sRNA-target Prediction Organizing Tool (SPOT) integrates computational and experimental data to facilitate functional characterization of bacterial small RNAs

Abstract

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.

Bacterial cyclopropane fatty acid synthase mRNA is targeted by activating and repressing small RNAs

“…An experimental study based on the ligation of Hfq-associated RNAs (Melamed et al, 2016) confirmed the previously observed interaction between cfa mRNA and RydC sRNA, and in addition revealed the potential interactions of the cfa transcript to ArrS, CpxQ, and GadF sRNAs. To verify that ArrS, CpxQ, and GadF sRNAs alter cfa expression post-transcriptionally in vivo, we constructed two translational cfa’-’lacZ fusions under the control of the arabinose-inducible P BAD promoter (King et al, 2019). One fusion contains the 212-nt 5’ UTR and the first 36 codons of cfa (cfa’-’lacZ-Long, Fig.…”

“…cfa’-’lacZ -Long, cfa’-’lacZ -Short, cfa’-’lacZ -ΔsRNABS, and cfa’-’lacZ- ΔHfqBS were constructed by PCR amplifying the cfa 5’ UTR fragment of interest from DJ624 using a fusion-specific forward primer containing flanking homology to P BAD and the reverse primer O-AK24R, which contains flanking homology to lacZ and anneals in the cfa coding region (Table EV3). cfa’-’lacZ -Long and cfa’-’lacZ -Short were constructed by A.M. King (King et al, 2019). cfa’-’lacZ -Long H1 and H4 were constructed by using a forward primer containing the desired point mutations and flanking homology to P BAD and reverse primer O-AK24R to PCR amplify the cfa 5’ UTR fragment of interest from DJ624 (Table EV3).…”

“…Binding of RydC within the long cfa mRNA masks a recognition site for the major endonuclease RNase E and inhibits transcript degradation. In addition, RydC has been reported to regulate yejABEF (Antal, Bordeau et al, 2005), csgD (Bordeau & Felden, 2014), pheA and trpE (King, Vanderpool et al, 2019) mRNAs, though the conditions and signals stimulating RydC production and the physiological role of RydC in bacterial physiology remain unknown. In contrast, ArrS, CpxQ and GadF sRNAs were previously linked to cell envelope or acid stress responses.…”