IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v13y2022i1d10.1038_s41467-022-30889-1.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-30889-1
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-022-30889-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jason Youn & Navneet Rai & Ilias Tagkopoulos, 2022. "Knowledge integration and decision support for accelerated discovery of antibiotic resistance genes," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    2. Rachapun Rotrattanadumrong & Yohei Yokobayashi, 2022. "Experimental exploration of a ribozyme neutral network using evolutionary algorithm and deep learning," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    3. Jonathan Yaacov Weinstein & Carlos Martí-Gómez & Rosalie Lipsh-Sokolik & Shlomo Yakir Hoch & Demian Liebermann & Reinat Nevo & Haim Weissman & Ekaterina Petrovich-Kopitman & David Margulies & Dmitry I, 2023. "Designed active-site library reveals thousands of functional GFP variants," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    4. Andreas Wagner, 2023. "Evolvability-enhancing mutations in the fitness landscapes of an RNA and a protein," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    5. Liselot Dewachter & Aaron N. Brooks & Katherine Noon & Charlotte Cialek & Alia Clark-ElSayed & Thomas Schalck & Nandini Krishnamurthy & Wim Versées & Wim Vranken & Jan Michiels, 2023. "Deep mutational scanning of essential bacterial proteins can guide antibiotic development," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    6. Federica Luppino & Ivan A. Adzhubei & Christopher A. Cassa & Agnes Toth-Petroczy, 2023. "DeMAG predicts the effects of variants in clinically actionable genes by integrating structural and evolutionary epistatic features," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    7. 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.
    8. Xuanji Li & Asker Brejnrod & Jonathan Thorsen & Trine Zachariasen & Urvish Trivedi & Jakob Russel & Gisle Alberg Vestergaard & Jakob Stokholm & Morten Arendt Rasmussen & Søren Johannes Sørensen, 2023. "Differential responses of the gut microbiome and resistome to antibiotic exposures in infants and adults," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    9. Solip Park & Fran Supek & Ben Lehner, 2021. "Higher order genetic interactions switch cancer genes from two-hit to one-hit drivers," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    10. Remkes A. Scheele & Laurens H. Lindenburg & Maya Petek & Markus Schober & Kevin N. Dalby & Florian Hollfelder, 2022. "Droplet-based screening of phosphate transfer catalysis reveals how epistasis shapes MAP kinase interactions with substrates," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    11. Allison L Hicks & Nicole Wheeler & Leonor Sánchez-Busó & Jennifer L Rakeman & Simon R Harris & Yonatan H Grad, 2019. "Evaluation of parameters affecting performance and reliability of machine learning-based antibiotic susceptibility testing from whole genome sequencing data," PLOS Computational Biology, Public Library of Science, vol. 15(9), pages 1-21, September.
    12. Brian M. Petersen & Monica B. Kirby & Karson M. Chrispens & Olivia M. Irvin & Isabell K. Strawn & Cyrus M. Haas & Alexis M. Walker & Zachary T. Baumer & Sophia A. Ulmer & Edgardo Ayala & Emily R. Rhod, 2024. "An integrated technology for quantitative wide mutational scanning of human antibody Fab libraries," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    13. David Ding & Ada Y. Shaw & Sam Sinai & Nathan Rollins & Noam Prywes & David F. Savage & Michael T. Laub & Debora S. Marks, 2024. "Protein design using structure-based residue preferences," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-30889-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.