Back to Search Start Over

The antigenic landscape of human influenza N2 neuraminidases from 2009 until 2017.

Authors :
Portela Catani, João Paulo
Smet, Anouk
Ysenbaert, Tine
Vuylsteke, Marnik
Bottu, Guy
Mathys, Janick
Botzki, Alexander
Cortes-Garcia, Guadalupe
Strugnell, Tod
Gomila, Raul
Hamberger, John
Catalan, John
Ustyugova, Irina V.
Farrell, Timothy
Stegalkina, Svetlana
Ray, Satyajit
LaRue, Lauren
Saelens, Xavier
Vogel, Thorsten U.
Source :
eLife. 5/28/2024, p1-22. 22p.
Publication Year :
2024

Abstract

Human H3N2 influenza viruses are subject to rapid antigenic evolution which translates into frequent updates of the composition of seasonal influenza vaccines. Despite these updates, the effectiveness of influenza vaccines against H3N2-associated disease is suboptimal. Seasonal influenza vaccines primarily induce hemagglutinin-specific antibody responses. However, antibodies directed against influenza neuraminidase (NA) also contribute to protection. Here, we analysed the antigenic diversity of a panel of N2 NAs derived from human H3N2 viruses that circulated between 2009 and 2017. The antigenic breadth of these NAs was determined based on the NA inhibition (NAI) of a broad panel of ferret and mouse immune sera that were raised by infection and recombinant N2 NA immunisation. This assessment allowed us to distinguish at least four antigenic groups in the N2 NAs derived from human H3N2 viruses that circulated between 2009 and 2017. Computational analysis further revealed that the amino acid residues in N2 NA that have a major impact on susceptibility to NAI by immune sera are in proximity of the catalytic site. Finally, a machine learning method was developed that allowed to accurately predict the impact of mutations that are present in our N2 NA panel on NAI. These findings have important implications for the renewed interest to develop improved influenza vaccines based on the inclusion of a protective NA antigen formulation. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
2050084X
Database :
Academic Search Index
Journal :
eLife
Publication Type :
Academic Journal
Accession number :
177768376
Full Text :
https://doi.org/10.7554/eLife.90782