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

Author

Listed:
  • Jingzhi Lou

    (The Chinese University of Hong Kong (CUHK)
    Beth Bioinformatics Co. Ltd)

  • Weiwen Liang

    (The University of Hong Kong)

  • Lirong Cao

    (The Chinese University of Hong Kong (CUHK)
    CUHK Shenzhen Research Institute)

  • Inchi Hu

    (George Mason University)

  • Shi Zhao

    (The Chinese University of Hong Kong (CUHK)
    Tianjin Medical University)

  • Zigui Chen

    (CUHK)

  • Renee Wan Yi Chan

    (CUHK
    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)
    CUHK Shenzhen Research Institute)

  • Yexian Zhang

    (Beth Bioinformatics Co. Ltd
    CUHK Shenzhen Research Institute)

  • Eng-kiong Yeoh

    (The Chinese University of Hong Kong (CUHK)
    CUHK)

  • Paul Kay-Sheung Chan

    (CUHK
    CUHK)

  • Benny Chung Ying Zee

    (The Chinese University of Hong Kong (CUHK)
    CUHK Shenzhen Research Institute)

  • Chris Ka Pun Mok

    (The Chinese University of Hong Kong (CUHK)
    CUHK)

  • Maggie Haitian Wang

    (The Chinese University of Hong Kong (CUHK)
    CUHK Shenzhen Research Institute)

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.

Suggested Citation

  • 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 , 2024. "Predictive evolutionary modelling for influenza virus by site-based dynamics of mutations," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46918-0
    DOI: 10.1038/s41467-024-46918-0
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    References listed on IDEAS

    as
    1. Trevor Bedford & Steven Riley & Ian G. Barr & Shobha Broor & Mandeep Chadha & Nancy J. Cox & Rodney S. Daniels & C. Palani Gunasekaran & Aeron C. Hurt & Anne Kelso & Alexander Klimov & Nicola S. Lewis, 2015. "Global circulation patterns of seasonal influenza viruses vary with antigenic drift," Nature, Nature, vol. 523(7559), pages 217-220, July.
    2. Andrew Rambaut & Oliver G. Pybus & Martha I. Nelson & Cecile Viboud & Jeffery K. Taubenberger & Edward C. Holmes, 2008. "The genomic and epidemiological dynamics of human influenza A virus," Nature, Nature, vol. 453(7195), pages 615-619, May.
    3. Shimon Bershtein & Michal Segal & Roy Bekerman & Nobuhiko Tokuriki & Dan S. Tawfik, 2006. "Robustness–epistasis link shapes the fitness landscape of a randomly drifting protein," Nature, Nature, vol. 444(7121), pages 929-932, December.
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