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Detecting patterns of accessory genome coevolution in Staphylococcus aureus using data from thousands of genomes

Bacterial genomes exhibit widespread horizontal gene transfer, resulting in highly variable genome content that complicates the inference of genetic interactions. In this study, we develop a method for detecting coevolving genes from large datasets of bacterial genomes based on pairwise comparisons...

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Autores principales: Mehta, Rohan S, Petit, Robert A, Read, Timothy D, Weissman, Daniel B
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10251594/
https://www.ncbi.nlm.nih.gov/pubmed/37296404
http://dx.doi.org/10.1186/s12859-023-05363-4
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author Mehta, Rohan S
Petit, Robert A
Read, Timothy D
Weissman, Daniel B
author_facet Mehta, Rohan S
Petit, Robert A
Read, Timothy D
Weissman, Daniel B
author_sort Mehta, Rohan S
collection PubMed
description Bacterial genomes exhibit widespread horizontal gene transfer, resulting in highly variable genome content that complicates the inference of genetic interactions. In this study, we develop a method for detecting coevolving genes from large datasets of bacterial genomes based on pairwise comparisons of closely related individuals, analogous to a pedigree study in eukaryotic populations. We apply our method to pairs of genes from the Staphylococcus aureus accessory genome of over 75,000 annotated gene families using a database of over 40,000 whole genomes. We find many pairs of genes that appear to be gained or lost in a coordinated manner, as well as pairs where the gain of one gene is associated with the loss of the other. These pairs form networks of rapidly coevolving genes, primarily consisting of genes involved in virulence, mechanisms of horizontal gene transfer, and antibiotic resistance, particularly the SCCmec complex. While we focus on gene gain and loss, our method can also detect genes that tend to acquire substitutions in tandem, or genotype-phenotype or phenotype-phenotype coevolution. Finally, we present the R package DeCoTUR that allows for the computation of our method. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05363-4.
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spelling pubmed-102515942023-06-10 Detecting patterns of accessory genome coevolution in Staphylococcus aureus using data from thousands of genomes Mehta, Rohan S Petit, Robert A Read, Timothy D Weissman, Daniel B BMC Bioinformatics Research Bacterial genomes exhibit widespread horizontal gene transfer, resulting in highly variable genome content that complicates the inference of genetic interactions. In this study, we develop a method for detecting coevolving genes from large datasets of bacterial genomes based on pairwise comparisons of closely related individuals, analogous to a pedigree study in eukaryotic populations. We apply our method to pairs of genes from the Staphylococcus aureus accessory genome of over 75,000 annotated gene families using a database of over 40,000 whole genomes. We find many pairs of genes that appear to be gained or lost in a coordinated manner, as well as pairs where the gain of one gene is associated with the loss of the other. These pairs form networks of rapidly coevolving genes, primarily consisting of genes involved in virulence, mechanisms of horizontal gene transfer, and antibiotic resistance, particularly the SCCmec complex. While we focus on gene gain and loss, our method can also detect genes that tend to acquire substitutions in tandem, or genotype-phenotype or phenotype-phenotype coevolution. Finally, we present the R package DeCoTUR that allows for the computation of our method. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05363-4. BioMed Central 2023-06-09 /pmc/articles/PMC10251594/ /pubmed/37296404 http://dx.doi.org/10.1186/s12859-023-05363-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Mehta, Rohan S
Petit, Robert A
Read, Timothy D
Weissman, Daniel B
Detecting patterns of accessory genome coevolution in Staphylococcus aureus using data from thousands of genomes
title Detecting patterns of accessory genome coevolution in Staphylococcus aureus using data from thousands of genomes
title_full Detecting patterns of accessory genome coevolution in Staphylococcus aureus using data from thousands of genomes
title_fullStr Detecting patterns of accessory genome coevolution in Staphylococcus aureus using data from thousands of genomes
title_full_unstemmed Detecting patterns of accessory genome coevolution in Staphylococcus aureus using data from thousands of genomes
title_short Detecting patterns of accessory genome coevolution in Staphylococcus aureus using data from thousands of genomes
title_sort detecting patterns of accessory genome coevolution in staphylococcus aureus using data from thousands of genomes
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10251594/
https://www.ncbi.nlm.nih.gov/pubmed/37296404
http://dx.doi.org/10.1186/s12859-023-05363-4
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