Evolutionary dynamics of antibody affinity maturation

项目总结 为了抵御快速进化的病原体，有颌骨的脊椎动物有特殊的细胞--淋巴细胞-- 在人的一生中进化以产生适应性免疫反应。巨大的体细胞多样性是 维持在编码淋巴细胞上能够检测到外来抗原的受体的基因座上。B细胞-淋巴细胞 这使得抗体与B细胞受体(BCR)结合抗原，并在微观解剖结构上多样化 称为生发中心(GC)，它们在那里增殖，同时突变BCR。Affi成熟的这个过程- 经典的理解是选择增加fi的抗原结合：存活的细胞成熟 成为高afbcr记忆B细胞或分泌抗体的浆细胞(fi的可溶性形式)。 在实验、理论和计算之间进行富有成效的对话以学习新知识的潜力很大 来自这些系统的免疫学和新的进化动力学。事实上，最近的研究揭示了一种文献 Confl认为GC进化是对高affi的适应性进化。这个应用程序的假设是GC 进化动力学平衡对抗原物种fiCity的适应和中性fi阳离子FortifiES 抗原漂移。 研究策略是在紧反馈循环中开发GC进化的定量模型 模型生物实验设计日益复杂化。使用鼠标模型，可以施加控制 超过B细胞谱系的多样性、受体和fi的一致性以及抗原靶向性。GC的演变可以使用以下工具进行跟踪 受体测序和血统追踪技术。目标1将分析一只小鼠的GC bcr进化 具有单克隆性BCR的模型和模型抗原暴露，使得所有GC反应构成复制 从相同的祖先状态进化而来。目标2将开发理论和计算工具来推断fi特异度， 收敛，以及来自(准)复制进化动态的偶然性，这是我们的 实验模型和自然曲目。Aim 3将设计和分析两种小鼠模型 曲目复杂性，并调查对Modified抗原的召回反应。 培训计划旨在综合理论和计算进化生物学方面的专业知识。 拥有免疫学，并提高作为独立研究人员未来所需的硬技能和软技能。 共同发起人弗雷德里克·马森博士和凯利·哈里斯博士结合了数学和计算方面的专业知识- 国家生物学和免疫学，以及种群遗传学。B细胞免疫学家加布里埃尔·维克托拉博士将成为 密切的合作者；他的实验室将领导小鼠模型的实验开发。我也会合作 与理论物理学家Armita Nourmohammad博士合作，他在快速进化动力学方面拥有专业知识。 我的论文委员会增加了菲尔·格林博士和乔·费尔森斯坦博士作为计算和理论资源。 这个跨学科的研究团队与拟议的目标相匹配，作为不同fi领域的综合。

Project summary To defend against rapidly evolving pathogens, jawed vertebrates have specialized cells—lymphocytes—that evolve during an individual's lifetime to mount adaptive immune responses. Massive somatic diversity is maintained in loci that encode receptors on lymphocytes that can detect foreign antigen. B cells—lymphocytes that make antibodies—bind antigen with the B cell receptor (BCR), and diversify in microanatomical structures called germinal centers (GCs) where they proliferate while mutating the BCR. This process of affinity matura- tion is classically understood to impose selection for increased antigen binding affinity: surviving cells mature to become high-affinity memory B cells or plasma cells that secrete antibodies (the soluble form of the BCR). There is great potential for productive dialog between experiment, theory, and computation to learn new immunology and new evolutionary dynamics from these systems. Indeed, recent studies reveal a literature conflicted regarding GC evolution as adaptive toward high affinity. The hypothesis of this application is that GC evolutionary dynamics balance adaptation towards antigen specificity with neutral diversification that fortifies against antigenic drift. The research strategy is to develop quantitative models of GC evolution in a tight feedback loop with model organism experimental design of increasing complexity. Using mouse models, control can be exerted over B cell repertoire diversity, receptor affinity, and antigen targeting. Evolution in GCs can be tracked using both receptor sequencing and lineage tracing technology. Aim 1 will analyze GC BCR evolution in a mouse model with a monoclonal BCR and a model antigen exposure, such that all GC reactions constitute replicated evolution from the same ancestral state. Aim 2 will develop theoretical and computational tools to infer fitness, convergence, and contingency from the (quasi-)replicated evolutionary dynamics that characterize both our experimental models and natural repertoires. Aim 3 will design and analyze two mouse models of increasing repertoire complexity, and investigate recall responses to modified antigens. The training plan is designed to synthesize expertise in theoretical and computational evolutionary biology with immunology, and advance both hard and soft skills necessary for a future as a independent investigator. Co-sponsors Dr. Frederick Matsen and Dr. Kelley Harris combine expertise in mathematical and computa- tional biology and immunology, and population genetics. B cell immunologist Dr. Gabriel Victora will be a close collaborator; his lab will lead experimental development of the mouse models. I will also collaborate with theoretical physicist Dr. Armita Nourmohammad, who has expertise in rapid evolutionary dynamics. My thesis committee adds Dr. Phil Green and Dr. Joe Felsenstein as computational and theoretical resources. This interdisciplinary research team is matched to the proposed aims as a synthesis of distinct fields.