Distinguishing mutants that resist drugs via different mechanisms by examining fitness tradeoffs

Abstract

There is growing interest in designing multidrug therapies that leverage tradeoffs to combat drug resistance. Tradeoffs are common in evolution and occur when, for example, resistance to one drug results in sensitivity to another. Major questions remain about the extent to which tradeoffs are reliable, specifically, whether the mutants that provide resistance to a given drug all suffer similar tradeoffs. This question is difficult because the drug-resistant mutants observed in the clinic, and even those evolved in controlled laboratory settings, are often biased towards those that provide large fitness benefits. Thus, the mutations (and mechanisms) that provide drug resistance may be more diverse than current data suggests. Here, we perform evolution experiments utilizing lineage-tracking to capture a fuller spectrum of mutations that give yeast cells a fitness advantage in fluconazole, a common antifungal drug. We then quantify fitness tradeoffs for each of 774 evolved mutants across 12 environments, finding these mutants group into six classes with characteristically different tradeoffs. Their unique tradeoffs may imply that each group of mutants affects fitness through different molecular mechanisms. Some of the groupings we find are surprising. For example, we find some mutants that resist single drugs do not resist their combination, and some mutants to the same gene have different tradeoffs than others. These findings, on one hand, demonstrate the difficulty in relying on consistent or intuitive tradeoffs when designing multidrug treatments that thwart resistance. On the other hand, by demonstrating that hundreds of adaptive mutations can be reduced to a relatively smaller number of groups, our findings suggest that resistance evolves through a relatively small number of mechanisms such that multidrug strategies that hinder resistance may be possible. Finally, by grouping mutants that likely affect fitness through similar underlying mechanisms, our findings also guide efforts to map the phenotypic impacts of mutation and predict the impacts of some mutations from others.