Predicting Egg Passage Adaptations to Design Better Vaccines for the H3N2 Influenza Virus
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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.
I: Viruses, Bind 14, Nr. 9, 2065, 2022.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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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