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

• Type of Material: Article
• Authors: Rohan S Mehta, Emory University; Robert A Petit III, Emory University; Timothy Read, Emory University; Daniel Weissman, Emory University
• Language: English
• Date: 2023-06-09
• Publisher: BMC
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2023
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1186/s12859-023-05363-4
• Title of Journal or Parent Work: BMC BIOINFORMATICS
• Volume: 24
• Issue: 1
• Start Page: 243
• End Page: 243
• Grant/Funding Information: This work was funded by the National Institutes of Health [AI139188 to TDR], the Simons Foundation [508600 to DBW], the Alfred P. Sloan Foundation [FG-2021-16667 to DBW], the National Science Foundation [2146260 to DBW], and an Emory University Synergy II Nexus/MP3 Initiative seed grant to DBW and TDR.
• Keywords: WIDE ASSOCIATION; Biochemical Research Methods; Biotechnology & Applied Microbiology; EVOLUTION; Staphylococcus aureus; Genomics; Horizontal gene transfer; Life Sciences & Biomedicine; Biochemistry & Molecular Biology; Science & Technology; Genetic interaction; Software; Microbial genomics; Mathematical & Computational Biology; EPISTASIS
• Research Categories: Physics, General

Abstract

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.