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: 10889629
• 负责人: Kenneth Hoehn
• 金额: $ 24.9万
• 依托单位: DARTMOUTH COLLEGE
• 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 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10889629
• 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; Maps; 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 Sample; Tissues; Training; Translating; Trees; Vaccination; Vaccines; Variant; Viral; Virus; Work; adaptive immune response; 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.

B细胞在人类对感染的适应性免疫反应中起着基础性作用，发展保护性 免疫接种，以及许多自身免疫性疾病的病理。所有这些过程的核心是 B细胞在不同组织间的迁移和B细胞向功能亚群的分化。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，尤指在人类身上。我们之前的工作已经证明，有可能检测到 从B细胞受体(BCR)序列推断的进化树上的迁移和分化事件， 它们在适应性免疫过程中受到快速的体细胞超突变和抗原驱动的选择 回应。这类似于病毒系统地理学，使用进化树来跟踪病毒的传播 在流行病期间。然而，B细胞和病毒在生物学上有关键的区别，需要修改 以及扩展现有的方法，使其适用于B细胞。 这项提议的目标是使B细胞系统地理学成为可能。我们将开发新的计算 利用单个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 回应。这项建议的目的是解决系统地理学发展的障碍。 方法对B细胞进行检测。这些方法将通过仿真和实验分析进行验证。我们会工作的 与成熟的实验合作者一起将这些新方法转化为有意义的结果 流感疫苗应答和自身免疫性疾病治疗的研究，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，这将大大增加 它们有可能为针对艾滋病毒和SARS-CoV-2等其他病原体的疫苗接种战略以及 治疗其他B细胞介导的疾病，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史蒂文·克莱因斯坦教授指导。 BCR序列分析计算方法开发的世界领先者，以及 神经自身免疫性疾病B细胞病理学方面的领先实验生物学家。这位候选人是Dr。 Kenneth Hoehn，在遗传学和进化生物学方面有很强的背景，并有既定的记录 从BCR序列数据中开发进化模型来研究B细胞群体。中详细介绍的工作 这项提议将填补候选人在B细胞生物学、单细胞分析和软件方面的培训空白 发展。R00阶段将在此工作的基础上为以下内容开发高度可概括的框架 表征复杂的迁移和分化模式，这些模式是B细胞在疫苗接种和 自身免疫力。这将支持关于B细胞生物学的新的、与医学相关的发现，并作为 为候选人未来成为一名独立的计算免疫学家奠定基础。