Title |
Assessment of Bona Fide sRNAs in Staphylococcus aureus
|
---|---|
Published in |
Frontiers in Microbiology, February 2018
|
DOI | 10.3389/fmicb.2018.00228 |
Pubmed ID | |
Authors |
Wenfeng Liu, Tatiana Rochat, Claire Toffano-Nioche, Thao Nguyen Le Lam, Philippe Bouloc, Claire Morvan |
Abstract |
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 definedbona fidesRNAs as those that (i) likely act intransand (ii) are not expressed from the opposite strand of a coding gene. Using published data and our own RNA-seq data, we reviewed hundreds ofStaphylococcus aureusputative 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 forS. aureusgenetic regulation studies, has only about 50bona fidesRNAs, indicating that these RNAs are less numerous than commonly stated. Among them, about half are associated to theS. aureussp. core genome and a quarter are possibly expressed in otherStaphylococci. We hypothesize on their features and regulation using bioinformatic approaches. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 45 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 11 | 24% |
Researcher | 6 | 13% |
Student > Bachelor | 4 | 9% |
Student > Doctoral Student | 4 | 9% |
Professor > Associate Professor | 3 | 7% |
Other | 7 | 16% |
Unknown | 10 | 22% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 16 | 36% |
Immunology and Microbiology | 7 | 16% |
Agricultural and Biological Sciences | 5 | 11% |
Business, Management and Accounting | 1 | 2% |
Chemistry | 1 | 2% |
Other | 1 | 2% |
Unknown | 14 | 31% |