Translational control and target recognition by Escherichia coli small RNAs in vivo

Small non-coding RNAs (sRNAs) are an emerging class of regulators of bacterial gene expression. Most of the regulatory Escherichia coli sRNAs known to date modulate translation of trans-encoded target mRNAs. We studied the specificity of sRNA target interactions using gene fusions to green fluorescent protein (GFP) as a novel reporter of translational control by bacterial sRNAs in vivo. Target sequences were selected from both monocistronic and polycistronic mRNAs. Upon expression of the cognate sRNA (DsrA, GcvB, MicA, MicC, MicF, RprA, RyhB, SgrS and Spot42), we observed highly specific translation repression/activation of target fusions under various growth conditions. Target regulation was also tested in mutants that lacked Hfq or RNase III, or which expressed a truncated RNase E (rne701). We found that translational regulation by these sRNAs was largely independent of full-length RNase E, e.g. despite the fact that ompA fusion mRNA decay could no longer be promoted by MicA. This is the first study in which multiple well-defined E.coli sRNA target pairs have been studied in a uniform manner in vivo. We expect our GFP fusion approach to be applicable to sRNA targets of other bacteria, and also demonstrate that Vibrio RyhB sRNA represses a Vibrio sodB fusion when co-expressed in E.coli.     

Multiple factors dictate target selection by Hfq-binding small RNAs

Hfq-binding small RNAs (sRNAs) in bacteria modulate the stability and translational efficiency of target mRNAs through limited base-pairing interactions. While these sRNAs are known to regulate numerous mRNAs as part of stress responses, what distinguishes targets and non-targets among the mRNAs predicted to base pair with Hfq-binding sRNAs is poorly understood. Using the Hfq-binding sRNA Spot 42 of Escherichia coli as a model, we found that predictions using only the three unstructured regions of Spot 42 substantially improved the identification of previously known and novel Spot 42 targets. Furthermore, increasing the extent of base-pairing in single or multiple base-pairing regions improved the strength of regulation, but only for the unstructured regions of Spot 42. We also found that non-targets predicted to base pair with Spot 42 lacked an Hfq-binding site, folded into a secondary structure that occluded the Spot 42 targeting site, or had overlapping Hfq-binding and targeting sites. By modifying these features, we could impart Spot 42 regulation on non-target mRNAs. Our results thus provide valuable insights into the requirements for target selection by sRNAs.     

Antagonistic functions between the RNA chaperone Hfq and an sRNA regulate sensitivity to the antibiotic colicin

The RNA chaperone Hfq is a key regulator of the function of small RNAs (sRNAs). Hfq has been shown to facilitate sRNAs binding to target mRNAs and to directly regulate translation through the action of sRNAs. Here, we present evidence that Hfq acts as the repressor of cirA mRNA translation in the absence of sRNA. Hfq binding to cirA prevents translation initiation, which correlates with cirA mRNA instability. In contrast, RyhB pairing to cirA mRNA promotes changes in RNA structure that displace Hfq, thereby allowing efficient translation as well as mRNA stabilization. Because CirA is a receptor for the antibiotic colicin Ia, in addition to acting as an Fur (Ferric Uptake Regulator)‐regulated siderophore transporter, translational activation of cirA mRNA by RyhB promotes colicin sensitivity under conditions of iron starvation. Altogether, these results indicate that Fur and RyhB modulate an unexpected feed‐forward loop mechanism related to iron physiology and colicin sensitivity.     

sTarPicker: a method for efficient prediction of bacterial sRNA targets based on a two-step model for hybridization

Background

Bacterial 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.

Results

Here, 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.

Conclusions

sTarPicker 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/.     

Major role for mRNA binding and restructuring in sRNA recruitment by Hfq

Bacterial small RNAs (sRNAs) modulate gene expression by base-pairing with target mRNAs. Many sRNAs require the Sm-like RNA binding protein Hfq as a cofactor. Well-characterized interactions between DsrA sRNA and the rpoS mRNA leader were used to understand how Hfq stimulates sRNA pairing with target mRNAs. DsrA annealing stimulates expression of rpoS by disrupting a secondary structure in the rpoS leader, which otherwise prevents translation. Both RNAs bind Hfq with similar affinity but interact with opposite faces of the Hfq hexamer. Using mutations that block interactions between two of the three components, we demonstrate that Hfq binding to a functionally critical (AAN)(4) motif in rpoS mRNA rescues DsrA binding to a hyperstable rpoS mutant. We also show that Hfq cannot stably bridge the RNAs. Persistent ternary complexes only form when the two RNAs are complementary. Thus, Hfq mainly acts by binding and restructuring the rpoS mRNA. However, Hfq binding to DsrA is needed for maximum annealing in vitro, indicating that transient interactions with both RNAs contribute to the regulatory mechanism.     

细菌非编码小RNA的筛选及鉴定方法

细菌非编码小RNA（smallnon．codingRNAs,sRNAs）是一类长度为50～500nt、不编码蛋白质的功能RNA,在应对胁迫、毒力产生和新陈代谢等生命过程中起重要的调控作用。其主要通过碱基配对与靶mRNA发生作用,导致mRNA翻译和稳定性改变,从而在转录后水平调节基因的表达,最终影响细菌各种生命活动。近年来,利用生物信息学和分子生物学技术,已在细菌中筛选并鉴定得到了几百个sRNA。该文对细菌sRNA的筛选和鉴定方法作一简要综述。     

革兰氏阳性菌非编码小RNA的研究进展

sRNA（非编码小RNA）通过碱基配对的方式与靶mRNA结合,抑制或激活转录过程、调节蛋白质的表达,以核酸的形式发挥其生物学功能。随着RNA深度测序（RNAseq）技术、生物信息学预测以及实验分析手段的日渐发展和完善,数以百计的sRNA被发现并得到验证。作为转录后调控因子,sRNA因在诸多生理过程中起到了关键的调节作用而得到了广泛的关注。以革兰氏阳性菌为切入点,总结了近年来sRNA的筛选、鉴定和功能研究等方面取得的进展,梳理分析了sRNA调控与毒力因子、群体感应、铁代谢和双组分系统等之间的内在联系,并展望了sRNA未来的研究方向。     

Small RNA interactome of pathogenic E. coli revealed through crosslinking of RNase E

RNA sequencing studies have identified hundreds of non‐coding RNAs in bacteria, including regulatory small RNA (sRNA). However, our understanding of sRNA function has lagged behind their identification due to a lack of tools for the high‐throughput analysis of RNA–RNA interactions in bacteria. Here we demonstrate that in vivo sRNA–mRNA duplexes can be recovered using UV‐crosslinking, ligation and sequencing of hybrids (CLASH). Many sRNAs recruit the endoribonuclease, RNase E, to facilitate processing of mRNAs. We were able to recover base‐paired sRNA–mRNA duplexes in association with RNase E, allowing proximity‐dependent ligation and sequencing of cognate sRNA–mRNA pairs as chimeric reads. We verified that this approach captures bona fide sRNA–mRNA interactions. Clustering analyses identified novel sRNA seed regions and sets of potentially co‐regulated target mRNAs. We identified multiple mRNA targets for the pathotype‐specific sRNA Esr41, which was shown to regulate colicin sensitivity and iron transport in E. coli. Numerous sRNA interactions were also identified with non‐coding RNAs, including sRNAs and tRNAs, demonstrating the high complexity of the sRNA interactome.     

Molecular call and response: the physiology of bacterial small RNAs

The vital role of bacterial small RNAs (sRNAs) in cellular regulation is now well-established. Although many diverse mechanisms by which sRNAs bring about changes in gene expression have been thoroughly described, comparatively less is known about their biological roles and effects on cell physiology. Nevertheless, for some sRNAs, insight has been gained into the intricate regulatory interplay that is required to sense external environmental and internal metabolic cues and turn them into physiological outcomes. Here, we review examples of regulation by selected sRNAs, emphasizing signals and regulators required for sRNA expression, sRNA regulatory targets, and the resulting consequences for the cell. We highlight sRNAs involved in regulation of the processes of iron homeostasis (RyhB, PrrF, and FsrA) and carbon metabolism (Spot 42, CyaR, and SgrS).     

Alternative Hfq‐sRNA interaction modes dictate alternative mRNA recognition

Many bacteria use small RNAs (sRNAs) and the RNA chaperone Hfq to regulate mRNA stability and translation. Hfq, a ring‐shaped homohexamer, has multiple faces that can bind both sRNAs and their mRNA targets. We find that Hfq has at least two distinct ways in which it interacts with sRNAs; these different binding properties have strong effects on the stability of the sRNA in vivo and the sequence requirements of regulated mRNAs. Class I sRNAs depend on proximal and rim Hfq sites for stability and turn over rapidly. Class II sRNAs are more stable and depend on the proximal and distal Hfq sites for stabilization. Using deletions and chimeras, we find that while Class I sRNAs regulate mRNA targets with previously defined ARN repeats, Class II sRNAs regulate mRNAs carrying UA‐rich rim‐binding sites. We discuss how these different binding modes may correlate with different roles in the cell, with Class I sRNAs acting as emergency responders and Class II sRNAs acting as silencers.