ArticlePLoS biology2022
Analysis of the evolution of resistance to multiple antibiotics enables prediction of the Escherichia coli phenotype-based fitness landscape.
Article in PLoS biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Review
- Function-specific epistasis shapes evolutionary trajectories towards antibiotic resistance.Nature communications · 2026Article
- D-LIM: A neural network for interpretable gene-gene interactions.PLoS computational biology · 2026Article
- Neutral transcriptome rewiring promotes quantitative disease resistance evolvability at the species level.The Plant cell · 2025Article
- Unlocking the potential of experimental evolution to study drug resistance in pathogenic fungi.npj antimicrobials and resistance · 2024Review
- Distinguishing mutants that resist drugs via different mechanisms by examining fitness tradeoffs.eLife · 2024Article
- Development of specialized devices for microbial experimental evolution.Development, growth & differentiation · 2024Review
- Distinguishing mutants that resist drugs via different mechanisms by examining fitness tradeoffs.bioRxiv : the preprint server for biology · 2024Article
- Reinforcement learning informs optimal treatment strategies to limit antibiotic resistance.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Laboratory Evolution of Antimicrobial Resistance in Bacteria to Develop Rational Treatment Strategies.Antibiotics (Basel, Switzerland) · 2024Review
- Phenotype selection due to mutational robustness.PloS one · 2024Article
- Steering and controlling evolution - from bioengineering to fighting pathogens.Nature reviews. Genetics · 2023Review
- Reinforcement Learning informs optimal treatment strategies to limit antibiotic resistance.bioRxiv : the preprint server for biology · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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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.
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