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Evolutionary action of mutations reveals antimicrobial resistance genes in Escherichia coli

Author

Listed:
  • David C. Marciano

    (Baylor College of Medicine)

  • Chen Wang

    (Baylor College of Medicine)

  • Teng-Kuei Hsu

    (Baylor College of Medicine)

  • Thomas Bourquard

    (Baylor College of Medicine)

  • Benu Atri

    (Baylor College of Medicine
    Clara Analytics Inc.)

  • Ralf B. Nehring

    (Baylor College of Medicine
    Baylor College of Medicine
    Baylor College of Medicine
    Baylor College of Medicine)

  • Nicholas S. Abel

    (Baylor College of Medicine)

  • Elizabeth A. Bowling

    (Baylor College of Medicine)

  • Taylor J. Chen

    (Baylor College of Medicine)

  • Pamela D. Lurie

    (Baylor College of Medicine)

  • Panagiotis Katsonis

    (Baylor College of Medicine)

  • Susan M. Rosenberg

    (Baylor College of Medicine
    Baylor College of Medicine
    Baylor College of Medicine
    Baylor College of Medicine)

  • Christophe Herman

    (Baylor College of Medicine
    Baylor College of Medicine
    Baylor College of Medicine)

  • Olivier Lichtarge

    (Baylor College of Medicine
    Baylor College of Medicine
    Baylor College of Medicine
    Baylor College of Medicine)

Abstract

Since antibiotic development lags, we search for potential drug targets through directed evolution experiments. A challenge is that many resistance genes hide in a noisy mutational background as mutator clones emerge in the adaptive population. Here, to overcome this noise, we quantify the impact of mutations through evolutionary action (EA). After sequencing ciprofloxacin or colistin resistance strains grown under different mutational regimes, we find that an elevated sum of the evolutionary action of mutations in a gene identifies known resistance drivers. This EA integration approach also suggests new antibiotic resistance genes which are then shown to provide a fitness advantage in competition experiments. Moreover, EA integration analysis of clinical and environmental isolates of antibiotic resistant of E. coli identifies gene drivers of resistance where a standard approach fails. Together these results inform the genetic basis of de novo colistin resistance and support the robust discovery of phenotype-driving genes via the evolutionary action of genetic perturbations in fitness landscapes.

Suggested Citation

  • David C. Marciano & Chen Wang & Teng-Kuei Hsu & Thomas Bourquard & Benu Atri & Ralf B. Nehring & Nicholas S. Abel & Elizabeth A. Bowling & Taylor J. Chen & Pamela D. Lurie & Panagiotis Katsonis & Susa, 2022. "Evolutionary action of mutations reveals antimicrobial resistance genes in Escherichia coli," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-30889-1
    DOI: 10.1038/s41467-022-30889-1
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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. George L. Peabody V & Hao Li & Katy C. Kao, 2017. "Sexual recombination and increased mutation rate expedite evolution of Escherichia coli in varied fitness landscapes," Nature Communications, Nature, vol. 8(1), pages 1-9, December.
    3. Toon Swings & David C. Marciano & Benu Atri & Rachel E. Bosserman & Chen Wang & Marlies Leysen & Camille Bonte & Thomas Schalck & Ian Furey & Bram Van den Bergh & Natalie Verstraeten & Peter J. Christ, 2018. "CRISPR-FRT targets shared sites in a knock-out collection for off-the-shelf genome editing," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
    4. Frank J. Poelwijk & Michael Socolich & Rama Ranganathan, 2019. "Learning the pattern of epistasis linking genotype and phenotype in a protein," Nature Communications, Nature, vol. 10(1), pages 1-11, December.
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