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DYNAMICS AND EVOLUTION OF IMMUNE RESPONSES TO INFLUENZA VIRUSES

流感病毒免疫反应的动态和演变

基本信息

批准号：
10621337
负责人：
RUSTOM NOSHIR ANTIA
金额：
$ 114.6万
依托单位：
EMORY UNIVERSITY
依托单位国家：
美国
项目类别：
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/10621337
关键词：
Address Affect Antibodies Antibody Response Antigens Attenuated B-Cell Antigen Receptor B-Lymphocytes Binding Biological Models Blood Cells Bone Marrow CD8-Positive T-Lymphocytes CD8B1 gene Cells Complication Data Data Analyses 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 Seasons 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 migration 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

项目摘要

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.

项目总结/文摘