Cargando…
A framework for the computational prediction and analysis of non-coding RNAs in microbial environmental populations and their experimental validation
Small regulatory RNAs and antisense RNAs play important roles in the regulation of gene expression in bacteria but are underexplored, especially in natural populations. While environmentally relevant microbes often are not amenable to genetic manipulation or cannot be cultivated in the laboratory, e...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7368042/ https://www.ncbi.nlm.nih.gov/pubmed/32346084 http://dx.doi.org/10.1038/s41396-020-0658-7 |
_version_ | 1783560537050710016 |
---|---|
author | Lott, Steffen C. Voigt, Karsten Lambrecht, S. Joke Hess, Wolfgang R. Steglich, Claudia |
author_facet | Lott, Steffen C. Voigt, Karsten Lambrecht, S. Joke Hess, Wolfgang R. Steglich, Claudia |
author_sort | Lott, Steffen C. |
collection | PubMed |
description | Small regulatory RNAs and antisense RNAs play important roles in the regulation of gene expression in bacteria but are underexplored, especially in natural populations. While environmentally relevant microbes often are not amenable to genetic manipulation or cannot be cultivated in the laboratory, extensive metagenomic and metatranscriptomic datasets for these organisms might be available. Hence, dedicated workflows for specific analyses are needed to fully benefit from this information. Here, we identified abundant sRNAs from oceanic environmental populations of the ecologically important primary producer Prochlorococcus starting from a metatranscriptomic differential RNA-Seq (mdRNA-Seq) dataset. We tracked their homologs in laboratory isolates, and we provide a framework for their further detailed characterization. Several of the experimentally validated sRNAs responded to ecologically relevant changes in cultivation conditions. The expression of the here newly discovered sRNA Yfr28 was highly stimulated in low-nitrogen conditions. Its predicted top targets include mRNAs encoding cell division proteins, a sigma factor, and several enzymes and transporters, suggesting a pivotal role of Yfr28 in the coordination of primary metabolism and cell division. A cis-encoded antisense RNA was identified as a possible positive regulator of atpF encoding subunit b’ of the ATP synthase complex. The presented workflow will also be useful for other environmentally relevant microorganisms for which experimental validation abilities are frequently limiting although there is wealth of sequence information available. |
format | Online Article Text |
id | pubmed-7368042 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-73680422020-07-21 A framework for the computational prediction and analysis of non-coding RNAs in microbial environmental populations and their experimental validation Lott, Steffen C. Voigt, Karsten Lambrecht, S. Joke Hess, Wolfgang R. Steglich, Claudia ISME J Article Small regulatory RNAs and antisense RNAs play important roles in the regulation of gene expression in bacteria but are underexplored, especially in natural populations. While environmentally relevant microbes often are not amenable to genetic manipulation or cannot be cultivated in the laboratory, extensive metagenomic and metatranscriptomic datasets for these organisms might be available. Hence, dedicated workflows for specific analyses are needed to fully benefit from this information. Here, we identified abundant sRNAs from oceanic environmental populations of the ecologically important primary producer Prochlorococcus starting from a metatranscriptomic differential RNA-Seq (mdRNA-Seq) dataset. We tracked their homologs in laboratory isolates, and we provide a framework for their further detailed characterization. Several of the experimentally validated sRNAs responded to ecologically relevant changes in cultivation conditions. The expression of the here newly discovered sRNA Yfr28 was highly stimulated in low-nitrogen conditions. Its predicted top targets include mRNAs encoding cell division proteins, a sigma factor, and several enzymes and transporters, suggesting a pivotal role of Yfr28 in the coordination of primary metabolism and cell division. A cis-encoded antisense RNA was identified as a possible positive regulator of atpF encoding subunit b’ of the ATP synthase complex. The presented workflow will also be useful for other environmentally relevant microorganisms for which experimental validation abilities are frequently limiting although there is wealth of sequence information available. Nature Publishing Group UK 2020-04-28 2020-08 /pmc/articles/PMC7368042/ /pubmed/32346084 http://dx.doi.org/10.1038/s41396-020-0658-7 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Lott, Steffen C. Voigt, Karsten Lambrecht, S. Joke Hess, Wolfgang R. Steglich, Claudia A framework for the computational prediction and analysis of non-coding RNAs in microbial environmental populations and their experimental validation |
title | A framework for the computational prediction and analysis of non-coding RNAs in microbial environmental populations and their experimental validation |
title_full | A framework for the computational prediction and analysis of non-coding RNAs in microbial environmental populations and their experimental validation |
title_fullStr | A framework for the computational prediction and analysis of non-coding RNAs in microbial environmental populations and their experimental validation |
title_full_unstemmed | A framework for the computational prediction and analysis of non-coding RNAs in microbial environmental populations and their experimental validation |
title_short | A framework for the computational prediction and analysis of non-coding RNAs in microbial environmental populations and their experimental validation |
title_sort | framework for the computational prediction and analysis of non-coding rnas in microbial environmental populations and their experimental validation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7368042/ https://www.ncbi.nlm.nih.gov/pubmed/32346084 http://dx.doi.org/10.1038/s41396-020-0658-7 |
work_keys_str_mv | AT lottsteffenc aframeworkforthecomputationalpredictionandanalysisofnoncodingrnasinmicrobialenvironmentalpopulationsandtheirexperimentalvalidation AT voigtkarsten aframeworkforthecomputationalpredictionandanalysisofnoncodingrnasinmicrobialenvironmentalpopulationsandtheirexperimentalvalidation AT lambrechtsjoke aframeworkforthecomputationalpredictionandanalysisofnoncodingrnasinmicrobialenvironmentalpopulationsandtheirexperimentalvalidation AT hesswolfgangr aframeworkforthecomputationalpredictionandanalysisofnoncodingrnasinmicrobialenvironmentalpopulationsandtheirexperimentalvalidation AT steglichclaudia aframeworkforthecomputationalpredictionandanalysisofnoncodingrnasinmicrobialenvironmentalpopulationsandtheirexperimentalvalidation AT lottsteffenc frameworkforthecomputationalpredictionandanalysisofnoncodingrnasinmicrobialenvironmentalpopulationsandtheirexperimentalvalidation AT voigtkarsten frameworkforthecomputationalpredictionandanalysisofnoncodingrnasinmicrobialenvironmentalpopulationsandtheirexperimentalvalidation AT lambrechtsjoke frameworkforthecomputationalpredictionandanalysisofnoncodingrnasinmicrobialenvironmentalpopulationsandtheirexperimentalvalidation AT hesswolfgangr frameworkforthecomputationalpredictionandanalysisofnoncodingrnasinmicrobialenvironmentalpopulationsandtheirexperimentalvalidation AT steglichclaudia frameworkforthecomputationalpredictionandanalysisofnoncodingrnasinmicrobialenvironmentalpopulationsandtheirexperimentalvalidation |