Recent advances in high-throughput RNA sequencing (RNA-seq) have enabled tremendous leaps forward in our understanding of bacterial transcriptomes. However, computational methods for analysis of bacterial transcriptome data have not kept pace with the large and growing data sets generated by RNA-seq technology. Here, we present new algorithms, specific to bacterial gene structures and transcriptomes, for analysis of RNA-seq data. The algorithms are implemented in an open source software system called Rockhopper that supports various stages of bacterial RNA-seq data analysis, including aligning sequencing reads to a genome, constructing transcriptome maps, quantifying transcript abundance, testing for differential gene expression, determining operon structures and visualizing results. We demonstrate the performance of Rockhopper using 2.1 billion sequenced reads from 75 RNA-seq experiments conducted with Escherichia coli, Neisseria gonorrhoeae, Salmonella enterica, Streptococcus pyogenes and Xenorhabdus nematophila. We find that the transcriptome maps generated by our algorithms are highly accurate when compared with focused experimental data from E. coli and N. gonorrhoeae, and we validate our system’s ability to identify novel small RNAs, operons and transcription start sites. Our results suggest that Rockhopper can be used for efficient and accurate analysis of bacterial RNA-seq data, and that it can aid with elucidation of bacterial transcriptomes.

The Escherichia coli small RNA SgrS controls metabolic stress response that occurs upon accumulation of certain glycolytic intermediates. SgrS base pairs with and represses translation of ptsG and manXYZ mRNAs, which encode sugar transporters, and activates translation of yigL mRNA, encoding a sugar phosphatase. This study defines four new genes as direct targets of E. coli SgrS. These new targets, asd, adiY, folE and purR, encode transcription factors or enzymes of diverse metabolic pathways, including aspartate semialdehyde dehydrogenase, arginine decarboxylase gene activator, GTP cyclohydrolase I and a repressor of purine biosynthesis, respectively. SgrS represses translation of each of the four target mRNAs via distinct mechanisms. SgrS binding sites overlapping the Shine-Dalgarno sequences of adiY and folE mRNAs suggest that SgrS pairing with these targets directly occludes ribosome binding and prevents translation initiation. SgrS binding within the purR coding sequence recruits the RNA chaperone Hfq to directly repress purR translation. Two separate SgrS binding sites were found on asd mRNA, and both are required for full translational repression. Ectopic overexpression of asd, adiY and folE is specifically detrimental to cells experiencing glucose-phosphate stress, suggesting that SgrS-dependent repression of the metabolic functions encoded by these targets promotes recovery from glucose-phosphate stress.

Bacteria live in many dynamic environments with alternating cycles of feast or famine that have resulted in the evolution of mechanisms to quickly alter their metabolic capabilities. Such alterations often involve complex regulatory networks that modulate expression of genes involved in nutrient uptake and metabolism. A great number of protein regulators of metabolism have been characterized in depth. However, our ever-increasing understanding of the roles played by RNA regulators has revealed far greater complexity to regulation of metabolism in bacteria. Here, we review the mechanisms and functions of selected bacterial RNA regulators and discuss their importance in modulating nutrient uptake as well as primary and secondary metabolic pathways.

Bacteria adapt to ever-changing habitats through specific responses to internal and external stimuli that result in changes in gene regulation and metabolism. One internal metabolic cue affecting such changes in Escherichia coli and related enteric species is cytoplasmic accumulation of phosphorylated sugars such as glucose-6-phosphate or the non-metabolizable analog α-methylglucoside-6-phosphate. This “glucose-phosphate stress” triggers a dedicated stress response in γ-proteobacteria including several enteric pathogens. The major effector of this stress response is a small RNA (sRNA), SgrS. In E. coli and Salmonella, SgrS regulates numerous mRNA targets via base pairing interactions that result in alterations in mRNA translation and stability. Regulation of target mRNAs allows cells to reduce import of additional sugars and increase sugar efflux. SgrS is an unusual sRNA in that it also encodes a small protein, SgrT, which inhibits activity of the major glucose transporter. The two functions of SgrS, base pairing and production of SgrT, reduce accumulation of phosphorylated sugars and thereby relieve stress and promote growth. Examination of SgrS homologs in many enteric species suggests that SgrS has evolved to regulate distinct targets in different organisms. For example, in Salmonella, SgrS base pairs with sopD mRNA and represses production of the encoded effector protein, suggesting that SgrS may have a specific role in the pathogenesis of some γ-proteobacteria. In this review, we outline molecular mechanisms involved in SgrS regulation of its target mRNAs. We also discuss the response to glucose-phosphate stress in terms of its impact on metabolism, growth physiology, and pathogenesis.

Summary Small RNA (sRNA) regulators promote efficient responses to stress, but the mechanisms for prioritizing target mRNA regulation remain poorly understood. This study examines mechanisms underlying hierarchical regulation by the sRNA SgrS, found in enteric bacteria and produced under conditions of metabolic stress. SgrS posttranscriptionally coordinates a nine‐gene regulon to restore growth and homeostasis. An in vivo reporter system quantified SgrS‐dependent regulation of target genes and established that SgrS exhibits a clear target preference. Regulation of some targets is efficient even at low SgrS levels, whereas higher SgrS concentrations are required to regulate other targets. In vivo and in vitro analyses revealed that RNA structure and the number and position of base pairing sites relative to the start of translation impact the efficiency of regulation of SgrS targets. The RNA chaperone Hfq uses distinct modes of binding to different SgrS mRNA targets, which differentially influences positive and negative regulation. The RNA degradosome plays a larger role in regulation of some SgrS targets compared to others. Collectively, our results suggest that sRNA selection of target mRNAs and regulatory hierarchy are influenced by several molecular features and that the combination of these features precisely tunes the efficiency of regulation of multi‐target sRNA regulons.