Genetic Interactions and Synthetic Population Biology

描述（由申请人提供）：遗传相互作用和合成群体生物学细胞行为是由相互作用的基因和蛋白质的电路决定的。在远交种群中，这些回路的组成是多样的：个体生物体包含不同的等位基因组，导致表型性状的变异。由于等位基因之间可能存在复杂的相互作用或上位性，因此将基因型和表型变异相关联是具有挑战性的。也就是说，存在于一个基因座的等位基因可以改变另一个基因座的等位基因的相对效应。这些相互作用可以并且确实对表型产生巨大影响，从病毒获得耐药性到自闭症和其他复杂疾病。然而，很少有人知道的数量分布的遗传因素之间的上位性的强度和上位性相互作用的进化，特别是在有性繁殖的人口有什么后果。这些问题是具有挑战性的，因为它们需要与遗传多样性，而不是克隆，人口工作;依赖于有一个可靠的基因型和表型的地图和进化动力学的定量解释的数学框架。因此，拟议的工作将涉及理论，计算和实验工作的结合。实验平台将基于S.酿酒酵母和使用合成生物学方法来创建定义的相互作用的遗传电路，允许定量表征和操纵上位性的性质和强度，并能够直接真实的时间测量种群动态。我们的合作研究计划紧密结合了理论和实验方法以及Elowitz和Shraiman实验室的跨学科专业知识。 更具体地说：（1）建立上位性互作和重组对群体中等位基因和基因型动态的综合影响的理论模型。这些模型将用于分析关键的群体遗传现象，包括遗传变异的维持、连锁不平衡和远系繁殖衰退。(2)我们将构建一组合成遗传电路，每个组件的替代等位基因之间具有可编程的上位相互作用。该系统将基于S.酿酒酵母，并将允许使用高通量单细胞荧光显微镜实时监测基因多样性群体内的基因型分布。(3)我们将使用这个综合系统来检验有关遗传变异、远交衰退和连锁不平衡的具体理论预测。在这些实验中，遗传多样性酵母菌群（具有设计的相互作用等位基因）将在受控选择压力下经历生长循环，然后进行交配和重组。我们将使用单细胞荧光测量来跟踪基因型分布的动态，为与模型预测的比较提供定量数据。总之，这些目标将提供一个基础，既操纵上位相互作用在一个简化的远交实验室人口，并了解自然远交人口上位相互作用的后果。 公共卫生相关性：拟议的工作将连接系统生物学和群体遗传学。研究结果将为上位性相互作用对远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，因此对基因组医学的发展至关重要。

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.