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

Yunsong Liu; Hui Chen; Wenyuan Duan; Xinyi Zhang; Xionglei He; Rasmus Nielsen; Liang Ma; Weiwei Zhai

doi:10.3390/v14092065

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

Viruses. 2022 Sep 17;14(9):2065. doi: 10.3390/v14092065.

Authors

Yunsong Liu^{1

2}, Hui Chen³, Wenyuan Duan^{1

2}, Xinyi Zhang^{1

2}, Xionglei He⁴, Rasmus Nielsen^{5

6

7}, Liang Ma¹, Weiwei Zhai^{1

2

8}

Affiliations

¹ Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China.
² University of the Chinese Academy of Sciences, Beijing 100049, China.
³ Human Genetics, Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore 138672, Singapore.
⁴ MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China.
⁵ Department of Integrative Biology, University of California-Berkeley, Berkeley, CA 94707, USA.
⁶ Department of Statistics, University of California-Berkeley, Berkeley, CA 94707, USA.
⁷ Globe Institute, University of Copenhagen, 1350 København, Copenhagen, Denmark.
⁸ Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China.

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 passage substitutions for any candidate vaccine strain before the passage experiment, providing a unique opportunity for the selection of optimal vaccine viruses. Our study presents one of the most comprehensive characterizations of the fitness landscape of a virus and demonstrates that evolutionary trajectories can be harnessed for improved influenza vaccines.

Keywords: H3N2 influenza; convergent evolution; epistasis; fitness landscape; passage adaptation; vaccine efficacy.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Hemagglutinin Glycoproteins, Influenza Virus / genetics
Hemagglutinins
Humans
Influenza A Virus, H3N2 Subtype / genetics
Influenza Vaccines* / genetics
Influenza, Human*

Substances

Hemagglutinin Glycoproteins, Influenza Virus
Hemagglutinins
Influenza Vaccines

Grants and funding

M.L. is supported by the National Key R&D Program of China (grant no. 2019YFA0709501) and National Natural Science Foundation of China (grant no. 11971459). W.Z. is supported in part by the Strategic Priority Research Program of the Chinese Academy of Sciences (grant no. XDPB17), the National Natural Science Foundation of China (grant no. 31970566) and National Key R&D program of China (grant no. 2018YFC1406902 and 2018YFC0910400).