Frenkel Evgeni M, Good Benjamin H, Desai Michael M
Program in Biophysics, Harvard University, Cambridge, Massachusetts 02138.
Genetics. 2014 Apr;196(4):1217-26. doi: 10.1534/genetics.113.160069. Epub 2014 Feb 10.
The outcomes of evolution are determined by which mutations occur and fix. In rapidly adapting microbial populations, this process is particularly hard to predict because lineages with different beneficial mutations often spread simultaneously and interfere with one another's fixation. Hence to predict the fate of any individual variant, we must know the rate at which new mutations create competing lineages of higher fitness. Here, we directly measured the effect of this interference on the fates of specific adaptive variants in laboratory Saccharomyces cerevisiae populations and used these measurements to infer the distribution of fitness effects of new beneficial mutations. To do so, we seeded marked lineages with different fitness advantages into replicate populations and tracked their subsequent frequencies for hundreds of generations. Our results illustrate the transition between strongly advantageous lineages that decisively sweep to fixation and more moderately advantageous lineages that are often outcompeted by new mutations arising during the course of the experiment. We developed an approximate likelihood framework to compare our data to simulations and found that the effects of these competing beneficial mutations were best approximated by an exponential distribution, rather than one with a single effect size. We then used this inferred distribution of fitness effects to predict the rate of adaptation in a set of independent control populations. Finally, we discuss how our experimental design can serve as a screen for rare, large-effect beneficial mutations.
进化的结果取决于发生并固定下来的突变。在快速适应的微生物群体中，这一过程尤其难以预测，因为具有不同有益突变的谱系往往会同时传播并相互干扰对方的固定。因此，为了预测任何单个变体的命运，我们必须了解新突变产生更高适应性竞争谱系的速率。在这里，我们直接测量了这种干扰对实验室酿酒酵母群体中特定适应性变体命运的影响，并利用这些测量结果推断新的有益突变的适应性效应分布。为此，我们将具有不同适应性优势的标记谱系接种到重复群体中，并跟踪它们在数百代中的后续频率。我们的结果说明了具有决定性优势的谱系向固定状态的强烈转变，以及在实验过程中经常被新突变淘汰的优势较弱的谱系。我们开发了一个近似似然框架，将我们的数据与模拟结果进行比较，发现这些竞争性有益突变的效应最好用指数分布来近似，而不是用单一效应大小的分布。然后，我们使用这个推断出的适应性效应分布来预测一组独立对照群体中的适应速率。最后，我们讨论了我们的实验设计如何能够作为一种筛选罕见的、具有大效应的有益突变的方法。
