Predictive fitness models for influenza vaccine strain selection

项目总结 人类流感病毒在宿主免疫系统的挑战下经历了快速的抗原进化。 循环病毒是一种具有高度遗传和抗原多样性的移动准物种，其结构由 不同的分支代表着不同的逃避人类群体免疫的途径。具体来说， 病毒-免疫协同进化遵循红皇后相互竞争血统的动态模式，这进一步 受全球传播模式、宿主种群结构和之前获得的群体免疫力的调节 感染和疫苗接种。这一过程对疫苗株的选择提出了挑战：确定单个 从四个季节性谱系中的每一种中分离出的菌株，以提供对预期病毒的最佳保护 几乎提前一年就能占据主导地位。 在这里，我们假设全球病毒种群的形态和动态可以从 潜在的人类群体免疫力。在这个项目中，我们将制定一个全面而客观的 一种提供流感病毒-宿主免疫相互作用机制的计算方法 在宿主层面上，量化全球进化尺度上强加给病毒的选择。具体来说，我们 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，以 准确地描述人类群体的免疫结构，以最好地代表健康效应的作用 全球范围内的病毒。对于B细胞免疫识别模型(目标1)，我们将利用多样化 抗原性人类血清学分析，以准备突变和上位性抗原影响的详细地图 互动。这些数据将交叉映射到全球流行的病毒序列和我们的 四个季节谱系中每一个的详细系统发育。T细胞免疫识别(目标2)将基于 结合新的生物物理动机模型对表位的计算机器学习预测 用于预测免疫优势抗原。宿主人口地理上的多样性将是人类白细胞抗原的多样性 解释了对病毒种群施加的选择性压力的估计。 这些成分，连同一个描述先前疫苗接种所造成的选择压力的成分， 将用于优化关节健康模型(目标3)。将使用信息论方法来优化 并对组合模型的预测能力进行了评估。我们将客观量化每一项的重要性 并对历史序列和流行病学数据的预测进行了验证。与 由此产生的适应度模型我们将为疫苗株的选择定义原则标准，以优化疫苗的覆盖率和 疫苗在未来人类季节性流感病毒感染人群中的效力.

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.