Predictive evolutionary modelling for influenza virus by site-based dynamics of mutations

Jingzhi Lou, Weiwen Liang, Lirong Cao, Inchi Hu, Shi Zhao, Zigui Chen, Renee Wan Yi Chan, Peter Pak Hang Cheung, Hong Zheng, Caiqi Liu, Qi Li, Marc Ka Chun Chong, Yexian Zhang, Eng-kiong Yeoh, Paul Kay-Sheung Chan, Benny Chung Ying Zee, Chris Ka Pun Mok () and Maggie Haitian Wang ()
Additional contact information
Jingzhi Lou: The Chinese University of Hong Kong (CUHK)
Weiwen Liang: The University of Hong Kong
Lirong Cao: The Chinese University of Hong Kong (CUHK)
Inchi Hu: George Mason University
Shi Zhao: The Chinese University of Hong Kong (CUHK)
Zigui Chen: CUHK
Renee Wan Yi Chan: CUHK
Peter Pak Hang Cheung: CUHK
Hong Zheng: The Chinese University of Hong Kong (CUHK)
Caiqi Liu: The Chinese University of Hong Kong (CUHK)
Qi Li: The Chinese University of Hong Kong (CUHK)
Marc Ka Chun Chong: The Chinese University of Hong Kong (CUHK)
Yexian Zhang: Beth Bioinformatics Co. Ltd
Eng-kiong Yeoh: The Chinese University of Hong Kong (CUHK)
Paul Kay-Sheung Chan: CUHK
Benny Chung Ying Zee: The Chinese University of Hong Kong (CUHK)
Chris Ka Pun Mok: The Chinese University of Hong Kong (CUHK)
Maggie Haitian Wang: The Chinese University of Hong Kong (CUHK)

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-46918-0

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