Predicting antibiotic resistance evolution

Bacteria can rapidly evolve resistance to antibiotics. Resistance mutations confer a fitness advantage in the presence of the drug, which is frequently coupled to a fitness cost in drug-free environments. But how do these effects set the strength of resistance elicited by a given drug dosage and the resulting cell growth? Here we develop a fitness model that predicts dosage-dependent growth rates of common resistance mutations. Selection experiments in E. coli populations at moderate drug levels reveal multiple resistance mutations of different strength, most of which affect membrane genes. By reducing both drug and nutrient uptake, these mutations cause antagonistic effects on cell metabolism and growth. Our fitness model maps this tradeoff and defines a Pareto surface of resistance evolution. We show that optimal mutants elicited at a given drug level occur at a specific point on this surface, leading to predictable dosage-dependent growth. Our analysis delineates the dosage regime where broad, membrane-mediated resistance evolution is prevalent compared to mere physiological response and drug-specific target mutations. These results show that drug resistance evolution, by coupling major metabolic pathways, is strongly intertwined with the systems biology of the cell.