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

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 s...

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Bibliographic Details
Published in:PLoS biology 2022-12, Vol.20 (12), p.e3001920-e3001920
Main Authors: Iwasawa, Junichiro, Maeda, Tomoya, Shibai, Atsushi, Kotani, Hazuki, Kawada, Masako, Furusawa, Chikara
Format: Article
Language:English
Subjects:
Analysis
Anti-Bacterial Agents - pharmacology
Antibiotic resistance
Antibiotics
Biology and Life Sciences
Drug resistance
Drug resistance in microorganisms
E coli
Escherichia coli
Escherichia coli - genetics
Evolution
Evolution & development
Experiments
Fitness
Gene expression
Genetic aspects
Genetic Fitness
Genotype
Genotype & phenotype
Genotypes
Laboratories
Medicine and Health Sciences
Models, Genetic
Mutation
Mutation - genetics
Phenotype
Phenotypes
Physical Sciences
Predictions
Reproductive fitness
Research and Analysis Methods
Sodium
Time series
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Summary: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.
ISSN:1545-7885
1544-9173
1545-7885
DOI:10.1371/journal.pbio.3001920