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Common and phylogenetically widespread coding for peptides by bacterial small RNAs.
Friedman, Robin C; Kalkhof, Stefan; Doppelt-Azeroual, Olivia; Mueller, Stephan A; Chovancová, Martina; von Bergen, Martin; Schwikowski, Benno.
Afiliación
  • Friedman RC; Systems Biology Laboratory, Department of Genomes and Genetics, Institut Pasteur, Paris, France. robin.friedman@gmail.com.
  • Kalkhof S; Molecular Microbial Pathogenesis Unit, Department of Cell Biology and Infection, Institut Pasteur, Paris, France. robin.friedman@gmail.com.
  • Doppelt-Azeroual O; Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris, France. robin.friedman@gmail.com.
  • Mueller SA; Department of Molecular Systems Biology, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany.
  • Chovancová M; Current Address: Department of Bioanalytics, University of Applied Sciences and Arts of Coburg, Coburg, Germany.
  • von Bergen M; Bioinformatics and Biostatistics Hub, C3BI, USR 3756 IP CNRS, Institut Pasteur, Paris, France.
  • Schwikowski B; Department of Molecular Systems Biology, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany.
BMC Genomics ; 18(1): 553, 2017 07 21.
Article en En | MEDLINE | ID: mdl-28732463
BACKGROUND: While eukaryotic noncoding RNAs have recently received intense scrutiny, it is becoming clear that bacterial transcription is at least as pervasive. Bacterial small RNAs and antisense RNAs (sRNAs) are often assumed to be noncoding, due to their lack of long open reading frames (ORFs). However, there are numerous examples of sRNAs encoding for small proteins, whether or not they also have a regulatory role at the RNA level. METHODS: Here, we apply flexible machine learning techniques based on sequence features and comparative genomics to quantify the prevalence of sRNA ORFs under natural selection to maintain protein-coding function in 14 phylogenetically diverse bacteria. Importantly, we quantify uncertainty in our predictions, and follow up on them using mass spectrometry proteomics and comparison to datasets including ribosome profiling. RESULTS: A majority of annotated sRNAs have at least one ORF between 10 and 50 amino acids long, and we conservatively predict that 409±191.7 unannotated sRNA ORFs are under selection to maintain coding (mean estimate and 95% confidence interval), an average of 29 per species considered here. This implies that overall at least 10.3±0.5% of sRNAs have a coding ORF, and in some species around 20% do. 165±69 of these novel coding ORFs have some antisense overlap to annotated ORFs. As experimental validation, many of our predictions are translated in published ribosome profiling data and are identified via mass spectrometry shotgun proteomics. B. subtilis sRNAs with coding ORFs are enriched for high expression in biofilms and confluent growth, and S. pneumoniae sRNAs with coding ORFs are involved in virulence. sRNA coding ORFs are enriched for transmembrane domains and many are predicted novel components of type I toxin/antitoxin systems. CONCLUSIONS: We predict over two dozen new protein-coding genes per bacterial species, but crucially also quantified the uncertainty in this estimate. Our predictions for sRNA coding ORFs, along with predicted novel type I toxins and tools for sorting and visualizing genomic context, are freely available in a user-friendly format at http://disco-bac.web.pasteur.fr. We expect these easily-accessible predictions to be a valuable tool for the study not only of bacterial sRNAs and type I toxin-antitoxin systems, but also of bacterial genetics and genomics.
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Péptidos / Filogenia / Bacterias / ARN Bacteriano / ARN Pequeño no Traducido Tipo de estudio: Risk_factors_studies Idioma: En Revista: BMC Genomics Asunto de la revista: GENETICA Año: 2017 Tipo del documento: Article País de afiliación: Francia

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Péptidos / Filogenia / Bacterias / ARN Bacteriano / ARN Pequeño no Traducido Tipo de estudio: Risk_factors_studies Idioma: En Revista: BMC Genomics Asunto de la revista: GENETICA Año: 2017 Tipo del documento: Article País de afiliación: Francia