Fitness and Modularity of Stochastic Variation in Protein Expression Levels

描述(申请人提供)：拟议工作的目标是系统地识别蛋白质表达水平的随机细胞间差异的后果，这在所有生命系统中的许多基因中都是普遍存在的。为了做到这一点，我们将测量发芽酵母中一组蛋白质在细胞中的表达水平分布，在不同表达状态之间随机切换的速率和模块化，以及细胞间变异分布的自然遗传变异。人们假设，动态环境在随机表达变异的进化中起着推动作用。使用发芽酵母作为研究系统，将通过测量不同压力处理的随机表达的适合度结果的不同程度来系统地检验这一假设。这项调查还将系统地揭示押注套期保值的证据，这是一种策略，根据这种策略，随机表达变化有助于环境变化后种群内的差异生存。随机表达变异的一个基本参数是细胞在不同调控状态之间的转换速率，称为表达状态转换率(ESSR)。对于大多数蛋白质来说，ESSR以在种群中的细胞中连续分布表达的形式产生噪音。基因在ESSR中表现出差异的假设将得到系统的检验。虽然以前已经一次测量到一个蛋白质的细胞间差异，但尚不清楚这种随机性是否反映了整个基因组表达的更大规模的模块化重塑，例如作为环境应激反应的一部分，本研究将提供第一个关于随机表达变化的模块化的经验测试。BET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进化在遗传背景中的差异程度尚未得到系统测试。这将在一组遗传背景中对关键基因进行测试。细胞特异性蛋白表达水平的测量将使用流式细胞术。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时间推移显微镜和使用流式细胞术根据表达水平的差异将细胞分成不同的群体，然后测量每个亚群体的增长率和一段时间内蛋白质水平的变化来测量。在特定蛋白质中存在随机差异的细胞是否也在参与相同调控模块的其他基因中表现出表达差异，将通过对按表达水平排序的细胞群体应用转录组测序来测试。最后，将通过在基因和生态上不同的萌芽酵母菌株(包括致病菌株、农业菌株和野生菌株)之间创建GFP融合蛋白来分析随机表达的遗传变异及其在这些蛋白质中的后果。结果将是确定在这些环境中的一个或多个环境中表现出随机表达的遗传变异的蛋白质，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.