Forecasting Multitrait Resistance Evolution under Antibiotic Stress.

Many bacteria rely on efflux pumps to survive antibiotic stress and exposure to antibiotics often leads to mutations in pump genes or their regulators that increase pump expression. Predicting the spectrum of these mutations is important for designing effective antibiotic treatments, but the underlying regulatory networks are large and complex, making them difficult to map experimentally. To address this challenge, we developed a mathematical framework that integrates dynamical equations for efflux pump regulation with a genetic algorithm for parameter estimation and evolutionary simulations. Using this framework, we simulated in silico evolution of Pseudomonas aeruginosa under exposure to the antibiotics meropenem, tobramycin, and ciprofloxacin. The simulations revealed mutational spectra affecting the expression of four Resistance-Nodulation-Division efflux pumps and their shared regulatory network. The most frequently mutated genes were single-target regulators that matched well with previous observations in clinical and in vitro studies. The model also showed that the shared use of the OprM protein by two pumps is a key factor shaping their distinct mutational patterns. Mutations often produced multitrait phenotypes, manifesting as collateral sensitivity or cross-resistance to antibiotics not used for selection. While cross-resistance evolved readily, its extent depended on initial pump expression levels and thus may vary between strains. Finally, simulations of changing environments showed that efflux pump genes tend to be lost in the absence of antibiotics, suggesting a potential strategy to steer bacterial evolution toward reduced capacity to re-evolve resistance.