A quantitative examination of cellular mechanisms that modulate the impacts of genetic variation

项目摘要 数量遗传学和进化生物学的最大挑战之一是， 遗传变化的表型效应因环境和遗传背景而异。这 这使得识别复杂性状的遗传基础以及预测 当面临相似的选择时，进化将通过相同的遗传变化进行 压力当突变具有依赖于环境的影响时，它也可能导致根本性的 生物学研究中的可重复性问题。我实验室的目标是揭示背景- 通过从细胞生物学中汲取见解来依赖。我们假设细胞的基本特性 可以以可预测的方式改变突变的表型影响，并且存在这样的趋势， 解释特定类型的突变如何与特定类型的环境或遗传相互作用 扰动在接下来的五年里，我们将量化基因变化对 芽殖酵母的生长速率受（1）其他基因参与相同的调控， 网络，和（2）细胞中有毒错误折叠蛋白的积累。我们将设计大型工程 通过单点突变而不同的酵母菌株的数量，并量化这些突变的影响， 当我们系统地改变遗传背景（即损害每一个细胞）时， 网络中的基因，一次一个）或环境（逐渐增加压力水平 使蛋白质折叠不稳定）。这种策略-重新测量许多突变的影响， 我们慢慢地、系统地干扰系统--这使我们有能力区分一致的趋势 描述了突变的影响如何取决于环境。可行且具有成本效益的是， 测量数以千计的突变对数百个系统扰动的影响， 酵母遗传学的最新进展（例如对CRISPR系统和DNA的修饰） 条形码方法）。描述特定类型的突变如何响应 特定类型的扰动将使我们能够做出并测试关于（1） 未经测试的突变的影响将根据上下文而变化，以及（2） 实验室的进化实验将通过类似的遗传变化进行。总体而言，这 研究方向将导致生物学的一个机械和定量的理解， 最具挑战性的问题：突变的影响如何取决于环境。

Project summary One of the biggest challenges in quantitative genetics and evolutionary biology is that the phenotypic effects of genetic change differ across environments and genetic backgrounds. This makes it more difficult to identify the genetic basis of complex traits and to predict whether evolution will proceed via the same genetic changes when faced with similar selection pressures. When mutations have context-dependent effects, it can also lead to fundamental problems with reproducibility in biological research. My lab’s goal is to shed light on context- dependency by drawing insights from cell biology. We hypothesize that basic properties of cells can change the phenotypic impacts of mutation in predictable ways, and that trends exist that explain how specific types of mutations interact with specific types of environmental or genetic perturbations. In the next five years, we will quantify how the impact of genetic change on budding yeast’s growth rate is modulated by (1) other genes participating in the same regulatory network, and (2) accumulation of toxic misfolded proteins in cells. We will engineer large numbers of yeast strains that differ by single point mutations and quantify the impact of these mutations on cell growth as we systematically change the genetic background (i.e. impair each gene in the network, one at a time) or the environment (incrementally increase levels of a stress that destabilizes protein folding). This strategy – re-measuring the impacts of many mutations as we slowly and systematically perturb systems – gives us power to distinguish consistent trends that describe how the impact of mutations depends on context. It feasible and cost-effective to measure the impact of thousands of mutations across hundreds of systematic perturbations due to recent advances in yeast genetics (e.g. modifications to the CRISPR system and DNA barcoding approaches). The trends describing how particular types of mutations respond to particular types of perturbations will allow us to make, and to test, predictions about (1) how the impacts of untested mutations will change depending on context, and (2) ranges of conditions in which laboratory evolution experiments will proceed via similar genetic changes. Overall, this research direction will result in a mechanistic and quantitative understanding of one of biology’s most challenging questions: how the impacts of mutation depend on context.