Population genetic inference from the adaptive immune system

描述(由申请人提供)：我在理论种群遗传学方面的研究生培训教会了我如何利用种群中当前的遗传多样性模式来重建该种群所走的进化道路。新技术为这类建模和分析开辟了新的领域。特别是，人类适应性免疫系统通过V(D)J重组在单个个体内产生了大量的遗传多样性。与个体(即一个物种)群体水平上的进化类似，单个个体内的免疫细胞群体随着时间的推移而进化，以响应突变、选择、迁移和遗传漂移。测序技术的进步允许通过在多个时间点对多个基因座进行深度测序来对单个个体内的这种多样性进行精细的询问。然而，在回答生物学问题之前，这些新数据需要新的解释方法。这个项目的具体目标将是：(1)扩展现有的群体遗传模型，以从跟踪单个免疫细胞群体(例如CD8 T细胞)的多样性的时间序列数据中推断选择的强度，同时考虑到测序错误，(2)通过将这些模型应用于来自小鼠的大量T细胞数据来验证这些模型，其中可以使用侵入性实验技术，以及必须推断a-b关联的更受限的人类T细胞数据，(3)开发一种新的群体遗传模型，该模型同时连接在多个细胞群体中选择的效果，比如当CD4T细胞协同“帮助”CD8T细胞时，具有相似的特异性。AIM 2中使用的数据将由我的实验合作者拉菲·艾哈迈德博士和约瑟夫·布拉特曼博士提供。我的导师Rustom antia博士将指导我将进化论与他在理论免疫学方面的专长结合起来，以推动我开始一条新的个体内群体遗传学研究路线的目标。

DESCRIPTION (provided by applicant): My graduate training in theoretical population genetics taught me how to use the current patterns of genetic diversity in a population to reconstruct the evolutionary path taken by that population. New technology has opened new frontiers for this type of modeling and analysis. In particular, the human adaptive immune system generates a tremendous amount of genetic diversity within a single individual via V(D)J recombination. Similar to evolution at the level of a population of individuals (i.e. a species), the population of immune cells within a single individual evolves over time in response to mutation, selection, migration, and genetic drift. Advances in sequencing technology allow fine-scale interrogation of this diversity within single individuals by deep sequencing of multiple loci at multiple timepoints. However, these new data require new methods for interpretation before biological questions can be answered. The specific aims of this project will be to: (1) extend existing population genetic models to infer the strength of selection from time-series data that tracks the diversity of a single population of immune cells (e.g., CD8 T cells) while accounting for sequencing error, (2) validate these models by applying them to extensive T cell data from mice, where invasive experimental techniques can be used, and more restricted human T cell data where a-b associations must be inferred, (3) develop a novel population genetic model linking the effects of selection in multiple cell populations simultaneously, such as when CD4 T cells synergistically "help" CD8 T cells of similar specificity. The data used in Aim 2 will be provided by my experimental collaborators, Dr. Rafi Ahmed and Dr. Joseph Blattman. My mentor Dr. Rustom Antia will guide my uniting of evolutionary theory with his specialty in theoretical immunology to further my goal of starting a new line of research in intra-individual population genetics.