Fitness and Modularity of Stochastic Variation in Protein Expression Levels

描述（由申请人提供）：拟议工作的目标是系统地鉴定蛋白质表达水平的随机细胞间变异的后果，这对所有生命系统中的许多基因都是普遍存在的。为了做到这一点，我们将测量芽殖酵母中一组蛋白质的细胞间表达水平的分布，替代表达状态之间随机切换的速率和模块性，以及细胞间变异分布中的自然遗传变异。 它已被假设，动态环境中发挥了驱动作用的随机表达变化的演变。使用芽殖酵母作为研究系统，这一假设将通过测量随机表达的适应性后果在应力处理中的差异程度来系统地检验。这项调查还将系统地揭示赌注对冲的证据，这是一种策略，通过这种策略，随机表达变异有助于在环境发生变化后群体内的差异生存。随机表达变化的一个基本参数是细胞在替代调节状态之间切换的速率，称为表达状态切换速率（ESSR）。对于大多数蛋白质，ESSR以群体中细胞间表达的连续分布的形式产生噪音。基因在ESSR中表现出差异的假设将被系统地检验。尽管之前已经一次测量了一种蛋白质的细胞间变异，但尚不清楚这种随机性是否反映了整个基因组中更大规模的表达模块化重塑，例如作为环境应激反应的一部分，而这项研究将提供随机表达变异模块化的第一个实证测试。赌注对冲、细胞记忆和表观遗传的功能重要性表明它们是适应新环境和生态的重要机制。然而，随机蛋白质表达和ESSR进化的程度是可变的遗传背景之间还没有系统地测试。这将在一组遗传背景中测试关键基因。 将使用流式细胞术测量细胞特异性蛋白表达水平。这些蛋白质的表达状态转换率及其对细胞生长的影响将通过延时显微镜和通过使用流式细胞术基于表达的差异水平将细胞分选成单独的群体，然后测量生长率和每个亚群中蛋白质水平随时间的变化来测量。将通过对按表达水平分选的细胞群应用转录组测序来测试在特定蛋白质中具有随机差异的细胞是否也在相同调控模块中涉及的其他基因中表现出表达差异。最后，随机表达中的遗传变异及其在这些蛋白质中的每一种之间的后果将通过在芽殖酵母的遗传和生态多样性菌株（包括致病性菌株、农业菌株和野生菌株）中产生GFP融合蛋白来测定。其结果将是鉴定在一种或多种这些环境中随机表达中显示遗传变异的蛋白质，这不能用蛋白质丰度的简单变化来解释。

DESCRIPTION (provided by applicant): The goal of the proposed work is to systematically identify consequences of stochastic cell- to-cell variation in protein expression levels, which is pervasive for many genes in all living systems. In order to do this we will measure the distribution of expression levels among cells for a panel of proteins in budding yeast, the rate and modularity of stochastic switching between alternative expression states, and natural genetic variation in cell-to-cell variation distributions. It has been hypothesized that dynamic environments play a driving role in the evolution of stochastic expression variation. Using budding yeast as a study system, this hypothesis will be systematically tested by measuring the extent that the fitness consequences of stochastic expression differ across stress treatments. This survey will also systematically uncover evidence for bet hedging, a strategy whereby stochastic expression variation contributes to the differential survival within the population following a shift in the environment. A fundamental parameter of stochastic expression variation is the rate at which cells switch among alternative regulatory states, called the expression state switching rate (ESSR). For most proteins, the ESSR creates noise in the form of a continuous distribution of expression across cells in a population. The hypothesis that genes exhibit differences in ESSRs will be systematically tested. Although cell-to-cell variation has been previously measured one-protein at a time, it is not known whether such stochasticity reflects larger scale modular remodeling of expression across the genome, for example as part of the environmental stress response, and this study will provide the first empirical test of the modularity of stochastic expression variation. The functional importance of bet hedging, cellular memory, and epigenetic inheritance suggests they are important mechanisms for adaptation to new environments and ecologies. However, the extent that stochastic protein expression and ESSR evolve are variable among genetic backgrounds has not been systematically tested. This will be tested for key genes across a panel of genetic backgrounds. Measurement of cell-specific protein expression levels will be made using flow cytometry. Expression state switching rate for these proteins and its effect on cellular growth will be measured by time-lapse microscopy and by using flow cytometry to sort cells into separate populations based on the differential level of expression, and then measuring both growth rates and changes in protein levels in each sub-population over a time. Whether cells with stochastic differences in a particular protein also exhibit expression differences in other genes involved in the same regulatory modules will be tested by applying transcriptome sequencing to cell populations sorted by expression level. Finally, genetic variation in stochastic expression and its consequences among each of these proteins will be assayed by creating GFP-fusion proteins across genetically and ecologically diverse strains of budding yeast, including pathogenic, agricultural, and wild strains. The outcome will be identification of proteins that show genetic variation in stochastic expression in one or more of these environments, which cannot be explained by simple changes in protein abundance.