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A biochemically-interpretable machine learning classifier for microbial GWAS

Author

Listed:
  • Erol S. Kavvas

    (University of California)

  • Laurence Yang

    (Queen’s University)

  • Jonathan M. Monk

    (University of California)

  • David Heckmann

    (University of California)

  • Bernhard O. Palsson

    (University of California
    University of California)

Abstract

Current machine learning classifiers have successfully been applied to whole-genome sequencing data to identify genetic determinants of antimicrobial resistance (AMR), but they lack causal interpretation. Here we present a metabolic model-based machine learning classifier, named Metabolic Allele Classifier (MAC), that uses flux balance analysis to estimate the biochemical effects of alleles. We apply the MAC to a dataset of 1595 drug-tested Mycobacterium tuberculosis strains and show that MACs predict AMR phenotypes with accuracy on par with mechanism-agnostic machine learning models (isoniazid AUC = 0.93) while enabling a biochemical interpretation of the genotype-phenotype map. Interpretation of MACs for three antibiotics (pyrazinamide, para-aminosalicylic acid, and isoniazid) recapitulates known AMR mechanisms and suggest a biochemical basis for how the identified alleles cause AMR. Extending flux balance analysis to identify accurate sequence classifiers thus contributes mechanistic insights to GWAS, a field thus far dominated by mechanism-agnostic results.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-16310-9
    DOI: 10.1038/s41467-020-16310-9
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    Cited by:

    1. Michelle Baker & Xibin Zhang & Alexandre Maciel-Guerra & Kubra Babaarslan & Yinping Dong & Wei Wang & Yujie Hu & David Renney & Longhai Liu & Hui Li & Maqsud Hossain & Stephan Heeb & Zhiqin Tong & Nic, 2024. "Convergence of resistance and evolutionary responses in Escherichia coli and Salmonella enterica co-inhabiting chicken farms in China," Nature Communications, Nature, vol. 15(1), pages 1-21, December.
    2. 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.

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