Global pathogenomic analysis identifies known and novel genetic antimicrobial resistance determinants in twelve species

Abstract

AbstractSurveillance programs for managing antimicrobial resistance (AMR) have yielded thousands of genomes suited for data-driven mechanism discovery. We present a workflow integrating pangenomics, gene annotation, and machine learning to identify AMR genes at scale. Applied to 12 species, 27,155 genomes, and 69 drugs, we 1) found AMR gene transfer mostly confined within related species, with 925 genes in multiple species but just eight in multiple phylogenetic classes, 2) demonstrated that discovery-oriented support vector machines outperform contemporary methods at recovering known AMR genes, recovering 263 genes compared to 145 by Pyseer, and 3) identified 142 novel AMR gene candidates. Validation of two candidates inE. coliBW25113 revealed cases of conditional resistance:ΔcycAconferred ciprofloxacin resistance in minimal media with D-serine, andfrdDV111D conferred ampicillin resistance in the presence ofampCby modifying the overlapping promoter. We expect this approach to be adaptable to other species and phenotypes.