Segmented RNA viruses are ubiquitous pathogens, which include influenza viruses and rotaviruses. A major challenge in understanding their assembly is the combinatorial problem of a non-random selection of a full genomic set of distinct RNAs. This process involves complex RNA-RNA and protein-RNA interactions, which are often obscured by non-specific binding at concentrations approaching in vivo assembly conditions. Here, we present direct experimental evidence of sequence-specific inter-segment interactions between rotavirus RNAs, taking place in a complex RNA- and protein-rich milieu. We show that binding of the rotavirus-encoded non-structural protein NSP2 to viral ssRNAs results in the remodeling of RNA, which is conducive to formation of stable inter-segment contacts. To identify the sites of these interactions, we have developed an RNA-RNA SELEX approach for mapping the sequences involved in inter-segment base-pairing. Our findings elucidate the molecular basis underlying inter-segment interactions in rotaviruses, paving the way for delineating similar RNA-RNA interactions that govern assembly of other segmented RNA viruses. Rotavirus is a highly infectious virus that affects children worldwide, causing severe diarrhoea. Despite the introduction of several highly effective vaccines, more than 200,000 children still die from rotavirus each year. There are currently no drugs that can combat this disease once a child has been infected.Viruses carry the instructions that determine their properties and behavior in molecules of DNA or RNA. Unlike many other viruses, which typically have a single molecule of DNA or RNA, rotavirus has 11 distinct “RNA segments”. After invading a cell the virus begins to replicate itself. During replication, the RNA segments (which consist of two strands of RNA paired together) are copied many times. It is not clear how rotaviruses ‘count’ up to 11 so that each new virus acquires a single copy of each segment.Previous biochemical and structural studies of rotavirus replication suggest that selecting 11 distinct RNA segments must involve the RNAs forming complex interactions with proteins and other RNA molecules. Using a highly sensitive fluorescence-based approach, termed fluorescence cross-correlation spectroscopy, Borodavka et al. now present direct experimental evidence of interactions between the RNA segments that occur via single strands of the rotavirus RNA.These RNA-RNA interactions require the binding of a rotavirus protein NSP2 to the RNA strands, which results in the remodeling of the RNA; this remodeling is required to form stable contacts between different RNA segments. Furthermore, a new experimental approach  developed by Borodavka et al. identified the parts of the RNA segments that may take part in these interactions.The results presented by Borodavka et al. pave the way for identifying the RNA-RNA interactions that govern how other segmented RNA viruses can package their genetic material. Further work to uncover the entire RNA interaction network in rotaviruses would also accelerate the design of new vaccines and may help us to develop antiviral drugs to treat infections. Reoviridae family, comprise nine to twelve double-stranded (ds)RNA segments, co-packaged into each infectious virion. The RNA assortment process, during which a single distinct positive-sense ssRNA copy of each of the genomic segments is selected for packaging and replication, occurs within cytoplasmic inclusion bodies termed viroplasms (Genomes of rotaviruses (RVs), and other pathogens of the roplasms . Multiplroplasms . Currentroplasms . Howeverroplasms . This prroplasms . Recentlroplasms  and psorroplasms . While troplasms . MoreoveTo address these challenges, we have developed an experimental framework for interrogating RNA-RNA interactions by taking advantage of two-colour fluorescence cross-correlation spectroscopy combined with pulsed interleaved excitation (PIE-FCCS) . Such asTo gain further insights into specific inter-segment contacts of RV RNAs, we introduce an RNA-RNA SELEX approach for mapping the genomic sequences, mediating such interactions. Using FCCS methodology combined with RNA mutagenesis studies, we validate the sites of inter-segment RNA-RNA interactions, identified via RNA-RNA SELEX. This integrated approach provides unique insights into the stability of macromolecular complexes, containing multiple RNAs, and it can be universally applied for investigating assembly of other segmented RNA viruses and ribonucleoproteins.Unlike most ensemble methods previously used for detecting inter-segment interactions in segmented RNA viruses , fluoresh of the S11 RNA remained unchanged, consistent with lack of RNA oligomerization under those conditions , the latter capable of helix-unwinding and strand-annealing reactions in vitro , we firshR ~30 nm, significantly larger than the protein-free S11 and S10 ssRNAs . This result suggests that the observed S11:S10 interaction involves additional RNAs that may be required to form a larger supramolecular complex , we also investigated the effect of ATP on the formation of RNA-RNA contacts in the presence of NSP2. Addition of 1 mM ATP did not affect RNA binding by NSP2 octamers , nor hadh upon removal of NSP2 , and examined it by FCCS. Both heat-annealed RNAs strongly interacted , yieldinx S11:S5 . TogetheHaving established that S11 preferentially binds to S3, S5 and S6 RNAs, we devised an RNA-RNA SELEX methodology to identify the S11 RNA sequences that can form stable inter-molecular contacts with other segment ssRNAs. We used this approach to analyze the distribution of the RNA-RNA interaction sites in S11 alone, and in complex with NSP2 .13 unique 30-mer ssRNA sequences. Biotinylated S11 RNA was incubated with the naïve RNA library . The resulting histogram indicates the areas, in which multiple SELEX-enriched sequences align to S11 RNA . The analysis enabled us to precisely map the conserved helices H1-H3 , consistSimilarly, multiple histogram peaks aligned well to various loop regions that had been previously identified in S11 RNA . OverallBecause the folded structures of interacting RNAs in isolation would present a substantial energy barrier for forming inter-molecular base pairs between them, RNA-RNA SELEX favors selection of unstructured RNA sequences. We reasoned that binding of NSP2 to ssRNAs would alter their structures and, thus, accessibilities of the RNA-binding sites. Analysis of the RNA-RNA SELEX data revealed significant enrichment of the RNAs interacting with S11 in the presence of NSP2 . A compaTo further confirm that NSP2 binding to S11 results in conformational change of the RNA, we monitored the circular dichroism (CD) spectrum of S11 RNA in the presence of NSP2, as described in Materials and methods. The protein-free S11 RNA spectrum is characteristic of an A-type helical conformation , typicalHaving established that NSP2 binding is a prerequisite for inter-segment RNA-RNA interactions, we aligned the SELEX-enriched sequences to the S1-S10 RNAs . The anaRemarkably, the distribution of the identified peaks changes in the presence of NSP2 , compared to the non-rotaviral control sequence  resulted in a dramatic reduction of the CCF amplitude . Both com ~ 47°C .We then used the two-color FCCS approach to probe the stability of S5:S11 RNA complex. The FCCS analysis of the preformed S5:S11 RNA complex, incubated at elevated temperatures, yielded the estimated melting temperature, Tm ~ 47°C . This vaWe also introduced compensatory nucleotide substitutions in the putative interacting sequences of S11 and S5mut in order to test if the disrupted inter-segment contacts could be rescued by restoring the stable base-pairing between the mutated RNAs. The resulting mutated trans-complementary RNAs S5mut and comp_mutS11 strongly interacted with each other upon incubation with NSP2 . MoreoveWe also carried out similar mutagenesis analyses for segment precursors S3 and S6 , confirmSeveral models of segmented genome encapsidation have been proposed to explain selective packaging of eleven RNAs in RVs . Here, wThe data presented here collectively support the model in which RV ssRNAs can specifically interact with each other as a result of RNA remodeling brought about by the ssRNA-binding protein NSP2 . The reset al. reported that group C NSP2 could not rescue replication in NSP2-deficient cells infected with group A RV  and influenza A viruses (IAVs), may control selection of the correct RNA segments . In BTV,oup A RV . Our assInterestingly, the IAV segment ssRNAs were shown to form higher order species in vitro; however most of these interactions were only detectable upon co-incubation of the RNAs at 55°C . SimilarAlthough the precise mechanism of the selective packaging of eleven distinct RNAs in RVs is still largely unknown, our results pave the way for delineating the mechanistic details and the RNA sequences essential for this process. Combining our approach with a recently established reverse genetics system for RVs  will ultGenome segment precursors S1-S11 (GenBank IDs are listed in NcoI and XhoI restriction sites used for ligating the resulting double-digested NSP2-coding fragment into a linearized pET-28b vector. The resulting pET-28b-NSP2 construct was verified by sequencing and used for protein expression in BL21(DE3) E.coli as previously described (cDNA clone of gene 8 of rotavirus A (strain RF) was used to PCR-amplify NSP2 ORF  with Ncoescribed . NTA-affescribed . The conescribed . A plasmescribed . The resRNA transcripts were produced by in vitro transcription of linearized DNA templates  using th2, 150 mM NaCl, 0.05% Tween 20, 1 mM DTT).For FCCS, to minimize any potential RNA self-association , all RNA2. The diluted RNA samples (1 nM of each strand) were incubated at 37-70°C for 15 min, after which they were slowly cooled down to 25°C prior to FCCS measurements. S10 RNA oligonucleotide hybridization assay was performed by heat-annealing RNA substrates . RNA samples were mixed in reaction buffer  and allowed to interact for 30 min at 37°C. For NSP2-mediated RNA-RNA-interaction assays, labeled RNA samples  were incubated with 2.5–10 μM NSP2 (or 2.5–10 µM BSA for control reactions). Reactions were allowed to proceed at 37°C for 30 min before they were stopped by diluting RNA samples into assay buffer, as discussed above, to achieve a final concentration of labeled RNAs of 1 nM (each strand). These samples were further allowed to equilibrate at 25°C for 15 min prior to FCCS measurements. Proteinase K digestion of NSP2 was performed by adding 40 μg of proteinase K (800 U/ml) to the NSP2-RNA samples and incubating them for 30 min at 37°C, as previously described , prior tDual-color fluorescence cross-correlation spectroscopy is a technique that allows sensitive detection of interactions of differently labeled molecules at low concentrations, typically with single-molecule sensitivity . FCCS deFCCS measurements of ATTO647, and ATTO565 or Cy3-dye labeled RNAs were performed on a custom-built confocal microscope with time-correlated single photon counting (TCSPC) detection designed for pulsed interleaved excitation (PIE), as previously described . Two pulhttps://gitlab.com/PAM-PIE/PAM; a copy archived at https://github.com/elifesciences-publications/PAM-PIE-PAM), which includes tools for processing single point data collected using PIE =γN⋅N is the number of individually diffusing molecules in the focus volume, D is the diffusion coefficient, and rω and zω are the distances from the center of the focus to the point where the fluorescence intensity has decayed to 1/e2 of the maximum intensity for the lateral and axial dimensions, respectively. T and Tt denote the dark state fraction and the correlation time, respectively. G(∞) is a constant offset accounting for slow signal fluctuations. The geometric factor, γ, accounts for gradual intensity decrease of the assumed 3D Gaussian shape of the observation volume and is 0.35355. The CCF amplitudes were normalized by the N, measured for the ATTO647 labeled S11 RNA . These normalized CCFs yield the fraction of the interacting ATTO565- and ATTO647-dye labeled ssRNAs. Thus, the fraction of interacting species is only an estimate of the absolute CCF value, as the stochastic labeling of the RNAs leads to the non-trivial weighting of a different number of fluorophores. In the case of complete binding with 1:1 stoichiometry, the ratio of the CCF/ACF amplitudes is expected to be close to one . The resulting ACF and CCF amplitudes were averaged, yielding mean CCF amplitudes reported here.13 N30 random sequences flanked by primer sequences for RT-PCR amplification and transcription was produced by in vitro T7 transcription from a DNA template  was incubated with the S11 RNA target for 15 min at 37°C, either in the absence  or presence of 10 μM NSP2 (NSP2 +selection). 20 μL of RNAse-free Dynabeads MyOne Streptavidin T1 beads (Invitrogen) were re-suspended in SB buffer and added to the S11 RNA samples followed by further incubation at 37°C for 15 min. Captured RNA targets with bound 30-mer RNAs were washed with SB buffer (200 μl) 4 times for 10 min at 37°C during the first 3 rounds of selections, and the stringency of selection was increased after round 3 by increasing the number of washes up to 8 per round. Negative selections were carried out at every second round of SELEX using streptavidin-coated beads. S11-bound 30-mers were recovered by heat-elution  of the RNAs from carrier beads into 30 μl of RNAse-free water. Eluted RNA libraries were reverse-transcribed using Superscript III Reverse Transcriptase (Thermo Scientific) and primer P1, and further PCR-amplified using primers P1 and P2 (A naïve 30-mer RNA library (N30) consisting of ~3.6×10template . It was template . A naïve1 and P2 . PCR-ampWe identified 4,857,793 unique sequences from the RNA alone SELEX data and 3,308,107 sequences from the NSP2+-enriched data. The lower number of the unique sequences from the NSP2 +selection is due to selective enrichment of certain RNA sequences in the presence of NSP2. Each sequence from both data sets was aligned against all 10 segments (S1-10) and the single best alignment was identified using a probabilistic score (the Bernoulli score), which benchmarks the probability of a non-contiguous alignment to that of a contiguous alignment of N nucleotides . Thus, aoC.CD spectra were acquired between 240 nm and 320 nm in a 1 cm-long path cell, as previously described (26). CD spectra were recorded for the S11 RNA  before and after incubation with either 10 µM NSP2 or RNAse-free acetylated bovine serum albumin  for 15 min. RNA secondary structure transitions upon thermal melting were also monitored at various temperatures up to 90SCR_008543) with the folding restraints derived from the RNA-RNA SELEX experiments. Differences in target RNA accessibilities were expressed as a normalized number of sequence alignments in the presence of NSP2, after subtracting alignments, identified in the S11 structure alone. These values were used to calculate a color map, applied to the S11 RNA structure, shown in RNA secondary structures were predicted using minimum free energy (MFE) modeling in RNAfold to compute the thermodynamic ensemble of secondary structures and base-pairing probabilities. The centroid structures showing minimal base-pair distances to all other secondary structures in the Boltzmann ensemble were analyzed in order to identify regions of high and low accessibilities within S1-S10 RNAs. Similarly, the MFE structure of S11 RNA was calculated in Mfold (RRID:m) calculations were performed using nearest-neighbor parameters implemented in MELTING v.5.0 (56), estimated for 1 nM RNA strand concentration using the parameters matching the low-salt buffer conditions, used for FCCS measurements, as described above.RNA structures were visualized in VARNA , and jVi In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Wenhui Li as the Senior Editor. The following individual involved in review of your submission has agreed to reveal his identity: Bidadi Venkataram Prasad (Reviewer #2).Thank you for submitting your article "Protein-mediated RNA folding governs sequence-specific interactions involved in genome segment selection in rotaviruses" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Summary:This manuscript tries to address the fascinating and complex question of how eleven segments of RNA are selected for subsequent encapsidation in rotavirus. Rotavirus is a member of the Reoviridae. A common feature is that these viruses encapsidate 9–12 segments of dsRNA; rotaviruses, in particular, have 11 dsRNA segments. How these viruses in the Reoviridae selectively package a correct set of RNA segments has been an unanswered question. Borodavka et al., present an interesting set of experiments to address how the 11 segments of the rotavirus RNA genome are selected for packaging into virions. The data suggest that the rotavirus non-structural protein NSP2 remodels viral ssRNA to facilitate the formation of stable inter-segment contacts. In particular, authors show that segment S11  interacts with S10 and this association is further stabilized by the presence of other RNA segments and that S11 preferentially binds to S3, S5, and S6. Authors further show that these specific inter-segmental interactions result in the formation of an RNA oligomer which remains stable even in the absence of NSP2, thus suggesting that the role of NSP2 is to promote such specific inter-segmental contacts and facilitate the formation of an RNA oligomer presumably containing the correct set of 11 segments for subsequent packaging. Using RNA SELEX methods authors have mapped the sequences in S11 that are likely involved in the inter-segmental RNA-RNA base-pairing, which were further substantiated selective mutating the contact regions. Overall this is an elegant study, well designed, presenting novel experimental approaches. Previous studies have suggested or speculated the possibility of such interactions as a requirement of selective packaging of RNA segments, however, this is perhaps the first study to demonstrate the possibility of such inter-segmental interactions in the case of rotavirus.Overall, this is a very nice paper demonstrating the possibility of specific inter-segmental interactions among rotavirus RNAs that is promoted by NSP2 using a clever combination of various techniques. It is unfortunate that the reverse genetics system published recently for rotaviruses is not sufficiently robust to address the functional relevance of the identified interactions. However, even in the absence of such data, the current manuscript makes a significant step towards uncovering the molecular basis of RNA packaging in rotaviruses and the presented methodologies will be useful for analysing RNA packaging mechanisms in other segmented negative strand RNA viruses.Essential revisions:The data in One major concern of this paper is the lack of in vivo validation for the observations made, and a lack of a well-reasoned model for how the formation of the higher-order ssRNA-oligomer allows itself for replication, duplex formation, and packaging. As noted above, we understand that in vivo validation is not practical at this point. Still, there are several questions (some of which could be addressed by in vitro experiments as well) such as for example 1) given that NSP2 strongly interacts with NSP5 in the viroplasm (and perhaps with other proteins including VP1 and VP2), how do these interactions affect NSP2-RNA interactions and RNA remodeling? Can the experiments be done in the presence of both NSP2 and NSP5? Do NTPase/RTPase activities of NSP2 play a role in RNA-RNA interactions or remodeling? This can be tested perhaps by using a mutant of NSP2 that lacks NTPase/RTPase activities? Addition of NSP5 and/or elucidation of potential roles of NTPase/RTPase activities would greatly strengthen the manuscript by making the measurement and analysis more physiologically relevant. Essential revisions:The data in One major concern of this paper is the lack of in vivo validation for the observations made, and a lack of a well-reasoned model for how the formation of the higher-order ssRNA-oligomer allows itself for replication, duplex formation, and packaging. As noted above, we understand that in vivo validation is not practical at this point. Still, there are several questions (some of which could be addressed by in vitro experiments as well) such as for example 1) given that NSP2 strongly interacts with NSP5 in the viroplasm (and perhaps with other proteins including VP1 and VP2), how do these interactions affect NSP2-RNA interactions and RNA remodeling? Can the experiments be done in the presence of both NSP2 and NSP5? Do NTPase/RTPase activities of NSP2 play a role in RNA-RNA interactions or remodeling? This can be tested perhaps by using a mutant of NSP2 that lacks NTPase/RTPase activities? Addition of NSP5 and/or elucidation of potential roles of NTPase/RTPase activities would greatly strengthen the manuscript by making the measurement and analysis more physiologically relevant.Here we have tried our best to respond to the wider concerns expressed by the referees.In particular, we have addressed three general issues raised. These are:1) Can the disrupted interactions between the mutated RNA sequences be ‘rescued’ by introducing compensatory, ‘trans-complementary’ mutations?2) How does the viroplasm-forming RNA-binding protein NSP5 affect inter-segment RNA-RNA interactions alone or in the presence of NSP2?3) Does the enzymatic activity (NTPase/RTPase) of NSP2 have an effect on the detected RNA-RNA interactions?et al., 2017. S11 RNA was chosen for mutagenesis analysis due to its small size and the availability of secondary structure probing data that could be used for introducing nucleotide substitutions whilst minimizing their impact on the secondary structure of the RNA.Regarding point 1, we have included results obtained with mutated RNAs S5 (S5mut) and a mutant of S11 RNA (comp_mutS11) showing that stable base-pairing between S5mut and the interacting S11 RNA mutant is restored. The mutated RNA sequences strongly interact with each other, consistent with the predicted stability of the ‘rescued’ RNA-RNA contacts between the two RNAs. These results, together with extensive characterisation of the stability of the formed duplex . We have also added a new figure  presenti


Very few studies have been conducted on the long-term effects of typhoon damage on mesophotic coral reefs. This study investigates the long-term community dynamics of damage from Typhoon 17 (Jelawat) in 2012 on the coral community of the upper mesophotic Ryugu Reef in Okinawa, Japan. A shift from foliose to bushy coral morphologies between December 2012 and August 2015 was documented, especially on the area of the reef that was previously recorded to be poor in scleractinian genera diversity and dominated by foliose corals. Comparatively, an area with higher diversity of scleractinian coral genera was observed to be less affected by typhoon damage with more stable community structure due to less change in dominant coral morphologies. Despite some changes in the composition of dominant genera, the generally high coverage of the mesophotic coral community is facilitating the recovery of Ryugu Reef after typhoon damage. Scleractinian corals are the primary architects of reef ecosystems and the major contributors to reef rugosity, a fundamental parameter for the resilience of the ecosystem after a disturbance . TherefoThe recovery of coral reefs after disturbances is usually measured by changes in coral cover, abundance, species composition, and/or diversity . The typReports measuring coral reef recovery after storm disturbances based on coral cover on shallow reefs vary in time from two to 15 years, with little attention paid to species composition . EcologiAcropora reef in the Coral Sea declined from 80% to less than 10% after several storms over a two year period, leaving only encrusting and robust coral species  after sPachyseris foliosa from 32 to 45 m. Heading deeper, away from shore, Ryugu Reef transitions on a low-angle slope from rubble with some corals (Fungiidae) at 21 m; to a high diversity of corals at 26 m; a Pachyseris- dominated area at 31 m; and finally to sand at 42 m. The center of Typhoon 17 (Jelawat), the strongest typhoon ever recorded to hit Okinawa-jima Island, with wave heights up to 12 m, passed within 30 km of Ryugu Reef on 29 September 2012  were taken along a 10-m tape for each line transect 1.5 years after Typhoon 17 (designated ‘2014’ dataset) and 2.5–3 years after the typhoon (designated ‘2015’ dataset). Scleractinian coral  operational taxonomic units (OTUs) were identified in a similar manner as in Acropora . Time-period centroids  were overlaid to visualize temporal dynamics. Differences in the composition of the benthic assemblage between stations, and between temporal dynamics within each station were tested using permutational multivariate analysis of variance (ADONIS).Random line transects were surveyed at Ryugu Reef  are provided in Each coral OTU was described following similar methods to those described in All data were analyzed in R v3.2.3  using thR2 = 0.19, p < 0.001)  was greater than among years  was significant  at station 3 as of 2015, compared to 2012 before the typhoon (p < 0.05), an increase of bushy corals , and an increase in some plate-like corals (p < 0.05) (Pachyseris from 2012 (post-typhoon) to 2015  . Howeverng years . Stationr genera . An incr typhoon . Signifi < 0.05) . A major to 2015 .Percent cover of coral rubble/coralline algae decreased from 31% to 27% at station 2 between 2012b and 2014 from 27% to 7% between 2014 and 2015 . This foSymbiodinium from the water column (Pachyseris coverage (Pachyseris (foliose morphology) cover decreased immediately following the typhoon (p < 0.01). At least six of the ‘massive’ genera  have been noted to be ‘stress-tolerant’ and four ‘weedy’ genera (fast growing with high turnover) are known to do well under disturbance  (Pachyseris) on Ryugu Reef is a ‘generalist’  . Howevereralist’ . It appeal cover . At Ryugal cover , and PacPachyseris-dominated community  that stabilized with no significant increase or decrease in cover at station 3 . These results are similar to a previous shallow-water study on the Great Barrier Reef in which foliose coral colonies declined drastically after a storm event (p < 0.01). The functional shift shows that beyond the apparent recovery of Ryugu Reef, the trajectory followed could lead ultimately to a fundamentally different coral community, as previously reported from the Great Barrier Reef  in 2012, but showed signs of recovery in 2015 with only slight diversification of the coral community. This fast recovery, combined with behavioral characteristics, may enable Pachyseris spp. to outcompete other coral genera at upper mesophotic depths, allowing Ryugu Reef to return to the initial, pre-typhoon coral assemblage. However, if Ryugu Reef coral genera are limited by their growth rates as many other mesophotic coral genera are (Acropora corals around Okinawa Main Island (Acropora species, and such research remains to be conducted on corals of deeper reefs such as Ryugu Reef. The high live coral cover of Ryugu Reef suggests that although mesophotic reefs may be affected by large storms, recovery may be facilitated by the relative stability of the mesophotic zone.nera are , it is mnera are . While tn Island  and on tn Island  both sug10.7717/peerj.3573/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.3573/supp-2Table S1Morpho-functional groups of observed Scleractinia operational taxonomic units at Ryugu, Okinawa, Japan, 2012–2015.Click here for additional data file.


A→I) that is catalyzed by the members of the Adenosine deaminases that act on RNA (ADAR) family. Typically, editing sites are identified as RNA-DNA-differences (RDDs) in a comparison of genome and transcriptome data from next-generation sequencing experiments. However, a method for robust detection of site-specific editing events from replicate RNA-seq data has not been published so far. Even more surprising, condition-specific editing events, which would show up as differences in RNA-RNA comparisons (RRDs) and depend on particular cellular states, are rarely discussed in the literature.RNA editing is a co-transcriptional modification that increases the molecular diversity, alters secondary structure and protein coding sequences by changing the sequence of transcripts. The most common RNA editing modification is the single base substitution . In practice, JACUSA shows higher recall and comparable precision in detecting The online version of this article (doi:10.1186/s12859-016-1432-8) contains supplementary material, which is available to authorized users. A→I) and cytidin to uridine (C→U) conversions, have been characterised in detail over decades of research [A→I conversions, whereas APOBEC1 family members catalyse C→U conversions.RNA editing refers to co-transcriptional RNA base modifications that increase transcript sequence diversity without changing the underlying genome. Two types of single base modifications, namely adenosine to inosine conversions  of the nucleotide frequency spectrum at a given location are the most direct way of identifying editing sites, whereas RNA-RNA comparison may pinpoint differential editing events across samples and conditions . The availability of deep next-generation sequencing data enabled the transcriptome-wide discovery of RNA editing events. A direct comparison of gDNA and cDNA sequencing data has been proposed early on . HoweverSeveral software solutions have been suggested for calling SNV sites: SAMtools/BCFtools , REDItooJAVA framework for accurate SNV assessment (JACUSA). JACUSA is a fast and precise solution for quantitative single nucleotide variant detection in RNA-DNA or RNA-RNA comparisons. JACUSA is primarily designed for the detection of position-specific editing events and readily integrates information from replicate experiments.Based on our previous experience from developing ACCUSA2 , we implDrosophila melanogaster fly brains that either originate from a wild type strain or a strain with a genetically ablated dADAR gene. With the Drosophila samples, we specifically look at the identifiability of editing events in protein coding exons in neuronal tissue where they have been reported previously [In the next sections, we will present our statistical framework and data processing steps in detail. We benchmark JACUSA on simulated data sets and compare its performance to other available and popular variant callers: REDItools , MuTect eviously .In the following, we present the JACUSA software in detail, discuss the test statistics that supports replicate experiments and a set of positional filters that enable the pruning of false positive variants for a more accurate detection of SNVs. Equally important, we have implemented parallel and memory-efficient read processing routines for better performance and usability.The JACUSA software predicts single-nucleotide variant positions from head-to-head comparisons of read stacks/pileups from Illumina sequencing. In this manuscript, we focus on identifying nucleotide-level differences in RNA-DNA and RNA-RNA comparisons see Fig. a. Our mΣ={A,C,G,T}. We define p= to be a random probability vector, such that pk:k∈Σ represents the base or allele probability for base k and the elements sum to 1. We can model p with a Dirichlet distribution α=: Previously, it has been shown that allele frequencies/counts are not accurately modeled by over-simplistic statistical models  . Typicalα from base calls and their respective base call quality score using an empirical Bayesian method. The Dirichlet distribution x= represent the sum of base calls at some location and let x follow a multinomial distribution p we can combine x and p into the compound distribution that is called the Dirichlet-Multinomial: In , we estiwhere An alternative interpretation of the Dirichlet-Multinomial is that of a hierarchical model: D={x1,xi,…,xN}:i∈{1,⋯,N} represent the base count vectors in N replicates and let xi be identically and independently distributed. Then α can be estimated from D by maximum likelihood estimation of Let α we add a pseudocount term xP to the base call count vector: xP is calculated as a sum from observed quality score qBC (i.e. variable terms) and a fixed noise term ε (=0.01) which models sequencing independent errors, which were derived empirically. qBC is reported per base call as Phred quality score qBC, which is logarithmically related to the base-calling error probability eBC [In order to model uncertainty of ity eBC : 5\docueBC is independent of the called base. That is why, the error probability of an uncalled base is given by: In JACUSA, we assume that the error probability l, we define xP as: Using these considerations and referring to a specific base call by j∈{I,II} where the data of each sample is defined as the pseudocount adjusted base call vectors αj as explained in the previous section. We test against the null hypothesis H0 that both samples originate from the same underlying distribution. The log-likelihood score function z  and with less than three distinct base types (filter M). Moreover, we strongly recommend to remove PCR-duplicate reads from the input read sets to minimize biases, which are introduced by PCR amplification biases, before the actual JACUSA run  have been implanted into each corresponding cDNA BAM file. Additionally, to make the identification of variants more challenging, SNPs with pairwise similar base frequencies have been included into each cDNA BAM file  gDNAsee Fig. . We use β∈{10,50,100} representing sites with high, medium, and low variability around the expected target frequencies. Another parameter of our benchmark is the number of RNA-seq replicates. We benchmarked all scenarios with cDNA samples from 1 to 5 replicates. Each replicate setup is simulated 3 times, which amounts to 15 RNA-seq FASTQ files per benchmark , with 24 cycles  in a 4 °C water bath, including a brief centrifugation after 12 cycles. The resulting fragmented DNA was converted to a sequencing library using the TruSeq DNA kit (Illumina) with PCR enrichment and sequenced on a Illumina HiSeq 2500 machine. In total >109 reads have been sequenced  using the GenElute Mammalian Genomic DNA Miniprep Kit (Sigma-Aldrich). DNA was fragmented in the BioRuptor Plus . We processed the data in the same way as the HEK-293 data sets using the Ensembl 75 Drosophila genome and annotations.We obtained published replicate paired-end RNA-seq data from Drosophila fly heads   as compared to the other tested methods while being competitive at the level of precision  and false positives (FPs) into composite measures and observed 10−16see Fig. c. This see Fig. d.Additional results and details are given in Additional file To assess the performance of JACUSA in practice, we designed a controlled experiment to generate sequencing input data from cell culture experiments see Fig. a. BriefSubsequently, we conducted gDNA vs. cDNA comparisons on the aforementioned data sets and predicted RNA editing sites with SAMtools/BCFtools, MuTect, and JACUSA. For each variant caller, we selected optimal thresholds for the HEK-293 data set based on our results from the in silico data set: gDNA vs. cDNA score threshold is 1.15 and cDNA vs. cDNA threshold is 1.56. Additional details on selecting score thresholds are given in Additional file For MuTect and REDItools we adopted a strategy presented in  to utiliIn total, 2 biological replicates have been created per condition and were sequenced twice to assess the biological and technical variability. By computing RDDs on each replicate with JACUSA, we could show an excellent agreement among replicates from the same condition see Fig. a. Subse%A→G sites, see Fig. A→G) and, as expected, to 3371 sites for the siADAR RNA samples.Our comparison of gDNA vs. RNA from untreated cells yielded 15,461 variant sites for JACUSA (with a proportion of 92.2The siAPOBEC3 transfection experiment (mock) already leads to a reduction of editing sites. Editing levels are further reduced by targeting the correct enzyme class (siADAR experiment).A→G sites identified by JACUSA  editing is a known but rare modification that is mediated by APOBEC1 [T→C and G→A variants (39.2%), which are the reverse complement versions of the canonical editing events.Interestingly, the non  APOBEC1  and T→C A→G sites (92.2% vs 93.3% for SAMtools/BCFtools) among all tested variant callers  and showed a comparable fraction of % less) in comparison to the other variant callers while achieving second highest fraction of A→G sites (92.5%). This is in line with the in silico benchmark results on MuTect indicating a high precision but a lower recall. In summary, all variant callers identify RDDs with a fraction of A→G sites in the range of 90.1 and 93.3%, while the total number of variants varies greatly from 7605 (MuTect) up to 15,461 (JACUSA).MuTect identified far fewer RDDs (≈25A→G sites (94.6%) . Strikingly, JACUSA identifies 2,634 additional RDDs, which are not reported by any other software tool and yet attain a proportion of 87.9%A→G sites. This is far more than for sites that were exclusively reported by SAMtools/BCFtools (59.5%A→G sites), REDItools (35.8%) or MuTect (42.3%).All four software solutions report a set of 6064 shared RDD sites for the untreated RNA sample, which show a high proportion of see Fig. a. The sMoreover, a detailed assessment of RDDs sites, which have been exclusively reported by JACUSA, shows a low mean editing level and mean coverage see Fig. b and c.A→G substitutions. We classify these A→G sites as true positives if they show a drop in their editing frequency in an siADAR vs. siAPOBEC3 knockdown.To control the effect of any siRNA knockdown treatment on RNA editing levels see Fig. b, we co% show lower editing levels in siADAR samples than in samples from siAPOBEC3 treated cells. This means that JACUSA reports 6375 true positive A→G sites out of a set of 6,466 predicted sites, the highest among all tested variant callers. Figure A→G and could not be observed for any other base substitution  in the absence of genomic sequencing data. We reasoned that one way to validate RRD site detection and ultimately differential A→G editing and siRNA transfection effects cancel out in this comparison. “True” A→G editing sites should show a lower editing frequency in the siADAR knockdown. For the siADAR vs. siAPOBEC3 comparison, SAMtools/BCFtools predicts 6368 RRD sites and JACUSA predicts 5366 RRD sites  and JACUSA   Table We compared the fraction of RRDs that after coverage filtering were potential SNPs and found that SAMtools/BCFtools predictions contained 15% more putative polymorphic sites than JACUSA see Fig. b.A→G editing sites was higher than the candidates called by SAMtools/BCFtools. The editing frequency of 4,043 A→G sites was smaller in siADAR treated cells whereas only 8 would show a higher editing frequency in siADAR treated cells  and repeat-centric categories . As expected, most RDD predictions are made in regions that are annotated as Alu repeats. Prediction accuracy drops dramatically for non-Alu repeat regions and even more so for non-repeat regions. For details see Additional file A→G sites are predicted in non repeat regions, by both SAMtools and JACUSA.The same phenomenon becomes more evident for the RRD comparisons . This is a favorable system for fine-mapping editing sites as editing activity depends only on a single enzyme in the fruit fly. Moreover, editing in coding exons, which are expressed in the fly brain, has been described previously [A→G sites, just 86.3% of all SAMtools/BCFtools predictions are  and RNA-RNA differences (RRDs). While the first benchmark is the typical scenario for identifying RNA editing sites from homozygous genomic positions, the second benchmark represents another interesting case of identifying condition specific changes in editing frequencies.JACUSA shows the best recall and competitive precision in comparison to all tested software solutions. The performance gain over its competitors is especially visible for the detection of RNA-RNA differences. In terms of recall, JACUSA outperforms SAMtools/BCFtools in the RRD scenario by at least 40% when replicates are available and by over 35% when no replicates are available. Intriguingly, this is not at the expense of precision which is at least 10% better over all tested number of replicates.A→G editing sites in RNA-DNA comparisons.In practice, we tested JACUSA in a controlled experimental setup where we generated DNA and RNA-seq data from HEK-293 cells. Similar to the in silico benchmark, we first identified candidate sites of RNA editing via RDD comparisons and checked if their editing frequency would respond to changes in ADAR protein levels by siRNA knockdown experiments. With this setup, we could nicely demonstrate that JACUSA has a better recall and comparable precision to other tested variant callers in identifying A→G editing sites than SAMtools (83.0% vs 67.8%) in this RNA-RNA comparison scenario on HEK-293 RNA-seq data.Subsequently, we assessed the RRD or differential editing scenario by predicting SNVs between replicate siAPOBEC3 and siADAR RNA samples. Again, JACUSA overall predicts more sites in homozygous DNA positions and a greater proportion of Drosophila melanogaster heads. Herein, JACUSA reports the highest number of RNA editing sites (857 vs 674) with much higher precision .These results were further corroborated by looking at an independent RNA-seq data set from In summary, JACUSA is a versatile software for the precise and sensitive detection of single nucleotide level differences in DNA-RNA as well as RNA-RNA comparisons from Illumina sequencing data. In this manuscript, we have specifically explored its excellent ability to detect site-specific RNA editing events.Project name: JACUSAProject home page:https://github.com/dieterich-lab/JACUSAOperating system(s): N/AProgramming language: JAVA 1.6Other requirements: noneLicense: GPL-3.0gDNA-seq data have been deposited in the NCBI SRA under accession SRP050149.


In this paper, we presented a comprehensive review on computational methods for ncRNA prediction, which are generally grouped into four categories: (1) homology-based methods, that is, comparative methods involving evolutionarily conserved RNA sequences and structures, (2) de novo methods using RNA sequence and structure features, (3) transcriptional sequencing and assembling based methods, that is, methods designed for single and pair-ended reads generated from next-generation RNA sequencing, and (4) RNA family specific methods, for example, methods specific for microRNAs and long noncoding RNAs. In the end, we summarized the advantages and limitations of these methods and pointed out a few possible future directions for ncRNA prediction. In conclusion, many computational methods have been demonstrated to be effective in predicting ncRNAs for further experimental validation. They are critical in reducing the huge number of potential ncRNAs and pointing the community to high confidence candidates. In the future, high efficient mapping technology and more intrinsic sequence features (e.g., motif and  A noncoding RNA (ncRNA) is a functional RNA that is transcribed from a DNA but does not encode a protein. According to transcriptomic and bioinformatics studies, there are thousands of ncRNAs classified into different categories based on their functions and lengths including transfer RNA (tRNA), ribosomal RNA (rRNA), microRNA (miRNA), and long ncRNA (lncRNA) to name a few –3. ANRIL, one type of lncRNA, is related to coronary disease, type II diabetes, and intracranial aneurysm [These ncRNAs play important roles in various cellular processes. For example, rRNA catalyzes the peptide bond formation between amino acids in translation process , miRNA ianeurysm . The reaaneurysm  and Chenaneurysm  for moreaneurysm  providesaneurysm  discusseDue to the important roles of ncRNAs in cellular processes and disease development, many experimental and bioinformatics methods have been developed to predict ncRNAs and their functions. As for experimental methods, enzymatic and chemical RNA sequencing, parallel cloning of ncRNAs by specialized cDNA libraries, microarray analysis, and genomic SELEX are among the most popular ones. The readers are referred to a review paper for the details of these methods . HoweverGenerally speaking, there are three major categories of computational methods in predicting ncRNAs, namely, (1) homology-based methods involving evolutionarily conserved RNA sequences and structures, (2) de novo methods using RNA sequence and structure features, and (3) transcriptional sequencing and assembling based methods, according to chronological order of their occurrences. Since miRNA and lncRNA have very specific methods, we reviewed them separately and called these methods RNA family specific methods .As probably the earliest ncRNA prediction methods, homology-based methods assume that sequence or structure similar RNAs are evolved from a common ancestor and thus share function similarities , 19. GivE-value). The query ncRNA is classified into the family containing the ncRNA with the lowest E-value.These methods rely purely on sequence conservations inferred by alignment methods like BLAST  and BLATSequence-based methods are usually very fast. However, it is commonly believed that ncRNAs are less conserved in sequence. Thus, another category of homology-based methods is introduced based on structure conservations. Instead of sequence alignment, these methods use RNA secondary structure alignment to measure RNA similarity. Popular methods include QRNA  and RNAzA more robust RNA similarity measure was obtained by incorporating both sequence and structure information. For example, Infernal  uses covThough homology-based methods have been extensively used due to their advantages in speed, however, they have a few limitations. First, they compare the query RNA with known ncRNA families and thus are incapable of predicting new ncRNA families. Second, they rely on sequence or structure conservations and thus are inapplicable to predict ncRNAs lacking conservation in sequence and structure. As a result, de novo methods are proposed to solve such dilemma.k-mer features that native ncRNA sequences and structures acquire (Unlike homology methods which require the information of RNAs similar (or homologous) to the query RNA, de novo methods predict ncRNA from primary sequences or structure based on general principles that govern ncRNA folding energetics and/or statistical tendencies of  acquire . Based ok-mer (nucleotide sequence of length k) frequencies. Methods in this category exploit the finding that the frequencies of many k-mers for ncRNAs in a specific family usually share similar probability distribution. Thus, new ncRNAs can be predicted based on the distribution of their k-mer frequencies. For example, Panwar et al. used the trinucleotide composition  to predict ncRNA by a support vector machine (SVM) based algorithm [One important feature for sequence-based de novo methods is nucleotide composition, which applies for identifying ncRNAs in species with nucleotide compositional biases. For example, by calculating the GC content, Wang et al. identified ncRNA genes with stable secondary structure in an AT-rich extreme hyperthermophile . Anotherlgorithm . Sun et lgorithm . In addilgorithm .Since a single type of sequence feature might be insufficient in effectively identifying ncRNAs, other features have also been proposed in conjunction with nucleotide composition. We summarized a few popular sequence feature based de novo ncRNA identification methods in The secondary structures of some kinds of functional RNA are more conserved than their primary sequences . For exaAs a trend, more and more de novo methods tend to combine both RNA sequence and RNA structure to improve the sensitivity and specificity in predicting ncRNAs.For example, Gupta et al. developed a new algorithm ptRNApred to identify and classify posttranscriptional RNA with dinucleotide properties of sequence and secondary structure feature, for example, numbers of loops, bulges, and hairpins or the frequency of nucleotides involved in substructures . It can De novo methods are capable of predicting new ncRNA families and classifying ncRNAs lacking conservation with existing ones. They usually have higher sensitivity and lower specificity than homology-based methods. However, this kind of methods depends largely on the features extracted. With the enrichment of biological, chemical, and dynamic knowledge of ncRNA, there might be some further informative features to be extracted, which will greatly benefit de novo ncRNA prediction , 68.More recently, with the advances in next-generation sequencing (NGS), especially RNA sequencing (RNA-seq) techniques, more and more transcriptome data are available, which have been utilized to discover novel ncRNAs. A general workflow of transcriptional sequencing and assembling based ncRNA prediction method is described in In practice, RNA-seq data are usually combined with other features and methods including tilling array , graph-kk-mer frequency [As an advantage over homology-based methods and de novo methods, RNA-seq based methods can directly sequence coding and noncoding RNA transcripts with high sensitivity and low false positive rate. It can especially detect new scripts and alternative splicing. However, sometimes it is difficult to tell ncRNAs from protein-coding RNAs and thus other features like sequence conservation , decipherequency  are ofteSince miRNA and lncRNA are two special and important ncRNAs, we reviewed a few computational methods related to them separately .P value of randomization test to predict miRNA precursor with a random forest algorithm [miRNAs are very short in length, usually around 22 nt. The short length and relatively low conservation of pre-miRNA sequences restrict the usage of sequence-based methods in identifying miRNAs. Fortunately, it is known that miRNAs are mostly derived from regions of RNA transcripts that fold back on themselves to form short hairpins, which make this RNA relatively conserved in secondary structure. Thus, a few methods exploit more secondary features for new miRNA gene detection instances. For example, as a homology-based method, miRAlign employs sequence alignment, secondary structure alignment, and miRNA's position on the stem-loop structure to identify RNA homologs. It has higher sensitivity and comparable specificity than other homology-based methods . MiPred lgorithm . We summLong noncoding RNAs (lncRNAs) are ncRNAs longer than 200 nt, including long intronic noncoding RNA and intergenic noncoding RNA. lncRNAs are believed to regulate gene expression through changing chromatin state and correlate with cancer pathogenesis and various clinical traits –66, 71. Besides the above two RNA families, some specific classification and prediction methods have been developed for ncRNAs with strong conservation information, for example, tRNA –78, snoRIt is very important to predict ncRNAs since they are related to many diseases , 86. Mank-mer match or gapped k-mer  techniques [Homology search has become much faster with the development of bioinformatics tools, for example, from Smith-Waterman dynamic programming algorithm to BLAST or GMAP  based onchniques , 90. HowIn addition, it has become central for understanding biological process by studying RNA globally. However, methods like microarrays and short-read sequencing are incapable of describing the entire RNA molecule from 5′ to 3′ end. Scientists use single-molecule long-read sequencing technology from Pacific Biosciences to sequence the polyadenylated RNA complement for human, without the need for fragmentation or amplification . TGS canFinally, in order to assemble and correct long transcripts, one can integrate reads sequenced by five sequencing platforms including Illumina HiSeq, Life Technologies' PGM and Proton, Pacific Biosciences RS, and Roche's 454 . Softwar


Mycobacterium ulcerans infection. Buruli ulcer is caused by M. ulcerans. This neglected disease is the third most common mycobacterial disease worldwide. Without treatment, M. ulcerans provokes massive skin ulcers. A healing process may be observed in 5% of Buruli ulcer patients several months after the initiation of disease. This spontaneous healing process suggests that some hosts can counteract the development of the lesions caused by M. ulcerans. Deciphering the mechanisms involved in this process should open up new treatment possibilities. To this end, we recently developed the first mouse model for studies of the spontaneous healing process. We have shown that the healing process is based on mutual tolerance between the bacterium and its host. In this context, RNA-seq seems to be the most appropriate method for deciphering bacterial adaptation. However, due to the low bacterial load in host tissues, the isolation of mycobacterial RNA from skin tissue for RNA-seq analysis remains challenging. We developed a method for extracting and purifying mycobacterial RNA whilst minimizing the amount of host RNA in the sample. This approach was based on the extraction of bacterial RNA by a differential lysis method. The challenge in the development of this method was the choice of a lysis system favoring the removal of host RNA without damage to the bacterial cells. We made use of the thick, resistant cell wall of M. ulcerans to achieve this end.Bacterial transcriptome analyses during host colonization are essential to decipher the complexity of the relationship between the bacterium and its host. RNA sequencing (RNA-seq) is a promising approach providing valuable information about bacterial adaptation, the host response and, in some cases, mutual tolerance underlying crosstalk, as recently observed in the context of  Mycobacterium tuberculosis (RNA sequencing (RNA-seq), a next-generation sequencing technique, opens up unique opportunities for deciphering interactions between microorganisms and their hosts. It provides information about the relative levels of expression of the various genes and support for proteomic results. RNA-seq can also be used to identify the regulatory networks controlled by non-coding RNA, as reported for rculosis .M. ulcerans is a fascinating microorganism, with a complex biology due to the different facets of its life cycle. M. ulcerans is the causal agent of Buruli ulcer, a severe cutaneous infection  to humans, suggesting a “parasite lifestyle”  and European guidelines . All protocols were approved by the ethics committee of the Pays de la Loire region, under protocol nos. CEEA 2009.14 and CEEA 2012.145, and performed at the required biosafety level. Animals were maintained under specific pathogen-free conditions in the animal house facility of Angers University Hospital, France (agreement A 49 007 002).Mycobacterium ulcerans strain 01G897 was originally isolated from patients from French Guiana (5 acid-fast bacilli/ml for inoculation (50 μl) into the tail of 6-week-old females of the inbred FVB/N mouse strain .h Guiana . Bacterih Guiana , with adRNA was extracted from infected tail skins 30 days post-infection, by the Trizol/chloroform method (Method 1), which co-extracts host and bacterial RNA, or by the differential lysis method (Method 2), optimized and adapted from that described by , for the(i) Sample preparation: Tail skin was excised from infected mice and immediately placed in a Petri dish containing a mixture of 1 ml Trizol (Ambion) and 1 ml RLT buffer (Qiagen) supplemented with 1% β-mercaptoethanol. Skin tissues were cut into smaller pieces, transferred to round-bottomed tubes and broken up with a TissueRuptor (Qiagen).(ii) RNA extraction and purification: Samples were transferred to two bead beating tubes  and were shaken with TissueLyser (Qiagen) at 4°C for 5 min at 30 Hz. The samples were immediately placed on ice and centrifuged at 10,000 × g for 5 min at 4°C to remove cell debris. The supernatant was transferred to a 15 ml tube containing 1 ml Trizol. 200 μl chloroform/isoamyl alcohol (24:1) was added to the tube, which was then repeatedly inverted to mix and centrifuged at 10,000 × g for 5 min. The aqueous phase was transferred to a clean tube containing 400 μl chloroform/isoamyl alcohol and centrifuged again, as in the previous step. The aqueous phase was transferred to a clean tube containing 1 volume of 70% ethanol, and the tube was repeatedly gently inverted to mix. Total RNA was purified with the RNeasy Midi kit (Qiagen), with DNase treatment, according to the manufacturer’s protocol, and eluted in 100 μl RNase- and DNase-free water.(iii) DNase treatment: Contaminating DNA was removed by retreating the RNA with DNase, for 45 min at 37°C, with the TURBO DNA-free kit (Ambion), according to the manufacturer’s protocol.(iv) Eukaryotic RNA removal: To reduce the levels of contaminating host RNA from the samples, MICROBEnrich kit (Ambion) was used according to the manufacturer’s protocol.(i) Sample preparation: Tail skin was excised from infected mice and immediately placed in a Petri dish containing 2 ml Tris-EDTA (TE) buffer . Skin tissues were cut into smaller pieces, transferred to round-bottomed tubes and broken up with a TissueRuptor (Qiagen). 4 ml TE buffer was added and the tissue homogenates were digested with 1 ml of a 20 mg/ml proteinase K solution (Qiagen) for 10 min at 55°C (without shaking). Another 6 ml of TE buffer was added and the samples were centrifuged at 3,200 × g for 15 min at 4°C. The pellet, which contained the bacterial cells, was resuspended in 300 μl Tri-reagent (Zymo Research) and 300 μl RLT buffer (Qiagen) supplemented with 1% β-mercaptoethanol.(ii) RNA extraction and purification: The samples were transferred into a bead beating tube  and shaken with TissueLyser (Qiagen) at 4°C for 5 min at 30 Hz. The samples were immediately placed on ice and centrifuged at 10,000 × g for 5 min at 4°C to remove cell debris. The supernatant was transferred into a clean tube containing 1 volume of 100% ethanol, with which it was mixed by repeated gentle inversion. The RNA was purified and treated with DNase with the Direct-zol RNA MiniPrep kit (Zymo Research), according to the manufacturer’s protocol, and eluted in 50 μl of RNase- and DNase-free water.(iii) DNase treatment: Contaminating DNA was removed by retreating the RNA with DNase for 45 min at 37°C with the TURBO DNA-free kit (Ambion), according to the manufacturer’s protocol.(i) Quantification and purity analysis: Total RNA concentration and purity (A260nm/A280nm) were assessed with 1 μl of the RNA preparation, on a NanoDrop 1000TM (Thermo Scientific) spectrophotometer. A ratio greater than 1.8 is usually considered to indicate satisfactory RNA purity (A purity .(ii) Quality and integrity analysis: RNA quality and integrity were assessed with the Experion automated electrophoresis system (Bio-Rad). The total RNA sample (Method 1) was diluted 10-fold and the Experion RNA StdSens analysis chip was used (quantification of 5–500 ng/μl RNA). With enriched bacterial RNA preparations (Method 2), the RNA was diluted twofold and the Experion RNA HighSens analysis chip was used . The RNA quality indicator (RQI) method returns a number between 1 (highly degraded RNA) and 10 (intact RNA) for each RNA sample  was determined from the slope of a standard curve of cDNA serial dilutions.RT-qPCR targeting the (i) Reverse transcription: The first-strand cDNA was synthesized in a reaction volume of 20 μl containing 2 μl of total RNA, 500 ng of random primers (Invitrogen) and the M-MLV reverse transcriptase (Invitrogen). Contaminating DNA in RNA sample was checked by performing a negative control with no reverse transcriptase (RT-) for each sample.(ii) Quantitative real-time PCR: qPCR was performed in a reaction volume of 10 μl containing Absolute Blue qPCR mix (Thermo Scientific), 300 nM primers, 100 nM Taqman probe (Table 1) and 2.5 μl of diluted twofold dilution of cDNA/RT-. The sequences of the primers and probes used are provided in Table 1. Reactions were run on a AriaMx Thermocycler (Agilent), with the following program: 10 min at 95°C and 40 cycles of 10 s at 95°C and 1 min at 60°C. Each sample was analyzed in duplicate.(i) Ribosomal RNA (rRNA) removal: rRNA was depleted from the mycobacterial total RNA preparations with the RiboZero Epidemiology Illumina kit, which removes eukaryote and prokaryote rRNA in a single step.(ii) Preparation of RNA-seq libraries: The RNA-seq libraries were prepared with the TruSeq Stranded Total RNA LT Sample Prep kit (Illumina). The quality of all libraries was checked with the DNA-1000 kit (Agilent) on a 2100 Bioanalyzer and quantification was performed with Quant-It assays on a Qubit 1.0 fluorometer (Invitrogen).(iii) RNA-seq: Clusters were generated for the resulting libraries, with Illumina HiSeq SR Cluster Kit v4 reagents. Sequencing was performed with the Illumina HiSeq 2500 system and HiSeq SBS kit v4 reagents. Runs were carried out over 65 cycles, including seven indexing cycles, to obtain 65-bp single-end reads. Sequencing data were processed with Illumina Pipeline software (Casava version 1.9). All 65-bp reads were aligned against the complete genome and plasmid sequences of M. ulcerans (Agy99 strain) obtained from the Burulist database with Bowtie software, and against the mouse genome (GRCm38) obtained from the Ensembl database with STAR software. Data were normalized and analyzed in R, with the Bioconductor packages.M. ulcerans and the host. We therefore decided to develop a method for extracting bacterial RNA from host tissue in conditions suitable for high-throughput RNA-seq, to make it possible to decipher the regulation of toxin production. We evaluated two methods for isolating M. ulcerans RNA from host tissue (tail) for RNA-seq analysis. In this study, we inoculated the tails of mice that were then killed for analysis 30 days post-infection. All infected mice presented clinical edemas and bacterial load was estimated at about 106 CFU (8.9 × 105–2.1 × 106 CFU). Each of the RNA extraction methods was evaluated on three samples (taken from three mice).We recently developed a model for studies of the spontaneous healing process. Our studies in this model revealed that the host response modulated toxin synthesis, providing evidence for crosstalk between M. ulcerans, we initially tried to extract bacterial and host RNA together, from the tail skin of mice infected with M. ulcerans. The infected tissues were broken up with a TissueRuptor and subjected to chemical and mechanical lysis, to release the bacterial and eukaryotic RNA. The RNA was then subjected to Trizol/chloroform extraction and purified on a Qiagen midi column which allows the purification of up to 1 mg RNA .For studies of the interactions between the host and the bacteria during the infection of mice with Figure 2C). Electrophoretic analysis revealed strong bands corresponding to the 18S and 28S rRNA of eukaryotic cells, with no bacterial RNA bands corresponding to 16S and 23S rRNA in samples . The electropherogram trace confirmed this result, as it contained peaks only for mouse rRNA . The mycobacterial RNA transcripts in samples were assessed by a reverse transcription-polymerase chain reaction (RT-qPCR) targeting the M. ulcerans housekeeping gene, ppk. The samples contained only small amounts of mycobacterial RNA, with Ct values of 32–33 (Table 2). The A260nm/A280nm ratio exceeded 1.8, confirming the purity of the RNA in each sample , and electrophoresis showed a complete absence of RNA degradation, with an RQI value greater than 9 . However, we were unable to assess the integrity of bacterial RNA, because no bacterial RNA was visualized.With this approach, we obtained 200 to 300 μg of total RNA per sample . These data confirmed the predominance of eukaryotic ribosomal RNA in samples, and suggested that mouse mRNA would probably also predominate over bacterial transcripts. However, based on our qPCR demonstrating the presence of mycobacterial RNA in samples, we decided to attempt a deep sequencing approach (RNA-seq) on a sample before and after bacterial RNA enrichment with the MICROBEnrich kit. The obtained RNA-seq data showed an absence of reads aligned with the M. ulcerans genome, even though mycobacterial RNA transcripts were detected by RT-qPCR, a more sensitive technique. In this case, sequencing depth was insufficient to obtain reads mapping to the M. ulcerans genome, confirming that the removal of mouse RNA was not effective enough to achieve an enrichment of the sample in mycobacterial RNA. In conclusion, these data demonstrate that studies of the M. ulcerans transcriptome in vivo will require a method capable of significantly decreasing eukaryotic RNA levels, to facilitate the detection of mycobacterial transcripts by RNA-seq.To eliminate the host RNA from the samples, MICROBEnrich kit (Ambion) was used which selectively removes 18S, 28S rRNA, and polyadenylated mRNA. Following MICROBEnrich treatment, electrophoresis was performed and showed this treatment to have been poorly effective in our hands, because strong bands corresponding to the 18S and 28S rRNAs were still detected .We developed an alternative technique for the purification of bacterial RNA from infected host tissues by differential lysis, to achieve our goal. The principal challenge in this method was the choice of a lysis system resulting in host-cell lysis without damage to the bacterial cells. We recently demonstrated that the mechanical disruption of tissues had no effect on bacterial viability . We therFigure 3C), about one-thousandth the amount obtained with Method 1 . However, gel electrophoresis on the total RNA obtained with this method revealed the presence of bands corresponding to the 16S and 23S bacterial rRNA whereas the mouse 28S and 18S rRNA were not detected . This finding was confirmed by the electropherogram trace .We obtained 150 to 500 ng of total RNA per sample  was evaluated by RT-qPCR. The efficacy of eukaryotic RNA removal during sample preparation with Method 2 was evaluated by comparing equivalent fractions of enriched (Method 2) and non-enriched (Method 1) RNA samples. We quantified the expression of both bacterial and mouse genes in the samples obtained with the two methods.ppk transcription levels showed that the transcript of this gene was present in both cases, with similar Ct values (30–33). An analysis of murine gapdh gene transcription revealed a difference in Ct of 15 between Method 1 and Method 2 (Table 2), corresponding to the presence of five orders of magnitude less host RNA with Method 2 than with Method 1. These results clearly demonstrate the efficacy of Method 2 for removing eukaryotic RNA. This method was, thus, considered suitable for the enrichment of samples in M. ulcerans RNA. The purity and integrity of bacterial RNA were assessed by determining the A260nm/A280nm ratio and the RQI value, respectively. The A260nm/A280nm ratio was greater than 1.8  and RQI values ranged from 7.3 to 9 , indicating that the RNA was of sufficiently high quality for use in subsequent experiments, including transcriptional analyses.No PCR product was obtained if the amplification was performed without prior reverse transcription (RT-). There was therefore no detectable DNA contamination. A comparison of M. ulcerans genome, respectively, whereas no reads aligned with this genome sequence were detected with the samples obtained by Method 1 . This enrichment procedure therefore provides sufficient coverage of sequences mapping to the M. ulcerans, because a minimum of 2 to 5 million reads from a ribosomal RNA-depleted library is required to provide adequate coverage of the gene expression profiles of bacteria in RNA-seq experiments  on the enriched samples. The removal of rRNA is a crucial step in RNA-seq, because rRNA signals can prevent adequate coverage of the bacterial transcriptome. We therefore used the Ribo-Zero gold rRNA Removal Kit  to remove both mouse and bacterial rRNA before producing the cDNA library. The RNA-seq data confirmed that less than 5% of the reads mapped to rRNA, demonstrating the efficacy of the rRNA removal kit. The datasets for samples M2-1, M2-2 and M2-3 obtained by the optimized method (Method 2) contained 9.5, 7.6 and 7.9 million reads aligning with the eriments .r = 0.9813) indicated a similar overall pattern of relative gene expression in the biological replicates, indicating that Method 2 was reproducible . This optimized method can therefore be used to study the mycobacterial transcriptome in a mouse model.Finally, two biological replicates, M2-1 and M2-2, were used to evaluate the reproducibility of the gene expression profiles obtained with RNA-seq technology. Spearman’s coefficient of correlation between samples  to lyse eukaryotic cells without damaging M. ulcerans cells, (ii) to remove large amounts of eukaryotic RNA by simple centrifugation and, (iii) to obtain high-quality bacterial RNA for RNA-seq analysis.This method is based on the extraction of bacterial RNA by a differential lysis method. The principal challenge in this method is the choice of a system capable of lysing the host cells without damaging the bacterial cells. Chemical or mechanical differential lysis methods are generally used for RNA isolation in vivo . Our metM. ulcerans RNA from host tissues. This method represents a significant improvement as it provides RNA of sufficiently high quality for RNA-seq analysis. This strategy will make it possible to perform in vivo studies of the interactions between mycobacteria and their host, improving our understanding of the molecular mechanisms underlying infection. Finally, it may be possible to adapt this method for the isolation of other mycobacteria in the host colonization context.We have developed the first simple protocol for the selective extraction of MR-S, LM, and EM conceived and designed the experiments. MR-S, JB, OS, and EM performed the experiments. MR-S, OS, LM and EM performed data analysis. MR-S and LM wrote the paper.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Protein-coding and noncoding genes in eukaryotes are typically expressed as linear messenger RNAs, with exons arranged colinearly to their genomic order. Recent advances in sequencing and in mapping RNA reads to reference genomes have revealed that thousands of genes express also covalently closed circular RNAs. Many of these circRNAs are stable and contain exons, but are not translated into proteins. Here, we review the emerging understanding that both, circRNAs produced by co- and posttranscriptional head-to-tail “backsplicing” of a downstream splice donor to a more upstream splice acceptor, as well as circRNAs generated from intronic lariats during colinear splicing, may exhibit physiologically relevant regulatory functions in eukaryotes. We describe how circRNAs impact gene expression of their host gene locus by affecting transcriptional initiation and elongation or splicing, and how they partake in controlling the function of other molecules, for example by interacting with microRNAs and proteins. We conclude with an outlook how circRNA dysregulation affects disease, and how the stability of circRNAs might be exploited in biomedical applications. Compared to prokaryotes, eukaryotic genomes are compartmentalized in a nucleus, whereby DNA is functionally organized in chromatin by spooling around histone proteins and forming nucleosomes. In addition, gene organization is different, as eukaryotic genes are interrupted by intronic sequences. Before mRNA is translated, the introns in the pre-mRNA transcripts have to be removed by splicing, such that exons can be ligated to each other ; b. nuclear localized 3′ → 5′-linked circular RNAs consisting of both exonic and intronic sequences (EIciRNAs) and c. the most abundant class of circular RNAs, cytoplasmic 3′ → 5′-linked circRNAs consisting only of exonic sequences.Caenorhabditis elegans and Drosophila melanogaster [Tetrahymena rRNA [We will describe the molecular biogenesis of these types of circular RNAs which are expressed from endogenous genes and processed by the spliceosome in eukaryotes. We will not cover the biology of viruses and viroids with circular RNA genomes. We will also not cover RNA circularization through non-spliceosomal machineries, such as tRNA intron circularization by tRNA ligase  in Caenonogaster , or circena rRNA , 19. Whiena rRNA –22, whetena rRNA , 24.The focus of the review will be the current understanding of basic molecular functions of spliceosome-dependent circRNAs. Finally, we will also cover current studies that explore circRNAs in human disease.By numbers, the by far most typical circularization mode involves the spliceosome machinery and occurs in pre-messenger RNAs conventionally transcribed by RNA polymerase II (RNAP II) from nuclear-encoded genes Table . PhysiolIn the two transesterification steps of a conventional colinear splicing reaction, the adenosine at the intron branchpoint is linked to the 5′ of the excised intron, resulting in a lasso-like circular RNA, the 2′ → 5′-branched lariat. In this reaction, the exons are joined together in the typical colinear 5′ → 3′ order in the mRNA Fig. a. AlthouIn contrast to ciRNA formation after linear splicing, for a prototypical circularization to a 3′ → 5′-linked circular RNA the spliceosome performs a “backsplice” reaction, whereby a branchpoint upstream of the exon to be circularized attacks a downstream splice donor. This links the involved sequences by a 3′ → 5′ phosphodiester bond and produces a circRNA Fig. b, c. BacTogether, the class of 3′ → 5′-linked circRNAs make up the majority of all cellular circular RNAs. They arise from both protein-coding and noncoding genes in all eukaryotes studied so far, ranging from metazoans, plants, to unicellular eukaryotes including fungi, ciliata, plasmodia, amoebae, and mycetozoa. Therefore, RNA circularization is thought to be an evolutionarily old process inherent to the onset of splicing messenger RNAs in eukaryotes.A number of parameters and factors have been recently described that control the circularization of 3′ → 5′-linked RNAs. Overall, a major finding from the genome-wide analysis of circRNA formation was that 3′ → 5′-linked circRNA biogenesis in the large majority (70%) of all circularization events uses canonical splicing signals of the major U2-containing spliceosome. Thus, the GU motif defines the 5′ end of introns and AG their 3′ end Fig. . There ain cis [Alu elements [Alu insertions sites in the human genome [In the classical model, and pertaining to RNA circularization in higher eukaryotes including humans, base pairing between sequence-complementary inverted repeats in the two introns flanking the circularization event can assist the backsplicing reaction in cis , 37–41 , Sacchaes pombe  or Sacchrevisiae ). In therevisiae , 46–49 arevisiae . When a revisiae . Third, revisiae , 50, andrevisiae . Finallyin cis, such that the backsplicing sequences come close together. Genetic screens have revealed three RNA-binding proteins that increase the rate of circularization by homodimerizing and bridging relevant intronic sequences in the RNA: Quaking (QKI) [muscleblind (mbl) gene in Drosophila, as well as from the orthologous MBL gene in humans, was stimulated by the protein product of the mbl/MBL gene. MBL binds to MBL-binding sites in the introns flanking the circularizing exons of circMbl, consistent with intron-pairing-mediated circRNA formation [CircRNA formation can occur by yet an alternative mechanism through protein:protein interaction-mediated folding of the pre-mRNA Fig. b. This ing (QKI) , Musclebng (QKI)  and Fuseng (QKI) . All thrng (QKI) , 54. Empng (QKI) . QKI is ng (QKI) . CircRNAormation . Finallyormation . FUS is ormation . During ormation . These dormation   occurs in linear mRNAs, it has been found to recruit the protein YTHDF2, which relocates affected mRNAs to P-bodies for degradation [Rny1 has been recently identified as responsible RNase in yeast. This finding is potentially significant for circRNAs, as Rny1 is an endonuclease [First, circRNAs can be degraded by endonucleases, where microRNA/RISC/AGO2-mediated cleavage could be a dominant degradation mechanism. For example, the circRNA produced from the cleavage . Second,cleavage , 64), arcleavage , this firadation . Interfenuclease , but thenuclease –73. It hnuclease . ExperimCircular RNAs are expressed from thousands of RNAP II-transcribed genes Table . The totIn relation to linear RNA formation, circular RNA expression is less pervasive. It was found that in humans only about a third of all circular RNAs are expressed at substantial levels, that is at a ratio > 10% compared to the total transcript output of a given gene (linear + circular) . This cuIn contrast to the low number of molecules per circRNA species, the diversity of observed circRNA species is high and many circRNA isoforms can be expressed from a single gene. However, the executed number of backsplice reactions is actually rather small compared to all possible combinations of all downstream 3′ splice site to all existing upstream 5′ splice sites . StudiesWhether such a constraint was reflected by evolutionary selection of circRNA-hosting sequences was also investigated: Comparing human and mouse, at the developmental stage of embryogenesis, around 50% of all detected circRNA were found to be expressed from the same circRNA host genes , with 15Conceptually, circRNAs can exhibit functionalities in two principal ways, and there is experimental evidence for both: first of all, the process of circRNA formation itself, e.g. during transcription-coupled splicing, can have biological effects, and secondly, once formed, the circRNA can be functional as a trans-acting molecular entity. So far, only a few high-confidence circRNAs have been studied in greater detail. In the following, we will review reported functions starting with processes in the nucleus and finishing with roles in the cytoplasm Fig. .Fig. 2CeTogether, five functions have been assigned to how circRNAs participate in control of gene expression Fig. a–e: CircA number of recent studies have revealed that transcriptional regulation is a major function of circular RNAs. Several hundred circular RNAs have been associated with the control of the RNA polymerase II holocomplex during different phases of the transcription cycle affecting both, transcriptional initiation and elongation. Not only 3′ → 5′-linked exonic and intronic sequences-containing EIciRNAs but also circularized introns without any exons (ciRNAs) have been implicated in this type of regulation Fig. a.in cis.EIciRNAs have been found to co-immunoprecipitate with RNAP II, as shown by PAR CLIP : confineankrd52 circRNA and two more ciRNAs have been partially studied (ci-mcm5 and ci-sirt7) [ci-ankrd52, ciRNAs are not enriched for microRNA binding sites but rather seem to directly associate with the chromatinized DNA at their host locus and stimulate the expression of the host gene from which they derive [in cis, or due to their mode of production remained so far unclear. Both explanations are possible because ciRNA function could not be realized when simply overexpressing ciRNAs in trans from plasmids. As shown in detail for ci-ankrd52, this ciRNA immunoprecipitated with the productively elongating form of RNAP II, that can be recognized because of phosphorylation at serine 2 of its C-terminal repeat domain (CTD) [Similarly, ciRNAs are experimentally defined as physiologically relevant circular RNAs and they have been shown to affect RNA polymerase II activity in cells . ciRNAs i-sirt7) . As showy derive . Whetherin (CTD) . Thus, sin (CTD) . Howeverin (CTD) . Retentiin (CTD) , ciRNAs in (CTD) .Apart from affecting transcriptional initiation or elongation by RNA polymerase II, circular RNAs have recently been shown to affect gene expression also on another control level: Although circular splicing is > 100-fold less pervasive than linear splicing , the vieMbl gene locus. Experiments on this locus have paved the way for the generalization that linear splicing and backsplicing antagonize each other on a genome-wide level [Mbl candidate gene locus, it has been suggested that circMbl might titrate out the protein product of the very locus (MBL), whereby MBL is known to be required for robust linear Mbl mRNA transcription [Mbl candidate gene locus, and RNA turnover does not seem to be a factor during competitive regulation though [The first “passive” model has been determined by studying the de level . The undde level , but prede level , 41. Cende level . When trde level . This obde level –94 and tde level . If thisde level . Lastly,de level , 40. Expde level –101. Kinde level . Since ade level . The outde level . A separde level . This fide level . At the de level  raising cription . This iscription , 49. As cription , 46, 103cription . Linear n though .SEPALLATA3 gene, an important MADS-box transcription factor involved in floral organ development. Interestingly, and representing a major exception to most other circRNAs, 6-SEP3 circRNA was nuclear. In dot blot experiments, this circRNA hybridized to DNA of the host locus by forming RNA:DNA hybrids (R-loops), even with slightly higher efficiency than cognate linear RNA [SEP3 transcript) and caused floral phenotypes comparable with the overproduced linear exon 6-skipped mRNA [An independent second “active” model describes the relationship between linear splicing and backsplicing by focusing on the question whether circRNAs (as single-stranded RNAs) can hybridize with DNA in cells Fig. b. Indeednear RNA . Experimped mRNA . The plaped mRNA . Thus, wTogether, on a genome-wide level, the molecular mechanisms underlying mutual competition between linear splicing and backsplicing await further exploration and generalization. Several parallel mechanisms might be responsible, including the competition for splice sites, instability of the branched mRNA after backsplicing, and changes in exon skipping patterns. Since splicing can also reach back and change the chromatin landscape of genes (see for review ), circRNcircANRIL, by impairing the activity of a major rRNA-processing machinery, and hence downregulating protein translation overall, and a second, circPABPN1, by sequestering and inhibiting a central protein that is known to be required to bind and promote translation of a set of mRNAs. The relevant studies, thus, also show that circRNAs can bind to selected RNAs and proteins, to impose specific cellular functions.The bulk of circRNAs reside in the cytoplasm and in this location selected circRNAs impact translation of mRNAs Fig. c. As desANRIL, locating on chromosome 9p21, is known to modulate the risk for a number of diseases associated with this locus, including cancer [ANRIL has been classically studied as molecular cis-regulator of the INK4/ARF tumor suppressor locus on chromosome 9p21 [ANRIL has, however, also been found to affect the transcription of target genes on other chromosomes in trans. This type of regulation has been implicated in affecting gene cohorts responsible for modulating proliferation, apoptosis, and adhesion during atherogenesis [ANRIL, in addition to the 19 exons and 2 alternative exons contained in its linear transcripts, also yields several circular RNA isoforms by backsplicing [circANRIL from a minigene construct in cultured cells allowed identifying proteins interacting with circANRIL RNA [circANRIL was found to decrease the interaction of the PeBoW complex member PES1 with pre-rRNA intermediates during pre-rRNA processing, consistent with the possibility that high levels of circANRIL impaired the PeBoW complex by binding to it. This resulted in reduced ribosome biogenesis, nucleolar fragmentation and stress signaling, including p53 activation, as shown by transcriptional profiling and proteomic analyses [ANRIL is known to be overexpressed in disease and mediates opposite effects, increased proliferation and reduced apoptosis [circANRIL has the potential to block the pathological and disease-associated cell overproliferation executed by its cognate host linear RNA. Since circANRIL, when expressed in trans, did not reactivate INK4/ARF expression [ANRIL may be an interesting special locus, where the circRNA antagonizes the cognate linear RNA transcript, not by competing with linear RNA production, but by antagonizing downstream cellular functions controlled by the linear transcript.Variation of expression of the long noncoding RNA g cancer –113 and g cancer , 115. ANome 9p21 –122. ANRogenesis . ANRIL, splicing , 123. Ovpoptosis . Thus, cCircPABPN1 is the second known circular RNA that impacts protein translation from mRNAs rather directly , which is a known multifunctional RNA binding protein (RBP) that can specify the site of polyadenylation and poly(A)-tail length in target mRNAs, at least in metazoans, as well as modulate mRNA stability via intron retention. Using RNA immunoprecipitation and circRNA profiling, and supported by previously published reciprocal PAR-CLIP datasets, HuR was found to bind a large part (> 56%) of human circRNAs, with circPABPN1 as most robustly interacting circular RNA in HeLa cells [circPABPN1, it was shown that circPABPN1 overexpression from plasmids impaired HuR’s normal interaction with some linear mRNAs, and among all tested targets, most strongly impacted its interaction with the PABPN1 mRNA [PABPN1 mRNA was translated to protein in these experimental conditions [PABPN1 mRNA and the circPABPN1, these data are consistent with the possibility that circPABPN1 competes with its parental pre-mRNA for binding to HuR [en route to translation. Such a relation between circular RNA and linear host mRNA is conceptually intriguing, as it is hypothetically generalizable to a number of other RBPs [tly Fig. c. DifferLa cells . HuR is La cells . In contLa cells . FocusinPN1 mRNA . Likely nditions . Given tg to HuR . It is cher RBPs .antisense to the cerebellar degeneration-related protein 1 transcript (CDR1as) gene, which expresses a circRNA (CDR1as) that is enriched for microRNA binding sites [CDR1as contains 74 miR-7s binding sites. In addition, one other circular RNA that was studied very early on, the circRNA produced from the mouse testis-determining gene Sry, contained several (16) miR-138 binding sites [CDR1as and SRY genes suggested that the circRNAs sequestered microRNAs and inhibited thereby their biological availability and function, termed a sponge effect . SeparaCDR1as  but alsoCDR1as , 77, 131Saccharomyces cerevisiae or Plasmodium falciparum, which do not display an siRNA pathway and, hence, cannot form AGO2:circRNA complexes, and where microRNA sponging, as we know it, would not be effective at all in regulating mRNAs stability [It has, however, also been convincingly argued that it is unlikely that microRNA sponging is a general intrinsic function of circRNAs because circRNAs are also equally present in lower eukaryotes such as tability . Summaritability .Since most backsplicing involves complete exons and occurs on protein-coding genes, the reasonable question arose whether portions of proteins can be translated from circRNAs and whether this might involve translation of known open-reading frames (ORFs) or of alternative ORFs that we have so far not been considered. Such a translation would in theory largely increase the coding potential of the genome and also have important evolutionary implications. In addition, a higher number of genes/transcripts encoding functional proteins may be hidden in our genomes: the minimal length of a functional ORF is not so clear after all and micropolypeptides as small as 10–20 amino acids have recently been found to be functional , 134.Drosophila and rat brains and mouse liver and muscle cells [Recent experiments have started to address how frequently ribosomes productively associated with circular RNAs. The standard mode of translation initiation in eukaryotes is Kozak sequence/5′Cap-dependent ribosome entry , but whele cells . Of thesle cells   nucleic acids in circulating blood during prenatal diagnostics or disease state profiling –151, recIndeed, circRNAs are a normal part of the cell-free blood transcriptome , 152–154To date, relating to profiling circRNAs in whole blood, serum or in blood cells, a few dozen circRNAs have been implicated as potential blood biomarkers for state or stage of coronary artery disease, distant solid tissue cancers, leukemias, diabetes or multiple sclerosis, and the numbers of studies searching for circRNAs as biomarkers is rising Tables , 4. The circANRIL is the only case, where the relation with cardiovascular disease bases on multiple independent pieces of evidence: the human ANRIL gene is non-protein coding and can give rise to the ANRIL lncRNA and the circular RNA isoforms (circANRIL) [ANRIL is an effector of a cardiovascular disease risk locus defined by GWAS data. ANRIL RNA expression and disease severity associate, and the expression state of the parental linear gene correlate with the occurrence of SNPs associated with cardiovascular disease risk as well as with splicing and circRNA occurrence [In the last decades, the identification of genomic loci that govern atherosclerosis, both by quantitative trait locus (QTL) mapping approaches and by genome-wide association (GWAS) studies in human cohorts , 163, harcANRIL) , 123. ANcurrence , 164–166ANRIL RNA expression promotes proatherogenic cell functions by multiple pathways. It stimulates proinflammatory signaling and ultimately the cell cycle of disease-relevant vascular cell types [circANRIL abundance anticorrelated with atherosclerosis risk genotypes and disease phenotype severity, indicating that circANRIL was potentially atheroprotective. Analyses of human vascular plaques and assays in accompanying cultured cells suggested that circANRIL might protect against atherosclerosis by negatively affecting rRNA maturation  has been identified as a predictor of left ventricular dysfunction as a consequence of myocardial infarction, but its molecular function is currently unknown [HRCR  [that miR-223 stimulated cardiac hypertrophy and heart failure by functioning as in vivo inhibitor of the known apoptosis repressor ARC. HRCR was identified because it was downregulated in a mouse injury model of cardiac hypertrophy and bound miR-223 when overexpressed from a minigene construct [HRCR attenuated cardiomyocyte hypertrophy depending on the presence of ARC in a transgenic mouse model. All these findings were consistent with the possibility that HRCR functioned as microRNA sponge for miR-223, and that a loss of HRCR function promoted heart failure because miR-223 was released to inhibit ARC [Other circRNAs were implicated in coronary artery disease (CAD) based on differential expression in patients or in diverse mouse models of heart failure, myocardial infarction or diabetes mellitus ore because they were expressed in hearts and mapped to previously reported risk genes Table . For exa unknown  Table 3-012559) . This ci-012559) . Based oonstruct . HRCR atibit ARC  (Table 3-012559 .Titin) [Ryr2 or Duchenne muscular dystrophy DMD or the sodium/calcium exchanger Slc8a1) [crc_0124644 as a diagnostic biomarker of coronary artery disease [circ_0054633 may serve as biomarker of pre-diabetes and type 2 diabetes mellitus [In another example, a set of circRNAs including cZNF292 were found to be upregulated in cultured endothelial cells under hypoxia, which is a known stimulus for angiogenesis . Other cTitin) , 172 (Ta Slc8a1) , 174. Fi disease , and cirmellitus . For an mellitus .circANRIL, HRCR, CDR1-as, circ_000203, circ_010567, see Table Taken together, to date five circRNAs have been functionally studied in the context of cardiometabolic disease   Table . Other tcircHIPK3 was implicated in cancer as microRNA sponging circRNA based on initial RNA expression profiling in tissues of colorectal cancer, gastric cancer bladder carcinoma, breast cancer, hepatocellular carcinoma, kidney clear cell carcinoma and prostate adenocarcinoma samples [circHIPK3 as a circRNA in the dataset that showed a high ratio compared to its linear host mRNA, and which also showed a significantly increased expression (2- to 4-fold) in at least one cancer cell type (HIPK3 circRNA). circHIPK3 contains binding sites for nine different microRNAs, with relatively few (1–2) copies of docking sites per microRNA, which would not classically be considered a microRNA sponge [circHIPK3 have been studied independently before and been implicated in growth-suppressive functions (such as miR-124). Targeting circHIPK3 by siRNAs reduced cell proliferation of typical cancer cell lines 1.5- to 2-fold [HIPK3 circRNA sequestered growth-suppressive microRNAs to promote cell proliferation. An overview of these and other circRNAs implicated in cancer can be found in existing reviews [In one exemplary report,  samples . ComplexA sponge . Yet, mio 2-fold . The pre reviews , 180. As reviews , 50. It  reviews .Drosophila to humans [CircRNAs have been found to be particularly enriched in neuronal tissues of different organisms ranging from o humans , 77, 131o humans . The ciro humans –187. Foro humans  as well o humans , and muto humans . More geo humans –192.Cdr1as circRNA developed neurological disorders associated with deficits in sensomotoric gating [Cdr1as was required for normal synaptic transmission in the brain by acting in its prototypical function as microRNA sponge for miR-7.Representing the currently most solid piece of evidence for a neurological role of a circRNA or circRNA biogenesis factor, it was recently reported that mice lacking the c gating . This stbona-fide neuronal function [hs-c-80, hs-c-84) were shown to be specifically downregulated in two hypomorphic FUS mutations that are characteristic of ALS patients [Another good example is FUS, a nuclear RNA-binding protein, splicing regulator and circRNA biogenesis factor, which is also mutated in familial amyotrophic lateral sclerosis (ALS) . ALS-spefunction . Among tpatients . WhetherDbr1 was shown to block the cytotoxic effects exerted by an ALS-causing mutant version of the TDP43 RNA-binding protein, and the authors suggested that the reason for this welcome effect was that the increased pool of unprocessed circular intronic lariats (after Dbr1 depletion) sequestered the mutant TDP43 [In a separate case, circular RNAs have been proposed as therapeutic agents : in thisnt TDP43 . In spitnt TDP43   circRNAs may still be bystanders without significant function. The recent observations that circRNA biogenesis can compete with linear splicing in principle, and that circRNAs can bind to their host locus by forming R-loops, point, however, to the direction that certain circRNAs act in processes where there is a near 1:1 ratio with the molecular target  or the target process (linear splicing). Thus, even when present in a single copy, circRNAs or the circRNA-generating process might be biologically relevant entities. Given the Janus-face nature of the mutual feedback between splicing and chromatin organization, an integrative view may be necessary to appreciate how circRNAs contribute to gene expression control on a genomic scale.In terms of using circRNAs as diagnostic and prognostic biomarkers, we are only at the beginning of research. The molecular analysis of blood (liquid biopsy) has so far mostly focused on analyzing cell-free fragments of genomic DNA (cfDNA, see  for revi