Evolutionary action of mutations reveals antimicrobial resistance genes in Escherichia coli

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, Susan M. Rosenberg, Christophe Herman and Olivier Lichtarge ()
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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
Ralf B. Nehring: 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
Christophe Herman: Baylor College of Medicine
Olivier Lichtarge: Baylor College of Medicine

Nature Communications, 2022, vol. 13, issue 1, 1-13

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-30889-1

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