Hfq assists small RNAs in binding to the coding sequence of ompD mRNA and in rearranging its structure

The bacterial protein Hfq participates in the regulation of translation by small noncoding RNAs (sRNAs). Several mechanisms have been proposed to explain the role of Hfq in the regulation by sRNAs binding to the 5′-untranslated mRNA regions. However, it remains unknown how Hfq affects those sRNAs that target the coding sequence. Here, the contribution of Hfq to the annealing of three sRNAs, RybB, SdsR, and MicC, to the coding sequence of Salmonella ompD mRNA was investigated. Hfq bound to ompD mRNA with tight, subnanomolar affinity. Moreover, Hfq strongly accelerated the rates of annealing of RybB and MicC sRNAs to this mRNA, and it also had a small effect on the annealing of SdsR. The experiments using truncated RNAs revealed that the contributions of Hfq to the annealing of each sRNA were individually adjusted depending on the structures of interacting RNAs. In agreement with that, the mRNA structure probing revealed different structural contexts of each sRNA binding site. Additionally, the annealing of RybB and MicC sRNAs induced specific conformational changes in ompD mRNA consistent with local unfolding of mRNA secondary structure. Finally, the mutation analysis showed that the long AU-rich sequence in the 5′-untranslated mRNA region served as an Hfq binding site essential for the annealing of sRNAs to the coding sequence. Overall, the data showed that the functional specificity of Hfq in the annealing of each sRNA to the ompD mRNA coding sequence was determined by the sequence and structure of the interacting RNAs.     

Identification of bacterial sRNA regulatory targets using ribosome profiling

Bacteria express large numbers of non-coding, regulatory RNAs known as ‘small RNAs’ (sRNAs). sRNAs typically regulate expression of multiple target messenger RNAs (mRNAs) through base-pairing interactions. sRNA:mRNA base-pairing often results in altered mRNA stability and/or altered translation initiation. Computational identification of sRNA targets is challenging due to the requirement for only short regions of base-pairing that can accommodate mismatches. Experimental approaches have been applied to identify sRNA targets on a genomic scale, but these focus only on those targets regulated at the level of mRNA stability. Here, we utilize ribosome profiling (Ribo-seq) to experimentally identify regulatory targets of the Escherichia coli sRNA RyhB. We not only validate a majority of known RyhB targets using the Ribo-seq approach, but also discover many novel ones. We further confirm regulation of a selection of known and novel targets using targeted reporter assays. By mutating nucleotides in the mRNA of a newly discovered target, we demonstrate direct regulation of this target by RyhB. Moreover, we show that Ribo-seq distinguishes between mRNAs regulated at the level of RNA stability and those regulated at the level of translation. Thus, Ribo-seq represents a powerful approach for genome-scale identification of sRNA targets.     

Plant Mobile Small RNAs

In plants, RNA silencing is a fundamental regulator of gene expression, heterochromatin formation, suppression of transposable elements, and defense against viruses. The sequence specificity of these processes relies on small noncoding RNA (sRNA) molecules. Although the spreading of RNA silencing across the plant has been recognized for nearly two decades, only recently have sRNAs been formally demonstrated as the mobile silencing signals. Here, we discuss the various types of mobile sRNA molecules, their short- and long-range movement, and their function in recipient cells.RNA silencing is a regulatory mechanism that controls the expression of endogenous genes and exogenous molecular parasites such as viruses, transgenes, and transposable elements. One of the most fascinating aspects of RNA silencing found in plants and invertebrates is its mobile nature—in other words, its ability to spread from the cell where it has been initiated to neighboring cells. This phenomenon relies on the movement of small noncoding RNA molecules (sRNA, 21–24 nucleotides [nt] in length) that provide the sequence specificity of the silencing effects. In plants, there are two major classes of sRNAs: short interfering RNAs (siRNAs) and micro RNAs (miRNAs). These sRNAs are generated by diverse and sometimes interacting biochemical pathways, which may influence their mobility. Movement of plant sRNAs falls into two main categories: cell-to-cell (short-range) and systemic (long-range) movement (Melnyk et al. 2011).     

Non-coding RNAs in marine Synechococcus and their regulation under environmentally relevant stress conditions

Regulatory small RNAs (sRNAs) have crucial roles in the adaptive responses of bacteria to changes in the environment. Thus far, potential regulatory RNAs have been studied mainly in marine picocyanobacteria in genetically intractable Prochlorococcus, rendering their molecular analysis difficult. Synechococcus sp. WH7803 is a model cyanobacterium, representative of the picocyanobacteria from the mesotrophic areas of the ocean. Similar to the closely related Prochlorococcus it possesses a relatively streamlined genome and a small number of genes, but is genetically tractable. Here, a comparative genome analysis was performed for this and four additional marine Synechococcus to identify the suite of possible sRNAs and other RNA elements. Based on the prediction and on complementary microarray profiling, we have identified several known as well as 32 novel sRNAs. Some sRNAs overlap adjacent coding regions, for instance for the central photosynthetic gene psbA. Several of these novel sRNAs responded specifically to environmentally relevant stress conditions. Among them are six sRNAs changing their accumulation level under cold stress, six responding to high light and two to iron limitation. Target predictions suggested genes encoding components of the light-harvesting apparatus as targets of sRNAs originating from genomic islands and that one of the iron-regulated sRNAs might be a functional homolog of RyhB. These data suggest that marine Synechococcus mount adaptive responses to these different stresses involving regulatory sRNAs.     

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.     

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/.