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 Enterococcus 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 because independent populations selected by the same drug can exhibit qualitatively different profiles of collateral sensitivity as well as markedly different fitness costs. Using whole-genome sequencing of evolved populations, we identified mutations in a number of known resistance determinants, including mutations in several genes previously linked with collateral sensitivity in other species. Although phenotypic drug sensitivity profiles show significant diversity, they cluster into statistically similar groups characterized by selecting drugs with similar mechanisms. To exploit the statistical structure in these resistance profiles, we develop a simple mathematical model based on a stochastic control process and use it 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. The approach reveals a new conceptual strategy for mitigating resistance by balancing short-term inhibition of pathogen growth with infrequent use of drugs intended to steer pathogen populations to a more vulnerable future state. Experiments in laboratory populations confirm that model-inspired sequences of four drugs reduce growth and slow adaptation relative to naive protocols involving the drugs alone, in pairwise cycles, or in a four-drug uniform cycle.
Highlights
The rapid emergence of drug resistance is an urgent threat to effective treatments for bacterial infections, cancers, and many viral infections [1,2,3,4,5,6]
By combining parallel experimental evolution of E. faecalis with high-throughput doseresponse measurements, we provide collateral sensitivity and resistance profiles for 60 strains evolved to 15 different antibiotics, yielding a total of 900 mutant–drug combinations
A mutant strain was defined to be collaterally sensitive if its half-maximal inhibitory concentration (IC50) had decreased by more than 3σa relative to the ancestral strain
Summary
The rapid emergence of drug resistance is an urgent threat to effective treatments for bacterial infections, cancers, and many viral infections [1,2,3,4,5,6]. Collateral evolution occurs when a population evolves resistance to a target drug while simultaneously exhibiting increased sensitivity or resistance to a different drug. Collateral effects are reminiscent of the trade-offs inherent when organisms are required to simultaneously adapt to different tasks, an optimization that is often surprisingly simple because it takes place on a low-dimensional phenotypic space [39, 40]. If tractable dynamics occur in the evolution of multidrug resistance, systematic optimization of drug deployment has the promise to mitigate the effects of resistance
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