Characterization of key amino acid substitutions and dynamics of the influenza virus H3N2 hemagglutinin. Issue 6 (December 2021) - Wang, Maggie Haitian; Lou, Jingzhi; Cao, Lirong; Zhao, Shi; Chan, Renee WY; Chan, Paul KS; Chan, Martin Chi-Wai; Chong, Marc KC; Wu, William KK; Wei, Yuchen; Zhang, Haoyang; Zee, Benny CY; et al.
Highlights: The effective mutations (EM) estimates key mutations associated with epidemics. The EM period (EMP) estimates duration of mutation selective advantage. The g -measure summarizes total genetic mutation activities in a period. 46 EMs on hemagglutinin were identified in 13-year global influenza H3N2 epidemics. Southeast Asia encompassed 90% of the common EMs and the longest EMP of H3N2. Summary: The annual epidemics of seasonal influenza is partly attributed to the continued virus evolution. It is challenging to evaluate the effect of influenza virus mutations on evading population immunity. In this study, we introduce a novel statistical and computational approach to measure the dynamic molecular determinants underlying epidemics using effective mutations (EMs), and account for the time of waning mutation advantage against herd immunity by measuring the effective mutation periods (EMPs). Extensive analysis is performed on the sequencing and epidemiology data of H3N2 epidemics in ten regions from season to season. We systematically identified 46 EMs in the hemagglutinin (HA) gene, in which the majority were antigenic sites. Eight EMs were located in immunosubdominant stalk domain, an important target for developing broadly reactive antibodies. The EMs might provide timely information on key substitutions for influenza vaccines antigen design. The EMP suggested that major genetic variants of H3N2 circulated in Southeast Asia for an average duration of 4.5 years (SD 2.4) compared to a significantly shorter 2.0 years (SD 1.0) in temperate regions. The proposed method bridges population epidemics and molecular characteristics of infectious diseases, and would find broad applications in various pathogens mutation estimations.