Small RNAs encoded within genetic islands of Salmonella typhimurium show host-induced expression and role in virulence

The emergence of pathogenic strains of enteric bacteria and their adaptation to unique niches are associated with the acquisition of foreign DNA segments termed ‘genetic islands’. We explored these islands for the occurrence of small RNA (sRNA) encoding genes. Previous systematic screens for enteric bacteria sRNAs were mainly carried out using the laboratory strain Escherichia coli K12, leading to the discovery of ~80 new sRNA genes. These searches were based on conservation within closely related members of enteric bacteria and thus, sRNAs, unique to pathogenic strains were excluded. Here we describe the identification and characterization of 19 novel unique sRNA genes encoded within the ‘genetic islands’ of the virulent strain Salmonella typhimurium. We show that the expression of many of the island-encoded genes is associated with stress conditions and stationary phase. Several of these sRNA genes are induced when Salmonella resides within macrophages. One sRNA, IsrJ, was further examined and found to affect the translocation efficiency of virulence-associated effector proteins into nonphagocytic cells. In addition, we report that unlike the majority of the E. coli sRNAs that are trans regulators, many of the island-encoded sRNAs affect the expression of cis-encoded genes. Our study suggests that the island encoded sRNA genes play an important role within the network that regulates bacterial adaptation to environmental changes and stress conditions and thus controls virulence.     

Identification of Novel Regulatory Small RNAs in Acinetobacter baumannii

Small RNA (sRNA) molecules are non-coding RNAs that have been implicated in regulation of various cellular processes in living systems, allowing them to adapt to changing environmental conditions. Till date, sRNAs have not been reported in Acinetobacter baumannii (A. baumannii), which has emerged as a significant multiple drug resistant nosocomial pathogen. In the present study, a combination of bioinformatic and experimental approach was used for identification of novel sRNAs. A total of 31 putative sRNAs were predicted by a combination of two algorithms, sRNAPredict and QRNA. Initially 10 sRNAs were chosen on the basis of lower E- value and three sRNAs (designated as AbsR11, 25 and 28) showed positive signal on Northern blot. These sRNAs are novel in nature as they do not have homologous sequences in other bacterial species. Expression of the three sRNAs was examined in various phases of bacterial growth. Further, the effect of various stress conditions on sRNA gene expression was determined. A detailed investigation revealed differential expression profile of AbsR25 in presence of varying amounts of ethidium bromide (EtBr), suggesting that its expression is influenced by environmental or internal signals such as stress response. A decrease in expression of AbsR25 and concomitant increase in the expression of bioinformatically predicted targets in presence of high EtBr was reverberated by the decrease in target gene expression when AbsR25 was overexpressed. This hints at the negative regulation of target genes by AbsR25. Interestingly, the putative targets include transporter genes and the degree of variation in expression of one of them (A1S_1331) suggests that AbsR25 is involved in regulation of a transporter. This study provides a perspective for future studies of sRNAs and their possible involvement in regulation of antibiotic resistance in bacteria specifically in cryptic A. baumannii.     

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/.     

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).     

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).