Inference of Bacterial Small RNA Regulatory Networks and Integration with Transcription Factor-Driven Regulatory Networks

ABSTRACT Small noncoding RNAs (sRNAs) are key regulators of bacterial gene expression. Through complementary base pairing, sRNAs affect mRNA stability and translation efficiency. Here, we describe a network inference approach designed to identify sRNA-mediated regulation of transcript levels. We use...

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Autores principales:	Mario L. Arrieta-Ortiz, Christoph Hafemeister, Bentley Shuster, Nitin S. Baliga, Richard Bonneau, Patrick Eichenberger
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2020
Materias:	gene networks global regulation small RNAs Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/1a14f9e1bbdd44feb3ac31d3a0bb9f34
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spelling	oai:doaj.org-article:1a14f9e1bbdd44feb3ac31d3a0bb9f342021-12-02T19:47:38ZInference of Bacterial Small RNA Regulatory Networks and Integration with Transcription Factor-Driven Regulatory Networks10.1128/mSystems.00057-202379-5077https://doaj.org/article/1a14f9e1bbdd44feb3ac31d3a0bb9f342020-06-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mSystems.00057-20https://doaj.org/toc/2379-5077ABSTRACT Small noncoding RNAs (sRNAs) are key regulators of bacterial gene expression. Through complementary base pairing, sRNAs affect mRNA stability and translation efficiency. Here, we describe a network inference approach designed to identify sRNA-mediated regulation of transcript levels. We use existing transcriptional data sets and prior knowledge to infer sRNA regulons using our network inference tool, the Inferelator. This approach produces genome-wide gene regulatory networks that include contributions by both transcription factors and sRNAs. We show the benefits of estimating and incorporating sRNA activities into network inference pipelines using available experimental data. We also demonstrate how these estimated sRNA regulatory activities can be mined to identify the experimental conditions where sRNAs are most active. We uncover 45 novel experimentally supported sRNA-mRNA interactions in Escherichia coli, outperforming previous network-based efforts. Additionally, our pipeline complements sequence-based sRNA-mRNA interaction prediction methods by adding a data-driven filtering step. Finally, we show the general applicability of our approach by identifying 24 novel, experimentally supported, sRNA-mRNA interactions in Pseudomonas aeruginosa, Staphylococcus aureus, and Bacillus subtilis. Overall, our strategy generates novel insights into the functional context of sRNA regulation in multiple bacterial species. IMPORTANCE Individual bacterial genomes can have dozens of small noncoding RNAs with largely unexplored regulatory functions. Although bacterial sRNAs influence a wide range of biological processes, including antibiotic resistance and pathogenicity, our current understanding of sRNA-mediated regulation is far from complete. Most of the available information is restricted to a few well-studied bacterial species; and even in those species, only partial sets of sRNA targets have been characterized in detail. To close this information gap, we developed a computational strategy that takes advantage of available transcriptional data and knowledge about validated and putative sRNA-mRNA interactions for inferring expanded sRNA regulons. Our approach facilitates the identification of experimentally supported novel interactions while filtering out false-positive results. Due to its data-driven nature, our method prioritizes biologically relevant interactions among lists of candidate sRNA-target pairs predicted in silico from sequence analysis or derived from sRNA-mRNA binding experiments.Mario L. Arrieta-OrtizChristoph HafemeisterBentley ShusterNitin S. BaligaRichard BonneauPatrick EichenbergerAmerican Society for Microbiologyarticlegene networksglobal regulationsmall RNAsMicrobiologyQR1-502ENmSystems, Vol 5, Iss 3 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	gene networks global regulation small RNAs Microbiology QR1-502
spellingShingle	gene networks global regulation small RNAs Microbiology QR1-502 Mario L. Arrieta-Ortiz Christoph Hafemeister Bentley Shuster Nitin S. Baliga Richard Bonneau Patrick Eichenberger Inference of Bacterial Small RNA Regulatory Networks and Integration with Transcription Factor-Driven Regulatory Networks
description	ABSTRACT Small noncoding RNAs (sRNAs) are key regulators of bacterial gene expression. Through complementary base pairing, sRNAs affect mRNA stability and translation efficiency. Here, we describe a network inference approach designed to identify sRNA-mediated regulation of transcript levels. We use existing transcriptional data sets and prior knowledge to infer sRNA regulons using our network inference tool, the Inferelator. This approach produces genome-wide gene regulatory networks that include contributions by both transcription factors and sRNAs. We show the benefits of estimating and incorporating sRNA activities into network inference pipelines using available experimental data. We also demonstrate how these estimated sRNA regulatory activities can be mined to identify the experimental conditions where sRNAs are most active. We uncover 45 novel experimentally supported sRNA-mRNA interactions in Escherichia coli, outperforming previous network-based efforts. Additionally, our pipeline complements sequence-based sRNA-mRNA interaction prediction methods by adding a data-driven filtering step. Finally, we show the general applicability of our approach by identifying 24 novel, experimentally supported, sRNA-mRNA interactions in Pseudomonas aeruginosa, Staphylococcus aureus, and Bacillus subtilis. Overall, our strategy generates novel insights into the functional context of sRNA regulation in multiple bacterial species. IMPORTANCE Individual bacterial genomes can have dozens of small noncoding RNAs with largely unexplored regulatory functions. Although bacterial sRNAs influence a wide range of biological processes, including antibiotic resistance and pathogenicity, our current understanding of sRNA-mediated regulation is far from complete. Most of the available information is restricted to a few well-studied bacterial species; and even in those species, only partial sets of sRNA targets have been characterized in detail. To close this information gap, we developed a computational strategy that takes advantage of available transcriptional data and knowledge about validated and putative sRNA-mRNA interactions for inferring expanded sRNA regulons. Our approach facilitates the identification of experimentally supported novel interactions while filtering out false-positive results. Due to its data-driven nature, our method prioritizes biologically relevant interactions among lists of candidate sRNA-target pairs predicted in silico from sequence analysis or derived from sRNA-mRNA binding experiments.
format	article
author	Mario L. Arrieta-Ortiz Christoph Hafemeister Bentley Shuster Nitin S. Baliga Richard Bonneau Patrick Eichenberger
author_facet	Mario L. Arrieta-Ortiz Christoph Hafemeister Bentley Shuster Nitin S. Baliga Richard Bonneau Patrick Eichenberger
author_sort	Mario L. Arrieta-Ortiz
title	Inference of Bacterial Small RNA Regulatory Networks and Integration with Transcription Factor-Driven Regulatory Networks
title_short	Inference of Bacterial Small RNA Regulatory Networks and Integration with Transcription Factor-Driven Regulatory Networks
title_full	Inference of Bacterial Small RNA Regulatory Networks and Integration with Transcription Factor-Driven Regulatory Networks
title_fullStr	Inference of Bacterial Small RNA Regulatory Networks and Integration with Transcription Factor-Driven Regulatory Networks
title_full_unstemmed	Inference of Bacterial Small RNA Regulatory Networks and Integration with Transcription Factor-Driven Regulatory Networks
title_sort	inference of bacterial small rna regulatory networks and integration with transcription factor-driven regulatory networks
publisher	American Society for Microbiology
publishDate	2020
url	https://doaj.org/article/1a14f9e1bbdd44feb3ac31d3a0bb9f34
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Inference of Bacterial Small RNA Regulatory Networks and Integration with Transcription Factor-Driven Regulatory Networks

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