Computational mapping of human B cell migration and differentiation pathways

人类 B 细胞迁移和分化途径的计算图谱

• 批准号: 10371370
• 负责人: Kenneth Hoehn
• 金额: $ 12.38万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-19 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10371370
• 关键词: 2019-nCoV; Adult; Affinity; Age; Antibody Affinity; Antibody-Producing Cells; Antigens; Asthma; Autoantibodies; Autoantigens; Autoimmune; Autoimmune Diseases; Autoimmune Diseases of the Nervous System; Autoimmunity; B cell differentiation; B-Cell Antigen Receptor; B-Cell Development; B-Lymphocytes; B-cell receptor repertoire sequencing; Binding; Biological Models; Biology; Blood; Blood specimen; Bone Marrow; COVID-19 pandemic; Cell Differentiation process; Cell Lineage; Cells; Cellular biology; Complex; Computer software; Computing Methodologies; DNA Sequence; Data; Development; Disease; Epidemic; Event; Evolution; Foundations; Future; Genetic; Geography; Goals; HIV; Human; Immune response; Immunity; Immunoglobulin Somatic Hypermutation; Immunologist; Immunology; Individual; Infection; Influenza vaccination; Knowledge; Lupus; Measures; Mediating; Medical; Memory; Methods; Modeling; Modeling of Cellular Pathways; Multiple Sclerosis; Mus; Mutation; Myasthenia Gravis; Neuromyelitis Optica; Outcome; Pathogenicity; Pathology; Pathway interactions; Patients; Pattern; Phase; Phylogenetic Analysis; Plasma Cells; Plasmablast; Play; Population; Process; RNA Sequences; Recording of previous events; Research; Research Personnel; Role; Scientist; Sequence Analysis; Source; Structure of germinal center of lymph node; System; Systemic Lupus Erythematosus; Technology; Testing; Time; Tissue Differentiation; Tissue Sample; Tissues; Training; Translating; Trees; Vaccination; Vaccines; Variant; Viral; Virus; Work; adaptive immune response; base; biological systems; cell motility; cell type; computer framework; data exchange; effectiveness evaluation; experimental analysis; flexibility; human model; influenza virus vaccine; lymph nodes; medical schools; method development; migration; model design; mouse model; neurological pathology; novel; pathogen; plasma cell differentiation; response; self-renewal; simulation; single cell analysis; single-cell RNA sequencing; software development; targeted treatment; tool; vaccination strategy; vaccine response

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.

人类 B 细胞在对感染的适应性免疫反应、保护性免疫反应的发展中发挥着重要作用。 疫苗接种产生的免疫力以及许多自身免疫性疾病的病理学是所有这些过程的核心。 B细胞在不同组织之间的迁移以及B细胞向功能亚型的分化。 对 B 细胞迁移和分化的理解有限，因为这些过程是动态的且困难的 直接观察，特别是在人类中，我们之前的工作已经证明可以检测到。 从 B 细胞受体 (BCR) 序列推断出沿树进化的迁移和分化事件， 在适应性免疫过程中受到快速体细胞超突变和抗原驱动的选择 这类似于病毒系统发育地理学，利用进化树来追踪病毒的传播。 然而，在流行病期间，B 细胞和病毒的生物学存在关键差异，需要进行修改。 以及现有的扩展方法，使它们适合 B 细胞。 该提案的目标是实现 B 细胞系统发育地理学。我们将开发新的计算方法。 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 组织之间的相互作用，并根据免疫过程中的激活状态分化为细胞亚型 该提案的目的是解决系统发育地理学发展的障碍。 我们将通过模拟和实验分析来验证这些方法。 与已建立的实验合作者合作，将这些新颖的方法转化为有意义的结果 流感疫苗反应和自身免疫性疾病治疗的研究，包括狼疮和 我们将在广泛使用的免费软件中实施这些方法，这将大大增加重症肌无力的风险。 它们有可能为针对 HIV 和 SARS-CoV-2 等其他病原体的疫苗接种策略提供信息，以及 治疗其他 B 细胞介导的疾病，例如多发性硬化症和哮喘。 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领域的世界领导者，Kevin O’Connor 教授 神经系统自身免疫性疾病 B 细胞病理学领域的领先实验生物学家。 Kenneth Hoehn，在遗传学和进化生物学方面拥有深厚的背景，并拥有良好的记录 开发进化模型来研究 BCR 序列数据中的 B 细胞群。 该提案将填补候选人在 B 细胞生物学、单细胞分析和软件培训方面的空白 R00 阶段将在这项工作的基础上开发一个高度通用的框架。 表征 B 细胞在疫苗接种和免疫接种中的作用的复杂迁移和分化模式 这将支持有关 B 细胞生物学的新的、医学相关的发现，并作为 为候选人未来作为独立计算免疫学家奠定基础。