Predicting trajectories and mechanisms of antibiotic resistance evolution

TL;DR

This study develops a fitness model to predict how antibiotic resistance mutations impact bacterial growth and resistance evolution across different drug and nutrient conditions, integrating systems biology and ecology insights.

Contribution

It introduces a metabolic fitness model that predicts resistance evolution trajectories and mechanisms based on metabolic effects and environmental factors.

Findings

Predicted growth rates and resistance levels across drug doses.

Identified resistance mechanisms prevalent under specific conditions.

Validated predictions with empirical growth and genomic data.

Abstract

Bacteria evolve resistance to antibiotics by a multitude of mechanisms. A central, yet unsolved question is how resistance evolution affects cell growth at different drug levels. Here we develop a fitness model that predicts growth rates of common resistance mutants from their effects on cell metabolism. We map metabolic effects of resistance mutations in drug-free environments and under drug challenge; the resulting fitness trade-off defines a Pareto surface of resistance evolution. We predict evolutionary trajectories of dosage-dependent growth rates and resistance levels, as well as the prevalent resistance mechanism depending on drug and nutrient levels. These predictions are confirmed by empirical growth curves and genomic data of E. coli populations. Our results show that resistance evolution, by coupling major metabolic pathways, is strongly intertwined with systems biology and ecology of microbial populations.