Characterizing variation in the local structure of fitness landscapes to assess the predictability of evolution

派：克里亚希姆斯基，谢尔盖 项目摘要/摘要 癌症会产生抗药性吗？一种病毒会跳跃宿主并导致新的大流行吗？这些问题的根源是 其他公共卫生问题是进化。支配进化的原则是众所周知的，但我们不能 预测它并回答上面提出的问题。我们可能对两种进化论预测感兴趣。 随着时间的推移，表型会发生怎样的变化？哪些基因变化会导致这种进化？在许多 人口，适应是由新的突变驱动的。一个主要的挑战是，新的突变对 表型和适合度取决于它们产生的遗传背景。这种依赖关系称为 “上位性”，在表型和遗传水平上都使进化预测复杂化。 可以预测的最简单的表型是健康状况。要做到这一点，我们需要知道新的突变是如何影响健康的。这 信息包含在称为新突变适合度效应分布(DFE)的数量中。上位性 可以导致DFE因不同的基因而不同，我们不知道它是如何变化的。目标1 这项研究是为了预测适应度的进化。为了实现这一点，我们将测量DFE在不同基因类型之间的差异 在进化中出现，并在这种变化中找到规律。 预测遗传进化更加困难，因为通常有太多不同的适应性突变 可能会出现在人群中。上位主义使情况进一步复杂化。如果没有上位性，或者如果有上位性 在一种基因中有益的所有突变在所有其他基因中也是有益的，那么只有一种 (即，高度可预测的)适应性进化的最终遗传结果，但突变路径的数量 导致它的将是非常大的。预测任何给定人口所走的道路很可能是不可能的。 另一方面，如果上位性使某些基因类型有益的某些突变对其他基因类型有害， 自然选择可获得的突变路径的数量将会减少，使它们更具可预测性。 然而，这些途径可能导致不同的最终基因型别，从而使进化的结果更难预测。 宝。不同类型的上位症的流行情况尚不清楚。这项研究的目标2是了解 可预测的突变路径和进化结果是。为了实现这一目标，我们将衡量 突变因基因类型而异，并可量化不同类型的上位性。 我们将在实验酵母种群中使用几种新的基因工程和 测序技术。我们将使用条形码谱系跟踪来估计多个酵母菌株的DFES，如 以及化学和插入突变与基于条形码测序的适合性分析相结合。我们将意味着- 使用一种新的Crispey方法--全基因组测序--研究数百个个体突变的适合度效应 适应性突变体的ING和插入突变。最后，我们将把这些数据合成到一个“适合度”模型中 景观“，并试图预测我们人口的进化。 1

PI: Kryazhimskiy, Sergey Project Summary/Abstract Will a cancer develop drug resistance? Will a virus jump hosts and cause a new pandemic? The root of these and other public health concerns is evolution. The principles that govern evolution are well understood, yet we cannot predict it and answer the questions posed above. We might be interested in two kinds of evolutionary predictions. How would a phenotype change over time? And which genetic changes would cause this evolution? In many populations, adaptation is driven by new mutations. A major challenge is that the effects of new mutations on phenotypes and on fitness depend on the genetic background in which they arise. Such dependencies, called “epistasis”, complicate evolutionary prediction at both the phenotypic and genetic levels. The simplest phenotype to predict is fitness. To do so, we need to know how new mutations affect fitness. This information is contained in the quantity called the distribution of fitness effects of new mutations (DFE). Epistasis can cause the DFE to vary from on genotype to another, and we do not understand how it varies. Objective 1 of this research is predict fitness evolution. To achieve it, we will measure how DFEs vary across genotypes that arise in evolution and find regularities in this variation. Predicting genetic evolution is more difficult because there are usually too many different adaptive mutations that can arise in the population. Epistasis complicates the situation further. If there is no epistasis or if epistasis is such that all mutations beneficial in one genotype are also beneficial in all other genotypes, there would be only one (i.e., highly predictable) eventual genetic outcome of adaptive evolution, but the number of mutational paths leading to it would be very large. Predicting which path any given population takes would likely be impossible. On the other hand, if epistasis makes certain mutations that are beneficial in some genotypes deleterious in others, the number of mutational paths accessible to natural selection would decrease, making them more predictable. However, these paths could lead to different eventual genotypes making the outcomes of evolution less predicta- ble. The prevalence of different types of epistasis is unknown. Objective 2 of this research is to understand how predictable mutational paths and evolutionary outcomes are. To achieve it, we will measure how the effects of mutations vary across genotypes and quantify different types of epistasis. We will approach our objectives in experimental yeast populations using several novel genetic engineering and sequencing techniques. We will estimate the DFEs in multiple yeast strains using barcode lineage tracking, as well as chemical and insertion mutagenesis coupled with barcode sequencing-based fitness assays. We will meas- ure the fitness effects of hundreds of individual mutations using a novel CRISPEY method, full-genome sequenc- ing of adaptive mutants and insertion mutagenesis. Finally, we will synthesize these data in a model of a “fitness landscape” and attempt to predict evolution of our populations. 1