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Optimization of lag time underlies antibiotic tolerance in evolved bacterial populations

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  • Ofer Fridman

    (Racah Institute of Physics, The Sudarsky Center for Computational Biology and the Center for NanoScience, Edmond J. Safra Campus, The Hebrew University, Jerusalem 91904, Israel)

  • Amir Goldberg

    (Racah Institute of Physics, The Sudarsky Center for Computational Biology and the Center for NanoScience, Edmond J. Safra Campus, The Hebrew University, Jerusalem 91904, Israel)

  • Irine Ronin

    (Racah Institute of Physics, The Sudarsky Center for Computational Biology and the Center for NanoScience, Edmond J. Safra Campus, The Hebrew University, Jerusalem 91904, Israel)

  • Noam Shoresh

    (Broad Institute of Harvard and MIT)

  • Nathalie Q. Balaban

    (Racah Institute of Physics, The Sudarsky Center for Computational Biology and the Center for NanoScience, Edmond J. Safra Campus, The Hebrew University, Jerusalem 91904, Israel)

Abstract

Repeated exposure of the bacterium Escherichia coli to clinically relevant concentrations of ampicillin results in the evolution of tolerance—the ability to survive until the antibiotic concentration diminishes—through an extension of the lag phase, a finding that has implications for slowing the evolution of antibiotic resistance.

Suggested Citation

  • Ofer Fridman & Amir Goldberg & Irine Ronin & Noam Shoresh & Nathalie Q. Balaban, 2014. "Optimization of lag time underlies antibiotic tolerance in evolved bacterial populations," Nature, Nature, vol. 513(7518), pages 418-421, September.
    • Handle: RePEc:nat:nature:v:513:y:2014:i:7518:d:10.1038_nature13469
    DOI: 10.1038/nature13469
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    Citations

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    Cited by:

    1. Jessica A Lee & Siavash Riazi & Shahla Nemati & Jannell V Bazurto & Andreas E Vasdekis & Benjamin J Ridenhour & Christopher H Remien & Christopher J Marx, 2019. "Microbial phenotypic heterogeneity in response to a metabolic toxin: Continuous, dynamically shifting distribution of formaldehyde tolerance in Methylobacterium extorquens populations," PLOS Genetics, Public Library of Science, vol. 15(11), pages 1-38, November.
    2. Elwood A. Mullins & Jonathan Dorival & Gong-Li Tang & Dale L. Boger & Brandt F. Eichman, 2021. "Structural evolution of a DNA repair self-resistance mechanism targeting genotoxic secondary metabolites," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    3. Marianne Bauer & Isabella R Graf & Vudtiwat Ngampruetikorn & Greg J Stephens & Erwin Frey, 2017. "Exploiting ecology in drug pulse sequences in favour of population reduction," PLOS Computational Biology, Public Library of Science, vol. 13(9), pages 1-17, September.
    4. Niclas Nordholt & Orestis Kanaris & Selina B. I. Schmidt & Frank Schreiber, 2021. "Persistence against benzalkonium chloride promotes rapid evolution of tolerance during periodic disinfection," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    5. José Camacho Mateu & Matteo Sireci & Miguel A Muñoz, 2021. "Phenotypic-dependent variability and the emergence of tolerance in bacterial populations," PLOS Computational Biology, Public Library of Science, vol. 17(9), pages 1-28, September.
    6. Sourav Chowdhury & Daniel C. Zielinski & Christopher Dalldorf & Joao V. Rodrigues & Bernhard O. Palsson & Eugene I. Shakhnovich, 2023. "Empowering drug off-target discovery with metabolic and structural analysis," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    7. Erica J. Zheng & Ian W. Andrews & Alexandra T. Grote & Abigail L. Manson & Miguel A. Alcantar & Ashlee M. Earl & James J. Collins, 2022. "Modulating the evolutionary trajectory of tolerance using antibiotics with different metabolic dependencies," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    8. Horvath, Denis & Brutovsky, Branislav, 2016. "Etiology of phenotype switching strategy in time varying stochastic environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 455-468.
    9. Alexander Sturm & Grzegorz Jóźwiak & Marta Pla Verge & Laura Munch & Gino Cathomen & Anthony Vocat & Amanda Luraschi-Eggemann & Clara Orlando & Katja Fromm & Eric Delarze & Michał Świątkowski & Grzego, 2024. "Accurate and rapid antibiotic susceptibility testing using a machine learning-assisted nanomotion technology platform," Nature Communications, Nature, vol. 15(1), pages 1-14, December.

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