We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Predictive evolutionary modelling for influenza virus by site-based dynamics of mutations.
- Authors
Lou, Jingzhi; Liang, Weiwen; Cao, Lirong; Hu, Inchi; Zhao, Shi; Chen, Zigui; Chan, Renee Wan Yi; Cheung, Peter Pak Hang; Zheng, Hong; Liu, Caiqi; Li, Qi; Chong, Marc Ka Chun; Zhang, Yexian; Yeoh, Eng-kiong; Chan, Paul Kay-Sheung; Zee, Benny Chung Ying; Mok, Chris Ka Pun; Wang, Maggie Haitian
- Abstract
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.Seasonal influenza vaccine effectiveness depends on including virus strains in the vaccine that closely match those circulating in the upcoming season. In this study, the authors develop a computational model of influenza virus evolution to predict future circulating strains and therefore support vaccine strain selection.
- Publication
Nature Communications, 2024, Vol 15, Issue 1, p1
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-024-46918-0