Drosophila melanogaster cells under native conditions and identified both the RNA and protein compositions of these RNP complexes. We identified “high occupancy target” (HOT) RNAs that interact with the majority of the RBPs we surveyed. HOT RNAs encode components of the nonsense-mediated decay and splicing machinery, as well as RNA-binding and translation initiation proteins. The RNP complexes contain proteins and mRNAs involved in RNA binding and post-transcriptional regulation. Genes with the capacity to produce hundreds of mRNA isoforms, ultracomplex genes, interact extensively with heterogeneous nuclear ribonuclear proteins (hnRNPs). Our data are consistent with a model in which subsets of RNPs include mRNA and protein products from the same gene, indicating the widespread existence of auto-regulatory RNPs. From the simultaneous acquisition and integrative analysis of protein and RNA constituents of RNPs, we identify extensive cross-regulatory and hierarchical interactions in post-transcriptional control.In eukaryotic cells, RNAs exist as ribonucleoprotein particles (RNPs). Despite the importance of these complexes in many biological processes, including splicing, polyadenylation, stability, transportation, localization, and translation, their compositions are largely unknown. We affinity-purified 20 distinct RNA-binding proteins (RBPs) from cultured  Gene expression involves a complex and often dynamic interplay between proteins and RNA. The synthesis and function of almost all known RNAs involve the formation of ribonucleoprotein particles (RNPs) . These RThe protein components of RNPs can either interact directly with RNA through one or more RNA-binding domains or can be associated indirectly through interaction with another protein that is itself directly bound to RNA . ProteinIn addition to the diversity of capture approaches used to study RNA–protein interactions, there are differences in the assays used to characterize the interacting molecules. Several groups have used probes to purify specific target RNAs and then identify the associated proteins, though these approaches often require tagging the target RNA , an essential component of the RNA-induced silencing complex (RISC) (RNA-binding proteins (RBPs) play a crucial role in cellular biology, particularly in higher eukaryotic organisms, where ∼3% of genes encode proteins that have either known or predicted RNA-binding domains . RBPs pax (RISC) . FMR1 alx (RISC)  and, in x (RISC) . Other px (RISC) , MBNL (Hx (RISC)  family px (RISC) . It is lDrosophila, and most eukaryotes, common classes of RNA-binding domains include the RNA-recognition motif (RRM), the K homology domain (KH), the double-stranded RNA-binding motif (dsRBM), and zinc-finger motifs. As with transcription factors, there is no one-to-one mapping between domains and functional roles, and many RBPs with characterized functions appear pleiotropic. Some RBPs have strong sequence specificity for cognate binding sites, including NOVA2 and the fly homolog PS, which bind to YCAY repeats in species from insects to mammals, although the RNA targets regulated by NOVA2 and PS have changed substantially across metazoans  showed that the Sm RNA targets fall into three categories: small nuclear RNAs (snRNAs), small Cajal bodies (scaRNAs), and mRNAs  or roles in RNA biology. We group these 20 RBPs into broad functional classes: Exon Junction Complex (EJC), which marks the location of splicing events and provides a link to processing events downstream from splicing, including nonsense-mediated decay    ; and, lastly, pleiotropic proteins, including factors with diverse functions in translational regulation and RNA localization .To explore the compositions of RNPs in r, Upf1) ; serine-4, tra2) ; spliceo U2af50) ; heterog U2af50)   RNAs. Several studies e.g.,  have shoDrosophila, we purified RNP complexes under native conditions (without crosslinking) from cultured cells expressing 20 different epitope-tagged RBPs and then analyzed the protein components by mass spectrometry and the RNA components by RNA sequencing (Drosophila Protein Interaction Map (DPiM) (To explore the composition of RNP complexes in quencing . The prop (DPiM) .Drosophila S2R+ cells in biological duplicate. We used as controls both empty vector and four different non-RBPs. For these experiments, cell lysates were prepared in the presence of RNase inhibitors to maintain an RNase-free environment to facilitate recovery of intact RNAs. The RNA–protein complexes were purified by immunoprecipitation (IP), and the products of each co-IP were split into two equal fractions. One fraction was used for LC/MS/MS analysis to define the protein composition of the sample and relate the proteins to the DPiM, and the second fraction was depleted of rRNAs and subjected to RNA sequencing to analyze the associated polyadenylated and nonpolyadenylated RNAs.Tagged RBPs were transfected into This experimental approach results in the identification of protein–RNA (RIP-seq) and protein–protein (MS/MS) interactions in a single IP reaction. Because we do not crosslink, we pull down whole complexes, and therefore, our data do not distinguish between direct and indirect interactions or binding events. When we identify interactions of two RBPs with mRNAs from the same gene, we conclude that these two factors share a common target, though the protein–RNA interactions can occur on either the same or different mRNA molecules. However, if we additionally observe protein–protein interactions between these RBPs, we conclude that there is evidence for the existence of an RNP complex that includes the target RNA and the two RBPs. RBPs interact with many RNAs and proteins present in S2R+ cells. Hence, our data are amenable to network analysis techniques that identify community structure. Because we observe whole complexes, not individual pairwise interactions, we expect stable RNPs to yield densely connected “cliques” of associated RNA and protein molecules. Our data are consistent with this model and are described as follows.Drosophila.To identify RNAs enriched by each RBP, we mapped sequenced reads to the genome and then quantified the capture level  of each gene (FlyBase r5.57) in both the IP and control experiments with DESeq (Methods) . We applsnRNA:U1:82Eb and snRNA:U1:95Cc are the two RNAs most strongly enriched by SNRNP70K, an integral component of the U1 snRNP. Consistent with the known interactions between the cap-binding complex and U snRNAs . Supplemental Table 5 shows that all but one RBP  show strong enrichment for spliced genes (P < 0.005). Hence the preference of most RBPs in this study to bind spliced RNAs supports their functional roles as splicing regulators.The majority of the factors in our study are involved in splicing regulation. In P < 0.01) . For example, Smg5 mRNA, which encodes an RBP involved in NMD, interacts with RNP complexes containing 15 of the 20 studied RBPs. Indeed there are six such mRNAs , which is considerably more than expected under an independence model (probability that the RNA bound by the most RBPs ≥15 is <0.001). The RNAs encoded by 282 genes  interact with half or more of the RBPs in our study , and we will refer to these RNAs as “high occupancy target” (HOT) RNAs. Under a model conservatively conditioned on the assumption that only RNAs associated with at least one RBP are available for binding, this constitutes 282-fold enrichment over expectation . This threshold ensures that HOT RNAs are the targets of a diverse group of RBPs, including multiple binding domains and functional families. We note that the qkr58E-1 and qkr54B mRNAs, which encode two of the RBPs we surveyed, are themselves HOT RNAs. Additionally, we note that the set of HOT RNAs, as well as RIP-seq targets in general, spans a wide range of gene expression levels  and is not biased toward highly expressed RNAs.Most of the RBPs in our study associate with overlapping sets of target RNAs. A total of 74% (141 out of 190) of pairwise intersections of RBP target RNAs across all pairs are larger than expected at random (hypergeometric P < 0.001), neurogenesis , and NMD . We also observe enrichment for the molecular function GO terms RNA binding  and translation initiation . When we rank the target RNAs by their local IDR, using this score as a proxy for a direct measure of binding affinity or fractional occupancy, we find that the most strongly associated RNAs drive the enrichment of the HOT RNA enriched GO terms (Dcr-2 and AGO2) and almost half (five out of 12) of the genes involved in NMD, despite consisting of only 3% of expressed genes (P < 0.0005). However, at least one hnRNP or quaking-related protein targets 92% of HOT RNAs and thus contributes strongly overall to the biological GO term enrichments of HOT RNAs.A number of biological process GO terms are strongly enriched in the HOT RNAs , with the strongest being nuclear mRNA splicing  between the splicing events altered upon RNAi knockdown of an RBP and the RIP-seq targets for the same RBP (P < 0.05). It is possible that the proteins were not sufficiently depleted to affect the splicing of many of the targets of TRA2 and FMR1. However this may also be reflective of biology as FMR1 is known to play a role in mRNA localization and has not been reported to directly regulate splicing. Similarly, although it is known that TRA2 regulates splicing, it is also possible that TRA2 has additional functions and that many of its interaction targets are not splicing regulatory targets. Nonetheless, we conclude that strong overlaps for the vast majority of tested RBPs provide statistical evidence for the functional importance of the interactions identified by RIP-seq (Fisher combined P < 10−100).To assess potential biological functions of the RNA–protein interactions identified in this study, we compared our RIP-seq results to RNA-seq data generated by same RBP . The ovewww.droidb.org), and found that 44.6% have been previously reported . The coassociated proteins are strongly enriched for mRNA-binding  molecular function despite removing many interactions between RBPs from this study due to possible cross-contamination . The associating proteins enriched for several terms are also enriched in the HOT RNAs, including both biological process and cellular component splicing-related terms. We observed highly significant overlap (hypergeometric P < 0.01) between the protein and the corresponding RNA targets for three  of the 20 RBPs  and note that there is a strong tendency among all RBPs to cobind proteins and their mRNAs (Fisher's method P < 10−8). For example, the B52 protein interacts with the protein and mRNA products of CG4849. Hence, RNP complex members interact with the RNAs encoding interacting proteins. This indicates that post-transcriptional regulation is highly interconnected, hierarchical, and cross-regulatory, operating at both the transcript and protein levels  of the 20 RBPs. We observed an average of 13 proteins associated with each RBP and a total of 198 proteins coassociated with at least one RBP. We confirmed protein–protein interactions for the pairs SC35:QKR58E-2, QKR58E-1:LARK, and U2AF50:UPF1 using reciprocal co-IP experiments with an alternative tag . These simultaneously validate the targeted interactions and our protein tagging strategy . We also compared our results to a database of published interactions (P < 10−7). Within this complex, we observe coordinated binding, where RBPs coimmunoprecipitate with proteins and the corresponding mRNAs that encode them , an integral component of the U2 snRNP complex. Furthermore, QKR58E-1 interacts with the CG2807 protein, and SNRNP70K interacts with the CG2807 RNA. CG16941, which encodes the SF3A1 subunit of U2 snRNP, is another hub of interactions with protein–protein interactions with QKR58E-1; RNA–protein interactions with SNRNP70K and CG6227, the ortholog of yeast Prp5; and both protein and RNA interactions with the SRP54, U2AF50, and B52 proteins. Finally, we observe that B52 appears to play a central role as it interacts with many U2 snRNP components, including the RNAs of four Sm proteins—D1, D2, D3, and F—and three proteins—CG16941, CG13900 and CG2807—as mentioned above. Together these results are consistent with an intricate network of cross-regulatory interactions that control expression of the U2 snRNP components.We find significant enrichment for genes encoding components of the U2 snRNP and related proteins . This complex includes six other proteins, four of which contain RNA-binding motifs  and a fifth that contains an RNA helicase domain and has been implicated in RNAi (CG6701). QKR54B and SYP, as well as QKR58E-1, associate with the transcripts encoding QKR58E-3 and CG6701. Additionally, we see that QKR58E-1 strongly associates with this complex through both protein and RNA interactions . We find reciprocal RNA binding between the pairs of SYP and QKR58E-1, as well as QKR54B and QKR58E-1. Seven gene products interact with two of these three RBPs, and four genes interact with all three.In addition to the experiments performed for this study, we investigated relationships to protein complexes and pathways reported in the DPiM, a protein–protein interaction map generated in the same cell line . The DPiDrosophila transcriptome; 255 are expressed in S2R+ cells. Nonetheless, we find that UCGs are enriched among the RNA targets of RBPs. UCGs are 25% more likely to be associated with at least one RBP than would be expected at random , and this enrichment is driven largely by hnRNP/QKRs (rank sum P < 0.0005) . Mice bearing mutations in the orthologs of qkr54B and qkr58E-1 exhibit neural developmental phenotypes (P < 5 × 10−10).Recently, a small subset of genes was identified that each generates more than 100 mRNAs via complex alternative splicing, promoter use, and polyadenylation and are referred to as “ultracomplex genes” (UCGs) . Most, benotypes . Our resP < 0.001) . The most striking examples are ELAV and MSI that have 26% and 19% of their RNA targets associated with no other RBP studied. Additionally, RNAs with low expression (RPKM < 1 in control samples) are 1.7-fold more likely to show binding to hnRNP/QKR . In particular, Ccn RNA, which encodes a growth factor implicated in neurogenesis, is detected at very low levels in the control samples (0.27 RPKM) and 19 of the IP samples (max of 0.72 RPKM) yet is enriched more than 7000-fold by SYP (592 RPKM). This indicates a highly specific and strong association between SYP and this neurogenesis-related mRNA.In addition to enrichment for UCGs, hnRNP/QKRs tend to associate with unique target RNAs that other RBPs from this study do not target . However, several other hnRNP/QKRs are also strongly associated with RNAs containing 3′ UTR extensions . We find that MSI associates with 52 RNAs containing 3′ UTR extensions that are not detectably associated with ELAV. We manually reviewed each of the eight ELAV targets reported by Fas1, where MSI associates with isoforms, including the extended 3′ UTR, and ELAV associates only with the shorter isoforms (It has been previously published  that elaR+ cells . We findisoforms A. These pasilla (ps) interacts specifically with repeats of YCAY  rather than small, RBP-protected fragments, identification of sequence motifs must be performed by considering the entire sequence of the all possible RNAs at each enriched locus. Extant methods are not available to determine sequence specificity given a set of bound loci within a complex transcriptome, where many genes encode multiple transcripts. We developed a method that identifies enriched sequence signatures within a set of RNAs compared with all expressed genes and, if statistically significant sequence signatures are found, combines these to produce a sequence motif for each RBP (Methods). We identified motifs for each factor. The RBP encoded by  of YCAY , a motif of YCAY  and founCirl (a HOT RNA) reveals a consistent pattern of UTR binding for some factors and of CDS binding for others  and SR proteins show a mean 2.7 enrichment z-score (P < 0.01) for motif hits in CDS regions. The EJC release factor UPF1 shows strong binding to the CDS, consistent with its role in NMD that detects splicing irregularities in coding sequence.The motif enrichment across target mRNA gene structure suggests that motif analysis may provide insight into the regions of mRNAs bound by particular RBPs. For instance, analysis of the gene r others B. We askr others C, thoughsnRNA:U5:14B, snRNA:U2:14B, and snRNA:U2:34ABb). For example, RBP1, MUB, and MSI all significantly enrich for the snRNA:7SK RNA; in fact, MSI enriches for this RNA over 19-fold. Intriguingly, ELAV displays a very strong (590-fold) interaction with snRNA:U5:35D, and QKR58E-1 enriches RNaseP:RNA over sevenfold. In addition, there are 236 annotated noncoding RNAs  that interact with between one and 11 RBPs: For example, CR31044, which encodes a ∼5-kbp RNA that contains miR-279 and miR-996, interacts with 11 RBPs, the strongest of which is SYP. Similarly, 10 RBPs interact with CR43651, which encodes a ∼1-kbp RNA hosting miR-14; the strongest interactor in this case is PS with a 65-fold enrichment. These results identify RBPs that may participate in the biogenesis of specific miRNAs.We sequenced total RNA without a size fractionation step, thus recovering unpolyadenylated noncoding RNA targets, which include microRNAs (miRNAs), snRNAs, small nucleolar RNAs (snoRNAs), and scaRNAs, as well as unpolyadenylated long noncoding RNAs. We visually inspected many examples of these targeted RNAs and discovered that the majority of targets are due to the enrichment of the RNA precursors . However, we also observed several examples of significant enrichment for “intergenic” noncoding RNAs . These include six of 144 expressed snoRNAs, four of nine miRNAs, seven of 18 snRNAs, and two of 14 scaRNAs. Of the 10 RBPs, ELAV targets include the most: eight small functional RNAs. No other RBP targets more than four. As mentioned earlier, two U1 snRNAs,  complex , and areIn addition to investigating the enrichment of particular mRNA transcripts at the gene level, we also queried introns for evidence of enrichment (Methods). We find that while gene level enrichments correlate well (ρ = 0.62) with intron enrichment loci across RBPs, there are several factors with many gene level targets that do not show a similar signature at the intron level, consistent with intron targeting as a feature of some RBPs and not others A. Two faXrp1 locus, which encodes a DNA-binding protein and is a HOT RNA (targeted by 17 RBPs) . We find differential intronic enrichment for four of these introns. For example, the second intron is strongly targeted by MSI, but not any other factors, while the fourth intron is preferentially targeted by SRP54 and ELAV. Several other loci with marked differential intron retention include MRP and crol, as well as at the loci of two of the RBPs in this study, ps and Syp.We find individual introns are targets of multiple factors. We also find gene loci with multiple introns that are targets of distinct cohorts of factors. A striking example is the 17 RBPs) B. Xrp1 cWe obtained genome-wide RNA–protein and protein–protein interaction profiles for 20 RBPs. The combined use of next-generation sequencing and mass spectrometry on a single immunoprecipitate for each RBP provided new insights into the composition of ribonucleoprotein complexes in metazoans. Validation of RNA–protein interactions was performed by comparison to an RNA sequencing data set generated from RNAi-depleted cells  demonstrWe found that strongly bound RNAs included HOT RNAs that interact with most of the factors in our study. These included many of the genes encoding proteins in the RNAi and NMD pathways, those related to neurogenesis, other RNA-binding and splicing factors, and components of the proteasome. This is consistent with previous reports  that genSyp mRNA is itself a target of QKR58E-1 and, reciprocally, that SYP binds mRNAs of qkr58E-1, and we detected protein–protein interactions between SYP and QKR58E-1. The mRNAs of the quaking-related factor held out wings (how) are targets of both quaking-related factors we surveyed, and HOW is a protein interaction partner of SYP. Overall, we find extensive coregulation and interaction among UCGs and the RBPs that target them.The hnRNP/QKR proteins bound a more diverse repertoire of target RNAs than other classes of RBP. We found that hnRNP/QKRs in general were strongly associated with UCGs, genes with many promoters, alternative splicing events, and/or polyadenylation sites. In contrast, SR proteins bound largely overlapping sets of post-transcriptional regulators, with few targets bound by only a single member of this class. This is consistent with their known biochemical redundancy . We findWe found that splicing factors and the EJC component UPF1 tend to bind CDSs of target mRNAs, while hnRNP/QKRs are enriched in UTRs. While several factors, including ELAV, are strongly enriched in 3′ UTRs, we found that other hnRNP/QKRs, particularly MSI, show even stronger association with 3′ UTR extensions. It was previously reported that ELAV is both necessary and sufficient for these extensions to exist at eight genes , but gloOur data are consistent with the colocalization of mRNAs of RBPs and the proteins they encode. Furthermore, these associations between interacting proteins and mRNA products from the same genes could be ribosome proximal or ribosome mediated. It could be that the protein complexes studied here undergo cotranslational assembly. This is also an intriguing possibility. Importantly, our assays measure time and space averages across ensembles of homogeneous, but not identical or synchronized cells. Hence, while it is clear that the proteins copurify and bind the same RNA targets, it may be that these associations occur on different individual RNA molecules that are neither spatially nor temporally localized with the proteins they encode. Additional assays, particularly high-content imaging approaches, will be needed to resolve these possibilities and to elucidate the intriguing biology at the basis of feedback in post-transcriptional regulatory networks.Drosophila S2R+ cell cultures. Expression of the tagged protein was induced for a 24-h period before harvesting cells. For details, see Supplemental Methods within the sections Construction of the Flag-HA Expression Clone Set and Transfection and Recombinant Expression of Bait Proteins in Cell Culture.Full-length ORFs for each RBP were obtained from an expression clone set generated for the DPiM project . These OCell lysate was clarified and put through a single-step anti-HA purification step. Unbound proteins were removed with extensive washes, and then bound complexes were released by competition. Of each sample, 30% was processed for LC/MS/MS, 60% was used for RNA purification, and 10% was used for quality controls. For details, see Supplemental Methods within the sections Protein Isolation and Liquid Chromatography-Mass Spectrometry (LC/MS/MS).Drosophila genome using TopHat  selection and RNA fragmentation steps were omitted. Libraries were sequenced on an Illumina HiSeq 2000 to generate single-end 50-bp reads. Reads were mapped to the g TopHat  as previg TopHat . For detP-values, from biological replicates, using the IDR R package  under accession number GSE37756.All mapped reads data from this study have been submitted to the NCBI Gene Expression Omnibus (GEO;


Streptococcus agalactiae, or Group B Streptococcus, is a leading cause of neonatal infections and an increasing cause of infections in adults with underlying diseases. In an effort to reconstruct the transcriptional networks involved in S. agalactiae physiology and pathogenesis, we performed an extensive and robust characterization of its transcriptome through a combination of differential RNA-sequencing in eight different growth conditions or genetic backgrounds and strand-specific RNA-sequencing.S. agalactiae. In particular, 40% of the TSSs upstream genes involved in nucleotide metabolism show reiterative transcription potentially regulating gene expression, as exemplified for pyrG and thyA encoding the CTP synthase and the thymidylate synthase respectively.Our study identified 1,210 transcription start sites (TSSs) and 655 transcript ends as well as 39 riboswitches and cis-regulatory regions, 39 cis-antisense non-coding RNAs and 47 small RNAs potentially acting in trans. Among these putative regulatory RNAs, ten were differentially expressed in response to an acid stress and two riboswitches sensed directly or indirectly the pH modification. Strikingly, 15% of the TSSs identified were associated with the incorporation of pseudo-templated nucleotides, showing that reiterative transcription is a pervasive process in S. agalactiae. It also provides the basis for in depth analyses of transcriptional networks in S. agalactiae and of the regulatory role of reiterative transcription following variations of intra-cellular nucleotide pools.This comprehensive map of the transcriptome at the single nucleotide resolution led to the discovery of new regulatory mechanisms in The online version of this article (doi:10.1186/s12864-015-1583-4) contains supplementary material, which is available to authorized users. Streptococcus agalactiae, or Group B Streptococcus, is a Gram-positive bacterium known as a commensal of the digestive and genitourinary tracts of 10–30% of the human population [S. agalactiae is frequently associated with animal diseases, being responsible for bovine mastitis [S. agalactiae factors involved in host colonization or in virulence have been described [S. agalactiae genomes [S. agalactiae, governing directly or indirectly the expression of more than 7% of the genome, including many virulence associated genes [S. agalactiae encounters acidic pH at crucial steps of the colonization and invasion processes, such as during the adaptation to the vaginal cavity or during intracellular survival in macrophages [pulation ,3. It alpulation . In addimastitis  and for mastitis . While aescribed ,8, the tescribed ,10. As m genomes ,12. Five genomes -17. In ped genes . The exped genes . S. agalrophages .Over the past ten years, our understanding of bacterial transcription has greatly advanced thanks to the combination of high-throughput technologies, namely tiling arrays and deep RNA sequencing (RNA-seq) with classical genetics and biochemical assays. In particular, RNA-seq has offered tremendous power for high-resolution transcriptome characterization, allowing both differential-expression analysis and identification of new transcripts not predicted by bioinformatics. Characterization of the primary transcriptome, which is a catalog of all expressed RNA molecules that carry a 5′-triphosphate group indicative of transcription start sites (TSSs), has also greatly facilitated the identification of promoter regions and their interspecies comparisons . These gS. agalactiae by a thorough identification of the promoter regions and operon structure, as well as of sRNAs and cis regulatory 5′ untranslated regions (5′UTR). We combined differential RNA-seq (dRNA-seq), and strand-specific RNA-seq on multiple RNA samples from the model strain NEM316 to maximize the identification of 5′ and 3′ ends. This revealed an unexpected high number of TSSs associated with a reiterative transcription suggesting new potential regulatory mechanisms. Among 39 cis-regulatory regions identified, eleven were novel and two were found to respond to acidic conditions. Ten out of 47 intergenic sRNAs were also predicted to be involved in S. agalactiae response to acid stress. This comprehensive characterization of S. agalactiae transcriptional landscape paves the way for further deciphering the regulatory networks that coordinate gene expression during the progression from commensalism to virulence.In this work, we aimed at characterizing the transcriptional landscape of S. agalactiae, we used a differential RNA-seq (dRNA-seq) strategy, based on selective Tobacco Acid Pyrophosphatase (TAP) treatment and 5′ adapter ligation, which differentiates primary transcripts and processed RNAs [ciaRH, relRS or covRS TCS loci were deleted. As these TCS may act as positive or negative regulators depending on their targets, we speculated that their inactivation would increase the transcription of genes weakly or not expressed in the WT strain under the conditions tested. Two growth conditions  were used for the covRS mutant, while the relRS and ciaRH mutants were grown to early or late exponential phases, respectively.To globally identify TSSs in sed RNAs ,27. To iS. agalactiae NEM316 genome sequence [S. agalactiae, with 81% of the coding sequences transcribed in the same direction as the movement of the replication fork. TAP+ and TAP- profiles were similar except at discrete positions, mostly found close to the annotated start codon of coding sequences (CDSs), where the number of reads under TAP+ conditions largely exceeded that under TAP- conditions  were TSSs upstream protein-coding genes, among which 891 corresponded to primary TSS and 36 to secondary TSS. We identified 34 and two protein-coding genes preceded by two promoters and three promoters, respectively. The start codons for 11 genes, in which the TSS was downstream the previously predicted translation initiation codon, were corrected and two new potentially coding genes were annotated. 225 TSSs were located inside CDS, 191 initiating transcription in the same orientation as the CDS and 34 in the opposite orientation. Five supplementary TSS, close to CDS (<100 nt) but in opposite orientation also initiated an antisense transcription. Finally 53 TSSs in intergenic regions were upstream potential sRNA and tRNA genes (Table 15/21bp-TATAAT) [Bacillus subtilis and Escherichia coli [By analyzing the 50 nucleotides upstream each TSS, we predicted promoter sequences related to the prototype Sigma70 promoter (TTGACA-X-TATAAT) ) upstrea-TATAAT) . For apphia coli ,29. For pyrB1, pyrG, carAB, codBA, upp-uraA in E. coli or B. subtilis and for the gal operon in E. coli [. We developed a method to mine dRNA-seq data for reiterative transcription at a genome-wide level. Indeed, while the RNA-polymerase stuttering at a promoter may lead to non-productive transcription, the switch to a non-reiterative nucleotide addition will generate a transcript differing in its 5′ end from its DNA-template, by one or several nucleotides detectable by RNA-seq.Reiterative transcription, also known as transcript slippage or pseudo-templated transcription was documented as an intrinsic property of RNA polymerase from as early as 1964 . However E. coli -33. We dS. agalactiae TSSs were associated with a reiterative transcription , might also be regulated by reiterative transcription in S. agalactiae. Indeed, transcription of thyA occurs at a G or at the adjacent upstream T, where it leads to reiterative addition of up to six U residues . This suggests a mechanism where, depending on the intracellular pools of GTP and UTP, the amplitude of reiterative transcription might modulate thyA mRNA translation initiation. Interestingly the proportion of RNAs with pseudo-templated nucleotides varied according to the growth condition tested, supporting that reiterative transcription has a regulatory function . Finally, 484 genes are transcribed as monocistronic units  on 138 genes  [Deinocococcus deserti (60%) [E. coli [S. agalactiae. Interestingly, the holA/gbs0807 and gbs0266 leaderless transcripts, encoding the DNA polymerase III subunit delta and a NAD-dependent dehydrogenase also showed an addition of pseudo-templated nucleotides at their 5′ ends and their translation might vary according to the amplitude of reiterative transcription. On the other hand, 84 transcription units, including genes involved in virulence such as the cyl (hemolysin) operon, the scpB gene encoding the C5A-peptidase, and the major TCS regulator of virulence covRS displayed 5′UTR longer than 100 nucleotides possibly involved in the regulation of transcript stability and translation.Analysis of the distance between TSSs and the translation initiating codons in e Figure  showed ti (1.5%)  and Deinti (60%) . All of [E. coli ,41, suggin silico analysis was then confronted to experimental evidence: premature termination of transcription and production of a sRNA deduced from RNA-seq data. Altogether, 28 regulatory regions belonging to known Rfam families were detected  were screened for similarity to known cis-regulatory sequences in the Rfam database  and for 5 nt were2603 V/R . For 19 gbs0560) are still unknown. Interestingly, we observed that the efficiency of transcription termination directed by the yybP-ykoY leader increased in conditions of acid stress  (n = 2), for thiamine pyrophosphate (TPP) (n = 2), for purine (n = 1), for glycine (n = 1) and for prequeuosine (n = 2), as well as seven T-box leaders responsible for the response to uncharged tRNAs  [S. agalactiae virulence by interfering with the stability or translation of their cognate mRNA.TSS mapping identified 39 TSSs potentially initiating an antisense transcript, as well as 44 TSSs in intergenic regions not directly linked to protein coding genes or to tRNA genes. The RNA-seq data confirmed transcription of these 73 antisense or intergenic sRNA and identified three supplementary sRNAs: two located in intergenic regions, which likely result from the processing of a longer RNA, as no TSS was identified by dRNA-seq experiments and one sRNA initiated at a TSS located at the end of a non-expressed coding gene Table . 11 amonnse RNAs . The incnse RNAs  and may ing sfbA  or the ggbs1306) , might basd and Srn073) were chosen for further expression analysis by Northern blot  were first described in utolysis , regulatutolysis  and viruutolysis . Based oalactiae . Here, w results . Signifigbs0291), for a RNA methylase potentially involved in the modification of 23S RNA m2G2445 (gbs0292) and for a protein of unknown function (gbs0293). The association between the three coding genes and RNaseP mRNA is conserved among all streptococci, suggesting a functional and regulatory link between these genes. Recently, RNaseJ was shown to be implicated in generating rnpB 5′ end in Staphylococcus aureus [In addition to the four csRNAs, 10 other sRNAs matched Rfam families, including transfer-messenger RNA (tmRNA/SsrA) involved in the recycling of stalled ribosomes, 6S/SsrS RNA, signal recognition particle RNA (SRP/4.5S) and M1/RnpB RNA, the RNA component of RibonucleaseP (RNaseP) involved in the maturation of tRNAs and of some sRNAs. Combined TSS mapping and RNA-seq revealed that RnpB RNA likely results from the processing of a longer transcript, comprising the CDS for the cell division protein GpsB  system. As first shown in elements . Two trapyogenes   (MidE), late exponential phase (OD600 = 1–1.2) (LE) or stationary (OD600 = 1.6-1.8) phase (ST). The strain NEM316 was also grown to OD600 = 0.3-0.4 in RPMI medium, without phenol red, supplemented by 50 mM Hepes, pH 7 and 1% glucose. This OD600 corresponded to early stationary growth phase as, in this culture medium, the growth plateau was reached at OD600 = 0.35-0.40. For RNA-seq and dRNA-seq experiments assessing the effects of acid stress, strain NEM316 was grown to OD600 = 0.4 in TH medium, centrifuged and resuspended in fresh TH medium adjusted or not to pH 5.2 and further incubated for 20 additional minutes at 37°C.All experiments were conducted in the n NEM316 . The NEMescribed  and the Total RNA was prepared as previously described . ResiduaDepleted RNA samples were divided into two subsamples (hereby TAP+ and TAP-) containing the equivalent of 7.5 μg of total RNA and used for dRNA-seq library preparation as described . The TAPStrand-specific RNA-seq libraries were generated with the Illumina primer ligation method, as described  with thewww.ebi.ac.uk/arrayexpress) under accession number: E-MTAB-3200.The raw dRNA-seq and RNA-seq reads are available in the ArrayExpress database  by the software Bowtie (version 0.12.7) [Sequencing reads generated from dRNA-seq and RNA-seq libraries were trimmed for adapter sequences with Cutadapt  and read 0.12.7)  with a shttp://meme.nbcr.net/meme/tools/meme) and custom Python scripts for promoter motifs.For each dRNA-seq experiment, the positions of the 5′ ends of the reads were recorded and the numbers of reads under TAP+ and TAP- conditions at each position were counted. Positions where the sum of reads in TAP+ and TAP- conditions was less than six were not further considered. A statistical assessment of TSS positions was performed by EdgeR (version 3.2.4)  for eachReads generated from strand-specific RNA-seq experiments were aligned to the genome of strain NEM316. The number of reads mapping to each predicted CDS was determined by Rsamtools (version 1.13.35), GenomicRanges (version 1.13.39) and GenomicFeatures (version 1.12.3) in R 3.0.1. The gene expression values were quantified in terms of reads per million (RPKM) defined as the total number of reads mapping to the feature divided by feature length (in kbp) normalized by the total number of reads (in millions) . For difet al. [http://rna.igmors.u-psud.fr/toolbox/arnold/) or TranstermHP (http://transterm.cbcb.umd.edu/). Two consecutive genes in the same orientation were considered as belonging to different transcription units (TU) if transcription of the first gene ends with a 100% efficient termination site or if a primary TSS was detected upstream the second gene. Accordingly, TU were classified into one of three categories: 1) monocistronic TU; 2) simple operons, composed of several genes preceded by a primary TSS and separated from the next TU by a 100% efficient termination site and/or a primary TSS; 3) composite operons preceded by a primary TSS and characterized by the presence of at least one internal TSS and/or one inefficient terminator leading to variations in gene expression levels along the operon.To map transcript 3′ ends, the coverage per nucleotide was determined along both strands of the genome sequence by using the SAMtools   at 37°C. Samples were harvested at OD600 = 0.3, 0.6, 1.2, 2 and late stationary phase (overnight culture). To study the effect of stress conditions, the strain NEM316 was grown in THY medium. At OD600 = 0.6, the medium was either supplemented with lysozyme (200 ng/ml) or bacteria were centrifuged and resuspended in fresh THY adjusted to pH 4.5, or supplemented with 1 M NaCl or with 4 mM H202, final concentrations. Extraction of total RNA and Northern blot hybridization with α32-P 3′-labeled oligonucleotides (described in Additional file RNA were extracted from


Nostoc sp. PCC 7120. Here we combine this dataset with a prediction of Rho-independent transcriptional terminators and an analysis of phylogenetic conservation of potential sRNAs among 89 available cyanobacterial genomes. In contrast to predictive genome-wide approaches, the use of an experimental dataset comprising all active transcriptional start sites  facilitates the identification of bona fide sRNAs. The output of our approach is a dataset of predicted potential sRNAs in Nostoc sp. PCC 7120, with different degrees of phylogenetic conservation across the 89 cyanobacterial genomes analyzed. Previously described sRNAs appear among the predicted sRNAs, demonstrating the performance of the algorithm. In addition, new predicted sRNAs are now identified that can be involved in regulation of different aspects of cyanobacterial physiology, including adaptation to nitrogen stress, the condition that triggers differentiation of heterocysts . Transcription of several predicted sRNAs that appear exclusively in the genomes of heterocystous cyanobacteria is experimentally verified by Northern blot. Cell-specific transcription of one of these sRNAs, NsiR8 (nitrogen stress-induced RNA 8), in developing heterocysts is also demonstrated.Small RNAs (sRNAs) are a growing class of non-protein-coding transcripts that participate in the regulation of virtually every aspect of bacterial physiology. Heterocystous cyanobacteria are a group of photosynthetic organisms that exhibit multicellular behavior and developmental alternatives involving specific transcriptomes exclusive of a given physiological condition or even a cell type. In the context of our ongoing effort to understand developmental decisions in these organisms we have undertaken an approach to the global identification of sRNAs. Using differential RNA-Seq we have previously identified transcriptional start sites for the model heterocystous cyanobacterium  Small RNAs (sRNAs) are involved in virtually every aspect of bacterial physiology and especially in adaptation to variable nutrient availability and biotic or abiotic stresses  sp. PCC 7120, a model heterocyst-forming cyanobacteria, resulting in a genome-wide map of all the positions in which active promoters lead to transcription of different types of RNAs, including antisense and putative sRNAs , we compare all sequences possibly encoding sRNAs in the reference genome with all other genomes available for cyanobacteria with the exception of the fast-evolving clade of Prochlorococcus and marine Synechococcus. Experimental validation confirms transcription of several previously unknown sRNAs predicted by the algorithm implemented, including NsiR8 (nitrogen stress induced RNA 8), a new HetR-dependent heterocyst-specific sRNA.In this work we have undertaken a predictive approach to the identification of sequences transcribed as sRNAs in cyanobacteria, using Nostoc sp. PCC 7120, hetR mutant 216  and grown photoautotrophically at 30°C in BG110C medium methyl-2-aminoethanesulfonic acid-NaOH buffer (pH 7.5) until exponential phase. To induce nitrogen deficiency, cells grown in the presence of ammonium were collected by filtration using nitrocellulose membranes (0.45 μm pore diameter), washed with and resuspended in nitrogen-free BG110C medium. RNA samples were isolated from cells taken at the time points indicated after removing combined nitrogen from the media. For the experiment shown in Figure 4D, the cells were grown at 30°C in BG11C medium containing NaNO3 . 32P-labeled oligonucleotides (labeled with polynucleotide kinase and γ-32P-dATP) were used for primer extension analysis and Northern blot of the RNA transcribed from position 4547237r. Oligonucleotides used for primer extension and to amplify PCR products used as templates to prepare 32P-labeled probes are shown in Table Total RNA was isolated using hot phenol as described  were downloaded from RefSeq (NCBI Reference Sequence Database). Rho-independent transcriptional terminators were predicted on each replicon using TransTermHP 2.08 with default parameters  of the reference genome used  rendering a total of 3364 possible terminators.We have carried out a prediction of genomic sequences that could encode sRNAs using a TSS dataset from a dRNA-Seq-based transcriptomic analysis of We implemented a pipeline  and the two nutritional conditions analyzed (growth in the presence of ammonia or in the absence of combined nitrogen for 8 h) is also included.Upon inspection of the selected sequences we noticed that for some of the predicted sRNAs there were several nearly identical copies in the reference genome that appeared among the sequences selected by the algorithm, indicating that more than one copy was associated to a nTSS defined by RNA-Seq (and therefore being actively transcribed under the growth conditions used). Thus, we pooled all predicted sequences resulting from the algorithm and compared them by BLASTn, so that identical sequences were further grouped into families. Each family is represented by only one member in the output table , therefore leading to a final number of 327 entries. The complete table containing all results for the seven replicons is presented in Table Nostoc sp. PCC 7120. Previously described NsiR1  or because their TSS are not annotated as nTSS in the RNA-Seq dataset (ssaA encoding 6S RNA and ffs encoding 4.5S RNA). The latter is also the case for NsiR4  and other 88 completed or draft genomes from phylogenetically diverse cyanobacteria  appear in every cyanobacterial genome analyzed Figure . Yfr1, iNostoc sp. PCC 7120 and a weblogo obtained for the putatively translated peptide in 83 strains are shown in Figure Careful inspection of some of the alignments for sRNAs with many homologs allows the identification of conserved features. For instance, conservation of the sequence 5′CACUCCUCACACC3′ in all the homologs of Yfr1 Figure . SimilarThe approach we described here is based on a dataset of transcriptional start sites defined by dRNA-Seq. The number of reads associated to each TSS by RNA-Seq is influenced by several factors, including size of the transcript  or transcription under specific physiological conditions different from the standard growth conditions . Also, sRNAs that are processed in the 5′ end are under-represented because the dRNA-Seq procedure counter-selects processed, non-primary 5′ ends. In this context we hypothesized that the analysis of phylogenetic conservation of possibly transcribed sequences could facilitate the identification of true sRNAs with small numbers of reads. As an example, among the phylogenetically conserved predicted sRNAs (with homologs in more than 10 heterocystous strains), we have experimentally verified transcription of one of the predicted sRNAs with the lowest number of reads Figure .According to our predictive approach, transcription from position 4547237r, for which only eight reads are mapped in dRNA-Seq, would produce an sRNA slightly above 50 nucleotides in size, therefore in the lower size limit for the fractionated RNA sample used in RNA-Seq. 12 out of the 27 genomes for heterocystous strains included in the homology search contain sequences that the algorithm identifies as homologs with the parameters used Figure . The con2. The TSS dataset for the model heterocystous cyanobacterium Nostoc sp. PCC 7120 contains combined information for the transcriptome of the wild-type strain and a hetR mutant (heterocyst-defective) both in the presence of combined nitrogen and after 8 h of nitrogen deficiency  and differentiation of heterocysts (HetR). Therefore, we expect these two sRNAs to be involved in the response to other stresses as well.Table hetR mutant, therefore suggesting the last two sRNAs could be related to heterocysts differentiation or function. An schematic representation of the genomic context of the sRNAs included in Figure In addition to previously described NsiR3 , the algorithm identifies a transcribed sRNA.Differential RNA-Seq-based datasets containing genome-wide maps of active promoters provide unprecedented details on transcription in bacteria. Using a previously available dataset for the model organism Nostoc sp. PCC 7120, whereas some of the sRNAs predicted by the algorithm appear widely distributed among relatively distant cyanobacterial strains , homologs to a few predicted sRNAs are exclusively found in the genomes of heterocystous strains . For some of the predicted sRNAs, in particular those that are specifically transcribed in heterocysts  a possible function related to heterocyst differentiation or function can be suspected. Finally, phylogenetic conservation of certain sequences in the predicted sRNAs can suggest translation of a peptide  or possible interactions with other RNA or protein molecules . The predicted sRNAs described in this work, several of them experimentally validated here, constitute a set of potentially regulatory elements to be analyzed in further detail in heterocystous cyanobacteria.Phylogenetic conservation of sequences predicted to be transcribed as sRNAs in related (or even distant) genomes provides additional information concerning a possible functional role. In the case of gfp fusions and performed confocal fluorescence microscopy analysis. All authors were involved in analyzing the results and writing the manuscript.AM and MB designed the computational approach. MB wrote scripts and implemented the sRNAs prediction. MB and AV performed all other computational tasks. MB, EO, and AM performed experimental validation of sRNAs. AM prepared This work was supported by grant BFU2013-48282-C2-1  to AM. MB is the recipient of a fellowship from Agencia Estatal CSIC, Spain (Jae Intro) and a contract from Ministerio de Educación Cultura y Deporte, Spain (FPU014/05123).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.


Temperature is one of the decisive signals that a mammalian pathogen has entered its warm-blooded host. Among the many ways to register temperature changes, bacteria often use temperature-modulated structures in the untranslated region of mRNAs. In this article, we describe how such RNA thermometers (RNATs) have been discovered one by one upstream of heat shock and virulence genes in the past, and how next-generation sequencing approaches are able to reveal novel temperature-responsive RNA structures on a global scale. RNA molecules are not linear, but fold into complex three-dimensional structures. Base pairing of proximal nucleotides generates secondary structures, like stem-loops. Long distance interactions allow formation of tertiary structures, like pseudoknots or kissing loops. It is well established that the structure is fundamental for the biological function of non-coding RNA such as tRNA and rRNA. What is now emerging is that the three-dimensional architecture of mRNA influences its entire life cycle: transcription, maturation, translation and degradation. RNA-mediated gene regulation is relevant as it is fast and energy-saving because it bypasses the expression of transcription factors. Structured RNA elements are known to respond to different stimuli, for example metabolite-sensing riboswitches  , ATP-independent chaperones that maintain client proteins in a folding-competent state. The systematic search for potential RNATs upstream of sHSP genes has led to the identification of more than 40 ROSE-type RNATs in diverse alpha- and gamma-proteobacteria  , kethoxal, and 1-cyclohexyl-(2-morpholinoethyl)carbodiimide metho-p-toluene sulphonate (CMCT). Enzymatic single-strand specific probes include nucleases S1, A, T1, I. The RNase V1 enzyme cleaves at double-stranded nucleotides. Hydroxyl radicals cleave at RNA bases that are solvent-exposed. Upon treatment, modified or cut positions are mapped by polyacrylamide gel electrophoresis, if necessary after reverse transcription. These approaches are well established and were successfully applied in the structural analysis of a wide range of RNA molecules.The genome-wide assessment of RNA structures relies on structure-probing techniques able to distinguish single- and double-stranded regions. Traditional structure-probing experiments are based on the in vivo. Chemicals that quickly penetrate the cell membrane, such as Pb2+  approach takes advantage of capillary electrophoresis of cDNA obtained from RNA treated with NMIA, or its derivatives  transcriptome and mice nuclear transcriptome, respectively  (Kertesz et al., y Figure . In a tyin vitro and partially digested with the single-strand specific nuclease P1, has been analyzed by Frag-seq. This approach could successfully confirm single-strand regions of non-coding RNA with known structure and unveiled the secondary structure of non-coding RNA with previously unknown structure (Underwood et al., Arabidopsis thaliana (Zheng et al., Drosophila melanogaster and Caenorhabditis elegans (Li et al., Purified nuclear RNA from mice, which was refolded It is easily conceivable that temperature-responsive structures such as RNATs can be discovered by this technology and a first step in this direction has been undertaken by probing the whole yeast transcriptome at different temperatures (Wan et al., In vivo global structure probing strategies have been attempted only very recently and applied to A. thaliana seedlings (Ding et al., S. cerevisiae and mammalian cells (Rouskin et al., S. cerevisiae (Talkish et al., in vitro and in vivo data will ultimately provide a detailed picture of the RNA structurome in its physiological context.RNA structure discovery has entered a new era by coupling structure probing to next-generation sequencing. Quite surprisingly, no prokaryotic RNA structurome has been reported so far. Given that the principle has been established in yeast (Wan et al., 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.


RNA 3′ uridylation occurs pervasively in eukaryotes, but is poorly characterized in viruses. In this study, we demonstrate that a broad array of RNA viruses, including mycoviruses, plant viruses and animal viruses, possess a novel population of RNA species bearing nontemplated oligo(U) or (U)-rich tails, suggesting widespread 3′ uridylation in eukaryotic viruses. Given the biological relevance of 3′ uridylation to eukaryotic RNA degradation, we propose a conserved but as-yet-unknown mechanism in virus-host interaction. Addition of non-templated nucleotides to 3′ -ends of RNA transcripts is a well-known type of post-transcriptional RNA modification in kingdoms of life. The most understood example within this category is polyadenylation, by which multiple adenosines are progressively added to the 3′ -ends of RNA substrates by the canonical poly(A) polymerase (PAP) or non-canonical PAP, thereby resulting in poly(A) or poly(A)-rich tails12125Leishmania121718C. elegansC. reinhardtii23C. elegans2627Along with polyadenylation, uridylation is another important type of RNA tailing, and has been recently observed in various eukaryotes, from fission yeast to human7881011Viruses are not included in kingdoms of life, but are small infectious agents which can replicate only within the living cells of organisms. It has long been known that RNAs of many DNA and RNA viruses in eukaryotes undergo polyadenylation, thereby generating 3′ poly(A) tails to maintain RNA stability as well as initiate translation303133Beet necrotic yellow vein virus (BNYVV), Sindbis virus (SIN), coxsackievirus B3 (CVB3) and hepatitis C virus (HCV) once disclosed that after removal of the 3′ poly(A) tails from genomes of these four polyadenylated positive-strand RNA viruses, their progeny would regain a 3′ tail that contain not just a poly(A) tail but also a U-rich or AU-rich linker preceding the poly(A)353637pol mRNA cleaved by a virus-encoded miRNA identified a non-templated AU-rich region followed by a poly(A) tail3932In contrast to the familiar 3′ polyadenylation, RNA 3′ uridylation of viruses is poorly characterized. However, a few studies on To determine the extent of RNA 3′ uridylation in viruses, herein we examined a broad range of RNA viruses infecting either lower eukaryotes (fungi) or higher eukaryotes . By sequencing 3′ -termini of the viral RNAs, we show that, although belonging to phylogenetically distinct groups, none of the tested RNA eukaryotic viruses is free of 3′ uridylation. The data unambiguously demonstrated the widespread 3′ uridylation in eukaryotic RNA viruses, suggesting that viral RNA 3′ uridylation is conserved across eukaryotes and may play an unknown role in host and virus interaction.Tobacco mosaic virus , one distinguished RNA plant virus whose genome terminates with tRNA-like structures (TLS)33Nicotiana benthamiana leaves was first reverse transcribed with an anchored oligo(dA) primer PA18 followed by a nested PCR with the primer pair of P1/TMV-5372-94 and P2/TMV-6023-44 (353637in vitro TMV RNA transcripts known to lacking oligo(U) tails and 0.9 μ g total RNA from healthy N. benthamiana leaves with the same approach. As a result, no viral RNA with 3′ uridine tail was cloned (data not shown), thus confirming 3′ uridylation of TMV RNAs.Following the previous evidence that non-templated 3′ uridine addition takes place in BNYVV, SIN, CVB3, HCV and EBV, the viral genomic RNAs or mRNAs of which all bear 3′ poly(A) tails353637-6023-44 . The res-6023-44 . Of noteTo better understand structures of the 3′ uridine tails added to viral RNAs, we totally cloned and analyzed 60 uridylated TMV RNA molecules. Two types of tails were identified, including 45 oligo(U) tails comprised exclusively of uridines, and 15 oligo(U)-rich tails that contain mostly uridines (96.77%) with a few guanosines (0.9%), adenosines (0.9%) and cytosines 1.43%) interspersed. In particular, the oligo(U) tails ranged from 14 nt to 32 nt with a mean size of 17.87 nt, whereas the oligo(U)-rich tails averaged up to 34.93 nt with a significant variation from 14 nt to 79 nt. However, it is worthy to emphasize that true length of the oligo(U) and oligo(U)-rich tails added to TMV RNAs is probably longer than those we detected, regarding that the oligo(dA)-dependent RT-PCR used here does not allow properly scaling the full length of the 3′ uridine tails, as the limitation of the oligo(dT)-dependent RT-PCR discussed previously33% interspN. benthamiana might tend to uridylate the TMV RNA species ended with adenosine or uridine.A particular interest was also given to the junction sites between the TMV RNAs and non-templated 3′ uridine tails. By thoroughly dissecting the collected TMV RNA-oligo(U) species, we identified that 51 out of 60 tails were appended to nts 6209–6211, 6299–6301 and 6394–6395 of TMV genome, which were assumed as hotspots for uridine addition. The data also pinpointed that a large majority of cloned tails were located at positions ranging from 1 to 175 nt upstream of the mature 3′ -end of TMV genome, whereas only 17 tails directly added to the viral RNAs bearing intact 3′ -end, suggesting the viral RNAs with incomplete 3′ -ends, in comparison with the full length TMV genomes, were more likely targeted for 3′ uridylation. In addition, we noted that the 3′ terminal-nucleotides of the TMV RNAs adjunct with uridine tails were mostly adenosines and uridines (> 61.7%), as observed in the TMV RNA species carrying poly(A) or poly(A)-rich tailsCucumber mosaic virus, Cucumovirus, Bromoviridae), TRV  and ORSV  terminate with 3′ TLS, PVX , PVY , PEDV , PRRSV  and FgHV2  bear 3′ poly(A) tail, and TCV  has 3′ Het. As is known, FgHV2 belongs to mycovirus, PEDV and PRRSV are animal viruses, and CMV, TRV, ORSV, PVX, PVY and TCV infect plants. Hosts of these viruses range from lower eukaryotes to higher eukaryotes, presenting a vast phylogenetic diversity.Presence of 3′ uridine tails in TMV RNAs prompted us to explore if RNA 3′ uridylation generally occurs in positive-strand RNA viruses, the largest class within the eukaryotic RNA virusesFusarium graminearum, the PEDV-infected Vero cells, the PRRSV-infected MARC-145 cells, and the Nicotiana benthamiana leaves inoculated with CMV, TRV, ORSV, PVX, PVY and TCV, respectively. With the corresponding primer pairs  primed RT-PCR was used to scrutinize the putative uridylated viral RNA species from total RNA of the FgHV2-infected er pairs , we idener pairs  and 3. Wer pairs  and 3A, er pairs . By conter pairs  and 3B. er pairs , suggestThus, the data unambiguously demonstrated presence of the 3′ uridine tails in positive-strand RNA eukaryotic viruses varied with 3′ terminal structure  as well as host range . Regarding prevalence of 3′ uridylation in positive-strand RNA viruses, we next investigated uridylation in another type of single-stranded RNA viruses, negative-strand RNA viruses.Influenzavirus A, Orthomyxoviridae) and Rice stripe virus , two viruses with segmented negative-sense RNA genomes were examined. By the oligo(dA) primed RT-PCR with specific primers  virus , a typical double-stranded RNA plant virus containing 12 dsRNA segments, was tested. Using total RNA of RDV-infected rice leaves as template, we analyzed both plus and minus strands of RDV segments 11 and 12 with oligo(dA) primed RT-PCR. The result demonstrated that all the tested RNA strands were subjected to 3′ uridylation (Alternaria longipes dsRNA virus 1 (AlRV1), one unclassified dsRNA mycovirus containing a single genome. Consistently, 3′ uridine tails were also present in the plus strand of AlRV1 dsRNAs Discovery of 3′ uridine tails in viruses with either positive-sense or negative-sense RNA genome was indicative of the wide occurrence of uridylation on single-stranded RNA viruses. Accordingly, we asked if double-stranded RNA eukaryotic viruses are subjected to RNA 3′ uridylation. To address this concern, dylation . Interes1 dsRNAs . Such taIn summary, this study characterized a novel type of viral RNA species bearing 3′ uridine tails from positive strand, negative strand or double-stranded RNA viruses with hosts ranging from fungi to plants and animal. Although the impacts of the oligo(U) and (U)-rich tails on viral RNAs remain unknown yet, the tails strictly resemble the previously determined degradation-stimulating oligo(U) tails associated with eukaryotic RNAs121846474849N. benthamiana plants cultured at 25 °C with 16h light/8 h dark cycle, following collection of the inoculated leaves at 3 dpi as describedO. sativa spp. japonica) seedlings grown at 28~30 °C under natural sunlight through the viruliferous (RSV and RDV-carrying) insects of L. striatellus and N. cincticeps, respectively51A total of nine positive strand RNA viruses, TMV, ORSV, CGMMV, CMV, TRV, TCV, TNV, PVX and PVY, were mechanically inoculated in 4-week-old Two positive strand RNA viruses, PRRSV HB-1/3.9Fusarium graminearum strain JS16 with the positive strand RNA virus FgHV2Alternaria longipes strain HN28 with the double-stranded RNA virus AlRV1Mycelial plugs of 18 PA18  following the manufacture’s protocol. To clone the 3′ uridine tails associated with the viral RNAs, the approach of oligo(dA) primed RT-PCR was designed according to the oligo(dT) primed RT-PCR previously described3318 PA18 , and ther primer . After tr primer  were treHow to cite this article: Huo, Y. et al. Widespread 3′-end uridylation in eukaryotic RNA viruses. Sci. Rep.6, 25454; doi: 10.1038/srep25454 (2016).


Saccharomyces cerevisiae, the Nrd1-dependent termination and processing pathways play an important role in surveillance and processing of non-coding ribonucleic acids (RNAs). The termination and subsequent processing is dependent on the Nrd1 complex consisting of two RNA-binding proteins Nrd1 and Nab3 and Sen1 helicase. It is established that Nrd1 and Nab3 cooperatively recognize specific termination elements within nascent RNA, GUA[A/G] and UCUU[G], respectively. Interestingly, some transcripts do not require GUA[A/G] motif for transcription termination in vivo and binding in vitro, suggesting the existence of alternative Nrd1-binding motifs. Here we studied the structure and RNA-binding properties of Nrd1 using nuclear magnetic resonance (NMR), fluorescence anisotropy and phenotypic analyses in vivo. We determined the solution structure of a two-domain RNA-binding fragment of Nrd1, formed by an RNA-recognition motif and helix–loop bundle. NMR and fluorescence data show that not only GUA[A/G] but also several other G-rich and AU-rich motifs are able to bind Nrd1 with affinity in a low micromolar range. The broad substrate specificity is achieved by adaptable interaction surfaces of the RNA-recognition motif and helix–loop bundle domains that sandwich the RNA substrates. Our findings have implication for the role of Nrd1 in termination and processing of many non-coding RNAs arising from bidirectional pervasive transcription.In  In yeast, RNA polymerase II (RNAPII) transcribes not only protein coding genes but also a subset of non-coding RNAs (ncRNAs) such as small nuclear (snRNAs), small nucleolar (snoRNAs), micro-RNA precursors, cryptic unstable transcripts (CUTs) and other intergenic and noncoding genes . WhereasNRD1 gene encodes a CTD-interacting domain (CID) and an RNA-recognition motif (RRM) at its N- and C-termini, respectively  of RNAPII, when phosphorylated at Ser5  and Nrd1 (nuclear pre-mRNA downregulation 1) and the putative helicase Sen1 ,8. Nrd1 y Figure . Althougy Figure ,12. In a at Ser5 . As a conscripts . Thus, t5 phosphorylation. For instance, the pre-mRNA of CTH2 gene is terminated by the Nrd1 pathway around 1.6 kb in order to be post-transcriptionally processed by TRAMP and exosome  measurement in order to describe RNA binding of Nrd1 protein. Surprisingly, our data show that Nrd1 is able to recognize a wide range of RNA motifs, all with affinity in a low micromolar range. The three-dimensional solution structure of RNA-binding fragment of Nrd1 reveals a two-domain architecture composed of a canonical βαββαβ RRM and an extra helix–loop bundle domain. Using NMR titration technique, we analyzed the Nrd1–RNA interactions and found two distinct but partly overlapping RNA-binding regions in the RRM and helix–loop bundle domains for AU-rich and G-rich sequences. These data are supported by the site-specific mutagenesis and the importance of mutated residues is confirmed by FA as well as phenotypic analysis 6-tag was verified by DNA sequencing and then transformed into Escherichia coli BL21-Codon Plus (DE3)-RIPL cells (Stratagene). Bacterial culture was grown at 37°C until OD600 ∼ 0.3–0.6 and induced with 1 mM IPTG . Protein was overexpressed at 30°C overnight in LB (Luria-Bertani) or minimal M9 medium for FA or NMR measurement, respectively, always supplemented with 50 mg/l of ampicillin. 15NH4Cl and [U-13C6]-glucose were added to the M9 medium as a source for isotopic labeling. For expression of protein in highly deuterated background, culture was grown in the M9 medium containing D2O (99% atom D) instead of normal water and -glucose . Above that, to prepare protein sample with selectively protonated valine, leucine and isoleucine amino acids in highly deuterated background, 1H-13C-labeled precursors were added to the M9 medium 1 h before induction. Fifty milligram per liter of 1H-13C-α-ketobutyrate and 90 mg/l of 1H-13C-α-ketoisovalerate precursors were sufficient amounts to incorporate into isoleucine and valine/leucine amino acids, respectively (Deoxyribonucleic acid (DNA) sequence including Nrd1 RRM (307–491) was amplified by polymerase chain reaction and cloned into pET22b plasmid (Novagen) using NdeI and XhoI restriction sites. Resulting construct containing C-terminal Hisectively . Cells wSite-specific Nrd1 mutants were prepared with the QuikChange site-directed mutagenesis kit (Stratagene) and the point mutations were verified by DNA sequence analysis.1H-15N-labeled sample where the protein was titrated with aliquots of non-labeled RNA substrate (synthesized by Sigma-Aldrich).All NMR experiments were measured on Bruker Avance III systems equipped with cryoprobes of proton frequencies of 600, 700, 900 and 950 MHz at 20°C. The raw data were acquired and processed using Bruker TopSpin 3.0 and analyzed with the use of Sparky 3.113. The resonance assignment of backbone nuclei of Nrd1 RRM was achieved following the standard triple resonance protocol using HNCA, HN(CO)CA, HNCACB and CBCA(CO)NH spectra, further supplemented with deuterated HNCO and HN(CA)CO experiments . For the1H spectrum. Next, Nrd1 RRM sample was added to the RNA to reach the concentration ratio 1:1 and the 1D spectrum was re-measured. Newly appeared peaks of tryptophan aromatic protons and amide protons in the 10.0 and 9.5 ppm spectral region were compared to those of Nrd1 RRM sample bound to GCGGGGC single-stranded RNA in the ratio of 1:1 after titration experiment.Intermolecular G-quadruplex was prepared as described previously . The GCGR2 and R1 relaxation parameters, series of 9–11 two-dimensional 1H-15N spectra were measured in a pseudo-three-dimensional manner on a 700 MHz Bruker Avance III spectrometer equipped with cryoprobe using the pulse schemes as described previously (For the estimation of eviously . Spectrahttp://www.pymol.org) were used for visualization of the Nrd1 molecules.The preliminary structure determinations of the free Nrd1 RRM protein were performed with the automated NOE (Nuclear Overhauser Effect) assignment module implemented in the CYANA program . This auThe equilibrium binding of Nrd1 to RNA was characterized by fluorescence anisotropy measurement. The RNA was labeled at 5′ end with fluorescein or TAMRA fluorophore. The fluorescein was excited at 488 nm and its emission was collected at 520 nm. For TAMRA fluorophore was set up 561 nm and 581 nm for excitation and emission, respectively. The width of both excitation and emission monochromatic slits was varying from 9 to 14 nm depending on measured RNA sequence. Integration time was set to 3 s. All measurements were conducted on a FluoroMax-4 spectrofluorometer (Horiba Jobin-Yvon). The instrument was equipped with a thermostated cell holder with a Neslab RTE7 water bath (Thermo Scientific). The system was operated by FluorEssence software . All measurements were performed at 20°C in 50 mM sodium phosphate buffer supplemented with 150 mM sodium chloride and 10 mM beta-mercaptoethanol (pH = 8). Ten nanomolar RNA (in a volume of 1.4 ml) was titrated with increasing amounts of Nrd1 protein sample . Each data point is an average of three measurements. The data were analyzed using Gnuplot (version 4.4.3) and Xmgrace (version 5.1.16). The data were normalized for visualization purposes and the experimental isotherms were fit to a single-site binding model according to Heyduk and Lee using non-linear least squares regression.NRD1 gene was used as a template for site-directed mutagenesis . All mutations were verified by DNA sequencing. Wild-type and mutated plasmids were transformed into W303 yeast strain using lithium acetate method. Yeasts were grown in SD-leu-his medium + 2% galactose at 30°C until OD600∼1, serially diluted by a factor of 10 in a 96-well plate and dropped on plates with SD-leu-his solid medium supplemented with 2% glucose to repress endogenous NRD1 gene expression. SD-leu medium containing 2% galactose was used as a positive control. Plates were grown at 30°C and 37°C.The pRS415 plasmid  containing the Saccharomyces cerevisiae Nrd1, we prepared a number of protein constructs . First, based on the secondary structure prediction we designed a set of constructs containing the predicted RRM domain (340–410) with various N- and C-terminal extensions . In E. coli, these constructs expressed only insoluble proteins in all tested conditions in which we varied temperatures, IPTG concentrations, vectors, expression cell lines and solubility enhancing tags. Although it was possible to refold the insoluble material after purification under denaturing conditions, the refolded proteins did not give a typical fingerprint of a folded protein in the 1H-15N HSQC spectra. Hence, we expressed the entire C-terminal part of Nrd1 (307–560), which was soluble, but the quality of 1H-15N HSQC spectra suffered from a large number of overlapping sharp lines arising from the unstructured C-terminal region of this construct. This construct was subsequently subjected to the limited proteolysis and mass spectrometry, which helped us to identify the domain boundaries for around the RRM of Nrd1. The final construct involves residues from 307 to 491 (Nrd1307–491) and it provides a well-dispersed spectrum . Furthermore, the 1H-15N HSQC spectra of Nrd1307–491 suffered from line width broadening at higher concentrations. Therefore, we set out to investigate whether Nrd1307–491 can dimerize or oligomerize with the increasing protein concentration. The backbone resonances of Nrd1307–491 (concentration was kept ≤0.4 mM) were assigned using a standard set of double- and triple-resonance experiments . The R1 rates decrease with the increasing size of a molecule, whereas the R2 rates increase  of Nrd1307–491, the average ratio of R2/R1 is 30.96 ± 10.88, whereas at the higher concentration (1.6 mM), the average ratio of R2/R1 increases to 43.41 ± 16.12, which is an increase of about 50% . The rotational correlation time (τc) derived from the whole 15N-1H T1/T2 data set for the monomeric state of Nrd1307–491 is ∼15 ns. Furthermore, we observed the variation of chemical shifts upon raising the concentration of Nrd1307–491 . Altogether, these data are consistent with oligomerization and/or aggregation of Nrd1307–491 at higher concentrations.In our initial NMR experiments, we observed that the eriments . The cheM Figure . Knowingincrease , thus th1H,13C,15N] and  samples was used to obtain the backbone resonance assignments . With this approach we were able to assign 91% of the backbone resonances in the structured part of the protein (residues 323–456). In addition, several residues in the loops showed no NMR signals. These missing signals are likely a result from the relatively high pH used in the NMR study that was necessary to prevent the precipitation of Nrd1307–491. To assign the side-chain resonances, we acquired the 3D HCCH-TOCSY, 3D 15N- and 13C-separated NOESY-HSQC experiments on the -labeled sample. Even though these spectra were acquired on a 900-MHz spectrometer equipped with a cryoprobe, they suffered from a low signal-to-noise ratio  and did not contain enough information for the side-chain assignments nor the structural determination. Therefore, we adopted a selective protonation strategy via α-ketoisovalerate and α-ketobutyrate precursors to introduce methyl- and ethyl-protonated valines, leucines, and isoleucines into 2H, 13C, 15N-labeled protein  and 10 mM β-mercaptoethanol] to prevent protein precipitation. A combination of two data sets acquired on .


MCC (Matthews correlation coefficient) values for the training dataset were 0.866 and 0.742, respectively; for the testing dataset, the accuracy and MCC were 0.876 and 0.576, respectively. The performance was higher using 18 features than all 77, suggesting that a small feature set was sufficient to achieve accurate prediction. Analysis of the 18 features was performed and may shed light on the mechanism and dominant factors of RNA editing, providing a basis for future experimental validation.RNA editing is a post-transcriptional RNA process that provides RNA and protein complexity for regulating gene expression in eukaryotes. It is challenging to predict RNA editing by computational methods. In this study, we developed a novel method to predict RNA editing based on a random forest method. A careful feature selection procedure was performed based on the Maximum Relevance Minimum Redundancy (mRMR) and Incremental Feature Selection (IFS) algorithms. Eighteen optimal features were selected from the 77 features in our dataset and used to construct a final predictor. The accuracy and  Adar1 gene from mice and found that Adar1−/− mice died early during embryonic development due to liver disintegration Adar2−/− mice have been found to be prone to seizures and to die young, and Higuchi et al. have rescued the lethality by mutating the Q/R sites of GluR-B mRNA, which were totally edited in normal mice Adar1−/− mice, −/−Apobec-1 mice are healthy and viable RNA editing is a post-transcriptional RNA process that provides RNA and protein complexity for regulating gene expression in eukaryotes. After editing, RNAs include changes in their nucleotide sequence, leading to non-synonymous codon changes, modulation of alternative splicing patterns, introduction/removal of stop codons and modification of regulatory RNAs It is important to note that RNA editing occurs within a dsRNA structure because ADAR always acts on double-stranded RNAs Here, we developed a novel method to predict RNA editing sites based on a random forest method. We conducted a careful feature selection procedure to select a small subset of all features as the optimal feature set to build the model. We found that by using only a subset of features, we can achieve accurate predictions.Drosophila and RNAs from an ADAR null mutant that lacks the enzyme to mediate adenosine-to-inosine (A-to-I) RNA editing. The DNA data from wild-type Drosophila and RNA data from the ADAR null mutant removed the effects of DNA polymorphisms and other non-ADAR artifacts on the RNA-editing sites discovered from RNA data on wild-type Drosophila. The sequencing data can be found in the NCBI database (http://www.ncbi.nlm.nih.gov/sra?term=SRP028559).The RNA-editing dataset used in this study was obtained from the work of Laurent and his colleagues Information S1 in our study. The testing dataset contained 533 positive and 90 negative samples.The training dataset was retrieved from the training samples used to build their Random Forest algorithm-based model, RFss1, as well as for training their sparse partial least-squares (SPLS)- and artificial neural network (ANN)-based models. There were 127 RNA-editing (positive) and 127 non-RNA-editing (negative) samples in the training dataset. The testing dataset was their ‘all but training samples’ dataset used to validate the RFss1 model and the SPLS- and ANN-based models . These two datasets are provided in the The 77 features used were obtained from Laurent et al.'s Table S2, which described the variables used in their Random Forest (RF) algorithm. We performed careful feature selection to identify an optimal feature set to build our final model.In this study, the RF was employed in prediction model construction. The RF method is a commonly-used classification method containing a number of decision trees. A final classification label was determined based on the class with the most votes from all trees. The RF algorithm has been successfully applied in several bioinformatics studies, such as Not all 77 features contributed to the final prediction. A smaller but more effective set should be selected from the 77 features to build the final model. The Maximum Relevance Minimum Redundancy (mRMR) algorithm was used in this study. The mRMR criteria can be used to rank the importance of the 77 features. A feature was deemed more important if it had more relevance to the target but less redundancy among the features themselves. After calculation by the mRMR method, the 77 features were ordered from maximum relevance but minimum redundancy to minimum relevance but maximum redundancy. In the ordered list, a feature with a smaller index indicated that it had a better trade-off between the maximum relevance and the minimum redundancy and was therefore more important. For mRMR method details, please see To determine the optimal feature set, the Incremental Feature Selection (IFS) method The reasons we used the Maximum Relevance Minimum Redundancy (mRMR) and the Incremental Feature Selection (IFS) method are as follows: “mRMR+IFS” allows us to select a compact set of features at very low time cost because the order in which to add features to final feature set in the IFS is determined by the mRMR, which requires low CPU time. However, in the method of backward feature selection and forward feature selection, the order in which to add features is determined by an extended computation to seek the best one, and therefore the time cost increases exponentially. Ref In this study, we used the 10-fold cross validation method to assess the performance of the prediction model. The complete training dataset was randomly split into 10 equal parts, with each part being used in turn as test data and the remaining 9 parts as training data. This process was iterated 10 times.Sn and Sp represent the sensitivity and specificity, respectively. MCC (Matthews correlation coefficient) was used throughout this study as the main evaluator.The following formulas were used in this study: Information S2. We plotted the predictor MCC values in MCC 0.741). These 18 features were considered to compose the optimal feature set and were used to construct the final prediction model. The corresponding Sn, Sp, and Acc values for the final model training process are listed in In the training dataset, from the corresponding mRMR table, the ranked 77 features were added one by one to generate 77 different feature sets. Accordingly, we constructed 77 individual predictors. The classification performances are found in The final model was tested on our independent testing dataset, and the results are shown in The final model suggested that 18 features were important for the prediction, which are listed in Drosophila and from ADAR knockout (ADAR-) Drosophila.Although RNA editing is reported to be a ubiquitous biological event, and our understanding of such events has increased, our understanding of the mechanism of RNA editing is still far from complete. In our study, we used several features to predict RNA editing sites. These features are applied to two types of RNAs: from wild-type Drosophila results in a deficiency of A to I substitution; hence, the G reads produced by substitution in ADAR-RNA represent the background noise compared with the G reads in WT RNA. The length of G reads for total alignments is also included in our algorithm. In the alignment procedures, the mapping read length is critical for alignment accuracy and precision Although dozens of features were tested, 18 relatively important features are analyzed below. The first feature is the total number of reads from total alignments in the wild-type RNA (wt_treads). As we know, the RNA editing site annotations are more accurate at deeper alignment depths accuracy and MCC, respectively, were 0.866 and 0.741 on the training dataset and 0.876 and 0.576 on the testing dataset. Furthermore, the 18 features provided clues about the mechanism and dominant factors in RNA editing.As an important post-transcriptional RNA regulation mechanism, RNA editing increases the diversity of gene expression. Understanding the features that affect RNA editing is challenging but fascinating for both experimental biologists and computational biologists because it is essential for accurate genetic manipulation. Based on publicly available datasets, we developed a random forest based method to predict RNA editing sites. Using sophisticated feature selection methods, 18 optimal features were selected. Their prediction Information S1The complete dataset used in this study, consisting of the training and testing datasets. There were 127 positive and 127 negative samples in the training dataset and 533 positive and 90 negative samples in the testing dataset.(ZIP)Click here for additional data file.Information S2The performances of the different predictors constructed using different numbers of top features selected from the corresponding mRMR table during the IFS process.Sn, Sp, Acc, and MCC are given. MCC was used as the main evaluator. Upon selection of the first 18 features, MCC reached the maximum value of 0.742.(XLS)Click here for additional data file.


However, recent research has shown that the rate at which people visually search through a display is also affected by cognitive strategies. In this study, we investigated the role of choice in visual search, by asking whether giving people a choice alters both preference for a cognitively neutral task and search behavior. Two visual search conditions were examined: one in which participants were given a choice of visual search task (the choice condition), and one in which participants did not have a choice (the no-choice condition). The results showed that the participants in the choice condition rated the task as both more enjoyable and likeable than did the participants in the no-choice condition. However, despite their preferences, actual search performance was slower and less efficient in the choice condition than in the no-choice condition (Exp. 1). Experiment 2 showed that the difference in search performance between the choice and no-choice conditions disappeared when central executive processes became occupied with a task-switching task. These data concur with a  Search efficiency is determined by the RT × Set Size function, which measures how RTs increase with the number of items (set size), to give the Recent work, however, has suggested that cognitive strategies may also influence how people search a display. For example, Smilek, Enns, Eastwood, and Merikle  found thMuch research has shown that the role of choice has a strong influence on people’s preferences and motivation . In this study, we tested whether having a choice in itself is inherently valuable, by using a cognitively neutral task of little personal value to the participant. Here we gave participants a visual search task, asking them to search for an affectively neutral target (the letter T) among affectively neutral distractors (letter Ls). We used these stimuli to ensure that nothing was inherent in the task for people to either like or dislike . Two conditions were tested: a “choice” condition, in which we gave people a choice of which visual search task they could take part in , and a “no-choice” condition, in which people were assigned to a predetermined visual search task. Importantly, unbeknownst to participants in the choice condition, all three visual search tasks were exactly the same (search for a T among Ls). Following Experiment 1, participants were asked to rate how much they enjoyed the task, how much they liked the task, and how difficult they found the task overall.choice-neutral hypothesis. In contrast, giving people a choice could lead them to have increased motivation in the task and to adopt a more active role when searching. It is well-documented that people engage and persist more in a task if they have chosen it  took part in Experiment 1, and 20 participants took part in Experiment 2Displays were generated and responses recorded using Blitz 3-D programs. The distractors were L shapes presented randomly in one of four orientations  on a black background. In Experiment 1, the target was a T, rotated 90° or 270° with equal probabilities, and in Experiment 2, the target was a T, rotated 0° or 180° with equal probabilities. Each L contained a small offset (~0.5°) at the line junction to make search more difficult. All stimuli subtended 1.7° × 1.7°, at a distance of 57.4 cm. Each display had a set size of 4, 8, or 12 items, with individual stimuli positioned within a 6 × 6 invisible matrix. Each display was uniquely generated, and therefore displays were not repeated across the experiment. In Experiment 1, all of the stimuli were white. In Experiment 2, on half of the trials all stimuli were red, and on the other half all stimuli were green.Participants completed an experimental block of 90 trials (30 for each set size). On each trial, a fixation dot was presented for 500 ms, followed by the distractors and target letter. The stimuli were presented on the screen until participants made a response. If participants had not responded in 10,000 ms, the display timed out and the next trial was presented. Participants were asked to respond as quickly and as accurately as possible, and completed a short practice session before each block. Example displays are shown in Fig. not at all) to 7 (very much).In the choice conditions, participants were asked to choose which experimental block they would like to complete. Their choice options were that they could complete either “Condition A,” “Condition B,” or “Condition C.” No other information about the blocks was given to participants. Once participants had chosen, they were given the corresponding condition. However, unknown to the participants, all conditions were the same in every way but their name. In the no-choice conditions, participants were told they had been assigned to one of the conditions and were not given a choice. In Experiment 1, participants were asked to rate how much they enjoyed the task, how much they liked the task, and how difficult they found the task. Responses were collected using three Likert scale questions ranging from 1  = 7.2, p < .01, ηp2 = .068, in which RTs in the choice condition  were slower than those in the no-choice condition , and a main effect of set size, F = 449.2, p < .01, ηp2 = .821, in which RTs increased with set size. The Choice × Set Size interaction was also significant, F = 9.0, p < .01, ηp2 = .084. RTs increased more with set size, showing less efficient search, in the choice condition (80.7 ms/item) than in the no-choice condition (61.2 ms/item).In Experiment 1, RTs less than 200 ms and greater than 4,000 ms were removed as outliers. This led to the removal of 1.4 % and 1.7 % of the data from the choice and no-choice conditions, respectively. RTs less than 200 ms were also removed from Experiment 2 (0 % of the data); however, because the task was more difficult, no upper outlier boundary was applied.F = 25.7, p < .01, ηp2 = .588, and of set size, F = 112.4, p < .01, ηp2 = .862. RTs for target-present trials were faster than target-absent ones, and RTs increased with set size. However, we found no main effect of task switch (F < 1), nor a main effect of choice (F < 1). A significant Target Presence × Set Size interaction was apparent, F = 3.2, p = .05, ηp2 = .151, in which RTs increased more with set size for target-absent than for target-present trials, as well as a significant Task Switch × Set Size interaction, F = 3.8, p < .05, ηp2 = .174, in which RTs increased more with set size for no-task-switch than for task-switch trials. The four-way Target Presence × Task Switch × Set Size × Choice interaction was also significant, F = 3.2, p = .05, ηp2 = .150. None of the other interactions were significant. Error rates are shown in Table F = 8.6, p = .01, ηp2 = .309, in which more errors occurred on target-present trials (4.04 %) than on target-absent trials (4.02 %), and a main effect of set size, in which errors increased with set size, F = 4.0, p < .05, ηp2 = .181. However, none of the other main effects or interactions in either experiments were significant.However, this difference disappeared in Experiment 2, with the engagement of executive processes in the task-switching task. Here, a mixed-design 2 (choice vs. no choice) × 3  × 2 (task switch vs. no task switch) × 2 (present vs. absent) ANOVA on correct participant mean RTs was conducted, with the within-participants factors Set Size, Task Switch, and Target Presence, and the between-participants factor Choice. We observed main effects of target presence, U tests showed that participants in the choice condition rated the visual search task to be more enjoyable than did participants in the no-choice task . Furthermore, participants in the choice task showed a more positive rating when asked how much they liked the task, as compared with participants in the no-choice task . However, no differences emerged in ratings of how difficult participants found the task across the choice and no-choice groups .Figure Previous work has shown that people prefer options that give them a choice . Furthermore, response speed in the choice condition was impaired relative to the no-choice condition. These results cannot be explained by a speed–accuracy trade-off, since error rates were low and there were no differences in errors for choice across conditions. Instead, the data are consistent with the n as on trial n–1  in Experiment 2 were similar to those in Experiment 1, and therefore should have been affected by choice. However, crucially, in Experiment 2 participants were required to maintain the task instructions in working memory throughout the experiment . Therefore, because executive processes were being utilized on all trial types, they would not be available to affect search in the choice condition. The ideas of executive control and automatic mechanisms could be considered similar to people’s dual processing system, commonly known in the field of decision making as System 1 and System 2. System 1 refers to automatic procedures that are quick, effortless, and occur in parallel, whereas System 2 requires much more detailed, controlled, and complex cognitive resources (Kahneman, Previous research has shown that if participants adopt an active search strategy, they show less efficient search than do those who use a more passive strategy (Smilek et al., The present data show that although presenting people with a choice led to greater enjoyment, it did so at the price of impairing search performance. It is generally considered that having a choice is beneficial (Patall et al.,