Genetic Interactions and Synthetic Population Biology

描述(申请人提供)：遗传相互作用和合成种群生物学细胞行为由相互作用的基因和蛋白质的回路决定。在远缘繁殖的种群中，这些环路的组成是不同的：单个生物体包含不同的等位基因集，导致表型特征的变异。由于等位基因之间可能存在复杂的相互作用或上位性，因此将基因变异和表型变异联系起来是具有挑战性的。也就是说，一个基因座上的等位基因可以改变另一个基因座上的等位基因的相对效应。这些相互作用可以，也确实会对表型产生很大影响，从病毒获得抗药性到自闭症和其他复杂的疾病。然而，关于上位性强度在遗传因素之间的数量分布以及上位性相互作用对进化的影响，特别是在有性繁殖种群中，人们知之甚少。这些问题具有挑战性，因为它们需要处理遗传多样性的种群，而不是克隆种群；依赖于拥有可靠的基因型和表型图谱以及用于定量解释进化动态的数学框架。因此，拟议的工作将涉及理论、计算和实验工作的结合。实验平台将以酿酒酵母为基础，使用合成生物学方法创建明确的相互作用的遗传电路，允许对上位性的性质和强度进行定量表征和操纵，并能够直接实时测量种群动态。我们的合作研究计划将理论和实验方法以及Elowitz和Shraiman实验室的跨学科专业知识紧密结合在一起。更具体地说：(1)我们将建立理论模型，描述上位性相互作用和重组对群体中等位基因和基因型动态的综合影响。这些模型将被用来分析关键的群体遗传现象，包括遗传变异的维持、连锁不平衡和远缘繁殖衰退。(2)我们将构建一组具有可编程的上位性交互作用的合成遗传回路，每个分量的备选等位基因之间存在相互作用。该系统将基于酿酒酵母中的锌指转录调控模块，并将允许使用高通量单细胞荧光显微镜实时监测遗传多样性群体中的基因分布。(3)我们将利用这个综合系统来检验关于遗传变异、远缘繁殖衰退和连锁不平衡的具体理论预测。在这些实验中，遗传多样性的酵母种群(带有设计的相互作用的等位基因)将在受控的选择压力下经历生长周期，随后进行交配和重组。我们将使用单细胞荧光测量来跟踪基因分布的动态，为与模型预测进行比较提供定量数据。总而言之，这些目标将为在简化的近交实验室种群中操纵上位性交互作用，以及理解自然近亲交配种群中上位性交互作用的后果提供基础。 公共卫生相关性：拟议的工作将架起系统生物学和种群遗传学之间的桥梁。这一结果将为上位性互作对近交群体遗传结构的影响提供定量的见解。这些基本问题对于理解群体遗传学和观察到的复杂疾病表型之间的联系至关重要，因此也是基因组医学发展的核心。

DESCRIPTION (provided by applicant): Genetic Interactions and Synthetic Population Biology Cellular behavior is determined by circuits of interacting genes and proteins. In outbred populations, the components of these circuits are diverse: individual organisms contain different sets of alleles causing variation in phenotypic traits. Relating genotypic and phenotypic variation is challenging because of the possibility of complex interactions, or epistasis, between the alleles. That is, the allele present at one locus can change the relative effect of an allele at another locus. These interactions can, and do, have large effects on phenotypes ranging from acquisition of drug resistance by viruses to autism and other complex disorders. However, little is known about the quantitative distribution of strength of epistasis between genetic elements and what consequences epistatic interactions have for evolution, especially in sexually reproducing populations. These questions are challenging because they require working with genetically diverse, rather than clonal, populations; depend on having a reliable map of genotypes and phenotypes and a mathematical framework for quantitative interpretation of evolutionary dynamics. Proposed work therefore will involve a combination of theoretical, computational and experimental work. The experimental platform will be based on S. cerevisiae and use the synthetic biology approach to create defined interacting genetic circuits, allow quantitative characterization and manipulation of the nature and strength of epistasis, and enable direct real time measurements of population dynamics. Our collaborative research plan tightly integrates theoretical and experimental approaches and the interdisciplinary expertise of the Elowitz and Shraiman laboratories. More specifically: (1) We will develop theoretical models describing the combined effect of epistatic interactions and recombination on the dynamics of alleles and genotypes in populations. These models will be used to analyze key population genetic phenomena, including maintenance of genetic variation, linkage disequilibrium, and outbreeding depression. (2) We will construct a set of synthetic genetic circuits with programmable epistatic interactions between alternative alleles for each component. This system will be based on zinc finger transcriptional regulation modules in S. cerevisiae, and will allow real-time monitoring of the genotype distribution within a genetically diverse population using high-throughput single-cell fluorescence microscopy. (3) We shall use this synthetic system to test specific theoretical predictions concerning genetic variation, outbreeding depression and linkage disequilibrium. In these experiments, genetically diverse yeast populations (with designed interacting alleles) will be subjected to cycles of growth under controlled selection pressure followed by mating and recombination. We will use single cell fluorescence measurements to follow the dynamics of the genotype distribution, providing quantitative data for comparison with model predictions. Together, these aims will provide a foundation for both manipulating epistatic interactions in a simplified outbred laboratory population, and understanding the consequences of epistatic interactions in natural outbred populations. PUBLIC HEALTH RELEVANCE: Proposed work will bridge systems biology and population genetics. The results will provide quantitative insight into the effect of epistatic interactions on the genetic structure of outbred populations. These fundamental issues are critical for understanding the link between population genetics and the observed complex disease phenotypes, and are thus central to the development of genomic medicine.