Identification of 17 Pseudomonas aeruginosa sRNAs and prediction of sRNA-encoding genes in 10 diverse pathogens using the bioinformatic tool sRNAPredict2

Abstract

sRNAs are small, non-coding RNA species that control numerous cellular processes. Although it iswidely accepted that sRNAs are encoded by most if not all bacteria, genome-wide annotations for sRNA-encoding genes have been conducted in only a few of the nearly 300 bacterial species sequenced to date. To facilitate the efficient annotation of bacterial genomes for sRNA-encoding genes, we developed a program, sRNAPredict2, that identifies putative sRNAs by searching for co-localization of genetic features commonly associated with sRNA-encoding genes. Using sRNAPredict2, we conducted genome-wide annotations for putative sRNA-encoding genes in the intergenic regions of 11 diverse pathogens. In total, 2759 previously unannotated candidate sRNA loci were predicted. There was considerable range in the number of sRNAs predicted in the different pathogens analyzed, raising the possibility that there are species-specific differences in the reliance on sRNA-mediated regulation. Of 34 previously unannotated sRNAs predicted in the opportunistic pathogen Pseudomonas aeruginosa, 31 were experimentally tested and 17 were found to encode sRNA transcripts. Our findings suggest that numerous genes have been missed in the current annotations of bacterial genomes and that, by using improved bioinformatic approaches and tools, much remains to be discovered in ‘intergenic’ sequences.

Global discovery of small RNAs in Yersinia pseudotuberculosis identifies Yersinia -specific small, noncoding RNAs required for virulence

“…The ability to identify all the "miscellaneous" RNAs in an impartial manner validated the deep sequencing approach for discovery of previously unidentified sRNAs. The number of putative sRNAs is much smaller than the 1,478 sRNAs predicted for the closely related Y. pestis by Livny et al (31) who used a computer algorithm that relies on conservation of intergenic sequences and ρ-independent terminators, but is comparable to the number of sRNAs predicted and verified in other enteric bacteria (3). It is possible, however, that additional sRNAs may be expressed under conditions not examined here, such as upon host-cell contact or during animal infection.…”

Legionella pneumophila 6S RNA optimizes intracellular multiplication

“…The predictive algorithm first identified the intergenic regions in the genome of the L. pneumophila Philadelphia-1 genome, and then searched them for Rho-independent terminators. To this end, we used the search module for the Rho-independent terminator developed by us previously based on their sequence and structure properties (18), as well as two other search strategies, RNAMotif (29) and TransTerm (30), implemented in the sRNAPredict2 algorithm (17). The sequence upstream of the terminator was then checked for conservation against the whole-genome sequences of 590 bacterial strains available at the time of the analysis, including the other three sequenced strains of L. pneumophila: Paris, Lens, and Corby.…”

Small RNAs in streptococci

“…A large number of sRNAs were predicted bioinformatically using the softwares sRNApredict2, 33,34 sRNA identification protocol using high-throughput technologies (SIHPT), 35 molecular sequence suite (moses), 36 and a more recently described in silico model to predict sRNAs and anti-sense sRNAs 21 ( Table 1). The programs available for identifying sRNAs and their target mRNAs were developed mainly on the basis of sRNA features from Gram-negative genomes.…”

“…The expression of some of them was validated by RT-PCR and northern blot analysis 21,[33][34][35]38,39 (Table 1). These sRNAs include trans.…”