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

项目总结 数量遗传学和进化生物学面临的最大挑战之一是 遗传变化的表型效应因环境和遗传背景而异。这 使得识别复杂性状的遗传基础和预测 当面临相似的选择时，进化将通过相同的基因变化进行 压力。当突变具有上下文相关效应时，它还可能导致根本的 生物学研究中的重复性问题。我的实验室的目标是阐明背景- 通过从细胞生物学中汲取洞察力来实现对药物的依赖。我们假设细胞的基本性质 可以以可预测的方式改变突变的表型影响，而且存在这样的趋势 解释特定类型的突变如何与特定类型的环境或基因相互作用 微扰。在接下来的五年里，我们将量化基因变化对 芽殖酵母的生长速度受(1)参与同一调控的其他基因的调节 以及(2)细胞内有毒的错误折叠蛋白的积累。我们将设计大型工程 单点突变差异的酵母菌株数量和量化这些突变的影响 突变对细胞生长的影响，因为我们系统性地改变了遗传背景(即每个突变都受到损害 网络中的基因，一次一个)或环境(逐渐增加压力水平 这会破坏蛋白质折叠的稳定性)。这一策略-重新衡量许多突变的影响，如 我们缓慢而系统地扰乱系统--让我们有能力辨别一致的趋势 它们描述了突变的影响如何依赖于环境。这是可行的和具有成本效益的 衡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的修改 条形码方法)。描述特定类型的突变如何响应的趋势 特定类型的扰动将允许我们做出并测试关于(1)如何 未经测试的突变的影响将根据环境而变化，以及(2) 哪些实验室进化实验将通过类似的基因变化进行。总体而言，这 研究方向将导致对生物学的一种机械和定量的理解 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.