Assessing the importance of resistance, persistence and hyper-mutation for antibiotic treatment success with stochastic modelling

Abstract

Antimicrobial resistance poses a rising threat to global health, making it crucial to understand the routes of bacterial survival during antimicrobial treatments. Treatment failure can result from genetic or phenotypic mechanisms, which diminish the effect of antibiotics. By assembling empirical data, we find that, for example, Pseudomonas aeruginosa infections in cystic fibrosis patients frequently contain persisters, transiently non-growing and antibiotic-refractory subpopulations, and hyper-mutators, mutants with elevated mutation rates and thus higher probability of genetic resistance emergence. Resistance, persistence and hyper-mutation dynamics are difficult to disentangle experimentally. Hence, we use stochastic population modelling and deterministic fitness calculations of bacterial evolution under antibiotic treatment to investigate how genetic resistance and phenotypic mechanisms affect treatment success. We find that treatment failure is caused by resistant mutants at lower antibiotic concentrations (with high final bacterial numbers), but by persistence phenotypes at higher antibiotic concentrations (with low final bacterial numbers). Facilitation of resistance occurs through hyper-mutators during treatment, but through persistence only after treatment is discontinued, which allows for persisters to resume growth and evolve resistance in the absence of antibiotics. Our findings highlight the time- and concentration-dependence of different bacterial mechanisms to escape antibiotic killing, which should be considered when designing resistance-proof antimicrobial treatments.