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

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Predicting Egg Passage Adaptations to Design Better Vaccines for the H3N2 Influenza Virus. / Liu, Yunsong; Chen, Hui; Duan, Wenyuan; Zhang, Xinyi; He, Xionglei; Nielsen, Rasmus; Ma, Liang; Zhai, Weiwei.

In: Viruses, Vol. 14, No. 9, 2065, 2022.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Liu, Y, Chen, H, Duan, W, Zhang, X, He, X, Nielsen, R, Ma, L & Zhai, W 2022, 'Predicting Egg Passage Adaptations to Design Better Vaccines for the H3N2 Influenza Virus', Viruses, vol. 14, no. 9, 2065. https://doi.org/10.3390/v14092065

APA

Liu, Y., Chen, H., Duan, W., Zhang, X., He, X., Nielsen, R., Ma, L., & Zhai, W. (2022). Predicting Egg Passage Adaptations to Design Better Vaccines for the H3N2 Influenza Virus. Viruses, 14(9), [2065]. https://doi.org/10.3390/v14092065

Vancouver

Liu Y, Chen H, Duan W, Zhang X, He X, Nielsen R et al. Predicting Egg Passage Adaptations to Design Better Vaccines for the H3N2 Influenza Virus. Viruses. 2022;14(9). 2065. https://doi.org/10.3390/v14092065

Author

Liu, Yunsong ; Chen, Hui ; Duan, Wenyuan ; Zhang, Xinyi ; He, Xionglei ; Nielsen, Rasmus ; Ma, Liang ; Zhai, Weiwei. / Predicting Egg Passage Adaptations to Design Better Vaccines for the H3N2 Influenza Virus. In: Viruses. 2022 ; Vol. 14, No. 9.

Bibtex

@article{d07de181cdff43f28a994a591be8cb66,
title = "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 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 = "convergent evolution, epistasis, fitness landscape, H3N2 influenza, passage adaptation, vaccine efficacy",
author = "Yunsong Liu and Hui Chen and Wenyuan Duan and Xinyi Zhang and Xionglei He and Rasmus Nielsen and Liang Ma and Weiwei Zhai",
note = "Publisher Copyright: {\textcopyright} 2022 by the authors.",
year = "2022",
doi = "10.3390/v14092065",
language = "English",
volume = "14",
journal = "Viruses",
issn = "1999-4915",
publisher = "M D P I AG",
number = "9",

}

RIS

TY - JOUR

T1 - Predicting Egg Passage Adaptations to Design Better Vaccines for the H3N2 Influenza Virus

AU - Liu, Yunsong

AU - Chen, Hui

AU - Duan, Wenyuan

AU - Zhang, Xinyi

AU - He, Xionglei

AU - Nielsen, Rasmus

AU - Ma, Liang

AU - Zhai, Weiwei

N1 - Publisher Copyright: © 2022 by the authors.

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - convergent evolution

KW - epistasis

KW - fitness landscape

KW - H3N2 influenza

KW - passage adaptation

KW - vaccine efficacy

U2 - 10.3390/v14092065

DO - 10.3390/v14092065

M3 - Journal article

C2 - 36146872

AN - SCOPUS:85138626270

VL - 14

JO - Viruses

JF - Viruses

SN - 1999-4915

IS - 9

M1 - 2065

ER -

ID: 321839991