Selection of antigenically advanced variants of seasonal influenza viruses View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2016-05-23

AUTHORS

Chengjun Li, Masato Hatta, David F Burke, Jihui Ping, Ying Zhang, Makoto Ozawa, Andrew S Taft, Subash C Das, Anthony P Hanson, Jiasheng Song, Masaki Imai, Peter R Wilker, Tokiko Watanabe, Shinji Watanabe, Mutsumi Ito, Kiyoko Iwatsuki-Horimoto, Colin A Russell, Sarah L James, Eugene Skepner, Eileen A Maher, Gabriele Neumann, Alexander I Klimov, Anne Kelso, John McCauley, Dayan Wang, Yuelong Shu, Takato Odagiri, Masato Tashiro, Xiyan Xu, David E Wentworth, Jacqueline M Katz, Nancy J Cox, Derek J Smith, Yoshihiro Kawaoka

ABSTRACT

Influenza viruses mutate frequently, necessitating constant updates of vaccine viruses. To establish experimental approaches that may complement the current vaccine strain selection process, we selected antigenic variants from human H1N1 and H3N2 influenza virus libraries possessing random mutations in the globular head of the haemagglutinin protein (which includes the antigenic sites) by incubating them with human and/or ferret convalescent sera to human H1N1 and H3N2 viruses. We also selected antigenic escape variants from human viruses treated with convalescent sera and from mice that had been previously immunized against human influenza viruses. Our pilot studies with past influenza viruses identified escape mutants that were antigenically similar to variants that emerged in nature, establishing the feasibility of our approach. Our studies with contemporary human influenza viruses identified escape mutants before they caused an epidemic in 2014-2015. This approach may aid in the prediction of potential antigenic escape variants and the selection of future vaccine candidates before they become widespread in nature. More... »

PAGES

16058

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/nmicrobiol.2016.58

DOI

http://dx.doi.org/10.1038/nmicrobiol.2016.58

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1008966416

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/27572841


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