Population genetic inference from the adaptive immune system

从适应性免疫系统进行群体遗传推断

• 批准号: 9011108
• 负责人: Philip Lee Falk Johnson
• 金额: $ 24.9万
• 依托单位: UNIV OF MARYLAND, COLLEGE PARK
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-05-02 至 2018-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9011108
• 关键词: Accounting; Affect; Aging; Alleles; Autoimmune Diseases; B-Lymphocytes; Biological; Biology; CD4 Positive T Lymphocytes; CD8B1 gene; Cells; Collaborations; Data; Educational process of instructing; Evolution; Exposure to; Frequencies; Genetic Drift; Genetic Models; Genetic Recombination; Genetic Variation; Genome; Goals; Haplotypes; Health; High-Throughput Nucleotide Sequencing; Human; Immune; Immune system; Immunology; Individual; Investigation; Link; Mentors; Methods; Modeling; Mus; Mutation; Pattern; Phase; Play; Population; Population Genetics; Process; Research; Role; Sampling; Series; Specificity; T-Lymphocyte; Techniques; Technology; Time; Training; V(D)J Recombination; Vaccination; deep sequencing; frontier; medical specialties; migration; new technology; novel; pathogen; response; theories

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.

描述（由申请人提供）：我在理论种群遗传学方面的研究生训练教会了我如何利用种群中遗传多样性的当前模式来重建该种群所采取的进化路径。新技术为这种类型的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巨大的遗传多样性。与个体群体（即物种）水平上的进化类似，单个个体内的免疫细胞群体在对突变、选择、迁移和遗传漂变作出反应的过程中随着时间的推移而进化。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，对单个个体内的这种多样性进行精细的调查。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。该项目的具体目标是：(1)扩展现有的群体遗传模型，从追踪单个免疫细胞群体（例如CD8 T细胞）多样性的时间序列数据中推断出选择的强度，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（可以使用侵入性实验技术）和更有限的人类T细胞数据（必须推断出a-b关联）来验证这些模型；(3)建立一种新的群体遗传模型，将同时在多个细胞群体中的选择效应联系起来，例如当CD4 T细胞协同“帮助”具有相似特异性的CD8 T细胞时。Aim 2中使用的数据将由我的实验合作者Rafi Ahmed博士和Joseph Blattman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，以进一步实现我在个体内群体遗传学方面开辟一条新研究路线的目标。