Fitness variation across subtle environmental perturbations reveals local modularity and global pleiotropy of adaptation.
Fitness variation across subtle environmental perturbations reveals local modularity and global pleiotropy of adaptation.
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DOI：
10.7554/elife.61271
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发表时间：
2020-12-02
期刊：
影响因子：
7.7
通讯作者：
Petrov DA
中科院分区：
文献类型：
--
作者：
Kinsler G;Geiler-Samerotte K;Petrov DA
建立适应的基因型-表型-适应度图是进化生物学的中心目标。即使适应性突变已知，这也是很困难的，因为很难枚举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如何响应微妙的环境变化来解决这个问题。然后，我们通过分解这些适应度变异模式来模拟这些突变共同影响的表型数量。我们发现少量推断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附近的适应性。重要的是，推断的表型对于处于或接近进化条件的适应性影响不大，但在遥远的环境中却很重要。这表明适应性突变是局部模块化的——影响少数表型，这些表型对它们在进化环境中的适应性很重要——但具有全局多效性——影响可能减少或提高新环境适应性的其他表型。进化生物学的目标之一是了解基因型、表型和适应性之间的关系。有机体的基因（基因型）决定其身体和行为特征（表型）。反过来，表型又会影响生物体的生存和繁殖机会——即其适应性。然而，绘制这三个变量之间的关系绝非易事。最近，研究人员已经能够识别出许多增强生物体健康的基因突变，但要弄清楚这些突变如何影响生物体的表型以及它们为何有益却更加困难。帮助生物体在特定环境中茁壮成长的突变通常仅限于影响类似生物过程的少数基因。例如，在糖分有限的环境中生长的微生物往往会在涉及系统的基因中积累突变，这些系统决定是快速而粗心地生长，还是小心翼翼地生长，以防糖分永远得不到补充。这些突变可能都会影响相同的一种或两种表型，例如生长或蹲下的决定。如果是这样的话，研究人员应该能够轻松预测这些生物体适应新环境的能力。然而，特定的突变可能会影响几种表型，但这些额外的影响在环境发生变化并且这些表型被揭示之前仍然是看不见的。为了探索这种可能性，Kinsler、Geiler-Samerotte 和 Petrov 获得了数百个单独的酵母菌株，每个菌株都含有不同的突变，可以提高酵母在低糖环境中的适应性。他们将这些菌株放入类似的环境中并测量它们的适应性。观察到的模式被用来建立几个模型，预测每个突变必须影响多少表型才能解释适应性的变化。 Kinsler、Geiler-Samerotte 和 Petrov 发现，只有五种表型受到突变影响的模型能够预测酵母在低糖环境中的适应性。然而，为了预测相同突变在非常不同的环境中的适应性，该模型必须包括八种表型。这表明，尽管帮助酵母在低糖环境中表现良好的突变在这种环境中的益处相似，但它们并不完全相同。事实上，一些突变在其隐藏的表型效应方面与其他突变有很大不同。突变的隐藏影响可以是积极的，也可以是消极的。一种突变可能会导致有机体在新环境中死亡，而另一种突变可能会让它茁壮成长。了解其工作原理不仅对进化生物学有影响，而且对医学研究也有影响。引起感染的病原体和引起癌症的细胞通常会在少量关键基因中积累突变。了解这些突变如何影响随着环境变化而变得重要的表型（例如当肿瘤生长时细胞遇到新的挑战）以及不同的突变是否具有不同的隐藏效应，可以改善未来的治疗。
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. One of the goals of evolutionary biology is to understand the relationship between genotype, phenotype, and fitness. An organism's genes – its genotype – determine its physical and behavioral traits – its phenotype. Phenotypes, in turn, affect the organisms’ chances of survival and reproduction – its fitness. However, mapping the relationships among these three variables is far from easy. Recently researchers have become able to identify many genetic mutations that increase an organism's fitness, but it is more difficult to work out how these mutations affect an organism’s phenotype, and why they are beneficial. The mutations that help organisms thrive in a particular environment are often limited to a handful of genes that affect similar biological processes. For example, microbes that grow in environments with limited sugar tend to accumulate mutations in genes involved in systems that determine whether to grow fast and carelessly or to be careful in case the sugar is never replenished. It is possible that these mutations all affect the same one or two phenotypes, such as the decision to grow or to hunker down. If this were the case, researchers should be able to easily predict how well these organisms adapt to new environments. However, it is possible that specific mutations affect several phenotypes, but these extra effects remain invisible until the environment changes and these phenotypes are revealed. To explore this possibility, Kinsler, Geiler-Samerotte, and Petrov obtained hundreds of individual yeast strains that each contained a different mutation that improved the yeast's fitness in a low sugar environment. They placed these strains into similar environments and measured their fitness. The patterns observed were used to build several models that predicted how many phenotypes each mutation must affect to explain the changes in fitness. Kinsler, Geiler-Samerotte and Petrov found that the model in which only five phenotypes were affected by the mutations was able to predict the fitness of the yeast in low-sugar environments. However, to predict the fitness of the same mutations in environments that were very different, the model had to include eight phenotypes. This suggests that although the mutations that helped yeast do well in the low sugar environment were similar in their benefits in this environment, they were not truly all the same. In fact, some mutations were quite different from the others in terms of their hidden phenotypic effects. The hidden effects of mutations can be positive or negative. One mutation might cause an organism to die in a new environment, whereas another might allow it to thrive. Understanding how this works has implications not only for evolutionary biology, but also for medical research. Pathogens that cause infection, and cells that cause cancer, often accumulate mutations in small numbers of crucial genes. Understanding how these mutations affect phenotypes that become important as the environment changes – for instance as the cells encounter new challenges as a tumor grows – and whether different mutations have different hidden effects, could improve treatments in the future.