sRNA Target Prediction Organizing Tool (SPOT) Integrates Computational and Experimental Data To Facilitate Functional Characterization of Bacterial Small RNAs

ABSTRACT Small RNAs (sRNAs) posttranscriptionally regulate mRNA targets, typically under conditions of environmental stress. Although hundreds of sRNAs have been discovered in diverse bacterial genomes, most sRNAs remain uncharacterized, even in model organisms. Identification of mRNA targets direct...

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Autores principales:	Alisa M. King, Carin K. Vanderpool, Patrick H. Degnan
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2019
Materias:	Hfq IntaRNA RNA RNase E Starpicker TargetRNA2 Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/571c317f22b34c4c8b28c3a27ce399c9
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spelling	oai:doaj.org-article:571c317f22b34c4c8b28c3a27ce399c92021-11-15T15:22:04ZsRNA Target Prediction Organizing Tool (SPOT) Integrates Computational and Experimental Data To Facilitate Functional Characterization of Bacterial Small RNAs10.1128/mSphere.00561-182379-5042https://doaj.org/article/571c317f22b34c4c8b28c3a27ce399c92019-02-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mSphere.00561-18https://doaj.org/toc/2379-5042ABSTRACT Small RNAs (sRNAs) posttranscriptionally regulate mRNA targets, typically under conditions of environmental stress. Although hundreds of sRNAs have been discovered in diverse bacterial genomes, most sRNAs remain uncharacterized, even in model organisms. Identification of mRNA targets directly regulated by sRNAs is rate-limiting for sRNA functional characterization. To address this, we developed a computational pipeline that we named SPOT for sRNA target prediction organizing tool. SPOT incorporates existing computational tools to search for sRNA binding sites, allows filtering based on experimental data, and organizes the results into a standardized report. SPOT sensitivity (number of correctly predicted targets/number of total known targets) was equal to or exceeded any individual method when used on 12 characterized sRNAs. Using SPOT, we generated a set of target predictions for the sRNA RydC, which was previously shown to positively regulate cfa mRNA, encoding cyclopropane fatty acid synthase. SPOT identified cfa along with additional putative mRNA targets, which we then tested experimentally. Our results demonstrated that in addition to cfa mRNA, RydC also regulates trpE and pheA mRNAs, which encode aromatic amino acid biosynthesis enzymes. Our results suggest that SPOT can facilitate elucidation of sRNA target regulons to expand our understanding of the many regulatory roles played by bacterial sRNAs. IMPORTANCE Small RNAs (sRNAs) regulate gene expression in diverse bacteria by interacting with mRNAs to change their structure, stability, or translation. Hundreds of sRNAs have been identified in bacteria, but characterization of their regulatory functions is limited by difficulty with sensitive and accurate identification of mRNA targets. Thus, new robust methods of bacterial sRNA target identification are in demand. Here, we describe our small RNA target prediction organizing tool (SPOT), which streamlines the process of sRNA target prediction by providing a single pipeline that combines available computational prediction tools with customizable results filtering based on experimental data. SPOT allows the user to rapidly produce a prioritized list of predicted sRNA-target mRNA interactions that serves as a basis for further experimental characterization. This tool will facilitate elucidation of sRNA regulons in bacteria, allowing new discoveries regarding the roles of sRNAs in bacterial stress responses and metabolic regulation.Alisa M. KingCarin K. VanderpoolPatrick H. DegnanAmerican Society for MicrobiologyarticleHfqIntaRNARNARNase EStarpickerTargetRNA2MicrobiologyQR1-502ENmSphere, Vol 4, Iss 1 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Hfq IntaRNA RNA RNase E Starpicker TargetRNA2 Microbiology QR1-502
spellingShingle	Hfq IntaRNA RNA RNase E Starpicker TargetRNA2 Microbiology QR1-502 Alisa M. King Carin K. Vanderpool Patrick H. Degnan sRNA Target Prediction Organizing Tool (SPOT) Integrates Computational and Experimental Data To Facilitate Functional Characterization of Bacterial Small RNAs
description	ABSTRACT Small RNAs (sRNAs) posttranscriptionally regulate mRNA targets, typically under conditions of environmental stress. Although hundreds of sRNAs have been discovered in diverse bacterial genomes, most sRNAs remain uncharacterized, even in model organisms. Identification of mRNA targets directly regulated by sRNAs is rate-limiting for sRNA functional characterization. To address this, we developed a computational pipeline that we named SPOT for sRNA target prediction organizing tool. SPOT incorporates existing computational tools to search for sRNA binding sites, allows filtering based on experimental data, and organizes the results into a standardized report. SPOT sensitivity (number of correctly predicted targets/number of total known targets) was equal to or exceeded any individual method when used on 12 characterized sRNAs. Using SPOT, we generated a set of target predictions for the sRNA RydC, which was previously shown to positively regulate cfa mRNA, encoding cyclopropane fatty acid synthase. SPOT identified cfa along with additional putative mRNA targets, which we then tested experimentally. Our results demonstrated that in addition to cfa mRNA, RydC also regulates trpE and pheA mRNAs, which encode aromatic amino acid biosynthesis enzymes. Our results suggest that SPOT can facilitate elucidation of sRNA target regulons to expand our understanding of the many regulatory roles played by bacterial sRNAs. IMPORTANCE Small RNAs (sRNAs) regulate gene expression in diverse bacteria by interacting with mRNAs to change their structure, stability, or translation. Hundreds of sRNAs have been identified in bacteria, but characterization of their regulatory functions is limited by difficulty with sensitive and accurate identification of mRNA targets. Thus, new robust methods of bacterial sRNA target identification are in demand. Here, we describe our small RNA target prediction organizing tool (SPOT), which streamlines the process of sRNA target prediction by providing a single pipeline that combines available computational prediction tools with customizable results filtering based on experimental data. SPOT allows the user to rapidly produce a prioritized list of predicted sRNA-target mRNA interactions that serves as a basis for further experimental characterization. This tool will facilitate elucidation of sRNA regulons in bacteria, allowing new discoveries regarding the roles of sRNAs in bacterial stress responses and metabolic regulation.
format	article
author	Alisa M. King Carin K. Vanderpool Patrick H. Degnan
author_facet	Alisa M. King Carin K. Vanderpool Patrick H. Degnan
author_sort	Alisa M. King
title	sRNA Target Prediction Organizing Tool (SPOT) Integrates Computational and Experimental Data To Facilitate Functional Characterization of Bacterial Small RNAs
title_short	sRNA Target Prediction Organizing Tool (SPOT) Integrates Computational and Experimental Data To Facilitate Functional Characterization of Bacterial Small RNAs
title_full	sRNA Target Prediction Organizing Tool (SPOT) Integrates Computational and Experimental Data To Facilitate Functional Characterization of Bacterial Small RNAs
title_fullStr	sRNA Target Prediction Organizing Tool (SPOT) Integrates Computational and Experimental Data To Facilitate Functional Characterization of Bacterial Small RNAs
title_full_unstemmed	sRNA Target Prediction Organizing Tool (SPOT) Integrates Computational and Experimental Data To Facilitate Functional Characterization of Bacterial Small RNAs
title_sort	srna target prediction organizing tool (spot) integrates computational and experimental data to facilitate functional characterization of bacterial small rnas
publisher	American Society for Microbiology
publishDate	2019
url	https://doaj.org/article/571c317f22b34c4c8b28c3a27ce399c9
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_version_	1718428071024918528

sRNA Target Prediction Organizing Tool (SPOT) Integrates Computational and Experimental Data To Facilitate Functional Characterization of Bacterial Small RNAs

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