establishing best practices are underway . For these tutorials and the following analysis discussions, we selected the “tuxedo” suite and other commonly used tools to illustrate an example RNA-seq analysis workflow. These specific tools were selected because of their widespread use. In our opinion, they are some of the better-engineered options and have acceptable levels of documentation. However, for each analysis application . This approach has several advantages for both RNA-seq users and instructors. It ensures a consistent computing environment across all students, and the elasticity of cloud computing allows the same tutorials to be run by small or large groups, even allowing for the possibility of a massive open online course (MOOC). We are able to store machine instances that represent multiple states during the tutorial exercises, including the starting point , an intermediate state with all necessary tools installed, and finally an instance with all analyses complete. These represent useful reference points for comparison to the student’s own work. By performing all tutorials on the cloud, the students learn the basics of cloud computing as they learn about RNA-seq. Using the cloud for instruction also allows the student to easily establish an RNA-seq pipeline in his or her own lab that is based directly on the tutorials, operates in the same environment, and does not require purchasing or administering the substantial hardware that may be needed for RNA-seq data analysis. We provide an extensive introduction to cloud-computing concepts and specific cloud administration skills in the Supplementary Tutorials online . Once read trimming is complete, the next step in most RNA-seq applications is alignment [In order to understand RNA-seq raw data and alignments, one must develop an understanding of the file formats and underlying data models they represent. These include the FASTA format for storing reference genome data , the genlignment  or assemlignment ,61. RNA-lignment , Savant lignment , and intlignment  are capalignment  of IGV aOne of the most widely used applications of RNA-seq is the estimation of gene or transcript abundance and comparison of these abundances across biological conditions . Gene abhttp://biorxiv.org/content/early/2014/07/14/007088).While the raw read count methods are well developed and have been validated as a robust alternative to expression microarrays and gold standard assays such as qPCR , they hawww.rnaseq.wiki), we explore the use of both count based and “FPKM style” expression estimation and associated differential expression tools.In order to estimate transcript abundance, Cufflinks first builds transcript isoforms by identifying overlapping “bundles” of fragment alignments. These are assembled, fragments are connected in an overlap graph, and transcript isoforms are inferred from the minimum paths required to cover the graph. Following use of Cufflinks to estimate transcript structures and abundance in each sample, Cuffmerge is used to merge several Cufflinks assemblies together . This ishttp://www.R-project.org) and Bioconductor [Interpretation, summarization, and visualization of expression and differential expression results can be just as involved as generating these results . CummeRbonductor . While wonductor ,72 as a onductor , GAGE , an online question-and-answer forum for bioinformatics [Certain questions consistently arise among researchers performing RNA-seq analysis. To avoid repetition of effort, we advocate for these questions to be asked and answered within “BioStars” (ormatics  in whichwww.rnaseq.wiki) using example data sets (CBW), Cold Spring Harbor Laboratories (CSHL), and future collaborating partners. We recognize that the current example workflow relies heavily on the existence of a reference genome sequence. We hope in the future to add example workflows that use reference-free or alignment-free methods of RNA quantification Click here for additional data file.S2 Data(PDF)Click here for additional data file.S1 TableThere are several downstream analysis goals for which RNA-seq is well suited. The main categories of these are described briefly below with reference to supporting materials. Refer to (PDF)Click here for additional data file.S2 Tablewww.rnaseq.wiki) are referenced below (tool name in bold) along with alternative tools in each category. Whenever possible, a citation is provided. Links are also provided to help the user evaluate the code and the level of maintenance. Whenever possible, the link goes directly to a source controlled repository such as a git repo. Additional lists of tools can be found here: Alamancos et al. (arXiv), the rna-seqblog, and RNA-seq—Protocols and Algorithms. This table is meant to be comprehensive but not exhaustive. Some RNA-seq analysis topics that are not explicitly covered here include co-regulation (co-expression), disease classification, time series, expression compendium databases, outlier expression, data normalization, and miRNA analysis.All tools used in the online tutorial ((PDF)Click here for additional data file.S3 TableSequencing Quality Control (SEQC) consortium, the Encyclopedia of DNA Elements (ENCODE) consortium, the Roadmap Epigenomics Mapping Consortium (REMC), and the Beta Cell Biology Consortium (BCBC).The following table summarizes several key concepts relating to sample preparation and library construction that may influence analysis and interpretation of RNA-seq data. Several initiatives are underway to develop standards and best practices that cover many of these concepts. These include the (PDF)Click here for additional data file.S4 TableA description of three RNA enrichment strategies is provided, along with their anticipated effects on RNA-seq library construction and data interpretation. For a visual depiction of the concepts discussed here, refer to (PDF)Click here for additional data file.S5 TableIGV’s read orientation codes for RNA-seq data viewed in the browser. Also provided are recommended software settings for three additional tools involved in common RNA-seq analysis workflows: TopHat, HTSeq, and Picard. Each of these explanations/settings is provided for several commonly used RNA-seq library construction kits that produce either stranded or unstranded data.This table provides further explanation of (PDF)Click here for additional data file.S6 Tablewww.rnaseq.wiki).The following table describes several file formats used in most RNA-seq analysis workflows as well as several files specific to the expression analysis tools used by the online tutorials that accompany this article (at (PDF)Click here for additional data file.S7 TableThe following table summarizes a list of commonly asked questions relating to RNA-seq analysis, with links to BioStar posts in which these questions have been addressed by the community.(PDF)Click here for additional data file.S8 Tableresources section of the online tutorial at www.rnaseq.wiki.The following table provides a list of general resources to help understand the background of RNA biology, next-generation sequencing, RNA-seq laboratory methods, and RNA-seq analysis. Additional educational resources can be found in the (PDF)Click here for additional data file.S9 Tablehttps://www.biostars.org/p/79845/ and https://www.biostars.org/p/11034/.The following table lists RNA-seq workshops and other tutorials complementary to this article. These examples are limited to online materials or short workshops. Not listed here are formal training programs or degrees in bioinformatics. For ongoing discussion of this topic, refer to these BioStar posts: (PDF)Click here for additional data file.S1 Text(PDF)Click here for additional data file.


Jasus edwardsii. Experiments were conducted using in situ binary choice chambers together with replayed recording of underwater reef sound. The experiment was conducted in a sandy lagoon under varying wind conditions. A significant proportion of puerulus (69%) swam towards the reef sound in calm wind conditions. However, in windy conditions (>25 m s-1) the orientation behaviour appeared to be less consistent with the inclusion of these results, reducing the overall proportion of pueruli that swam towards the reef sound (59.3%). These results resolve previous speculation that underwater reef sound is used as an orientation cue in the shoreward migration of the puerulus of spiny lobsters, and suggest that sea surface winds may moderate the ability of migrating pueruli to use this cue to locate coastal reef habitat to settle. Underwater sound may increase the chance of successful settlement and survival of this valuable species.The post-larval or puerulus stage of spiny, or rock, lobsters  swim many kilometres from open oceans into coastal waters where they subsequently settle. The orientation cues used by the puerulus for this migration are unclear, but are presumed to be critical to finding a place to settle. Understanding this process may help explain the biological processes of dispersal and settlement, and be useful for developing realistic dispersal models. In this study, we examined the use of reef sound as an orientation cue by the puerulus stage of the southern rock lobster,  The ability of post-larvae to locate suitable habitat in which to settle is critical to the successful recruitment of many marine species that have a planktonic larval dispersal stage . SettlemJasus edwardsii, is distributed across southern Australia and around the coast of New Zealand, and supports a valuable fishery with approximately 6,500 tonnes harvested per year [J. edwardsii pueruli to particular cues have not been reported. The pelagic pueruli  upon reof Booth , 19. Durof Booth , 20.J. edwardsii or any other spiny lobster species. However, underwater sound was implicated as a possible cause for more than 4,000 pueruli caught in the seawater intake of a power station on the west coast of New Zealand [in situ orientation response of swimming puerulus of J. edwardsii exposed to underwater sound from a natural reef.Recently, the sensory abilities and the behaviour of settlement stages of a variety of marine organisms have received considerable attention, with growing evidence that underwater sound plays an important role in the onshore orientation of coral reef fishes and the post-larvae of some crab species , 21, 22. Zealand , 32, and Zealand . The aimJ. edwardsii in response to an ambient underwater reef sound was conducted in a field experiment in a sandy lagoon that was protected from waves at Castle Point, New Zealand   . This ex13.8' E) .-1 wind speed; Climate Station, Leigh Marine Laboratory) so sound was primarily biological in origin rather than from waves or wind. Three typical 2 min sound sequences from the original habitat recording were randomly selected from a total of two hours of recording taken from a coastal reef in northeastern New Zealand on two separate nights and transferred to a MP3 player and used for playback in the experiments , whereby a single puerulus (n = 64) was transferred from the 40 ml floating jar to a sealed plastic holding container (400 ml). This container was then placed at the centre of the choice chamber by a diver on snorkel . Both prhttp://www.metservice.com/) and the wind data from the National Climate Database (http://cliflo.niwa.co.nz/). The logistic regression models measured the relationship between a dichotomous categorical dependent variable (puerulus choice) and independent variables that could be continuous and/or categorical (ambient conditions) by using probability scores as predicted values of the dependent variable [-1) and non-windy days with two simple goodness-of-fit tests using an equal preference for both sides of the choice chamber as the expected frequency [Underwater sound was recorded only during the 2015 experimentation with a calibrated remote hydrophone  that was located 1 m perpendicular to the choice chambers. This recorded the potential variation in ambient and replayed sound at the experimental site  Files. Tvariable . We inclvariable , 41. Thevariable . Additiorequency . Data werequency .-1 . In contrast, pueruli exhibited no significant preference for either side of the choice chamber  when the wind gust speed was higher than 25 m s-1.Of the 64 pueruli tested, 59 92.2%) moved and were trapped into one of the two sides of the experimental chamber. The remaining five hid in the delayed release mechanism so were excluded from analyses. Overall, the majority of the pueruli  moved from the choice arena towards the sound, independent of the speaker position , wind direction  or tide phases  during the summer of 2015 . In cont00 Hz durJ. edwardsii actively moved toward the projected sound of a natural reef, suggesting that they could use reef sound as a cue to find reef habitats in which to settle. Pueruli made directional choices within the confines of a choice chamber within which there would have been a negligible sound pressure gradient, given that there is very low attention of sound propagated in seawater, especially at the lower frequencies that dominated the replayed reef noise [-1) as a result of the disturbance of the sea surface or breaking waves on the nearby reefs [A significant majority of pueruli of ef noise . Therefoef noise , 44–47. by reefs , 43. Thiby reefs , 43. Scaby reefs .J. edwardsii is mainly influenced by large scale transport processes, such as currents and eddies, where diurnal vertical migration may result in their retention 100’s of km from benthic populations [The distribution of the phyllosoma stages of ulations . The phyulations  and the ulations . There iulations . Howeverulations .J. edwardsii are a non-feeding (lecithotrophic) stage that depend only on the energy reserves stored during the preceding phyllosoma phase so the duration of the pueruli phase is constrained by these limited energy reserves [Panulirus argus [P. cygnus have been found at the surface of the sea in rough sea conditions, possibly using directional cues from waves, such as Stoke’s drift [in situ experiments on tethered pueruli of P. argus found they were orientating in response to wind direction as well as tidal flow [J. edwardsii pueruli may use other directional cues at times when strong winds mask underwater sound orientation cues.Pueruli of reserves , 53. Thereserves , 55. In reserves –16. For us argus . Higher ’s drift . SimilarJ. edwardsii can swim at velocities around 10–40 cm s-1 for sustained periods providing the capacity to move considerable distances shoreward provided effective orientation cues are in use [The temporal and spatial pattern of settlement of spiny lobster pueruli is difficult to predict, but it is important for estimating future recruitment levels of valuable fisheries for these species –59. There in use , 60.J. edwardsii lobsters were present in northeastern New Zealand [J. edwardsii could also be applied to test the effect of different sources of underwater sound from different coastal settlement habitats. Additional research is also required to determine the range offshore over which the underwater sound may provide an effective orientation cue, including confirming the extent to which wind on the sea surface would mask the reliable detection of the sound cue [The experiment in the current study used only underwater sound previously recorded from a reef habitat in which  Zealand , 37 sugg Zealand , 61–63. ound cue . Previouound cue . ImproviJ. edwardsii and this could influence spatial patterns in abundance [J. edwardsii because there have been recent dramatic changes in coastal reef communities across part of their range as a result of climate change and the removal of predators of sea urchins [J. edwardsii as occur in other species [In the megalopae of a number of crab species, the habitat type or habitat quality influences settlement behaviour , 62. Habbundance . This is urchins –68. Chan urchins , 25, 69  species , 61, 62.J. edwardsii appears to involve active nocturnal searching [Overall, the settlement of earching  where muearching . Underwaearching , 23 so iS1 Sound File(WAV)Click here for additional data file.S2 Sound File(WAV)Click here for additional data file.S3 Sound File(WAV)Click here for additional data file.


Theoretical reference points have been proposed to differentiate probabilistic gains from probabilistic losses in humans, but such a phenomenon in non-human animals has yet to be thoroughly elucidated. Three experiments evaluated the effect of reward magnitude on probabilistic choice in rats, seeking to determine reference point use by examining the effect of previous outcome magnitude(s) on subsequent choice behavior. Rats were trained to choose between an outcome that always delivered reward (low-uncertainty choice) and one that probabilistically delivered reward (high-uncertainty). The probability of high-uncertainty outcome receipt and the magnitudes of low-uncertainty and high-uncertainty outcomes were manipulated within and between experiments. Both the low- and high-uncertainty outcomes involved variable reward magnitudes, so that either a smaller or larger magnitude was probabilistically delivered, as well as reward omission following high-uncertainty choices. In Experiments 1 and 2, the between groups factor was the magnitude of the high-uncertainty-smaller (H-S) and high-uncertainty-larger (H-L) outcome, respectively. The H-S magnitude manipulation differentiated the groups, while the H-L magnitude manipulation did not. Experiment 3 showed that manipulating the probability of differential losses as well as the expected value of the low-uncertainty choice produced systematic effects on choice behavior. The results suggest that the reference point for probabilistic gains and losses was the expected value of the low-uncertainty choice. Current theories of probabilistic choice behavior have difficulty accounting for the present results, so an integrated theoretical framework is proposed. Overall, the present results have implications for understanding individual differences and corresponding underlying mechanisms of probabilistic choice behavior. The analysis of risk-sensitive decision making has become a well-established area of research in fields such as human judgment and decision making, animal choice behavior, and neuroeconomics ,2. ProbaOne of the inherent properties of probabilistic decision making that has been overlooked in the development of animal models is the presence of and sensitivity to differential gains and losses. Gains and losses are judged relative to subjective reference points –19, in wrelatively decreased rate of repeating the same behavior, as previous research has also demonstrated relatively more shifting  behavior following losses relative to gains  equal to .5. Phase 1 lasted for 11 sessions. Phases 2–4 lasted for 10 sessions.Data analysis. Data analysis was the same as in Experiments 1 and 2. For analysis of high-uncertainty probability training, the final five sessions of Phases 2–7 were used for data analyses. Due to an equipment error that resulted in data loss, sessions 6, 7, 8, 10, and 11 of Phase 3 were used for analysis of one rat, and sessions 6, 7, 9, 10, and 11 of Phase 5 were used for analysis of four rats. The rats’ molar choice behavior was stable across the final five sessions of each phase. Across Phases 2–7, the mean absolute deviation in choice percentages between each of the final five sessions and the mean of the final five sessions of each phase was 3.5%. Three rats were removed from the molecular analyses of the high-uncertainty probability training phase due to missing data. Specifically, of the 240 data points involved in this analysis (24 subjects × 5 previous outcomes × 2 probability conditions), there were 3 missing data points, one per each of the rats removed from this analysis. Two rats did not receive any H-S outcomes and one rat did not receive any H-L outcomes in the P[0] condition, such that there were not any data as a function of these previous outcomes. The missing data occurred because of general risk aversion and relatively small number of high-uncertainty choices in those individuals. For low-uncertainty magnitude training, the final five sessions of Phases 2–4 were used for data analyses. The rats’ molar choice behavior was stable across the final five sessions of each phase. Across Phases 2–4, the mean absolute deviation in choice percentages between each of the final five sessions and the mean of the final five sessions of each phase was 5.5%. Seven rats were removed from the molecular analyses of the low-uncertainty magnitude training phases due to missing data, leaving a total of 17 rats in this analysis. Of the 288 data points involved in this analysis (24 subjects × 4 previous outcomes × 3 low-uncertainty magnitudes), there were 12 missing data points. Half of these missing data points were due to the absence of data following a high-uncertainty magnitude when the low-uncertainty magnitude was equal to 5 pellets . With the exception of one rat that was missing four data points due to general risk aversion, the maximum number of missing data points per rat was two.Molar analysis:Experiment 3A: High-uncertainty probability training. F = 14.02, p < .001, ηp2 = .389, and a Condition × Probability interaction, F = 5.95, p = .005, ηp2 = .213. Simple effects analyses  revealed significantly lower high-uncertainty choices in the P[0] condition compared to the p[1] condition at the probability of. 9, but no significant differences at other probabilities. Molecular analysis:F = 114.92, p < .001, ηp2 = .858, and a Condition × Previous Outcome interaction, F = 10.06, p < .001, ηp2 = .346. Paired samples t-tests conducted on the interaction disclosed significantly fewer high-uncertainty choices following H-Z outcomes in the P[0] condition than in the p[1] condition, and significantly fewer high-uncertainty choices following H-S outcomes in the p[1] condition than in the P[0] condition.r = .64, p = .001  = 32.01, p < .001, ηp2 = .582. Molecular analysis:F = 38.30, p < .001, ηp2 = .705, a main effect of previous outcome, F = 86.00, p < .001, ηp2 = .843, and a Low-Uncertainty Outcome Magnitude × Previous Outcome interaction, F = 2.70, p = .018, ηp2 = .144. Paired-sample t-tests were conducted on post H-Z and post H-S choice behavior to explore the interaction. When the low-uncertainty outcome was 1 pellet, the rats were significantly more likely to make high-uncertainty choices following the H-S outcome than following the H-Z outcome, t(16) = 2.28, p = .037. However, when the low-uncertainty outcome was 3 or 5 pellets, the rats did not differ in their choices following H-S and H-Z outcomes.The goal of the current experiment was to further investigate the hypothesis that the expected value of the low-uncertainty choice served as the reference point for distinguishing high-uncertainty gains from losses. During high-uncertainty probability training, the rats showed an increase in high-uncertainty choice behavior as a function of the probability of the H-L outcome, similar to results shown in Experiments 1 and 2. Interestingly, this effect of H-L outcome probability was moderated by condition ; the P[0The results from the molar analyses were complemented by comparable results at the molecular level. Similar to Experiments 1 and 2, the rats made more low-uncertainty choices following low-uncertainty outcomes and more high-uncertainty choices following high-uncertainty outcomes . The absUltimately, as the delivery of a 1-pellet outcome resulted in a subsequent reduction rather than increase in high-uncertainty choice behavior, the present results lend direct support to the idea that uncertain outcomes in the present experiment were distinguished as gains and losses relative to the expected value of the low-uncertainty choice. The results of the low-uncertainty magnitude training phases corroborated this explanation, as global choice behavior was substantially affected by the objective value of the low-uncertainty outcome . SpecifiThe current results, especially the effect of the 1-pellet outcome, serve as an intriguing complement to more recent research that has identified “losses-disguised-as-wins” as a potentially relevant factor driving the onset of maladaptive gambling behavior. Dixon et al.  describeThese results are especially relevant to the present experiments, as the 1-pellet outcomes in Experiments 1, 3A, and 3B may be viewed as losses-disguised-as-wins; they were less than the expected value of the low-uncertainty choice (loss) but were also positive food amounts delivered in the same way as the larger 11-pellet outcomes (win). Given the individual differences in processing the 0- and 1-pellet losses , it is pThe primary goal of the present experiments was to differentiate between three possible reference points that may affect judgments of high-uncertainty gains versus losses in rats: high-uncertainty, zero-outcome, and low-uncertainty. The collective results suggest that the rats used the expected value of the low-uncertainty outcome as a reference point for gauging high-uncertainty gains and losses see  and 11. In conjunction with the results suggesting a low-uncertainty reference point, a second relatively novel finding of the present experiments was that rats were more sensitive to differential losses than they were to differential gains, as has been described previously in humans . InteresSeveral theoretical frameworks have incorporated reference points in evaluating choice outcomes as gains and losses. While these frameworks have been frequently shown to account for molar choice behavior, a more critical assessment would include their ability to account for choice behavior on a molecular scale ,71. Two Other theoretical frameworks predict greater risk seeking following gains than following losses, such as the reinforcement learning model  and the In the quasi-hedonic editing hypothesis, the differences in choice behavior following prior losses and prior gains are due to the differential integration of prior outcomes with prospective outcomes. Following gains, individuals are predicted to be risk seeking because subsequent losses are perceived as decreases in current gains; alternatively, individuals are risk averse following losses due to aversion to additional losses . AccordiA more recent theoretical framework that is potentially relevant to the present studies is tri-reference point theory . Rather The theories of choice behavior described above posit distinct mechanisms to account for probabilistic decision making. While each theory may not be able to fully account for the present data, there are individual elements that are applicable. For example, prospect theory suggests that the subjective value of a gain is a positively increasing, negatively accelerating function of the objective magnitude of the gain . AccordiXRP demarcating the low-uncertainty reference point of 3 pellets in this example. Note that high-uncertainty outcomes greater than XRP are gains and outcomes less than XRP are losses, and the relative value function reflects the experienced degree of relative gain or loss. The difference between the subjective values of previous high-uncertainty losses  is greater than the differences between the subjective values of previous high-uncertainty gains, and as a result differential losses are predicted to elicit greater differences in subsequent choice behavior than differential gains , (2) an augmented sensitivity to losses over gains due to the steeper function associated with smaller high-uncertainty magnitudes (losses), and (3) an effect of non-zero losses distinguishable from effects of both zero-magnitude losses and non-zero gains. This model also predicts the strong effect of the low-uncertainty outcome magnitude on choice behavior that was observed in Experiment 3B (XRP.iment 3B  as this In conjunction with the present results, previous evidence supports the descriptive model diagrammed in low-uncertainty choice. Specifically, as the abscissa in Figs. As described above, diminishing marginal utility is a critical component of the model in the gain and loss domain, as increased separation of the outcomes from the reference point should attenuate the impact of the individual outcomes. While the case could be made that the present results could be purely explained in terms of marginal utility, there does seem to be reason to believe that a reference point account is mutually exclusive from a marginal utility account. Specifically, the criterion from which utility diminishes may be regarded as an individual’s reference point. Indeed, in non-human primates, Stauffer et al.  demonstrProbabilistic gains and losses drive the decisions among multiple choice outcomes that differ in the magnitudes and probabilities of their outcomes. Behavioral, neurobiological, and neuro-economic accounts of such effects have shaped our understanding of probabilistic decision making ,67,87–91


Salvia miltiorrhiza is one of the most widely used medicinal plants. As a first step to develop a chloroplast-based genetic engineering method for the over-production of active components from S. miltiorrhiza, we have analyzed the genome, transcriptome, and base modifications of the S. miltiorrhiza chloroplast. Total genomic DNA and RNA were extracted from fresh leaves and then subjected to strand-specific RNA-Seq and Single-Molecule Real-Time (SMRT) sequencing analyses. Mapping the RNA-Seq reads to the genome assembly allowed us to determine the relative expression levels of 80 protein-coding genes. In addition, we identified 19 polycistronic transcription units and 136 putative antisense and intergenic noncoding RNA (ncRNA) genes. Comparison of the abundance of protein-coding transcripts (cRNA) with and without overlapping antisense ncRNAs (asRNA) suggest that the presence of asRNA is associated with increased cRNA abundance (p<0.05). Using the SMRT Portal software (v1.3.2), 2687 potential DNA modification sites and two potential DNA modification motifs were predicted. The two motifs include a TATA box–like motif , and an unknown motif (CPGDMM2 “WNYANTGAW”). Specifically, 35 of the 97 CPGDMM1 motifs (36.1%) and 91 of the 369 CPGDMM2 motifs (24.7%) were found to be significantly modified (p<0.01). Analysis of genes downstream of the CPGDMM1 motif revealed the significantly increased abundance of ncRNA genes that are less than 400 bp away from the significantly modified CPGDMM1motif (p<0.01). Taking together, the present study revealed a complex interplay among DNA modifications, ncRNA and cRNA expression in chloroplast genome. Salvia miltiorrhiza, also known as Chinese sage, tan shen, or danshen, is highly valued in traditional Chinese medicine for its roots or rhizomes S. miltiorrhiza extracts are the main components of two of the ten best-selling traditional Chinese medical products, each with a total sale of around 100 million US dollars. To date, more than 70 active components have been isolated from S. miltiorrhizaS. miltiorrhiza, their overproduction remains an active research area. Recent studies have shown that chloroplasts are ideal hosts for the expression of genes related to the production of secondary metabolites Given the pharmacological and economic importance of the active components of S. miltiorrhiza chloroplast genome has been recently assembled and analyzed Chloroplasts are plant-specific organelles for photosynthesis, starch storage, nitrogen metabolism, sulfate reduction, fatty acid synthesis, DNA and RNA synthesis, and the synthesis of 50% of soluble protein in leaves ncRNAs are RNAs that do not encode proteins, but have multiple functions. ncRNAs can be classified into two broad categories: house-keeping and regulatory. House-keeping ncRNA, which include ribosomal, small nuclear, small nucleolar, and transfer RNA (tRNA), is constitutively expressed ndhB asRNA covers two editing sites of the ndhB gene and a group II intron splice acceptor site, and might play important role in RNA maturation or stability psbT gene were found to form double-stranded RNA/RNA hybrids, which results in translational inactivation of the psbT mRNA. The hybrid was further hypothesized to provide protection against nucleolytic degradation of mRNA during photo-oxydative stress conditions rbcL and atpB genes form stable double-stranded molecules with sense strand transcripts and prevents their translation RpoB-2 from chloroplast transgenes affects editing efficiency of transgene and other endogenous chloroplast transcripts S. miltiorrhiza chloroplast genome would help reveal the functions of this important group of gene expression regulators.Previous studies have identified dozens of ncRNA in chloroplasts S. miltiorrhiza chloroplast genome would provide valuable information on the epigenetic regulation of its gene expression.DNA contains a large variety of functionally important modifications S. miltiorrhiza chloroplast genome, transcriptome, and DNA modifications. Detailed analyses of DNA modifications as well as of ncRNA and cRNA expression suggest a complex interplay between these processes. The results of this study would provide valuable information for the construction of chloroplast-based transgenic systems, as well as lay the foundation for research on ncRNA and DNA modifications in regulating the expression of coding genes in chloroplasts from other organisms.In this study, we used the Single-Molecule Real-Time (SMRT) Sequencing and strand-specific RNA-Seq technologies to characterize in detail the S. miltiorrhiza Bunge cv 99-3 were collected from the Institute of Medicinal Plant Development, Beijing, China. The leaves are pooled together and total DNA was extracted from the leaves using a plant genomic DNA kit . The genomic DNA of S. miltiorrhiza were isolated and subjected to DNA sequencing using SMRT sequencing, a parallelized single molecule DNA sequencing by synthesis technology developed by PacBio . The DNA sequencing is done on a SMRTcell containing hundreds thousand of zero-mode waveguide (ZMW) nano-pores. Inside each ZMW, a single active DNA polymerase with a single molecule of single stranded DNA template is immobilized to the bottom through which light can penetrate and create a visualization chamber. The nucleotide substrate is phosphor-linked to a fluorescence dye. As the DNA synthesis proceeds, the fluorescence signal is detected when a nucleotide is held by the DNA polymerase before the nucleotide being incorporated into the DNA strand, resulting in the DNA sequencing in real time. We choose SMRT technology for this study as it is the only technology that is currently capable to directly detect potential modifications on native DNA without PCR amplification Fresh leaves from three plants of S. miltiorrhiza. Similar reads with an E value < 1e-5 were extracted and subjected to sequence assembly using Seqman (version 9.0). The genome sequence of Sesamum indicum, which has the highest overall sequence similarity with S. miltiorrhiza reads, was used as a reference to order the contigs. The gaps were filled by iteratively searching the PacBio S. miltiorrhiza reads using the contigs, extracting reads that share sequence similarities, extending the ends, adding the reads to the assembly, and then performing reassembly. After obtaining an initial assembly, we designed primers  separately following the manufacturer's recommendation. The three RNA samples were pooled and the cDNA was amplified using a random-priming protocol. Ribosomal RNA was removed from total RNA using MICROBExpress Bacterial mRNA Enrichment Kit . A RNA-Seq library was constructed following a strand-specific protocol  S. miltiorrhiza genome assembly. The 5′ and 3′ ends were determined based on the very ends of each contig. The RNA-Seq data were visualized and quantified using SeqMonk  and Tablet (v1.12.12.05) Total RNA was extracted from fresh leaves of three miltiorrhiza  at the 5′ end  was performed as described previously S. miltiorrhiza plants were analyzed using ss-qPCR as described above. The expression quantification by qPCR was performed as described previously atpB gene was chosen as the internal control for each ss-qPCR experiment.To validate the strand specificity and expression abundance of ncRNA detected in our RNA-Seq data, three total RNA samples extracted from leaves of three t test.For RNA-Seq data, we first specified the start and end of the overlapping regions for each pair of ncRNA and cRNA. SeqMonk was used to calculate the RPMs for ncRNA and cRNA for the overlapping regions. Validation of differential gene expression between ncRNA and cRNA was performed as previously described The total DNA was sequenced using SMRT technology following the standard procedures as described above. Sequence preprocessing and DNA modification detection were performed using the SMRT Analysis pipeline (version 1.3.2). The DNA modification motifs were identified using MotifFinder (PacBio).t test and analysis of variance, were conducted using the JMP software  and customer scripts written with Perl module Statistics::Distributions (v1.02).Statistical analyses, including one-sample S. miltiorrhiza chloroplast. An ideogram summarizing our results is shown in S. miltiorrhiza chloroplast, which are described in detail below.We used strand-specific RNA-Seq, and SMRT technologies  were embedded in long noncoding transcripts (see below).We conducted strand-specific RNA-Seq analysis of the psbA, psbB, psbC, psbD, and rbcL. This result is consistent with the involvement of rRNA in protein synthesis as well as with the participation of psb genes and rbcL in photosynthesis. Interestingly, two tRNA genes, namely, trnA-UGC and trnI-UUC, were highly expressed at levels comparable to those of rRNA genes and psb subunit genes. The trnA-UGC and trnI-GAU transcripts accounted for 60.60% and 38.18% of all tRNA transcripts, respectively, even though alanine and isoleucine only account for 2/30 (6.7%) of the amino acids in the S. miltiorrhiza genome. Detailed examination showed that these two tRNA genes are located in polycistron pc17, which contains the rRNA genes. Their expression levels are consistent with those of other genes in the same polycistron. Therefore, this region is a potential site for foreign gene insertion (see below).These genes were quantified using the Seqmonk software. The results are shown in S. miltiorrhiza chloroplast genome based on the presence of a continuous transcript. In total, we found 19 polycistronic transcripts containing 71 genes, which include 58 coding-protein genes, 9 tRNA, and all 4 rRNA. The polycistron identity, its member genes, and the strand on which the gene is located are shown in atpI, rpoC2) were not entirely covered by the RNA-Seq reads. However, based on the results of a previous study Most genes from chloroplast genomes are transcribed in polycistrons S. miltiorrhiza. To be consistent, we defined coding RNA (cRNA) as any RNA transcripts transcribed from genes encoding proteins. We define non-coding RNA (ncRNA) as any RNA transcripts transcribed that do not encode proteins and have a length > = 100 bp. We identified 136 ncRNA transcripts based on these criteria , 3′ (B2), coding (B3), span (B4) and intron (B5) asRNA . B1 inclA. thaliana. A previous study has identified 107 ncRNA in A. thaliana chloroplast genome S. miltiorrhiza ncRNA were found similar to 39 A. thaliana ncRNA using BLASTN with an a cutoff of E value <1e-5,  was similar to two A. thaliana ncRNA , a 1:n mapping. Last, nc92 and nc93 of S. miltiorrhiza are similar to trnL-ndhBint and ndhB-3′ of A. thaliana, a n:n mapping. However, we can not exclude that possibilities that some of these n:1,1:n and n:n mappings resulted from partial transcripts. These conserved ncRNA between S. miltiorrhiza and A. thaliana may have similar functions. Further investigations are required to test this possibility.To determine if these ncRNA are conserved across various plant chloroplast genomes, we compared these sequences with those from e <1e-5, . Four tyWe first calculated the correlation between the RNA abundance obtained from RNA-Seq and the three ss-qPCR results respectively. For these 24 pairs of ncRNA and cRNA, pearson correlation coefficients of 0.749, 0.764 and 0.779 were obtained (data not shown).t test  did not exhibit significantly different gene expression between RNA-Seq and ss-qPCR results   . This re(p<0.05) . The avep<0.01) putatively modified bases throughout the genome  is the time difference between two “pulses”. IPD ratio is calculated as [IPD at a site at a condition under study]/[IPD at the same site at the control condition], The IPD changes significantly in the presence of DNA modifications. Based on the IPD ratio, the DNA modification status of the base can be inferred e genome . Among te genome  and “WNYe genome , whose pe genome . Detailep<0.01  the region should have minimal effects on the expression of endogenous chloroplast genes; and (2) it has potential to be highly expressed. In particular, we selected locations that are in the intergenic regions of polycistronic sites and at which the relative expression levels of genes are high. The top-ranking regions are presented in trnA-UGC , which hS. miltiorrihiza chloroplast genome. RNA-Seq is a revolutionary tool for transcriptomic analysis The current study takes advantage of two advanced technologies, RNA-Seq and SMRT, to identify asRNA and DNA modifications and then characterize their effects on gene expression in the SMRT is a novel technology for detecting DNA methylation while simultaneously determining the context of the corresponding DNA sequence Several mechanisms have been proposed to explain the regulatory role of ncRNA, including transcription itself , as well as the formation of duplexes of RNA–DNA (chromatin remodeling) and RNA–RNA (dsRNA-dependent RNA formation) Although the majority of studies on asRNA focused on those from nuclear genomes, some studies focused on asRNA in the chloroplast genome. For example, overexpression of a natural chloroplast-encoded antisense RNA (AS5) in tobacco destabilizes 5S rRNA and retards plant growth, possibly because AS5 prevents the accumulation of misprocessed 5S rRNA and controls its stoichiometry p<0.05)  . In othePrevious studies indicated that gene transcription and DNA methylation are closely interwoven processes Although the expression regulation of chloroplast genes has been intensively studied S. miltiorrhiza. Chloroplast genomes are most abundant in the leaves. Therefore, focusing on the chloroplast genome characteristics in the leaves is reasonable. After obtaining a detailed expression map of S. miltiorrhiza chloroplasts in the leaves, we systematically predict regions that are potential insertion sites for foreign genes. Two of them have already been commonly used as sites for foreign gene insertion. Furthermore, the region between psaA and psaB on polycistron pc8 and that between ndhK and ndhC on polycistron pc9  length distribution of sequence reads; (B) depth of coverage across the assembled chloroplast genome; and (C) abundance of various lengths of reads mapped to the chloroplast genome. Table S1. Primers used for sequence assembly validation. Table S2. Primer sets used for strand-specific real-time qPCR. Table S3. Statistical testing of RNA-Seq and ss-qPCR results using one-sample t test. Table S4. Detailed information on motif CPGDMM1. Table S5. Detailed information on motif CPGDMM2. Table S6. Associations between DNA modification site (DMS) and downstream ncRNA expression. Table S7. Analysis of the effect of DNA modification at the CPGDMM1 motif on the abundance of ncRNA using ANOVA.(DOC)Click here for additional data file.


Selective 2′ Hydroxyl Acylation analyzed by Primer Extension (SHAPE) is an accurate method for probing of RNA secondary structure. In existing SHAPE methods, the SHAPE probing signal is normalized to a no-reagent control to correct for the background caused by premature termination of the reverse transcriptase. Here, we introduce a SHAPE Selection (SHAPES) reagent, N-propanone isatoic anhydride (NPIA), which retains the ability of SHAPE reagents to accurately probe RNA structure, but also allows covalent coupling between the SHAPES reagent and a biotin molecule. We demonstrate that SHAPES-based selection of cDNA–RNA hybrids on streptavidin beads effectively removes the large majority of background signal present in SHAPE probing data and that sequencing-based SHAPES data contain the same amount of RNA structure data as regular sequencing-based SHAPE data obtained through normalization to a no-reagent control. Moreover, the selection efficiently enriches for probed RNAs, suggesting that the SHAPES strategy will be useful for applications with high-background and low-probing signal such as in vivo RNA structure probing. Under physiological conditions, RNA has the ability to form structures through internal base-pairing. This additional layer of information encoded in the RNA sequence will in many cases be key to understanding the function of RNA molecules. This is true for the abundant noncoding RNAs, but also for protein-coding mRNAs, which often contain functional regulatory RNA structures. For highly conserved structural RNAs, comparative data allow the secondary structure to be deduced . In the Arabidopsis thaliana seedlings , which retains the ability of SHAPE reagents to accurately probe RNA structure, but also allows covalent coupling to a biotin molecule. We demonstrate that RNase I treatment of cDNA/RNA–NPIA–biotin hybrids followed by selection on streptavidin beads enriched for probed RNAs and effectively removes the large majority of background signal present in SHAPE probing data. Moreover, we have adapted SHAPES to randomly primed reverse transcription and sequencing-based readout of reverse transcription termination sites, making the probing of long RNAs without normalization with a no-reagent control possible. We believe that the SHAPES strategy will provide an alternative to regular SHAPE methods for in vitro probing of RNA structure and will be especially useful for applications with high-background and low-probing signal such as in vivo structure probing.Here, we describe a novel SHAPE Selection (SHAPES) reagent, N-propanone isatoic anhydride (NPIA), which is identical to the canonical SHAPE reagent N-methyl isatoic anhydride (NMIA), except that the N-methyl group has been exchanged with an N-propanone group  , we set ne group A. Like ane group B. We tesne group C. Subseqne group . We findne group .B. subtilis RNase P RNA with NMIA, NPIA and with DMSO as a no-reagent control. Following the SHAPE-Seq set-up (R = 0.98), indicating that NPIA and NMIA react with RNA in a nearly identical fashion. Termination counts from the two reactions are also quite strongly correlated to the termination counts from the DMSO control (R = 0.88 for both), demonstrating that both probing reactions contain a substantial amount of background. Reactions were carried out at conditions that favored single-hit kinetics and as expected all three reactions have a high percentage of reads mapping to the RNase P 5′ end (R = 0.83) (data not shown). In contrast, for the selected NPIA experiment the correlation with the control sample is much lower (R = 0.63) and the run-off count constitutes a much smaller fraction of the total count in the selected sample compared with the unselected sample (0.10 versus 0.71). Moreover, the dominant termination count peaks observed in the DMSO sample are not present, demonstrating that the signal caused by nonprobed RNAs or premature termination of reverse transcriptases has been effectively depleted (RNase P RNA either by capillary electrophoresis (SHAPE-CE)   or sequeHAPE-CE) . Both ofHAPE-CE) , whereasHAPE-CE) . For comHAPE-CE) . The strHAPE-CE) . Our strHAPE-CE) B.E. coli ribosomes using a gentle purification protocol to preserve the overall RNA fold , which is a novel strategy for probing of secondary RNA structure. We demonstrate that SHAPES effectively removes the background signal present in SHAPE-based RNA structure-probing data by selection of informative cDNA–RNA hybrids from cDNAs caused by pretermination of reverse transcriptase or RNA 5′ end run off. Our method is conceptually similar to the CAGE method for identifying the capped end of mRNAs, which has been extensively validated . The majSHAPES offers two major advantages compared with standard SHAPE. First, the selection procedure makes the no-reagent control typically used for SHAPE probing experiments unnecessary, meaning that only one sample needs to be sequenced. This will reduce the costs associated with this kind of sequencing-based probing experiment. On the other hand, the selection step increases the time required to perform the experiment with an additional day. We believe that in this type of experiment, where data analysis is a major part of the project, most researchers would like to get the double amount of data for the same cost at the expense of spending an additional day in the laboratory. Second, in cases with high background, the selection will enrich for the probed RNAs, thereby allowing RNA structure-probing data to be obtained. For the recently published DMS based in vivo RNA structure probing in human cells, stringent size selection was performed first on the probed and fragmented RNA and subsequently on the cDNA obtained by reverse transcription . ThroughIn our 16S rRNA experiment, we have introduced random priming for sequencing-based SHAPE structure probing, which facilitates the structural probing of long RNA molecules. Our priming strategy is similar to the strategy used for priming in the DMS-based Structure-Seq  and in HIn this study, we demonstrate that NPIA can be used as a SHAPES reagent, allowing both structure probing and coupling to a solid support. We have tested the NPIA reagent for in vivo use, but found that the reactive propanone group reacts with other molecules than RNA in vivo. Thus to use the SHAPES strategy inside cells, we have synthesized SHAPES probing reagents that have the biotin molecule coupled directly to the SHAPE reactive group and pilot experiments show that these molecules can enter human cells and probe RNA structure. In this way, the probed RNA is directly biotin labeled and can be used directly for selection. Alternatively, other chemical methods for coupling to a solid support, such as click chemistry, could be used to allow in vivo SHAPES.B. subtilis RNase P specificity domain RNA and for the E. coli 16S rRNA. However, for both RNAs our signal is not as strong as the signal obtained by the Weeks group using the traditional SHAPE setup and normalization with a DMSO control  was obtained from Enamine Ltd., product number EN300-78111 and N-methyl isatoic anhydride (NMIA) was from Life Technologies, product number M-25. The excitation/emission profile of NPIA and NMIA was determined using the Fluorescence Profiler feature on a NanoDrop 3300 (Thermo Scientific), having excitation optimum at 375 nm and emission optimum at 440 nm. To determine hydrolysis reaction rates, 1 μL 10 mM NPIA or NMIA was added to 1 mL reaction buffer , and the formation of the hydrolysis product was measured at 25°C for 50 min with 30-sec time intervals as an increase in fluorescence. Nonlinear regression  was fitted to the data using the SigmaPlot v11.0 software and the equation f = y0 + a × [1 − exp(−b × x)], where y0 is the offset, a is the amplitude, and b is the decay constant. We found that NMIA had a b value of 0.042 and a half-life of 13.5 min. NPIA had a b value of 0.088 and a half-life of 5.9 min.2, and 100 mM NaCl . One microliter of each of the reactions was mixed with 2 µL 1 M Na-citrate pH 6.0 and 6.75 µL 50 mM biotin (long arm) hydrazide dissolved in DMSO, and water to a final volume of 28.75 µL. A control reagent with DMSO instead of biotin (long arm) hydrazide was included. The reactions were incubated 12 h at 25°C in the dark. One microliter of each reaction was mixed with 9 µL water and 2 µL 6× loading buffer, and loaded on a 30% native polyacrylamide gel . After electrophoresis , the result was analyzed with phosphorimaging .Radiolabeled dCTP and ATP  was incubated with 50 mM NPIA or DMSO in 100 mM HEPES pH 8.0, 6 mM MgClB. subtilis RNase P specificity domain inserted in a structure cassette as previously described (E. coli cells (Invitrogen). The RNase P specificity domain sequence was verified by Sanger sequencing. The plasmid was linearized with BsaI-HF (New England Biolabs) and used as a template for in vitro transcription. The in vitro transcription reaction  contained T7 RNA polymerase, 2 mM of each NTP, 40 mM Tris–HCl pH 8.0, 6 mM MgCl2, 1 mM Spermidin, 5 mM DTT, and 1 μg linearized DNA template. After transcription, the RNA was ethanol precipitated, centrifuged and resolved on a 5% polyacrylamide, 7 M urea, 1× TBE gel. It was detected with UV shadowing as a single band on the gel. A gel slice containing the band was cut out, and the RNA was eluted overnight in a buffer containing phenol, 250 mM NaOAc and 1 mM EDTA. The aqueous phase was extracted with chloroform, and after ethanol precipitation and centrifugation, the RNA was resuspended in water. The RNA was folded as previously described , under nondenaturing conditions as previously described (600∼0.6), and the cells were transferred to 4°C for 20 min. After collection by centrifugation , the cells were resuspended in 1 mL buffer A (15 mM Tris–HCl (pH 8), 450 mM sucrose, and 8 mM EDTA), and lysozyme (Sigma-Aldrich) was added to a final concentration of 0.4 mg/mL. The cells were incubated at 22°C for 5 min and kept on ice for 10 min. The protoplasts were collected by centrifugation , and resuspended in 120 µL buffer B . After 5-min incubation at 22°C and 5 min on ice, 30 µL buffer C  was added, and the sample was centrifuged  to precipitate the SDS. The pelleted SDS was discarded and the buffer was exchanged by gel filtration using a NucAway column (Ambion) that was preequilibrated with buffer D . The RNA was then extracted three times with 1 vol. phenol (pH 8.0):chloroform:isoamyl alcohol; 25:24:1, and three times with chloroform. The RNA quality was verified with a Bioanalyzer Pico assay (Agilent) before structure probing.Total RNA was isolated from the escribed . BacteriE. coli was treated  with 1/10 vol. NPIA or NMIA dissolved in DMSO (60 mM), or treated with DMSO as a control. After probing, the RNA was precipitated with ethanol, centrifuged and the pelleted RNA was dissolved in water.Folded RNase P or total RNA from 2], 7.5 μL 2.5 mM dNTPs, 7.5 μL 3.3 M/0.6 M sorbitol/trehalose mix, 2.5 μL PrimeScript Reverse Transcriptase (TAKARA), and 5 μL water) was added. After mixing, the reaction was incubated 1 min at 25°C, 30 min at 42°C, 10 min at 50°C, 10 min at 56°C, and 10 min at 60°C, and kept on ice before purification. The reaction conditions for RNase P and total RNA primer extension were the same, except that the first step in the reverse transcription (1 min at 25°C) was omitted for RNase P. The cDNA/RNA hybrids were purified using Agencourt RNAClean XP kit (cDNA/RNA:beads ratio 1:1.8), as previously described . Reverse transcription of total RNA was carried out with random priming . Reverse transcription was performed as described previously  with modescribed  The cDNAE. coli tRNA mix for 30 min at room temperature, separated from the supernatant on a magnetic stand and washed twice with 50 μL wash buffer 1  before being resuspended in 80 μL wash buffer 1. The 40 μL cDNA/RNA sample was added to the beads, and the sample was incubated 30 min at room temperature, vortexing every 5 min. The beads were separated on the magnetic stand, and the supernatant was discarded. The beads were then extensively washed , using 150 μL of the buffers in each wash. To elute the full-length cDNA, 60 μL 50 mM NaOH were added to the beads, and they were incubated 10 min at room temperature. After separation on the magnetic stand, the supernatant was recovered and kept on ice. To neutralize the solution, 12 μL of 1 M Tris–HCl (pH 7) was added. The cDNA was then precipitated with ethanol, centrifuged, and resuspended in 8 μL water.Biotinylation, RNase I treatment and full-length cDNA selection were performed as previously described  with modE. coli total RNA). For ligation, 3 µL cDNA was mixed with 7 μL ligation mix . The mixture was incubated for 2 h at 60°C, then 1 h at 68°C and 10 min at 80°C. After the cDNA was precipitated with ethanol, it was dissolved in 30 μL H2O (RNase P) or 10 µL H2O . PCR was performed using 5 μL of the adapter-ligated cDNA, mixed with 45 μL PCR mix  reverse primer , 10 volumes of Phusion 5× HF buffer, 4 volumes of 2.5 mM dNTPs, 27.5 volume H2O, and 1 volume Phusion Polymerase). Reactions underwent thermal cycling with the following program: 1× (98°C for 3 min), 4× , 15× , 1× (72°C for 5 min). The generated PCR amplicons were purified with Ampure XP beads using the ratio 1:1.8 as previously described (E. coli RNA library was sequenced with the 100-nt paired-end protocol. The raw sequencing data are available here: http://people.binf.ku.dk/~jvinther/data/SHAPES-Seq/.cDNA was diluted to the concentration 0.66 ng/μL (RNase P) or 0.5 ng/µL , followed by normalization with the 95th percentile to give reactivities between 0 and 1. The final reactivity for each position was obtained as the average of the reactivities obtained for that particular position in the different windows.Data analysis was carried out in R . For botB. subtilis RNase P specificity domain RNA and the E. coli 16S rRNA was obtained from the RNA Mapping Database  and the RNase P RNA data (http://rmdb.stanford.edu/repository/detail/RNASEP_SHP_0000). For normalization of the RNase P and 16S rRNA data with the DMSO control, the coverage at each position was calculated by summing the counts for all fragments spanning the position or terminating at the position immediately before the position in question. To avoid bias from size selection, fragments were only used for calculation of coverage for a given position, if the distance between the position and the priming position was at least 100 nt. A termination coverage Ratio (TCR) was calculated by dividing the termination count with the coverage for each position. For calculation of normalized values ΔTCR, we used the formula described by Weeks and colleagues (Secondary structure annotation for the Database  and CRW Database , respectDatabase . The pRODatabase  was usedescribed . The cap16S rRNA  were obtRNA data  were proRNA data  were obtRNA data  (http://lleagues :ΔTCR=TCE. coli 16S rRNA secondary structure as the binary classifier.Instead of estimating the scaling factor α, we tested a wide range of α values and used the value that gave the best possible structure signal as estimated by the AUC of the ROC curve using the Supplemental material is available for this article.


A number of different genes have been found mutated in patients with Amyotrophic Lateral Sclerosis (ALS). Several of these genes encode for proteins involved in multiple steps of RNA processing, suggesting that mRNA dys-metabolism has a role in the degeneration of motor neurons. This is the case also for FUS-linked ALS. FUS (Fused in Sarcoma) is a DNA/RNA binding proteins with an established, yet not completely clear, role in the regulation of RNA transcription, splicing, transport and translation. However, recent evidence indicates that (similarly to mutant ALS-linked SOD1) the toxic function of this protein may lie also in its propensity to aggregate and sequester other proteins, and/or in its ability to induce mitochondrial damage and oxidative stress. In this presentation I will discuss our recent work on the molecular mechanisms underlying these effects and their potential relevance in the pathogenesis of ALS.


Most significantly the production rates on Acropora-dominated reefs, the corals most severely impacted in 1998, averaged +8.4 G by 2015, comparable with estimates under pre-human (Holocene) disturbance conditions. These positive budgets are reflected in high reef growth rates (4.2 mm yr−1) on Acropora-dominated reefs, demonstrating that carbonate budgets on these remote reefs have recovered rapidly from major climate-driven disturbances. Critically, these reefs retain the capacity to grow at rates exceeding measured regional mid-late Holocene and 20th century sea-level rise, and close to IPCC sea-level rise projections through to 2100.Climate-induced disturbances are contributing to rapid, global-scale changes in coral reef ecology. As a consequence, reef carbonate budgets are declining, threatening reef growth potential and thus capacity to track rising sea-levels. Whether disturbed reefs can recover their growth potential and how rapidly, are thus critical research questions. Here we address these questions by measuring the carbonate budgets of 28 reefs across the Chagos Archipelago (Indian Ocean) which, while geographically remote and largely isolated from compounding human impacts, experienced severe (>90%) coral mortality during the 1998 warming event. Coral communities on most reefs recovered rapidly and we show that carbonate budgets in 2015 average +3.7 G (G = kg CaCO Warming sea waters, ocean acidification and rising sea levels are effects of climate that pose a major threat to coral reefs globally1356112133m−2 yr−1), and use these data to address two important questions: 1) have these reefs proved sufficiently resilient to recent climatic disturbances to regain rates of carbonate production and erosion that can be considered typical of undisturbed Indo-Pacific reefs? 2) do these reefs retain the capacity to accrete at rates sufficient to offset projected increases in sea level?Whilst many reefs have succumbed to the combined impacts of climate- and human-driven pressures, there remain regions largely free of direct human pressures due to their geographic remoteness. This raises the question of whether such reefs are better able to cope with global climate change impacts. The geographically isolated Chagos Archipelago, central Indian Ocean , is one 24,82 = 8.584, p < 0.001), although not among atolls . To explore the influence of wave exposure on carbonate budgets we compared averaged net and gross rates of production and erosion for sheltered and exposed reefs within each atoll, grouped based on wave energy regime. Average net G ranged from +0.2 to +6.0 G .Net carbonate production rates across all reefs averaged +3.7 G, with rates at the individual reef scale ranging from +9.8 to −5.0 G. Most reefs  had net positive budgets, and eight (29%) had budgets exceeding 5 G. Only 3 of the 28 reefs had net negative budgets (range: −0.3 to −5.0 G). There was, however, considerable heterogeneity in net G , with sio +6.0 G , with hio +6.0 G . Net G d24,82 = 7.165, p < 0.001). Highest production rates were measured at Ile Poule on the sheltered margin of Peros Banhos (+11.2 G), and the lowest at Nelson at the northern margin of the Gt. Chagos Bank (+1.3 G), a reef with very low live coral cover (3.6%) as a result of recent (2015) Crown-of-Thorns starfish predation. Grouped- exposure/atoll average production ranged from +2.4 to +9.5 G . Bioerosion rates averaged 3.4 G across all reefs, with grouped-exposure/atoll averages ranging from −2.0 to −4.2 G . Bioerosion rates were significantly different between reefs , but not between atolls . Highest rates were measured at Petite Coquillage, Peros Banhos (7.4 G) and the lowest at East Island, Diego Garcia (1.4 G)  see .Porites sp. and Pocillopora (mainly P. eydouxi), typically increased , are close to the production rates (range ~5 to 9 G) reported as typical for undisturbed Acropora-dominated Indo-Pacific fore-reef settingsAcropora spp., contemporary rates have now recovered to be close to optimal for Indian Ocean reefs. This contrasts directly with the fundamental budget changes that persist at post-disturbance sites across the Caribbean, such that average carbonate production and erosion rates are higher (28% and 40% respectively) in Chagos relative to the Caribbean, and with particularly high coral production rates resulting in net G being twice as high compared to the Caribbean  an estimated order of magnitude lower than that measured in regional Holocene core records for equivalent depth assemblages−1 across all sites, but are higher (mean: 4.2 mm yr−1) within Acropora-dominated sites, compared to Porites/Pocillopora dominated sites (mean: 0.9 mm yr−1) (−1) and Salomon (3.74 mm yr−1), and on the Brothers reefs   . Considemm yr−1) . The lowAcropora spp., should have the potential to accrete at rates above those measured using sea level altimetry data over the period 1950 to 2000 , suggesting that even under these elevated rise rates that most Acropora-dominated reefs have the potential to closely track rising sea-levels over the coming century. However, higher rates predicted under RCP 6.0 and 8.5 scenarios would lead to a slight deepening (in the order of a few decimetres) over the fore-reef slope habitats by 2100.Predicting reef structural integrity and growth potential under present and future sea-level rise scenarios is more problematic and depends on the interaction between reef accretion potential (driven partly by reef ecology) and sea-level rise rates. A comparison of our accretion rate estimates against recent regional sea-level trends indicates that many Chagos reefs, including all those dominated by  to 2000 . ProjectAcropora sp. tend to be highly susceptible to episodic disturbances26A. cythereaPorites and Pocillopora spp. drive carbonate production. Long-term potential accretion rates may therefore not be so different between the two, especially where coral cover and thus carbonate production (see Acropora-dominated reefs to ~2.9 mm yr−1 (range 2.3 to 4.5 mm yr−1). Thus, even accounting for framework removal the best available estimates would suggest that most ‘sheltered’ reefs will retain the capacity, providing Acropora cover is maintained, to accrete at rates above those measured across the region over the last ~50–60 years, and within or close to the rates projected for the next 100 years  in the absence of Acropora, although we must assume, given the higher susceptibility of Acropora spp. to episodic disturbance, that the accretion potential of such reefs may inherently fluctuate over time. This raises an important note of warning in relation to any increase in the magnitude and frequency of disturbance events, since these would most likely preferentially impact those coral taxa with the highest growth ratesCollectively, these findings highlight the capacity of the Chagos reefs, which are both geographically remote and isolated from compounding human impacts, to not only recover their ecological and geomorphic functions relatively rapidly following major past climate-driven perturbations, but also to retain the capacity to respond positively to future increases in sea level. Clear differences in accretion potential occur between sites, largely reflecting reef ecology and wave exposure regimes, but the highest accretion rates presently occur predominantly on those reefs where −3) or ‘sheltered’ (<1000 J m−3), this division being based on a natural break in the rank order of the data across all sites. We refer to these groupings in the text for descriptive purposes.Surveys were conducted during March/April 2015 on 28 reefs across the five islanded atolls of the Chagos Archipelago (numbers of survey sites in brackets): Diego Garcia (5); Peros Banhos (7), Salomon (6), Great Chagos Bank (8), and Egmont (1), as well as at one site on the submerged Blenheim Reef . A seaso3 m2 yr−1) we used a modified version of the Reef BudgetAcropora spp. dominated, and Porites/Pocillopora spp. dominated reefs as those where these taxa contribute to >50% of coral carbonate production. To test for differences in net and gross production and erosion between sites and atolls ANOVA tests were run with, where appropriate, a Tukey post-hoc test. Principal Component Analysis (PCA) was used to explore the relationships between reef carbonate production rates and reef ecology, physical structure and exposure regime.To quantify gross carbonate production and erosion and thus to determine net carbonate budgets . We used an approach previously applied to Caribbean reefsTo assess the accretion potential of reefs, and to explore their capacity to respond to future projected regional sea-level rise rates, we converted our net production rate estimates to potential accretion rates .


A foreign gene flanked by the 3’ UTR was encapsidated by supplying both viral NS3-NS5B proteins and Core-NS2 in trans. Mutations within the conserved stem-loops of the 3’ UTR were observed to dramatically diminish packaging efficiency, suggesting that the conserved apical motifs of the 3´ X region are important for HCV genome packaging. This study provides evidence of selective packaging of the HCV genome into viral particles and identified that the 3’ UTR acts as a cis-acting element for encapsidation.Although information regarding morphogenesis of the hepatitis C virus (HCV) is accumulating, the mechanism(s) by which the HCV genome encapsidated remains unknown. In the present study, in cell cultures producing HCV, the molecular ratios of 3’ end- to 5’ end-regions of the viral RNA population in the culture medium were markedly higher than those in the cells, and the ratio was highest in the virion-rich fraction. The interaction of the 3’ untranslated region (UTR) with Core  cis-elements for RNA packaging. In this study, we characterized the properties of HCV RNAs in a cell culture system by determining their integrity in virus-replicating cells and in culture supernatants, and we found that over-distributed 5’-subgenomes were negatively selected during virus assembly in the cells. Using trans-packaging systems with replication defective subgenomes, we identified the 3’UTR as a cis-acting element that was sufficient for packaging of not only a HCV subgenome but also a foreign gene into infectious particles. Mutagenesis analyses, together with an in vitro binding assay with Core demonstrated that, whereas the best encapsidation occurs with the entire 3’ UTR, the loop sequences of the 3’ X region appear to be essential for encapsidation. Our work opens new perspectives for understanding the molecular mechanisms that regulate the HCV life cycle and potentially paves a way to a new anti-viral therapy.Although cell culture systems provide a powerful tool for deciphering the life cycle of the hepatitis C virus (HCV), the mechanisms of encapsidation of the viral genome into infectious particles remain to be uncovered. The HCV genome is a positive RNA with one single reading frame flanked by 5’- and 3’ untranslated regions (UTRs). Thus far, there is no direct evidence that HCV employs a packaging-signal dependent- or replication-coupled mechanism of encapsidation of its genome. The possible overlap of RNA sequences that function in RNA replication with those that function in encapsidation may present an obstacle to investigation of the  Flaviviridae family.It is known that positive strand RNA viruses package their genome via replication-coupled processes –3. Nevercis-acting elements for RNA replication. The 3’ UTR contains a short variable region, a poly (U/UC) tract with an average length of 80 nt (nucleotide) and a conserved 98-nt X-tail region (3’ X). Direct interactions of the 3’ UTR and the translation machinery facilitate efficient initiation of subsequent translation . W. Wcis-acescribed .To produce HCVtcp, Huh7.5.1 cells were co-transfected with pHH-based plasmids expressing a replicative  or non-replicative  JFH-1 subgenome and a plasmid pCAG/C-NS2 encoding Core, E1, E2, p7 and NS2 proteins. HCVtcp was inoculated into naïve Huh7.5.1 cells and transduced HCV RNA was determined . Althougcis-elements of the HCV genome packaging, we removed the 5’- or the 3’ UTR of the GND subgenome. It was noted that production of HCVtcp dramatically decreased, close to the background level, when the 3’ UTR of pHHSGR-JFH1/Gluc/GND was deleted. Only a limited influence on HCVtcp production was observed by deletion of the 5’ UTR  of encapsidation of HCV RNA, we determined the interactions of Core with a variety of HCV RNA fragments that have conserved sequences among HCV isolates and that can potentially fold into highly ordered stem-loop structures . The intin vitro binding data shown in These findings encouraged us to test whether the 3’ UTR of HCV is sufficient to allow packaging of a foreign RNA sequence into HCVtcp. The HCV 3’ UTR sequence derived from JFH-1 (genotype 2a) or H77c (genotype 1a) isolates was inserted into a reporter plasmid, p/EmGFP, downstream of the coding region of Emerald Green Fluorescent Protein (EmGFP), yielding p/EmGFP-3’UTR . Huh7.5.Renilla luciferase (Rluc) RNA in this setting) packaging into HCVtcp, we assessed the localization of HCV proteins in producer cells using confocal laser scanning microscopy , and examined the effects of these mutations on the production of HCVtcp. The resultant mutants were ΔSLI, ΔSLII and ΔSLIII, in each of which one stem-loop of the 3’ X region was deleted; ΔVSL, in which the variable region and the poly (U/UC) tract were deleted; and ΔSLII&SLIII, ΔSLI&SLII, and Δ3’X, in each of which two or three stem-loops of the 3’ X region were deleted . Comparain vitro was significantly reduced when nucleotides in the loop regions of SLI and SLII were replaced with their paired counterparts   , upper.  LI&IIM) , lower.  LI&IIM) . When LI LI&IIM) . On day Based on the collected data, we propose a model whereby encapsidation of the HCV genome is potentially triggered by direct contact of Core with loop regions in the 3’ X region of the 3’ UTR, while other RNA structures of the 3’ terminus act as brace backbones to support this interaction and/or nucleocapsid formation.Evidence regarding the virion assembly of HCV has accumulated over the past years; however, the detailed mechanisms responsible for incorporation of viral genomes into progeny virions remain largely unclear.cis-packaging elements. Formation of a pseudoknot structure through a kissing interaction between the 3’ X region of the 3’ UTR and CRE within the NS5B region is essential for viral RNA replication [A large proportion of 5’ end subgenomic HCV RNA with heterogeneous termination sites has been reported in the livers of hepatitis C patients ,10, conslication . Taking in vitro interaction with Core. Interestingly, we found that the interaction of CRE in the NS5B-coding sequence with Core turned out not to be as strong as the interaction of the 3’ UTR with Core. When the 3’ UTR was linked to CRE (CRE3’UTR), the strength of its interaction with Core was lower than that of the 3’ UTR alone. It was likely that access of Core to the 3’ UTR was implicated in the conformational switch of RNA-RNA interactions such as the kissing-loop structure formed via the interaction between CRE and the 3’ X region. Previous observations suggested that structural rearrangements within the 3’ end region of the HCV genome are important for the regulation of switching between different steps of the HCV lifecycle [In general, initiation of selective encapsidation of a viral genome requires recognition of a packaging signal by the nucleocapsid protein. Several conserved RNA structures across the HCV genome  were tested for ifecycle .cis-acting element for RNA packaging. The available evidence suggests that the packaging signal for the HCV genome is not contained within the 5’ UTR [Furthermore, the HCVtcp system was modified by introducing a reporter gene cassette (EmGFP-3’ UTR) for encapsidation. Based on the results of the two HCVtcp systems and the RNA-Core interaction assay, we concluded that the 3’ UTR of the HCV genome functions as a e 5’ UTR . Our exptrans-packaging efficiency. Nevertheless, mutations in the stem regions did not influence Core-binding or HCVtcp production. The predicted structures (by RNAstructure software) of STIM and STIIM showed that, even when the substitutions in the stems dramatically changed the RNA structures, there were no major changes in the loop area. Thus, the stem mutants still contained the loop motifs of the wild-type RNA , using a linearized HCV cDNA clone as the template. The genome-size RNA was purified from agarose gel after formaldehyde denaturing agarose gel electrophoresis of the synthesized RNA. Uniformity of the IVT-F RNA was assessed by Agilent 2100 Bioanlyzer in combination with the Agilent RNA 6000 Nano Kit.The Quantification of HCV RNA was performed by qRT-PCRs using TaqMan EZ RT-RNA Core Reagents (Applied Biosystems). HCV RNA quantifications were done by targeting either 5’ UTR or NS5B, in two (q) RT-PCR sets with respective RNA standards. The primers and probes used for quantifying the 5’ UTR of HCV have been described previously . To set Total cellular RNA was extracted with ReliaPrep RNA purification kit (Promega) and treated with TURBO DNase (Life technologies), followed by cleaning up with a Nucleospin RNA clean-up kit (TaKaRa Bio). RNA from supernatant or density fractions was extracted with a SepaGene RNA extraction kit (Edia Japan) according to manufacturer’s instructions.2. After 3 hr inoculation, cells were washed with PBS and cultured 12 hr with fresh growth medium for RNA extraction.HCVtcp was generated by cotransfection with pCAG/C-NS2 and pHHSGR-JFH1/Gluc essentially as previously described . To prodTotal RNAs of transfected Huh7.5.1 cells were isolated with Tri-reagent (Sigma-Aldrich), at 72 hr post-transfection. The isolated RNAs were treated with TURBO DNase (Life technologies), followed by cleaning up with a Nucleospin RNA clean-up kit (TaKaRa Bio). The resulted RNAs were then analyzed by electrophoresis in a 1.5% agarose-2.2 M formaldehyde gel, followed by Northern hybridization by using a DIG Northern starter kit (Roche) according to the manufacturer’s instructions. DIG-labeled anti-sense RNA probe complementary to the positive strand of NS5B (nt 7951–8476) was used to detect HCV RNA.in vitro translation kit (CellFree Sciences) using pEU/core/wt as a template.Biotinylated RNA fragments were synthesized using a MEGAscript T7 kit (Life technologies) or provided by Sigma Aldrich. The size and stability of RNA fragments synthesized with MEGAscript kit were checked by agarose gel electrophoresis. RNA fragments purchased from Sigma Aldrich were provided in 0.05 μmole scale, HPLC grade purity. FLAG-tagged HCV Core and its mutants were synthesized using a WEPRO in vitro synthesized Core protein, 0.1% BSA (w/v), 40 U RNase inhibitor, 20 μg/ml anti-FLAGm2 acceptor beads, 20 μg/ml streptavidin donor beads, 2.5 μl of 10x AlphaScreen assay buffer (PerkinElmer), followed by 90 min of incubation at 25°C and then subjected for determination of interactions by AlphaScreen signals (photon counts at 630 nm/s). AlphaScreen signals were detected on an EnSpire plate reader (PerkinElmer).The RNA-Core interaction assays were carried out in a final reaction volume of 25 μl by adding each assay component to the following final concentrations: 0.32 nM, 1.6 nM, or 8 nM of biotinylated RNA, 20 nM of The culture supernatant of Huh7.5.1 cells infected with HCVcc was concentrated by ultrafiltration with a centrifugal filter device (Amicon), and filtered with a 0.22 μm Sterivex filter unit (Millipore). To release intracellular virus, cell pellets were re-suspended with Dulbecco’s modified Eagle’s medium (DMEM) containing 10% fetal bovine serum and subjected to four cycles of freezing and thawing, followed by centrifugation at 2,400 x g for 10 min to remove cell debris. Supernatant- or cell-derived samples were then layered on top of 30 ml of a 10% to 50% Opti-prep density gradient medium (Sigma-Aldrich) prepared in DMEM and centrifuged in a Beckman SW41Ti rotor (Beckman) at 25,000 rpm for 16 hr at 4°C. Fractions of 1 ml were collected from the bottom of each tube.Olympus FLUOVIEW FV1000 confocal laser scanning microscope. Co-localization of NS5A with Core or LD was quantified via Pearson’s correlation coefficient (PCC) [http://imagej.nih.gov/ij/).Immunostaining of LD, NS5A and Core were performed as previously described . Subcellnt (PCC)  and intent (PCC)  using ImS1 Text(DOCX)Click here for additional data file.S1 FigA) Schematic representation the qRT-PCR targets in HCV genome. The amplicons of two qRT-PCR assays together with their locations in the HCV genome were shown. (B) In vitro synthesized HCV RNA with approximate full genome (IVT-F RNA) was examined with Agilent RNA 6000 Nano Kit, in which 150 ng of RNA was used. (C) RNA copy numbers of IVT-F at each dilution were determined by two qRT-PCR assays with respective RNA standard. (D) The molecular ratios of NS5B to 5’ UTR of IVT-F in each dilution. The mean value of their ratios was set as the reference ratio. Values are presented as mean ± SEM, n = 2.((TIF)Click here for additional data file.S2 FigA) Distribution of HCV RNA (J6/JFH-1) in fractions from culture supernatant. Detailed information was described in B) The 3’:5’-end ratios of fractions in ((TIF)Click here for additional data file.S3 Fig10 μg of total RNA isolated from HCV infected or naïve Huh7.5.1 cells and 200 ng IVT-F RNA were loaded for detection.(TIF)Click here for additional data file.S4 FigDetailed information of production and detection of HCVtcp was described in (TIF)Click here for additional data file.S5 Fig1.5 μg of each RNA fragments were loaded to a 1% denaturing agarose gel for electrophoresis.(TIF)Click here for additional data file.S6 FigA) Schematic representation of the plasmids used in a trans-complementation system for producing HCVtcp carrying EmGFP RNA. pCAG/NS3-5B expresses NS3, NS4A, NS4B, NS5A, and NS5B under the CAG promoter. p/EmGFP encodes EmGFP. p/EmGFP-J3UTR and pEm/GFP-H3UTR contain a 3’ UTR sequence derived from JFH-1 or H77c isolates, respectively, downstream of the stop codon of EmGFP. p/5’UTR-EmGFP contains the 5’ UTR sequence of JFH-1 upstream of the start codon of EmGFP. p/5’UTR-EmGFP-J3’UTR, encoding EmGFP flanked by 5’UTR and 3’UTR of JFH1. (B) Expression of EmGFP in producer cells with or without co-expression of NS3-5B. The scale bar indicates 200 μm. (C) Expression of NS5A and Core in producer cells. Identities of the transfected plasmids are indicated at the top, as referenced in D) Detection of production of HCVtcp packaged EmGFP-3’ UTR RNA, with pretreatment of the virions containing medium by 30 IU benzonase for 1 hr. N/G3J and cont were as described in E) Competition between RNA fragments with 3’UTR for interaction with Core. 4 nM of non-labeled 3’UTR, 5’UTR, or GFP were pre-incubated with 20 nM Core for 30min and then mixed with 4 nM biotinylated 3’UTR (Bio-3’UTR), followed by AlphaScreen assay. pc: the reaction with 4 nM of Bio-3’UTR; nc: the reaction without RNA fragments. Values are the means ± SEM, n = 2.((TIF)Click here for additional data file.S7 FigPlasmids expressing subgenomic HCV mutants  were tra(TIF)Click here for additional data file.S8 FigLI&IIM Mutations in (TIF)Click here for additional data file.S9 FigThe RNA structures were generated by predicting the lowest free energy structure and a set of low free energy structures for the sequences, by using an RNAstructure software. The dashed line circles marked the loop motifs existing in the loops of wild-type SLI and SLII (as shown in (TIF)Click here for additional data file.