Abstract
Evolved resistance to one antibiotic may be associated with “collateral” sensitivity to other drugs. Here we provide an extensive quantitative characterization of collateral effects in E. faecalis, a gram-positive opportunistic pathogen. By combining parallel experimental evolution with high-throughput dose-response measurements, we measure phenotypic profiles of collateral sensitivity and resistance for a total of 900 mutant-drug combinations. We find that collateral effects are pervasive but difficult to predict, as independent populations selected by the same drug can exhibit qualitatively different profiles of collateral sensitivity. Despite this apparent complexity, however, the sensitivity profiles cluster into statistically similar groups characterized by selecting drugs with similar mechanisms. Using a simple mathematical framework, we leverage these phenotypic profiles to design optimal drug policies that assign a unique drug to every possible resistance profile. Stochastic simulations reveal that these optimal drug policies outperform intuitive cycling protocols by maintaining long-term sensitivity at the expense of short-term periods of high resistance. Finally, we performed whole-genome sequencing on single isolates from each population and identified candidate genes statistically associated with increased sensitivity to particular drugs.