Cairns Johannes, Borse Florian, Mononen Tommi, Hiltunen Teppo, Mustonen Ville
Organismal and Evolutionary Biology Research Programme (OEB), Department of Computer Science University of Helsinki Helsinki 00014 Finland.
Department of Microbiology University of Helsinki Helsinki 00014 Finland.
Evol Lett. 2022 May 26;6(3):266-279. doi: 10.1002/evl3.284. eCollection 2022 Jun.
The impact of fitness landscape features on evolutionary outcomes has attracted considerable interest in recent decades. However, evolution often occurs under time-dependent selection in so-called fitness seascapes where the landscape is under flux. Fitness seascapes are an inherent feature of natural environments, where the landscape changes owing both to the intrinsic fitness consequences of previous adaptations and extrinsic changes in selected traits caused by new environments. The complexity of such seascapes may curb the predictability of evolution. However, empirical efforts to test this question using a comprehensive set of regimes are lacking. Here, we employed an in vitro microbial model system to investigate differences in evolutionary outcomes between time-invariant and time-dependent environments, including all possible temporal permutations, with three subinhibitory antimicrobials and a viral parasite (phage) as selective agents. Expectedly, time-invariant environments caused stronger directional selection for resistances compared to time-dependent environments. Intriguingly, however, multidrug resistance outcomes in both cases were largely driven by two strong selective agents (rifampicin and phage) out of four agents in total. These agents either caused cross-resistance or obscured the phenotypic effect of other resistance mutations, modulating the evolutionary outcome overall in time-invariant environments and as a function of exposure epoch in time-dependent environments. This suggests that identifying strong selective agents and their pleiotropic effects is critical for predicting evolution in fitness seascapes, with ramifications for evolutionarily informed strategies to mitigate drug resistance evolution.
近几十年来，适应度景观特征对进化结果的影响引起了广泛关注。然而，进化往往发生在所谓的适应度海景中随时间变化的选择之下，其中景观处于不断变化之中。适应度海景是自然环境的一个固有特征，景观的变化既源于先前适应的内在适应度后果，也源于新环境导致的所选性状的外在变化。这种海景的复杂性可能会抑制进化的可预测性。然而，缺乏使用一套全面机制来检验这个问题的实证研究。在这里，我们采用了一个体外微生物模型系统，以研究在时间不变和随时间变化的环境之间进化结果的差异，包括所有可能的时间排列，使用三种亚抑制性抗菌剂和一种病毒寄生虫（噬菌体）作为选择剂。不出所料，与随时间变化的环境相比，时间不变的环境对耐药性产生了更强的定向选择。然而，有趣的是，在这两种情况下，多药耐药结果在很大程度上是由总共四种药剂中的两种强选择剂（利福平和平噬菌体）驱动的。这些药剂要么导致交叉耐药，要么掩盖了其他耐药突变的表型效应，在时间不变的环境中总体上调节进化结果，并在随时间变化的环境中作为暴露时期的函数调节进化结果。这表明，识别强选择剂及其多效性效应对于预测适应度海景中的进化至关重要，这对减轻耐药性进化的基于进化的策略具有影响。
