Antibiotic Interactions, Collateral Sensitivity, and the Evolution of Multidrug Resistance in E. faecalis

Abstract

Antibiotic resistance is a growing threat to public health, as modern medicine relies heavily on effective drugs for combatting bacterial infections. The emergence of multi-drug resistant pathogens combined with the sluggish pace of drug discovery underscore the need for new treatment strategies that balance short-term drug efficacy with long-term evolutionary considerations. Drug combinations are one potential solution to minimize or reverse antibiotic resistance, but multi-drug treatments are difficult to systematically design because drugs frequently interact, strengthening or weakening the overall effect of a drug cocktail in counterintuitive ways. Recent studies suggest that drug interactions can have a significant impact on the evolution of resistance, though predicting evolution in multi-drug environments remains a challenge, in part because resistance to one drug is often correlated with altered sensitivity to other drugs.

Drug combinations are particularly important for successful treatment of E. faecalis, an opportunistic pathogen that contributes to multiple human infections, including endocarditis, bacteremia, urinary tract infections, and medical device infections. While numerous synergistic drug combinations for E. faecalis have been identified—and several are commonly used in clinical practice—much less is known about how these combinations impact the rate of resistance evolution. In this work, we use high-throughput laboratory evolution experiments to quantify adaptation in growth rate and drug resistance of E. faecalis exposed to clinically relevant drug combinations exhibiting different classes of interactions, ranging from synergistic to suppressive. We identify a wide range of evolutionary behavior, including both increased and decreased rates of adaptation, depending on the specific interplay between drug interaction and collateral drug sensitivity. To disentangle these effects, we generalized previous quantitative models based on drug concentration rescaling to account for collateral sensitivity between drugs. Our results highlight trade-offs between drug interactions and collateral effects during the evolution of multi-drug resistance and, more specifically, emphasize unappreciated evolutionary benefits of particular drug pairs in targeting aminoglycoside-resistant enterococcus. Overall, the results represent a quantitative case study in the evolution of multidrug resistance in an opportunistic human pathogen and provide a general framework for evaluating and predicting resistance evolution in multi-stress environments.