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: 10204919
• 负责人: RUSTOM NOSHIR ANTIA
• 金额: $ 119.03万
• 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/10204919
• 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 我们的目标是为生成，增强和维护的定量框架开发一个定量框架 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免疫细胞种群的相互作用。我们首先 使用小鼠实验开发和验证模型。然后，我们使用这些经过验证的模型分析 来自人类疫苗接种研究的数据。 AIM 1询问先前的免疫力如何影响并有可能限制免疫力的增强，并将其应用于 流感。我们的方法是开发模型，以了解为什么先前的免疫力限制了促进 对病毒保守区域的抗体。具体来说，我们使用模型更好地了解 先前的免疫力可能会限制对抗体对保守区域的抗体反应的增强 血凝素分子是普遍流感疫苗的重点。 AIM 2考虑了影响体液免疫记忆耐用性的因素，并解决了问题 例如为什么用蛋白质抗原免疫产生的记忆力不如免疫 由病毒感染产生，以及先前的免疫力如何差异地影响增强和产生 记忆到新的流感菌株。 AIM 3认为CD8 T细胞记忆的产生是流感和黄热病。我们将确定 反复接触流感的如何影响产生的CD8 T细胞反应的多样性。我们有 访问遵循YFV特异性CD8 T细胞数量的唯一数据集，其变化 表型及其从重水标记研究中的营业额超过一年。我们的分析 该数据集将使我们能够解决有关是否是长期记忆的持续争议 CD8记忆干细胞在免疫后或仅随着时间的推移而逐渐生成。 AIM 4描述了我们将为B细胞受体序列分析和 可视化，以及模拟免疫反应的动力学。这些工具将被广泛 在线访问，并在我们组织的研讨会和科学研讨会上促进。