| Autor: |
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, Maggie Haitian Wang |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Nature Communications, Vol 15, Iss 1, Pp 1-11 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2041-1723 |
| DOI: |
10.1038/s41467-024-46918-0 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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