DYNAMICS AND EVOLUTION OF IMMUNE RESPONSES TO INFLUENZA VIRUSES

流感病毒免疫反应的动态和演变

• 批准号: 10407514
• 负责人: RUSTOM NOSHIR ANTIA
• 金额: $ 117.62万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-29 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10407514
• 关键词: Address; Affect; Antibodies; Antibody Response; Antigens; Attenuated; B-Cell Antigen Receptor; B-Lymphocytes; Binding; Biological Models; Bone Marrow; CD8-Positive T-Lymphocytes; CD8B1 gene; Cells; Complication; Data; Data Set; Deuterium Oxide; Development; Educational workshop; Epitopes; Evolution; Exposure to; Foundations; Generations; Goals; Hemagglutinin; Human; Humoral Immunities; Immune; Immune response; Immunity; Immunization; Immunologic Memory; Immunological Models; In Vitro; Infection; Influenza; Influenza A Virus, H1N1 Subtype; Influenza A Virus, H2N2 Subtype; Influenza A Virus, H3N2 Subtype; Influenza A Virus, H5N1 Subtype; Influenza Hemagglutinin; Influenza vaccination; Label; Location; Longevity; Maintenance; Masks; Memory; Memory B-Lymphocyte; Modeling; Mus; Passive Transfer of Immunity; Phenotype; Plasma Cells; Play; Population; Proteins; Role; Sequence Analysis; Serum; T cell response; T memory cell; T-Lymphocyte; T-Lymphocyte Epitopes; T-cell diversity; Testing; Time; Vaccination; Vaccines; Variant; Virus; Virus Diseases; Visualization; Yellow Fever; Yellow Fever Vaccine; Yellow fever virus; computerized tools; experimental study; human data; in vivo; influenza virus strain; influenzavirus; long term memory; mathematical model; mouse model; neutralizing antibody; pandemic disease; response; seasonal influenza; simulation; stem; stem cells; symposium; tool; tool development; universal influenza vaccine; universal vaccine; user-friendly; vaccination strategy; vaccine development; vaccine trial

PROJECT SUMMARY/ABSTRACT Our goal is to develop a quantitative framework for the generation, boosting and maintenance of immunological memory. Mathematical models are useful because immunization and infection involve the interaction of rapidly changing populations of virus and multiple populations of immune cells. We first develop and validate models using experiments in mice. We then use these validated models to analyze data from human vaccination studies. Aim 1 asks how prior immunity affects and potentially limit the boosting of immunity and apply this to influenza. Our approach is to develop models to understand why prior immunity limits boosting of antibodies to conserved regions of the virus. Specifically, we use our models to better understand how prior immunity might limit boosting of antibody responses to conserved regions on the stem of the hemagglutinin molecule that is the focus of universal influenza vaccines. Aim 2 considers the factors that affect the durability of humoral immune memory, and address questions such as why memory generated by immunization with protein antigens is less durable than immunity generated by virus infection, and how prior immunity can differentially affect the boosting and generation of memory to new strains of influenza. Aim 3 considers the generation of CD8 T cell memory to influenza and yellow fever. We will determine how repeated exposure to influenza affects the diversity of the CD8 T cell responses generated. We have access to a unique dataset that follows the number of YFV-specific CD8 T cells, changes in their phenotype, and their turnover from heavy water labelling studies for a period of over one year. Our analysis of this dataset will allow us to address an ongoing controversy regarding whether are long-term memory CD8 memory stem cells are generated rapidly after immunization or only gradually over time. Aim 4 describes computational tools that we will build for B cell receptor sequence analysis and visualization, and for simulation of the dynamics of immune responses. These tools will be widely accessible online, and promoted at workshops and scientific symposia we organize.

项目摘要/摘要 我们的目标是制定一个量化框架，以产生、促进和维护 免疫记忆。数学模型很有用，因为免疫和感染涉及 快速变化的病毒群体和多个免疫细胞群体的相互作用。我们首先 通过在小鼠身上的实验开发和验证模型。然后，我们使用这些经过验证的模型来分析 来自人类疫苗研究的数据。 目标1询问先前免疫如何影响和潜在地限制免疫的增强，并将其应用于 流行性感冒。我们的方法是开发模型来理解为什么先前的免疫力限制了 针对病毒保守区的抗体。具体地说，我们使用我们的模型更好地理解 先前的免疫可能会限制对杆状病毒茎上保守区域的抗体反应的增强 血凝素分子是通用流感疫苗的焦点。 目的2探讨影响体液免疫记忆持久性的因素，并提出问题 例如为什么用蛋白质抗原免疫产生的记忆不如免疫产生的持久 由病毒感染产生，以及先前的免疫如何以不同的方式影响增强和生成 对新流感病毒株的记忆。 目的3考虑CD8T细胞对流感和黄热病记忆的产生。我们将决定 反复接触流感如何影响产生的CD8T细胞反应的多样性。我们有 访问跟随YFV特异性CD8 T细胞数量的唯一数据集，其 表型，以及它们在一年多的重水标签研究中的营业额。我们的分析 这一数据集将使我们能够解决关于长期记忆是否存在的持续争议 CD8记忆干细胞在免疫后迅速产生，或者随着时间的推移才逐渐产生。 AIM 4描述了我们将构建的用于B细胞受体序列分析和 可视化，并用于模拟免疫反应的动态。这些工具将被广泛使用 可在线访问，并在我们组织的研讨会和科学研讨会上进行推广。