Forecasting influenza evolution on a heterogeneous immune landscape

项目摘要 流感病毒迅速进化，以逃避先前感染和疫苗接种引起的免疫力。打击这一 由于流感病毒的进化，流感疫苗中的毒株每个季节都会更新，以跟上病毒进化的步伐。 然而，目前很难准确预测哪种病毒株将主导下一季， 当选择了错误的疫苗菌株时，疫苗的效力就降低了。在这里，我们提出了一个组合 一种实验/计算方法，将克服目前使疫苗株 选择如此困难。首先，我们将使用一种新的深度突变扫描方法来直接绘制基因型 病毒表面蛋白的抗原表型与人类抗体免疫力的关系。二是 将使用这种新的实验方法来重建异质的人类免疫景观， 流感病毒进化。最后，我们将建立计算模型，预测流感如何演变， 一年来，人类免疫力。我们所有的数据和预测都将整合到易于使用的交互式 可视化。总的来说，这项工作将提高准确识别哪些病毒株应该被感染的能力。 选择季节性流感疫苗。

PROJECT SUMMARY Influenza virus evolves rapidly to escape immunity elicited by prior infections and vaccinations. To combat this evolution, the strains in the influenza vaccine are updated every season to keep pace with viral evolution. However, it is currently difficult to accurately forecast which viral strain will dominate the coming season, and vaccines are less effective when the wrong strain is chosen for the vaccine. Here we propose a combined experimental / computational approach that will overcome some of the factors that currently make vaccine strain selection so difficult. First, we will use a new deep mutational scanning approach to directly map the genotypes of the viral surface proteins to their antigenic phenotype with respect to human antibody immunity. Second, we will use this new experimental approach to reconstruct the heterogeneous human immune landscape over which influenza virus evolves. Finally, we will build computational models that forecast how influenza evolves year-to- year in response to human immunity. All of our data and forecasts will be integrated into easy-to-use interactive visualizations. Overall, this work will improve the capacity to accurately identify which viral strains should be selected for the seasonal influenza vaccine.