Assessment of Bona Fide sRNAs in Staphylococcus aureus

• Published in: Frontiers in microbiology Vol. 9; p. 228
• Main Authors: Liu, Wenfeng, Rochat, Tatiana, Toffano-Nioche, Claire, Le Lam, Thao Nguyen, Bouloc, Philippe, Morvan, Claire
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media 20-02-2018; Frontiers Media S.A
• Subjects: bona fide sRNA; gene regulation; Life Sciences; Microbiology; RNA-seq; Staphylococcus aureus HG003; transcription factors; RNA-seq; Staphylococcus aureus HG003; bona fide sRNA; gene regulation; transcription factors
• ISSN: 1664-302X; 1664-302X
• DOI: 10.3389/fmicb.2018.00228

Bacterial regulatory RNAs have been extensively studied for over a decade, and are progressively being integrated into the complex genetic regulatory network. Transcriptomic arrays, recent deep-sequencing data and bioinformatics suggest that bacterial genomes produce hundreds of regulatory RNAs. However, while some have been authenticated, the existence of the others varies according to strains and growth conditions, and their detection fluctuates with the methodologies used for data acquisition and interpretation. For example, several small RNA (sRNA) candidates are now known to be parts of UTR transcripts. Accurate annotation of regulatory RNAs is a complex task essential for molecular and functional studies. We defined
sRNAs as those that (i) likely act in
and (ii) are not expressed from the opposite strand of a coding gene. Using published data and our own RNA-seq data, we reviewed hundreds of
putative regulatory RNAs using the DETR'PROK computational pipeline and visual inspection of expression data, addressing the question of which transcriptional signals correspond to sRNAs. We conclude that the model strain HG003, a NCTC8325 derivative commonly used for
genetic regulation studies, has only about 50
sRNAs, indicating that these RNAs are less numerous than commonly stated. Among them, about half are associated to the
sp. core genome and a quarter are possibly expressed in other
. We hypothesize on their features and regulation using bioinformatic approaches.