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 that we call a “coevolution score”. The method is based on pairwise comparisons of closely related individuals, analogous to a pedigree study in eukaryotic populations. This approach avoids the need for an accurate phylogenetic tree and allows very large datasets to be analyzed for signatures of recent coevolution. We apply our method to all of the more than 3 million pairs of genes from the entire annotated Staphylococcus aureus accessory genome of 2,756 annotated genes using a database of over 40,000 whole genomes. We find many pairs of genes that 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 dozens of rapidly coevolving genes, primarily consisting of genes involved in metal resistance, virulence, mechanisms of horizontal gene transfer, and antibiotic resistance, particularly the SCCmec complex. Our results reflect the fact that the evolution of many bacterial pathogens since the middle of the twentieth century has largely been driven by antibiotic resistance gene gain, and in the case of S. aureus the SCCmec complex is the most prominent of these elements driving the evolution of resistance. The frequent coincidence of these gene gain or loss events suggests that S. aureus switch between antibiotic-resistant niches and antibiotic-susceptible ones. While we focus on gene gain and loss, our method can also detect genes which tend to acquire substitutions in tandem or, in datasets that include phenotypic information, genotype-phenotype or phenotype-phenotype coevolution.
Competing Interest Statement
The authors have declared no competing interest.
