JC School of Public Health and Primary Care (JCSPHPC), The Chinese University of Hong Kong (CUHK), Hong Kong SAR, China.
Beth Bioinformatics Co. Ltd, Hong Kong SAR, China.
Nat Commun. 2024 Mar 21;15(1):2546. doi: 10.1038/s41467-024-46918-0.
Influenza virus continuously evolves to escape human adaptive immunity and generates seasonal epidemics. Therefore, influenza vaccine strains need to be updated annually for the upcoming flu season to ensure vaccine effectiveness. We develop a computational approach, beth-1, to forecast virus evolution and select representative virus for influenza vaccine. The method involves modelling site-wise mutation fitness. Informed by virus genome and population sero-positivity, we calibrate transition time of mutations and project the fitness landscape to future time, based on which beth-1 selects the optimal vaccine strain. In season-to-season prediction in historical data for the influenza A pH1N1 and H3N2 viruses, beth-1 demonstrates superior genetic matching compared to existing approaches. In prospective validations, the model shows superior or non-inferior genetic matching and neutralization against circulating virus in mice immunization experiments compared to the current vaccine. The method offers a promising and ready-to-use tool to facilitate vaccine strain selection for the influenza virus through capturing heterogeneous evolutionary dynamics over genome space-time and linking molecular variants to population immune response.
流感病毒不断进化以逃避人体适应性免疫并引发季节性流行。因此，为确保疫苗有效性，流感疫苗株需要每年更新，以应对即将到来的流感季节。我们开发了一种计算方法 beth-1，用于预测病毒进化并选择具有代表性的流感疫苗病毒。该方法涉及对病毒的突变适应性进行基于位置的建模。根据病毒基因组和人群血清阳性率，我们校准突变的转变时间，并根据该时间将适应性景观投影到未来，基于此，beth-1 选择最佳的疫苗株。在对流感 A pH1N1 和 H3N2 病毒的季节性预测中，beth-1 与现有方法相比，在遗传匹配方面表现出更好的性能。在前瞻性验证中，与当前疫苗相比，该模型在小鼠免疫实验中对流行病毒具有更好或非劣效的遗传匹配和中和作用。该方法提供了一种有前途的即用型工具，通过捕捉基因组时空上的异质进化动态并将分子变体与人群免疫反应联系起来，有助于流感病毒疫苗株的选择。
