Comparative genomics boosts target prediction for bacterial small RNAs

• Published in: Proceedings of the National Academy of Sciences - PNAS 2013-09, Vol.110 (37), p.14827-14827
• Main Authors: Wright, Patrick R., Richter, Andreas S., Papenfort, Kai, Mann, Martin, Vogel, Jörg, Hess, Wolfgang R., Backofen, Rolf, Georg, Jens
• Format: Article
• Language: English
• Subjects: Algorithms; Bacteria; Base Sequence; Biological Sciences; Computational Biology; Enterobacteriaceae - classification; Enterobacteriaceae - genetics; Escherichia coli - classification; Escherichia coli - genetics; Eukaryotes; Evolution, Molecular; Gene expression; Gene Expression Regulation, Bacterial; Gene Regulatory Networks; genomics; Genomics - statistics & numerical data; Internet; messenger RNA; microRNA; Phylogenetics; Phylogeny; PNAS Plus; PNAS PLUS: SIGNIFICANCE STATEMENTS; prediction; regulator genes; Ribonucleic acid; RNA; RNA, Bacterial - chemistry; RNA, Bacterial - classification; RNA, Bacterial - genetics; Salmonella enterica - classification; Salmonella enterica - genetics; transcriptomics
• ISSN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.1303248110

Small RNAs (sRNAs) constitute a large and heterogeneous class of bacterial gene expression regulators. Much like eukaryotic microRNAs, these sRNAs typically target multiple mRNAs through short seed pairing, thereby acting as global posttranscriptional regulators. In some bacteria, evidence for hundreds to possibly more than 1,000 different sRNAs has been obtained by transcriptome sequencing. However, the experimental identification of possible targets and, therefore, their confirmation as functional regulators of gene expression has remained laborious. Here, we present a strategy that integrates phylogenetic information to predict sRNA targets at the genomic scale and reconstructs regulatory networks upon functional enrichment and network analysis (CopraRNA, for Comparative Prediction Algorithm for sRNA Targets). Furthermore, CopraRNA precisely predicts the sRNA domains for target recognition and interaction. When applied to several model sRNAs, CopraRNA revealed additional targets and functions for the sRNAs CyaR, FnrS, RybB, RyhB, SgrS, and Spot42. Moreover, the mRNAs gdhA, lrp, marA, nagZ, ptsI, sdhA, and yobF-cspC were suggested as regulatory hubs targeted by up to seven different sRNAs. The verification of many previously undetected targets by CopraRNA, even for extensively investigated sRNAs, demonstrates its advantages and shows that CopraRNA-based analyses can compete with experimental target prediction approaches. A Web interface allows high-confidence target prediction and efficient classification of bacterial sRNAs.