Center for Interdisciplinary Research in Biology (CIRB), College de France, CNRS, INSERM, Université PSL, 75005 Paris, France.
Centre d'Ecologie Fonctionnelle et Evolutive (CEFE), CNRS, Univ Montpellier, EPHE, IRD, 34293 Montpellier, France.
Genetics. 2023 May 26;224(2). doi: 10.1093/genetics/iyad001.
Population bottlenecks are commonplace in experimental evolution, specifically in serial passaging experiments where microbial populations alternate between growth and dilution. Natural populations also experience such fluctuations caused by seasonality, resource limitation, or host-to-host transmission for pathogens. Yet, how unlimited growth with periodic bottlenecks influence the adaptation of populations is not fully understood. Here, we study theoretically the effects of bottlenecks on the accessibility of evolutionary paths and on the rate of evolution. We model an asexual population evolving on a minimal fitness landscape consisting of two types of beneficial mutations with the empirically supported trade-off between mutation rate and fitness advantage, in the regime where multiple beneficial mutations may segregate simultaneously. In the limit of large population sizes and small mutation rates, we show the existence of a unique most likely evolutionary scenario, determined by the size of the wild-type population at the beginning and at the end of each cycle. These two key demographic parameters determine which adaptive paths may be taken by the evolving population by controlling the supply of mutants during growth and the loss of mutants at the bottleneck. We do not only show that bottlenecks act as a deterministic control of evolutionary paths but also that each possible evolutionary scenario can be forced to occur by tuning demographic parameters. This work unveils the effects of demography on adaptation of periodically bottlenecked populations and can guide the design of evolution experiments.
人口瓶颈在实验进化中很常见，特别是在微生物种群在生长和稀释之间交替的连续传代实验中。自然种群也会经历由于季节性、资源限制或病原体在宿主之间的传播而引起的这种波动。然而，周期性瓶颈的无限增长如何影响种群的适应还不完全清楚。在这里，我们从理论上研究了瓶颈对进化路径的可及性和进化速度的影响。我们在一个最小的适应度景观上模拟了一个无性繁殖种群的进化，该景观由两种类型的有益突变组成，突变率和适应优势之间存在经验支持的权衡，在这种情况下，多个有益突变可能同时分离。在大种群规模和小突变率的极限下，我们展示了在每个循环的开始和结束时野生型种群的大小决定了唯一最可能的进化情景的存在。这两个关键的人口统计参数通过控制生长过程中突变体的供应和瓶颈处突变体的损失，决定了进化种群可能采取的适应途径。我们不仅表明瓶颈作为进化途径的确定性控制起作用，而且通过调整人口统计参数，每个可能的进化情景都可以被强制发生。这项工作揭示了人口统计学对周期性瓶颈种群适应的影响，并可以为进化实验的设计提供指导。
