Back to Search Start Over

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

Authors :
Iwasawa J
Maeda T
Shibai A
Kotani H
Kawada M
Furusawa C
Source :
PLoS biology [PLoS Biol] 2022 Dec 13; Vol. 20 (12), pp. e3001920. Date of Electronic Publication: 2022 Dec 13 (Print Publication: 2022).
Publication Year :
2022

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.<br />Competing Interests: The authors have declared that no competing interests exist.<br /> (Copyright: © 2022 Iwasawa et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Details

Language :
English
ISSN :
1545-7885
Volume :
20
Issue :
12
Database :
MEDLINE
Journal :
PLoS biology
Publication Type :
Academic Journal
Accession number :
36512529
Full Text :
https://doi.org/10.1371/journal.pbio.3001920