sRNA Targets Research Articles

Identification and characterization of a new iron‐dependent sRNA in Klebsiella oxytoca (LB204)

Klebsiella sp. are responsible for many serious hospital‐acquired infections such as pneumonia, sepsis, urinary tract infections, and hemorrhagic colitis. Because iron is an essential nutrient required by virtually all organisms, the ability to acquire iron correlates with virulence and pathogenesis of bacterial infections. The discovery of the regulatory small RNA (sRNA) ryhB in Escherichia coli helped to identify similar iron‐regulated sRNA targets in other bacteria. We have identified a homologous gene, krrF, in Klebsiella oxytoca via in silico methods. We first confirmed the expression of this gene by applying non‐quantitative RT‐PCR. After proving krrF transcription, we demonstrated that its expression is dependent on extracellular iron levels by performing qRT‐PCR on RNA isolated from wild type K. oxytoca cultures grown in media with varying concentrations of iron. Results indicated that krrF transcription increased in cultures with lower concentrations of iron and in cultures to which an iron chelator was added, while cultures with the highest iron concentrations showed decreased krrF transcription. Moreover, we performed binding assays using purified K. oxytoca Ferric Uptake Regulator (Fur), which showed affinity of the Fur protein for the krrF gene promoter. In conclusion, we present krrF from K. oxytoca, a new sRNA likely regulated by the Fur protein in response to iron levels in the medium.

Central role for RNase YbeY in Hfq-dependent and Hfq-independent small-RNA regulation in bacteria

BackgroundConceptual parallels exist between bacterial and eukaryotic small-RNA (sRNA) pathways, yet relatively little is known about which protein may recognize and recruit bacterial sRNAs to interact with targets. In eukaryotes, Argonaute (AGO) proteins discharge such functions. The highly conserved bacterial YbeY RNase has structural similarities to the MID domain of AGOs. A limited study had indicated that in Sinorhizobium meliloti the YbeY ortholog regulates the accumulation of sRNAs as well as the target mRNAs, raising the possibility that YbeY may play a previously unrecognized role in bacterial sRNA regulation.ResultsWe have applied a multipronged approach of loss-of-function studies, genome-wide mRNA and sRNA expression profiling, pathway analysis, target prediction, literature mining and network analysis to unravel YbeY-dependent molecular responses of E. coli exposed to hydroxyurea (HU). Loss of ybeY function, which results in a marked resistance to HU, had global affects on sRNA-mediated gene expression. Of 54 detectable E. coli sRNAs in our microarray analysis, 30 sRNAs showed a differential expression upon HU stress, of which 28 sRNAs displayed a YbeY-dependent change in expression. These included 12 Hfq-dependent and 16 Hfq-independent sRNAs. We successfully identified at least 57 experimentally inferred sRNA-mRNA relationships. Further applying a ‘context likelihood of relatedness’ algorithm, we reverse engineered the YbeY-dependent Hfq-dependent sRNA-mRNA network as well as YbeY-dependent Hfq-independent sRNA-mRNA network.ConclusionYbeY extensively modulates Hfq-dependent and independent sRNA-mRNA interactions. YbeY-dependent sRNAs have central roles in modulating cellular response to HU stress.

Comparative genomics boosts target prediction for bacterial small RNAs

Small RNAs (sRNAs) constitute a large and heterogeneous class of bacterial gene expression regulators. Much like eukaryotic microRNAs, these sRNAs typically target multiple mRNAs through short seed pairing, thereby acting as global posttranscriptional regulators. In some bacteria, evidence for hundreds to possibly more than 1,000 different sRNAs has been obtained by transcriptome sequencing. However, the experimental identification of possible targets and, therefore, their confirmation as functional regulators of gene expression has remained laborious. Here, we present a strategy that integrates phylogenetic information to predict sRNA targets at the genomic scale and reconstructs regulatory networks upon functional enrichment and network analysis (CopraRNA, for Comparative Prediction Algorithm for sRNA Targets). Furthermore, CopraRNA precisely predicts the sRNA domains for target recognition and interaction. When applied to several model sRNAs, CopraRNA revealed additional targets and functions for the sRNAs CyaR, FnrS, RybB, RyhB, SgrS, and Spot42. Moreover, the mRNAs gdhA, lrp, marA, nagZ, ptsI, sdhA, and yobF-cspC were suggested as regulatory hubs targeted by up to seven different sRNAs. The verification of many previously undetected targets by CopraRNA, even for extensively investigated sRNAs, demonstrates its advantages and shows that CopraRNA-based analyses can compete with experimental target prediction approaches. A Web interface allows high-confidence target prediction and efficient classification of bacterial sRNAs.

Construction of small RNA-mediated gene regulatory networks in the roots of rice (Oryza sativa)

BackgroundThe root systems play essential roles for plants to anchorage to the soil, and to exploit the mineral and water resources. The molecular mechanisms underlying root development have been extensively studied to improve root system architecture, especially for the crops. Several microRNA (miRNA) families have been demonstrated to be involved in plant root development. However, whether the other small RNA (sRNA) species, which occupy a dominant portion of the plant endogenous sRNA population, possess potential roles in root development remains unclear.ResultsIn this study, by using sRNA high-throughput sequencing data, we made a comparison of the sRNA accumulation levels between the rice root tips and the whole roots. The sRNAs highly accumulated in the root tips and in the whole roots were extracted respectively. After Argonaute 1 (AGO1) enrichment analysis, the sRNAs with great potential of performing target cleavages were included for target prediction and degradome sequencing data-based validation. As a result, lists of the targets regulated by the AGO1-enriched sRNAs were obtained for both the root tips and the whole roots. Further evidences were identified from microarray data of the target genes to support some of the sRNA—target interactions. Specifically, the expression patterns of certain target genes in the root tips and the whole roots were contrary to those of the regulating sRNAs. Besides, several targets were indicated to play important roles in root development based on literature mining.ConclusionsTaken together, the regulatory networks mediated by the sRNAs highly accumulated in the root tips or in the whole roots could advance our current understanding of the sRNA-involved molecular mechanisms underlying rice root development. And, the sRNA—target lists could serve as the basis for further functional investigations.

Physiological Consequences of Multiple-Target Regulation by the Small RNA SgrS in Escherichia coli

Cells use complex mechanisms to regulate glucose transport and metabolism to achieve optimal energy and biomass production while avoiding accumulation of toxic metabolites. Glucose transport and glycolytic metabolism carry the risk of the buildup of phosphosugars, which can inhibit growth at high concentrations. Many enteric bacteria cope with phosphosugar accumulation and associated stress (i.e., sugar-phosphate stress) by producing a small RNA (sRNA) regulator, SgrS, which decreases phosphosugar accumulation in part by repressing translation of sugar transporter mRNAs (ptsG and manXYZ) and enhancing translation of a sugar phosphatase mRNA (yigL). Despite a molecular understanding of individual target regulation by SgrS, previously little was known about how coordinated regulation of these multiple targets contributes to the rescue of cell growth during sugar-phosphate stress. This study examines how SgrS regulation of different targets impacts growth under different nutritional conditions when sugar-phosphate stress is induced. The severity of stress-associated growth inhibition depended on nutrient availability. Stress in nutrient-rich media necessitated SgrS regulation of only sugar transporter mRNAs (ptsG or manXYZ). However, repression of transporter mRNAs was insufficient for growth rescue during stress in nutrient-poor media; here SgrS regulation of the phosphatase (yigL) and as-yet-undefined targets also contributed to growth rescue. The results of this study imply that regulation of only a subset of an sRNA's targets may be important in a given environment. Further, the results suggest that SgrS and perhaps other sRNAs are flexible regulators that modulate expression of multigene regulons to allow cells to adapt to an array of stress conditions.

Modification of the RpoS network with a synthetic small RNA

Translation of the sigma factor RpoS is activated by DsrA, RprA and ArcA, three small non-coding sRNAs (sRNA) that expose the ribosome-binding site (RBS) by opening up an inhibitory loop. In the RpoS network, no sRNAs have been found to pair with the RBS, a most common sRNA target site in bacteria. Here, we generate Ribo-0, an artificial sRNA, which represses rpoS translation by pairing with the RBS. Ribo-0 bypasses the RNA chaperon Hfq but requires the RBS to be loosely blocked. Ribo-0 interacts with DsrA and reshapes the RpoS network. Specifically, in the intact RpoS network, DsrA activates rpoS translation by freeing up the RBS. In the modified RpoS network where Ribo-0 is introduced, the DsrA-caused RBS exposure facilitates Ribo-0 binding, thereby strengthening Ribo-0 inhibition. In other words, Ribo-0 changes DsrA from an activator to an accomplice for repressing rpoS translation. This work presents an artificial mechanism of rpoS regulation, reveals mutual effects of native and synthetic players and demonstrates genetic context-dependency of their functions.

Conserved arginines on the rim of Hfq catalyze base pair formation and exchange

The Sm-like protein Hfq is required for gene regulation by small RNAs (sRNAs) in bacteria and facilitates base pairing between sRNAs and their mRNA targets. The proximal and distal faces of the Hfq hexamer specifically bind sRNA and mRNA targets, but they do not explain how Hfq accelerates the formation and exchange of RNA base pairs. Here, we show that conserved arginines on the outer rim of the hexamer that are known to interact with sRNA bodies are required for Hfq’s chaperone activity. Mutations in the arginine patch lower the ability of Hfq to act in sRNA regulation of rpoS translation and eliminate annealing of natural sRNAs or unstructured oligonucleotides, without preventing binding to either the proximal or distal face. Stopped-flow FRET and fluorescence anisotropy show that complementary RNAs transiently form a ternary complex with Hfq, but the RNAs are not released as a double helix in the absence of rim arginines. RNAs bound to either face of Hfq quench the fluorescence of a tryptophan adjacent to the arginine patch, demonstrating that the rim can simultaneously engage two RNA strands. We propose that the arginine patch overcomes entropic and electrostatic barriers to helix nucleation and constitutes the active site for Hfq’s chaperone function.

Modelling Translation Initiation under the Influence of sRNA

Bacterial small non-coding RNA (sRNA) plays an important role in post-transcriptional gene regulation. Although the number of annotated sRNA is steadily increasing, their functional characterization is still lagging behind. Various computational strategies for finding sRNA–mRNA interactions, and thus putative sRNA targets, were developed. Most of them suffer from a high false positive rate. Here, we present a qualitative model to simulate the effect of an sRNA on the translation initiation of a potential target. Information about the ribosome–mRNA interaction, sRNA–mRNA interaction and expression information from deep sequencing experiments is integrated to calculate the change in translation initiation complex formation, as a proxy for translational activity. This model can be used to post-evaluate predicted targets, hence condensing the list of potential targets. We show that our translation initiation model, under the influence of an sRNA, can successfully simulate thirteen out of fifteen tested sRNA–mRNA interactions in a qualitative manner. To show the gain in specificity, we applied our method to a target search for the Escherichia coli sRNA RyhB. Compared with simple target prediction without post-evaluation, we reduce the number of targets to less than one fourth potential targets, considerably reducing the burden of experimental validation.

Construction of gene regulatory networks mediated by vegetative and reproductive stage‐specific small RNAs in rice (Oryza sativa)

Although huge amounts of high-throughput sequencing (HTS) data are available, limited systematic analyses have been performed by integrating these valuable resources. Based on small RNA (sRNA), RNA and degradome HTS data, the sRNAs specifically expressed at vegetative and reproductive stages were identified separately in rice. Two distinct groups of sRNA HTS data, which were prepared during the vegetative and the reproductive stages, were utilized to extract stage-specific sRNAs. Degradome sequencing data were employed for sRNA target validation. RNA sequencing data were used to construct expression-based, sRNA-mediated networks. As a result, 26 microRNAs and 413 sRNAs were specifically expressed at the vegetative stage, and 79 microRNAs and 539 sRNAs were specifically expressed at the reproductive stage. In addition to the microRNAs, numerous stage-specific sRNAs enriched in ARGONAUTE1 showed great potential to perform cleavage-based repression on the targets. Several stage-specific sRNAs were indicated to result from the wobble effect of Dicer-like 1-mediated processing of microRNA precursors. The expression patterns of the sRNA targets, and the stage-specific cleavage signals strongly indicated the reliability of the constructed networks. A set of rice stage-specific sRNAs along with the regulatory cascades, which have great potential in regulating specific developmental stages, were provided for further investigation.

Accessibility and conservation: General features of bacterial small RNA–mRNA interactions?

Bacterial small RNAs (sRNAs) are a class of structural RNAs that often regulate mRNA targets via post-transcriptional base pair interactions. We determined features that discriminate functional from non-functional interactions and assessed the influence of these features on genome-wide target predictions. For this purpose, we compiled a set of 71 experimentally verified sRNA–target pairs from Escherichia coli and Salmonella enterica. Furthermore, we collected full-length 5′ untranslated regions by using genome-wide experimentally verified transcription start sites. Only interaction sites in sRNAs, but not in targets, show significant sequence conservation. In addition to this observation, we found that the base pairing between sRNAs and their targets is not conserved in general across more distantly related species. A closer inspection of RybB and RyhB sRNAs and their targets revealed that the base pairing complementarity is only conserved in a small subset of the targets. In contrast to conservation, accessibility of functional interaction sites is significantly higher in both sRNAs and targets in comparison to non-functional sites. Based on the above observations, we successfully used the following constraints to improve the specificity of genome-wide target predictions: the region of interaction initiation must be located in (1) highly accessible regions in both interaction partners or (2) unstructured conserved sRNA regions derived from reliability profiles of multiple sRNA alignments. Aligned sequences of homologous sRNAs, functional and non-functional targets, and a sup document with sup tables, figures and references are available at www.bioinf.uni-freiburg.de/Supplements/srna-interact-feat/.

PAREsnip: a tool for rapid genome-wide discovery of small RNA/target interactions evidenced through degradome sequencing

Small RNAs (sRNAs) are a class of short (20–25 nt) non-coding RNAs that play important regulatory roles in gene expression. An essential first step in understanding their function is to confidently identify sRNA targets. In plants, several classes of sRNAs such as microRNAs (miRNAs) and trans-acting small interfering RNAs have been shown to bind with near-perfect complementarity to their messenger RNA (mRNA) targets, generally leading to cleavage of the mRNA. Recently, a high-throughput technique known as Parallel Analysis of RNA Ends (PARE) has made it possible to sequence mRNA cleavage products on a large-scale. Computational methods now exist to use these data to find targets of conserved and newly identified miRNAs. Due to speed limitations such methods rely on the user knowing which sRNA sequences are likely to target a transcript. By limiting the search to a tiny subset of sRNAs it is likely that many other sRNA/mRNA interactions will be missed. Here, we describe a new software tool called PAREsnip that allows users to search for potential targets of all sRNAs obtained from high-throughput sequencing experiments. By searching for targets of a complete ‘sRNAome’ we can facilitate large-scale identification of sRNA targets, allowing us to discover regulatory interaction networks.

Esre: A novel essential non-coding RNA in Escherichia coli

YigP gene (GeneID: 948915) locates between ubiquinone biosynthetic genes ubiE and ubiB in Escherichia coli. GeneBank annotates yigP as a putative protein-coding gene. In this study, we found a new essential sRNA gene, esre, locates within the region of yigP. The E. coli strain with inactive esre must rely on a complementary plasmid to survive. Moreover, RACE experiments showed esre encodes an RNA molecule of 252 nt. Further experiments revealed esre gene is immune to frame shift mutations and the function of esre depends mostly on the RNA secondary structure, which are typical traits of sRNA. Since it is difficult to predict the target of an essential sRNA, more research is needed to reveal the function and mechanism of esre.

STarPicker: A Method for Efficient Prediction of Bacterial sRNA Targets Based on a Two-Step Model for Hybridization

BackgroundBacterial sRNAs are a class of small regulatory RNAs involved in regulation of expression of a variety of genes. Most sRNAs act in trans via base-pairing with target mRNAs, leading to repression or activation of translation or mRNA degradation. To date, more than 1,000 sRNAs have been identified. However, direct targets have been identified for only approximately 50 of these sRNAs. Computational predictions can provide candidates for target validation, thereby increasing the speed of sRNA target identification. Although several methods have been developed, target prediction for bacterial sRNAs remains challenging.ResultsHere, we propose a novel method for sRNA target prediction, termed sTarPicker, which was based on a two-step model for hybridization between an sRNA and an mRNA target. This method first selects stable duplexes after screening all possible duplexes between the sRNA and the potential mRNA target. Next, hybridization between the sRNA and the target is extended to span the entire binding site. Finally, quantitative predictions are produced with an ensemble classifier generated using machine-learning methods. In calculations to determine the hybridization energies of seed regions and binding regions, both thermodynamic stability and site accessibility of the sRNAs and targets were considered. Comparisons with the existing methods showed that sTarPicker performed best in both performance of target prediction and accuracy of the predicted binding sites.ConclusionssTarPicker can predict bacterial sRNA targets with higher efficiency and determine the exact locations of the interactions with a higher accuracy than competing programs. sTarPicker is available at http://ccb.bmi.ac.cn/starpicker/.

RNApredator: fast accessibility-based prediction of sRNA targets

Bacterial genomes encode a plethora of small RNAs (sRNAs), which are heterogeneous in size, structure and function. Most sRNAs act as post-transcriptional regulators by means of specific base pairing interactions with the 5′-untranslated region of mRNA transcripts, thereby modifying the stability of the target transcript and/or its ability to be translated. Here, we present RNApredator, a web server for the prediction of sRNA targets. The user can choose from a set of over 2155 genomes and plasmids from 1183 bacterial species. RNApredator then uses a dynamic programming approach, RNAplex, to compute putative targets. Compared to web servers with a similar task, RNApredator takes the accessibility of the target during the target search into account, improving the specificity of the predictions. Furthermore, enrichment in Gene Ontology terms, cellular pathways as well as changes in accessibilities along the target sequence can be done in fully automated post-processing steps. The predictive performance of the underlying dynamic programming approach RNAplex is similar to that of more complex methods, but needs at least three orders of magnitude less time to complete. RNApredator is available at http://rna.tbi.univie.ac.at/RNApredator.

StarBase: a database for exploring microRNA–mRNA interaction maps from Argonaute CLIP-Seq and Degradome-Seq data

MicroRNAs (miRNAs) represent an important class of small non-coding RNAs (sRNAs) that regulate gene expression by targeting messenger RNAs. However, assigning miRNAs to their regulatory target genes remains technically challenging. Recently, high-throughput CLIP-Seq and degradome sequencing (Degradome-Seq) methods have been applied to identify the sites of Argonaute interaction and miRNA cleavage sites, respectively. In this study, we introduce a novel database, starBase (sRNA target Base), which we have developed to facilitate the comprehensive exploration of miRNA–target interaction maps from CLIP-Seq and Degradome-Seq data. The current version includes high-throughput sequencing data generated from 21 CLIP-Seq and 10 Degradome-Seq experiments from six organisms. By analyzing millions of mapped CLIP-Seq and Degradome-Seq reads, we identified ∼1 million Ago-binding clusters and ∼2 million cleaved target clusters in animals and plants, respectively. Analyses of these clusters, and of target sites predicted by 6 miRNA target prediction programs, resulted in our identification of approximately 400 000 and approximately 66 000 miRNA-target regulatory relationships from CLIP-Seq and Degradome-Seq data, respectively. Furthermore, two web servers were provided to discover novel miRNA target sites from CLIP-Seq and Degradome-Seq data. Our web implementation supports diverse query types and exploration of common targets, gene ontologies and pathways. The starBase is available at http://starbase.sysu.edu.cn/.

Modularity of Escherichia coli sRNA regulation revealed by sRNA-target and protein network analysis

BackgroundsRNAs, which belong to the non-coding RNA family and range from approximately 50 to 400 nucleotides, serve various important gene regulatory roles. Most are believed to be trans-regulating and function by being complementary to their target mRNAs in order to inhibiting translation by ribosome occlusion. Despite this understanding of their functionality, the global properties associated with regulation by sRNAs are not yet understood. Here we use topological analysis of sRNA targets in terms of protein-protein interaction and transcription-regulatory networks in Escherichia coli to shed light on the global correlation between sRNA regulation and cellular control networks.ResultsThe analysis of sRNA targets in terms of their networks showed that some specific network properties could be identified. In protein-protein interaction network, sRNA targets tend to occupy more central positions (higher closeness centrality, p-val = 0.022) and more cliquish (larger clustering coefficient, p-val = 0.037). The targets of the same sRNA tend to form a network module (shorter characteristic path length, p-val = 0.015; larger density, p-val = 0.019; higher in-degree ratio, p-val = 0.009). Using the transcription-regulatory network, sRNA targets tend to be under multiple regulation (higher indegree, p-val = 0.013) and the targets usually are important to the transfer of regulatory signals (higher betweenness, p-val = 0.012). As was found for the protein-protein interaction network, the targets that are regulated by the same sRNA also tend to be closely knit within the transcription-regulatory network (larger density, p-val = 0.036), and inward interactions between them are greater than the outward interactions (higher in-degree ratio, p-val = 0.023). However, after incorporating information on predicted sRNAs and down-stream targets, the results are not as clear-cut, but the overall network modularity is still evident.ConclusionsOur results indicate that sRNA targeting tends to show a clustering pattern that is similar to the human microRNA regulation associated with protein-protein interaction network that was observed in a previous study. Namely, the sRNA targets show close interaction and forms a closely knit network module for both the protein-protein interaction and the transcription-regulatory networks. Thus, targets of the same sRNA work in a concerted way toward a specific goal. In addition, in the transcription-regulatory network, sRNA targets act as "multiplexor", accepting regulatory control from multiple sources and acting accordingly. Our results indicate that sRNA targeting shows different properties when compared to the proteins that form cellular networks.

SRNATarBase: A comprehensive database of bacterial sRNA targets verified by experiments

Bacterial sRNAs are an emerging class of small regulatory RNAs, 40-500 nt in length, which play a variety of important roles in many biological processes through binding to their mRNA or protein targets. A comprehensive database of experimentally confirmed sRNA targets would be helpful in understanding sRNA functions systematically and provide support for developing prediction models. Here we report on such a database--sRNATarBase. The database holds 138 sRNA-target interactions and 252 noninteraction entries, which were manually collected from peer-reviewed papers. The detailed information for each entry, such as supporting experimental protocols, BLAST-based phylogenetic analysis of sRNA-mRNA target interaction in closely related bacteria, predicted secondary structures for both sRNAs and their targets, and available binding regions, is provided as accurately as possible. This database also provides hyperlinks to other databases including GenBank, SWISS-PROT, and MPIDB. The database is available from the web page http://ccb.bmi.ac.cn/srnatarbase/.

Comparative genome‐wide analysis of small RNAs of major Gram‐positive pathogens: from identification to application

SummaryIn the recent years, the number of drug‐ and multi‐drug‐resistant microbial strains has increased rapidly. Therefore, the need to identify innovative approaches for development of novel anti‐infectives and new therapeutic targets is of high priority in global health care. The detection of small RNAs (sRNAs) in bacteria has attracted considerable attention as an emerging class of new gene expression regulators. Several experimental technologies to predict sRNA have been established for the Gram‐negative model organism Escherichia coli. In many respects, sRNA screens in this model system have set a blueprint for the global and functional identification of sRNAs for Gram‐positive microbes, but the functional role of sRNAs in colonization and pathogenicity for Listeria monocytogenes, Staphylococcus aureus, Streptococcus pyogenes, Enterococcus faecalis and Clostridium difficile is almost completely unknown. Here, we report the current knowledge about the sRNAs of these socioeconomically relevant Gram‐positive pathogens, overview the state‐of‐the‐art high‐throughput sRNA screening methods and summarize bioinformatics approaches for genome‐wide sRNA identification and target prediction. Finally, we discuss the use of modified peptide nucleic acids (PNAs) as a novel tool to inactivate potential sRNA and their applications in rapid and specific detection of pathogenic bacteria.

Degradome sequencing reveals endogenous small RNA targets in rice (Oryza sativa L. ssp. indica)

MicroRNAs (miRNAs) and small interfering RNAs (siRNAs) regulate gene expression in eukaryotes. Plant miRNAs modulate their targets mainly via messenger RNA (mRNA) cleavage. Small RNA (sRNA) targets have been extensively investigated in Arabidopsis using computational prediction, experimental validation, and degradome sequencing. However, small RNA targets are largely unknown in rice (Oryza sativa). Here, we report global identification of small RNA targets using high throughput degradome sequencing in the rice indica cultivar 93–11 (Oryza sativa L. ssp. indica). One hundred and seventy-seven transcripts targeted by a total of 87 unique miRNAs were identified. Of targets for the conserved miRNAs between Arabidopsis and rice, transcription factors comprise around 70% (58 in 82), indicating that these miRNAs act as masters of gene regulatory nodes in rice. In contrast, non-conserved miRNAs targeted diverse genes which provide more complex regulatory networks. In addition, 5 AUXIN RESPONSE FACTORs (ARFs) cleaved by the TAS3 derived ta-siRNAs were also detected. A total of 40 sRNA targets were further validated via RNA ligasemediated 5’ rapid amplification of cDNA ends (RLM 5’-RACE). Our degradome results present a detailed sRNA-target interaction atlas, which provides a guide for the study of the roles of sRNAs and their targets in rice.

Coding sequence targeting by MicC RNA reveals bacterial mRNA silencing downstream of translational initiation

Bacterial small noncoding RNAs (sRNAs) generally recognize target mRNAs in the 5' region to prevent 30S ribosomes from initiating translation. It was thought that the mRNA coding sequence (CDS) was refractory to sRNA-mediated repression, because elongating 70S ribosomes have an efficient RNA helicase activity that prevents stable target pairing. We report that the Hfq-associated MicC sRNA silences Salmonella typhimurium ompD mRNA via a <or=12-bp RNA duplex within the CDS (codons 23-26) that is essential and sufficient for repression. MicC does not inhibit translational initiation at this downstream position but instead acts by accelerating RNase E-dependent ompD mRNA decay. We propose an alternative gene-silencing pathway within bacterial CDS wherein sRNAs repress targets by endonucleolytic mRNA destabilization rather than by the prototypical inhibition of translational initiation. The discovery of CDS targeting markedly expands the sequence space for sRNA target predictions in bacteria.