History of Antibiotic Adaptation Influences Evolutionary Dynamics During Subsequent Treatment in Pseudomonas Aeruginosa

Antibiotic resistance is an increasingly serious global health problem that threatens the effective prevention and treatment of infections caused by bacterial pathogens. While there has been renewed effort in discovering new antibiotic compounds to combat these resistant bacterial pathogens, an equally important endeavor is studying how bacteria evolve to become resistant to the antibiotics that are currently available and commonly used. Having a clearer fundamental understanding of the adaptation process will allow for the development of new stewardship strategies of using the current antibiotics available in such a way that minimizes the risk of resistance evolution. Antibiotic regimens often include the sequential changing of drugs to limit development and evolution of resistance of bacterial pathogens. An open question in the field that has not been addressed is how history of prior adaptation to one antibiotic can influence the resistance profiles when bacteria subsequently adapt to a different antibiotic. In this dissertation, we aim to characterize the effects that prior drug adaptation has on influencing the potential future evolutionary dynamics of subsequent adaptation. We experimentally evolved the model organism Pseudomonas aeruginosa to six two-drug sequences. We observed drug order-specific effects whereby: adaptation to the first drug can limit the rate of subsequent adaptation to the second drug, adaptation to the second drug can restore susceptibility to the first drug, or final resistance levels depend on the order of the two-drug sequence. Furthermore, we used whole-genome sequencing to determine the genetic changes that occurred during drug adaptation to better understand the molecular basis of the drug order-specific effects. This body of work demonstrates how resistance not only depends on the current drug regimen but also history of past regimens. These order-specific effects may allow for rational forecasting of the evolutionary dynamics of bacteria given knowledge of past adaptations and provide support for the need to consider history of past drug exposure when designing strategies to mitigate resistance and combat bacterial infections. This work establishes a framework for a better fundamental understanding of how evolutionary historical context plays a role in antibiotic resistance evolution dynamics and how this knowledge can then hopefully be used to develop regimens that combat the development of resistance.