Predictive fitness models for influenza vaccine strain selection

流感疫苗株选择的预测适应性模型

• 批准号: 10549863
• 负责人: Marta Luksza
• 金额: $ 59.9万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-13 至 2026-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10549863
• 关键词: Algorithms; Antigenic Diversity; Antigens; B-Lymphocytes; Biological Assay; Biophysics; CD8-Positive T-Lymphocytes; Cancer Patient; Collaborations; Computer Models; Data; Data Set; Databases; Environment; Epidemiologic Factors; Epitopes; Evolution; Ferrets; Flu virus; Future; Genetic; Genetic Variation; Goals; Herd Immunity; Human; Immune; Immune response; Immune system; Immunity; Immunodominant Antigens; Immunological Models; Infection; Influenza; Ions; Joints; Machine Learning; Maps; Modeling; Mutation; Nature; New York City; Pattern; Phylogeny; Population; Population Dynamics; Process; Proteins; Publishing; Research Personnel; ST14 gene; Seasons; Serology; Serology test; Shapes; Source; Statistical Models; Structure; System; T cell response; T-Lymphocyte; T-Lymphocyte Epitopes; Testing; Vaccination; Vaccines; Viral; Virus; Work; acquired immunity; biophysical model; cohort; epidemiologic data; fitness; geographic population; immunogenicity; influenza surveillance; influenza virus vaccine; influenzavirus; machine learning prediction; multiple datasets; novel; phenotypic data; predictive modeling; pressure; reconstruction; response; seasonal influenza; transmission process; vaccine efficacy; virus genetics; whole genome

Project summary The human flu virus undergoes fast antigenic evolution driven by the challenge of the host immune system. Circulating viruses are a moving quasi-species of high genetic and antigenic diversity, which is structured in distinct clades representing niches with separate avenues of escape from human herd immunity. Specifically, viral-immune co-evolution follows a Red Queen’s dynamical pattern of competing lineages, which is further modulated by global transmission patterns, host population structure, and herd-immunity acquired by previous infections and vaccination. This process poses a challenge for vaccine strain selection: to determine a single strain from each of the four seasonal lineages to provide the best protection from the viruses that are expected to dominate almost a year in advance. Here we posit that shape and dynamics of the global viral population can be understood and predicted from the underlying human population immunity. In this project, we will develop a comprehensive and objective computational approach to provide a mechanistic understanding of the influenza virus-host immune interaction on the host level, to quantify the selection imposed on the virus on the global evolutionary scales. Specifically, we will build biophysical models, both for the B-cell and T-cell driven immune recognition of epitopes in a host, to accurately characterize the immune structure of the human population to best represent the fitness effects acting on the virus on the global scale. For the model of B-cell immune recognition (Aim 1), we will leverage diverse antigenic human serology assays to prepare a detailed map of antigenic effects of mutations and epistatic interactions. The data will be cross-mapped on the dataset of sequences of globally circulating viruses and our detailed phylogenies for each of the four seasonal lineages. The T-cell immune recognition (Aim 2) will be based on computational machine learning predictions of epitopes, combined with novel biophysically motivated models for prediction of immunodominant antigens. Host population geographically diverse HLA diversity will be accounted for estimating selective pressures imposed on the population of the virus. These components, together with a component describing the selective pressure due to previous vaccinations, will be used to optimize a joint fitness model (Aim 3). Information theoretic approaches will be used to optimize and evaluate the predictive power of the combined model. We will objectively quantify the significance of each of the components and validate the predictions on the historical sequence and epidemiological data. With the resulting fitness model we will define principled criteria for vaccine strain selection, to optimize the coverage and efficacy of the vaccine in the future populations of the of seasonal human influenza viruses.

项目摘要 人类流感病毒经历由宿主免疫系统的挑战驱动的快速抗原进化。 循环病毒是具有高度遗传和抗原多样性的移动准种，其结构为 不同的进化枝代表了具有不同的人类群体免疫逃逸途径的小生境。具体地说， 病毒免疫协同进化遵循红皇后竞争谱系的动态模式，这是进一步的 受全球传播模式、宿主种群结构和先前感染者获得的免疫力的影响， 感染和疫苗接种。这一过程对疫苗株的选择提出了挑战： 从四个季节性谱系的每一个菌株，以提供最好的保护，从病毒的预期 几乎提前一年占据主导地位。 在这里，我们认为，全球病毒种群的形状和动态可以从 潜在的人类免疫力在这个项目中，我们将制定一个全面和客观的 计算方法，以提供对流感病毒-宿主免疫相互作用的机制理解 在宿主水平上，以量化在全球进化尺度上对病毒的选择。我们特别 将建立生物物理模型，用于宿主中B细胞和T细胞驱动的表位免疫识别， 准确地描述人类群体的免疫结构，以最好地代表适应性效应， 在全球范围内对病毒的研究。对于B细胞免疫识别模型（目标1），我们将利用多种 抗原人类血清学测定，以制备突变和上位性的抗原效应的详细图谱 交互.这些数据将在全球流行病毒序列数据集和我们的 四季血统的详细遗传T细胞免疫识别（目标2）将基于 表位的计算机器学习预测，结合新的生物药理学模型 用于预测免疫显性抗原。宿主群体地理上的多样性HLA多样性将是 占估计的选择性压力施加在人口的病毒。 这些组成部分，连同描述由于先前接种而产生的选择性压力的组成部分， 将用于优化关节适应度模型（目标3）。信息论方法将用于优化 并评估组合模型的预测能力。我们将客观地量化每一个 的组成部分，并验证历史序列和流行病学数据的预测。与 由此产生的适应度模型，我们将定义疫苗株选择的原则性标准，以优化覆盖率， 疫苗在未来季节性人流感病毒感染人群中的有效性。