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B细胞在自适应免疫响应中起着基本作用，保护性的发展 免疫疫苗接种和许多自身免疫性疾病的病理。所有这些过程的中心是 B细胞在不同组织之间的迁移以及B细胞分化为功能亚型。现在， 了解B细胞迁移和分化是有限的，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。我们以前的工作表明可以检测到 从B细胞接收器（BCR）序列推断出的进化树的迁移和分化事件， 在自适应免疫期间，会受到快速的体细胞超突变和抗原驱动的选择 回答。这类似于病毒植物地理学，使用进化树来跟踪病毒的传播 在流行期间。但是，B细胞和病毒的生物学存在关键差异，需要修改 并扩展现有方法使其适合B细胞。 该建议的目的是启用B细胞植物地理学。我们将开发新颖的计算 利用单个B细胞测序技术最新进展的方法来推断B细胞如何迁移 在组织之间根据其在免疫期间的激活状态分化为细胞亚型 回答。该提案的目的集中于将障碍障碍的发展解决。 B细胞的方法。这些方法将通过模拟和实验分析来验证。我们将工作 与既定的实验合作者一起，将这些新颖方法转化为有意义的结果 影响Za疫苗反应和自身免疫性疾病的治疗，包括狼疮和 肌无力重症。我们将在广泛可用的免费软件中实施这些方法，这将大大增加 他们有可能针对其他病原体（如HIV和SARS-COV-2）提供疫苗接种策略的潜力，以及 处理其他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史蒂文·克莱恩斯坦教授指导 BCR序列分析计算方法开发的世界领导者，以及Kevin O'Connor教授 神经系统自身免疫性疾病的B细胞病理学中领先的实验生物学家。候选人，博士 肯尼斯·霍恩（Kenneth Hoehn），在遗传学和进化生物学方面具有很强的背景，并具有既定记录 开发从BCR序列数据研究B细胞群体的进化模型。详细介绍的作品 该建议将填补候选人在B细胞生物学，单细胞分析和软件中训练中的空白 发展。 R00阶段将建立在这项工作的基础上，以开发一个高度可推广的框架 表征B细胞在疫苗接种和 自身免疫性。这将支持有关B细胞生物学的新的，医学上与医学相关的发现，并充当 候选人作为独立计算免疫学家的未来的基础。