High-Throughput, Kingdom-Wide Prediction and Annotation of Bacterial Non-Coding RNAs

Jonathan Livny,Miron Livny,Matthew K Waldor,Hidayat Teonadi,Niyaz Ahmed

doi:10.1371/journal.pone.0003197

Abstract

BackgroundDiverse bacterial genomes encode numerous small non-coding RNAs (sRNAs) that regulate myriad biological processes. While bioinformatic algorithms have proven effective in identifying sRNA-encoding loci, the lack of tools and infrastructure with which to execute these computationally demanding algorithms has limited their utilization. Genome-wide predictions of sRNA-encoding genes have been conducted in less than 3% of all sequenced bacterial strains, leading to critical gaps in current annotations. The relative paucity of genome-wide sRNA prediction represents a critical gap in current annotations of bacterial genomes and has limited examination of larger issues in sRNA biology, such as sRNA evolution.Methodology/Principal FindingsWe have developed and deployed SIPHT, a high throughput computational tool that utilizes workflow management and distributed computing to effectively conduct kingdom-wide predictions and annotations of intergenic sRNA-encoding genes. Candidate sRNA-encoding loci are identified based on the presence of putative Rho-independent terminators downstream of conserved intergenic sequences, and each locus is annotated for several features, including conservation in other species, association with one of several transcription factor binding sites and homology to any of over 300 previously identified sRNAs and cis-regulatory RNA elements. Using SIPHT, we conducted searches for putative sRNA-encoding genes in all 932 bacterial replicons in the NCBI database. These searches yielded nearly 60% of previously confirmed sRNAs, hundreds of previously annotated cis-encoded regulatory RNA elements such as riboswitches, and over 45,000 novel candidate intergenic loci.Conclusions/SignificanceCandidate loci were identified across all branches of the bacterial evolutionary tree, suggesting a central and ubiquitous role for RNA-mediated regulation among bacterial species. Annotation of candidate loci by SIPHT provides clues into the potential biological function of thousands of previously confirmed and candidate regulatory RNAs and affords new insights into the evolution of bacterial riboregulation.

Highlights

SRNAs are small, noncoding bacterial RNAs that regulate gene expression, usually by interacting with specific mRNA targets to modulate message stability and/or accessibility to the translation machinery [1]. sRNAs have been shown to regulate a wide variety of cellular processes including secretion, quorum sensing, stress responses, and virulence [1,2]
Sequences and coordinates of physically tested sRNAs in V. cholerae, P. aeruginosa, B. subtilis, L. innocua, P. marinus, S. typhimurium and S. aureus were based on published results [7,13,14,15,16,17,18,19,20] and in S. meliloti on both published results [21] and unpublished results
Conserved intergenic sequences are identified by comparing the Intergenic regions (IGRs) of the replicon of interest (ROI) to all appropriate IGRs from other replicons using BLAST. sRNAPredictHT identifies candidate sRNA-encoding genes by searching IGRs for putative terminators located within or directly downstream of conserved sequences and annotates these candidates for the following features: the coordinate position and strand orientation of each predicted sRNA-encoding gene; the distances between the candidate gene and the genes flanking its IGR as well as the name and orientation of these genes; the BLAST E value and score of its associated conserved sequences; the number and name(s) of the replicons in which it is conserved; which program(s) predicted its associated terminator; and whether it corresponds to a previously annotated regulatory RNA

Summary

Introduction

SRNAs are small (typically 100–300 nucleotides in length), noncoding bacterial RNAs that regulate gene expression, usually by interacting with specific mRNA targets to modulate message stability and/or accessibility to the translation machinery [1]. sRNAs have been shown to regulate a wide variety of cellular processes including secretion, quorum sensing, stress responses, and virulence [1,2]. Genes encoding sRNAs are located far from genes encoding their mRNA targets and sRNA:mRNA hybridization occurs over relatively short regions of imperfect complimentarity Due to this limited sRNA:mRNA complimentarity, predicting the regulatory role of confirmed sRNAs through bioinformatic identification of their mRNA targets has proven very difficult. While the diversity of species known to encode sRNAs suggests that these riboregulators are common to most if not all branches of the bacterial evolutionary tree, genome-wide predictions of sRNAencoding genes have been conducted in only 19 of the over 550 sequenced bacterial strains [3]. Diverse bacterial genomes encode numerous small non-coding RNAs (sRNAs) that regulate myriad biological processes. Genomewide predictions of sRNA-encoding genes have been conducted in less than 3% of all sequenced bacterial strains, leading to critical gaps in current annotations. The relative paucity of genome-wide sRNA prediction represents a critical gap in current annotations of bacterial genomes and has limited examination of larger issues in sRNA biology, such as sRNA evolution

Methods

Results

Conclusion