Predicting Egg Passage Adaptations to Design Better Vaccines for the H3N2 Influenza Virus.

ABSTRACT: Seasonal H3N2 influenza evolves rapidly, leading to an extremely poor vaccine efficacy. Substitutions employed during vaccine production using embryonated eggs (i.e., egg passage adaptation) contribute to the poor vaccine efficacy (VE), but the evolutionary mechanism remains elusive. Using an unprecedented number of hemagglutinin sequences (n = 89,853), we found that the fitness landscape of passage adaptation is dominated by pervasive epistasis between two leading residues (186 and 194) and multiple other positions. Convergent evolutionary paths driven by strong epistasis explain most of the variation in VE, which has resulted in extremely poor vaccines for the past decade. Leveraging the unique fitness landscape, we developed a novel machine learning model that can predict egg pas

SUBMITTER: Liu Y 

PROVIDER: S-EPMC9501976 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature