Department of Biology, Stanford University, Stanford, United States.
Center for Mechanisms of Evolution, School of Life Sciences, Arizona State University, Tempe, United States.
Elife. 2020 Dec 2;9:e61271. doi: 10.7554/eLife.61271.
Building a genotype-phenotype-fitness map of adaptation is a central goal in evolutionary biology. It is difficult even when adaptive mutations are known because it is hard to enumerate which phenotypes make these mutations adaptive. We address this problem by first quantifying how the fitness of hundreds of adaptive yeast mutants responds to subtle environmental shifts. We then model the number of phenotypes these mutations collectively influence by decomposing these patterns of fitness variation. We find that a small number of inferred phenotypes can predict fitness of the adaptive mutations near their original glucose-limited evolution condition. Importantly, inferred phenotypes that matter little to fitness at or near the evolution condition can matter strongly in distant environments. This suggests that adaptive mutations are locally modular - affecting a small number of phenotypes that matter to fitness in the environment where they evolved - yet globally pleiotropic - affecting additional phenotypes that may reduce or improve fitness in new environments.
构建适应的基因型-表型-适应性图谱是进化生物学的一个核心目标。即使知道适应性突变,这也是困难的,因为很难确定哪些表型使这些突变具有适应性。我们通过首先量化数百种适应性酵母突变体的适应性对微妙环境变化的响应来解决这个问题。然后,我们通过分解这些适应性变化模式来模拟这些突变共同影响的表型数量。我们发现,少数推断出的表型可以预测适应性突变在其原始葡萄糖限制进化条件下的适应性。重要的是,在进化条件下或附近对适应性影响不大的推断表型在遥远的环境中可能具有很强的影响。这表明适应性突变是局部模块化的 - 只影响少数对进化环境中适应性重要的表型 - 但在全局上是多效性的 - 影响新环境中可能降低或提高适应性的其他表型。