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

PLoS Biol. 2022 Dec 13;20(12):e3001920. doi: 10.1371/journal.pbio.3001920. eCollection 2022 Dec.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Anti-Bacterial Agents / pharmacology
  • Escherichia coli* / genetics
  • Genetic Fitness*
  • Genotype
  • Models, Genetic
  • Mutation / genetics
  • Phenotype


  • Anti-Bacterial Agents

Grants and funding

This study was supported in part by JSPS KAKENHI (17H06389 and 19H05626 to C.F.;, JST ERATO (JPMJER1902 to C.F.; J.I. was supported by a Grant-in-Aid for the Japan Society for Promotion of Science Fellows (JP18J21942). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.