sRNA target prediction organizing tool (SPOT) integrates computational and experimental data to facilitate functional characterization of bacterial small RNAs

Alisa M. King, Carin K. Vanderpool, Patrick H. Degnan

Research output: Contribution to journalArticlepeer-review

Abstract

Small RNAs (sRNAs) posttranscriptionally 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 target prediction organizing tool. 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 (number of correctly predicted targets/number of 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 regulate cfa mRNA, encoding cyclopropane fatty acid synthase. SPOT identified cfa along with additional putative mRNA targets, which we then tested experimentally. Our results demonstrated that in addition to cfa mRNA, RydC also regulates trpE and pheA mRNAs, 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.

Original languageEnglish (US)
Article numbere00561-18
JournalmSphere
Volume4
Issue number1
DOIs
StatePublished - Jan 1 2019

Keywords

  • Hfq
  • IntaRNA
  • RNA
  • RNase E
  • Starpicker
  • TargetRNA2

ASJC Scopus subject areas

  • Microbiology
  • Molecular Biology

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