Analysis of the evolution of resistance to multiple antibiotics enables prediction of the Escherichia coli phenotype-based fitness landscape

Junichiro Iwasawa, Tomoya Maeda, Atsushi Shibai, Hazuki Kotani, Masako Kawada and Chikara Furusawa

PLOS Biology, 2022, vol. 20, issue 12, 1-19

Abstract: The fitness landscape represents the complex relationship between genotype or phenotype and fitness under a given environment, the structure of which allows the explanation and prediction of evolutionary trajectories. Although previous studies have constructed fitness landscapes by comprehensively studying the mutations in specific genes, the high dimensionality of genotypic changes prevents us from developing a fitness landscape capable of predicting evolution for the whole cell. Herein, we address this problem by inferring the phenotype-based fitness landscape for antibiotic resistance evolution by quantifying the multidimensional phenotypic changes, i.e., time-series data of resistance for eight different drugs. We show that different peaks of the landscape correspond to different drug resistance mechanisms, thus supporting the validity of the inferred phenotype-fitness landscape. We further discuss how inferred phenotype-fitness landscapes could contribute to the prediction and control of evolution. This approach bridges the gap between phenotypic/genotypic changes and fitness while contributing to a better understanding of drug resistance evolution.Laboratory evolution of Escherichia coli with simultaneous measurements of eight different drug resistance time-series reveals the phenotype-based fitness landscape, enabling the prediction of evolutionary trajectories.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pbio00:3001920

DOI: 10.1371/journal.pbio.3001920

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