Small RNAs (sRNAs) regulate gene expression in diverse bacteria by interacting with mRNAs to change their structure, stability, or translation. Hundreds of sRNAs have been identified in bacteria, but characterization of their regulatory functions is limited by difficulty with sensitive and accurate identification of mRNA targets. Thus, new robust methods of bacterial sRNA target identification are in demand. Here, we describe our small RNA target prediction organizing tool (SPOT), which streamlines the process of sRNA target prediction by providing a single pipeline that combines available computational prediction tools with customizable results filtering based on experimental data. SPOT allows the user to rapidly produce a prioritized list of predicted sRNA-target mRNA interactions that serves as a basis for further experimental characterization. This tool will facilitate elucidation of sRNA regulons in bacteria, allowing new discoveries regarding the roles of sRNAs in bacterial stress responses and metabolic regulation.

Bacteria are known to use RNA, either as mRNAs encoding proteins or as non-coding small RNAs (sRNAs), to regulate numerous biological processes. However, a few sRNAs have two functions: they act as base pairing RNAs and encode a small protein with additional regulatory functions. Thus, these so called “dual-function” sRNAs can serve as both a riboregulator and an mRNA. In some cases, these two functions can act independently within the same pathway while in other cases, the base-pairing function and protein function act in different pathways. Here, we discuss the five known dual-function sRNAs: SgrS from enteric species, RNAIII and Psm-mec from Staphylococcus aureus, Pel RNA from Streptococcus pyogenes, and SR1 from Bacillus subtilis, and review their mechanisms of action and roles in regulating diverse biological processes. We also discuss the prospect of finding additional dual-function sRNAs and future challenges in studying the overlap and competition between the functions.

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Small RNAs (sRNAs) post-transcriptionally regulate mRNA targets, typically under conditions of environmental stress. Although hundreds of sRNAs have been discovered in diverse bacterial genomes, most sRNAs remain uncharacterized, even in model organisms. Identification of mRNA targets directly regulated by sRNAs is rate-limiting for sRNA functional characterization. To address this, we developed a computational pipeline that we named SPOT for sRNA-targetPredictionOrganizingTool. SPOT incorporates existing computational tools to search for sRNA binding sites, allows filtering based on experimental data, and organizes the results into a standardized report. SPOT sensitivity (Correctly Predicted Targets/Total Known Targets) was equal to or exceeded any individual method when used on 12 characterized sRNAs. Using SPOT, we generated a set of target predictions for the sRNA RydC, which was previously shown to positively regulatecfamRNA, encoding cyclopropane fatty acid synthase. SPOT identifiedcfaalong with additional putative mRNA targets, which we then tested experimentally. Our results demonstrated that in addition tocfamRNA, RydC also regulatestrpEandpheAmRNAs, which encode aromatic amino acid biosynthesis enzymes. Our results suggest that SPOT can facilitate elucidation of sRNA target regulons to expand our understanding of the many regulatory roles played by bacterial sRNAs.IMPORTANCESmall RNAs (sRNAs) regulate gene expression in diverse bacteria by interacting with mRNAs to change their structure, stability or translation. Hundreds of sRNAs have been identified in bacteria, but characterization of their regulatory functions is limited by difficulty with sensitive and accurate identification of mRNA targets. Thus, new robust methods of bacterial sRNA target identification are in demand. Here, we describe ourSmall RNA-targetPredictionOrganizingTool, which streamlines the process of sRNA target prediction by providing a single pipeline that combines available computational prediction tools with customizable results filtering based on experimental data. SPOT allows the user to rapidly produce a prioritized list of predicted sRNA-target mRNA interactions that serves as a basis for further experimental characterization. This tool will facilitate elucidation of sRNA regulons in bacteria, allowing new discoveries regarding the roles of sRNAs in bacterial stress responses and metabolic regulation.