Computational mapping of human B cell migration and differentiation pathways

人类B细胞在针对感染的适应性免疫应答、保护性免疫应答的发展中起着重要作用。 免疫接种和许多自身免疫性疾病的病理学。所有这些进程的核心是 B细胞在不同组织间的迁移和B细胞分化成功能亚型。目前， 对B细胞迁移和分化的理解是有限的，因为这些过程是动态的和困难的 直接观察，特别是在人类身上。我们以前的工作已经证明， 从B细胞受体（BCR）序列推断的沿着进化树的迁移和分化事件， 其在适应性免疫期间经受快速体细胞超突变和抗原驱动选择 应答这类似于病毒地理学，即利用进化树来追踪病毒的传播 在流行病期间。然而，B细胞和病毒在生物学上存在关键差异，需要进行修饰 并扩展现有的方法使其适用于B细胞。 该提议的目标是实现B小区的地理分布。我们将开发新的计算 利用单B细胞测序技术的最新进展来推断B细胞如何迁移的方法 并根据免疫过程中的激活状态分化为细胞亚型 应答该提案的目的是解决发展地理信息系统的障碍， B细胞的方法。这些方法将通过仿真和实验分析进行验证。我们将 与已建立的实验合作者合作，将这些新方法转化为有意义的成果， 研究流感疫苗的反应和治疗自身免疫性疾病，包括狼疮和 重症肌无力我们将在广泛可用的自由软件中实现这些方法， 它们为针对其他病原体（如HIV和SARS-CoV-2）的疫苗接种策略提供信息的潜力，以及 治疗其他B细胞介导的病症，如多发性硬化和哮喘。 该提案的K99阶段将由耶鲁大学医学院的Steven Kleinstein教授指导， BCR序列分析计算方法开发的世界领导者，以及Kevin奥康纳教授， 神经系统自身免疫性疾病B细胞病理学的领先实验生物学家。候选人，博士。 Kenneth Hoehn在遗传学和进化生物学方面有很强的背景， 开发进化模型，从BCR序列数据研究B细胞群体。中详述的工作 该计划将填补候选人在B细胞生物学、单细胞分析和软件方面的培训空白 发展R 00阶段将在此基础上开发一个高度通用的框架， 表征B细胞在疫苗接种中的作用的基础的复杂的迁移和分化模式， 自身免疫这将支持关于B细胞生物学的新的、医学相关的发现，并作为 为候选人未来成为独立的计算免疫学家奠定了基础。

Human B cells play a fundamental role in the adaptive immune response to infection, development of protective immunity from vaccination, and pathology of many autoimmune diseases. Central to all of these processes are migration of B cells among different tissues and differentiation of B cells into functional subtypes. Currently, understanding B cell migration and differentiation is limited because these processes are dynamic and difficult to directly observe, particularly in humans. Our previous work has demonstrated that it is possible to detect migration and differentiation events along evolutionary trees inferred from B cell receptor (BCR) sequences, which are subject to rapid somatic hypermutation and antigen-driven selection during adaptive immune responses. This is analogous to viral phylogeography, the use of evolutionary trees to track the spread of viruses during epidemics. However, there are key differences in the biology of B cells and viruses that require modifying and extending existing approaches to make them appropriate for B cells. The goal of this proposal is to enable B cell phylogeography. We will develop novel computational methods that leverage recent advances in single B cell sequencing technology to infer how B cells migrate between tissues and differentiate into cellular subtypes based on their activation states during immune responses. The aims of this proposal focus on solving the roadblocks to the development of phylogeographic methods for B cells. These methods will be validated by simulations and experimental analysis. We will work with established experimental collaborators to translate these novel methods into meaningful outcomes in the research of influenza vaccine response and the treatment of autoimmune diseases, including lupus and myasthenia gravis. We will implement these methods in widely available free software, which will greatly increase their potential to inform vaccination strategies against other pathogens like HIV and SARS-CoV-2, as well as treatment of other B cell-mediated conditions such as multiple sclerosis and asthma. The K99 phase of this proposal will be guided by Prof. Steven Kleinstein at Yale School of Medicine, a world leader in computational methods development for BCR sequence analysis, and Prof. Kevin O’Connor, a leading experimental biologist in the B cell pathology of neurologic autoimmune diseases. The candidate, Dr. Kenneth Hoehn, has a strong background in genetics and evolutionary biology, and has an established record of developing evolutionary models to study B cell populations from BCR sequence data. The work detailed in this proposal will fill gaps in the candidate’s training in B cell biology, single cell analysis, and software development. The R00 phase will build off of this work to develop a highly generalizable framework for characterizing the complex migration and differentiation patterns that underlie B cells’ role in vaccination and autoimmunity. This will support new, medically-relevant discoveries about B cell biology, and serve as a foundation for the candidate’s future as an independent computational immunologist.