Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ.
School of Life Sciences, Arizona State University, Tempe AZ.
bioRxiv. 2024 Jun 11:2023.10.17.562616. doi: 10.1101/2023.10.17.562616.
There is growing interest in designing multidrug therapies that leverage tradeoffs to combat 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 6 classes with characteristically different tradeoffs. Their unique tradeoffs may imply that each group of mutants affects fitness through different underlying mechanisms. Some of the groupings we find are surprising. For example, we find some mutants that resist single drugs do not resist their combination, while others do. 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. On the other hand, by demonstrating that hundreds of adaptive mutations can be reduced to a few groups with characteristic tradeoffs, our findings may yet empower multidrug strategies that leverage tradeoffs to combat resistance. More generally speaking, by grouping mutants that likely affect fitness through similar underlying mechanisms, our work guides efforts to map the phenotypic effects of mutation.
设计利用权衡来对抗耐药性的多药疗法正引发越来越多的关注。权衡在进化中很常见，例如，对一种药物的耐药性会导致对另一种药物敏感时就会出现权衡。关于权衡在多大程度上可靠，尤其是对给定药物产生耐药性的突变体是否都经历类似的权衡，仍然存在重大问题。这个问题很难解决，因为在临床上观察到的耐药突变体，甚至在受控实验室环境中进化出的突变体，往往偏向于那些能带来巨大适应性益处的突变体。因此，产生耐药性的突变（和机制）可能比目前的数据所显示的更加多样。在这里，我们利用谱系追踪进行进化实验，以捕捉更全面的能使酵母细胞在常见抗真菌药物氟康唑中获得适应性优势的突变谱。然后，我们在12种环境中对774个进化突变体中的每一个进行适应性权衡量化，发现这些突变体分为6类，具有明显不同的权衡特征。它们独特的权衡可能意味着每组突变体通过不同的潜在机制影响适应性。我们发现的一些分组令人惊讶。例如，我们发现一些对单一药物有抗性的突变体对其组合药物没有抗性，而其他一些则有抗性。而且同一基因的一些突变体与其他突变体有不同的权衡。一方面，这些发现表明在设计多药治疗方案时，依赖一致或直观的权衡是困难的。另一方面，通过证明数百种适应性突变可以归纳为具有特征性权衡的少数几组，我们的发现可能会助力利用权衡来对抗耐药性的多药策略。更一般地说，通过将可能通过类似潜在机制影响适应性的突变体分组，我们的工作为绘制突变的表型效应提供了指导。
