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

Author

Listed:
  • Jason C. Hyun

    (University of California, San Diego)

  • Jonathan M. Monk

    (University of California, San Diego)

  • Richard Szubin

    (University of California, San Diego)

  • Ying Hefner

    (University of California, San Diego)

  • Bernhard O. Palsson

    (University of California, San Diego
    University of California, San Diego
    University of California, San Diego
    University of California, San Diego)

Abstract

Surveillance 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. When applied to 12 species, 27,155 genomes, and 69 drugs, we 1) find AMR gene transfer mostly confined within related species, with 925 genes in multiple species but just eight in multiple phylogenetic classes, 2) demonstrate that discovery-oriented support vector machines outperform contemporary methods at recovering known AMR genes, recovering 263 genes compared to 145 by Pyseer, and 3) identify 142 AMR gene candidates. Validation of two candidates in E. coli BW25113 reveals cases of conditional resistance: ΔcycA confers ciprofloxacin resistance in minimal media with D-serine, and frdD V111D confers ampicillin resistance in the presence of ampC by modifying the overlapping promoter. We expect this approach to be adaptable to other species and phenotypes.

Suggested Citation

  • Jason C. Hyun & Jonathan M. Monk & Richard Szubin & Ying Hefner & Bernhard O. Palsson, 2023. "Global pathogenomic analysis identifies known and candidate genetic antimicrobial resistance determinants in twelve species," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-43549-9
    DOI: 10.1038/s41467-023-43549-9
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    References listed on IDEAS

    as
    1. Danesh Moradigaravand & Martin Palm & Anne Farewell & Ville Mustonen & Jonas Warringer & Leopold Parts, 2018. "Prediction of antibiotic resistance in Escherichia coli from large-scale pan-genome data," PLOS Computational Biology, Public Library of Science, vol. 14(12), pages 1-17, December.
    2. Erol S. Kavvas & Laurence Yang & Jonathan M. Monk & David Heckmann & Bernhard O. Palsson, 2020. "A biochemically-interpretable machine learning classifier for microbial GWAS," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    3. Jiwoong Kim & David E Greenberg & Reed Pifer & Shuang Jiang & Guanghua Xiao & Samuel A Shelburne & Andrew Koh & Yang Xie & Xiaowei Zhan, 2020. "VAMPr: VAriant Mapping and Prediction of antibiotic resistance via explainable features and machine learning," PLOS Computational Biology, Public Library of Science, vol. 16(1), pages 1-17, January.
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