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Predictive evolutionary modelling for influenza virus by site-based dynamics of mutations
Jingzhi Lou1,2, Weiwen Liang3, Lirong Cao1,4
1JC School of Public Health and Primary Care (JCSPHPC), The Chinese University of Hong Kong (CUHK), Hong Kong SAR, China.
Nature Communications
|March 22, 2024
Summary
A new computational method, beth-1, forecasts influenza virus evolution to select optimal vaccine strains. This approach improves genetic matching and neutralization compared to current methods, aiding annual vaccine updates.
Area of Science:
- Virology
- Immunology
- Computational Biology
- Epidemiology
Background:
- Influenza virus constantly evolves, necessitating annual vaccine updates to match circulating strains and overcome human adaptive immunity.
- Seasonal epidemics are driven by influenza virus's continuous genetic diversification, posing a public health challenge.
- Current influenza vaccine strain selection relies on predicting future viral evolution, which is complex due to heterogeneous evolutionary dynamics.
Purpose of the Study:
- To develop and validate a computational approach, beth-1, for forecasting influenza virus evolution and selecting optimal vaccine strains.
- To improve the genetic matching and neutralization efficacy of influenza vaccines against circulating strains.
- To provide a ready-to-use tool for facilitating influenza vaccine strain selection by linking molecular variants to population immune response.
Main Methods:
- Developed beth-1, a computational method modeling site-wise mutation fitness to forecast virus evolution.
- Integrated virus genome data and population sero-positivity to calibrate mutation transition times.
- Projected the viral fitness landscape to future time points for optimal vaccine strain selection.
- Validated beth-1 using historical influenza A (pH1N1 and H3N2) data and prospective mouse immunization experiments.
Main Results:
- Beth-1 demonstrated superior genetic matching compared to existing approaches in season-to-season predictions for influenza A viruses.
- Prospective validations showed beth-1 achieved superior or non-inferior genetic matching against circulating viruses.
- Mice immunized with vaccines selected by beth-1 exhibited enhanced neutralization against circulating influenza strains.
- The model effectively captures heterogeneous evolutionary dynamics across the viral genome over space and time.
Conclusions:
- The beth-1 computational approach offers a promising tool for enhancing influenza vaccine strain selection.
- By accurately forecasting virus evolution and linking molecular changes to immune response, beth-1 can improve vaccine effectiveness.
- This method provides a ready-to-use solution to address the challenge of rapidly evolving influenza viruses and seasonal epidemics.
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