University of Minnesota
https://twin-cities.umn.edu/
612-625-5000
Milestone
1.2.c

H1N1 and H3N2 characterization

In progress
High priority

Continue to develop, harmonize, and implement methods (e.g., the use of predictive artificial intelligence and other new technologies) to inform antigenic characterization of H1N1 and H3N2 viruses.

Progress Highlights

Huang 2024 used a pseudovirus platform to evaluate the alignment between the 2023 and 2024 seasonal influenza vaccine H3N2 strain (A/Darwin/6/2021) and currently circulating influenza strains.

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Harvey 2023 developed a new approach using a Bayesian model for integrating genetic and antigenic data to identify genetic changes in H3N2 virus that underpin antigenic drift. 

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Peng 2023 developed a novel quantitative prediction method to predict the antigenic distance between virus strains using attribute network embedding techniques. Results show a strong positive correlation between supplementing genetic features and antigenic distance prediction accuracy. 

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The US Centers for Disease Control and Prevention is characterizing NA antigenicity of A(H3N2) viruses, which includes optimizing methods for analysis and assessing contemporary A(H3N2) viruses as part of the WHO vaccine recommendation process. 

Galli et al 2022 developed and described a high-throughput whole-genome sequencing protocol for A(H3N2) viruses, providing a fast and accurate method to characterize the complete genome of H3N2 viruses directly from clinical respiratory samples.

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The US Centers for Disease Control and Prevention is developing a human epitope map to identify escape variants. Researchers have screened hundreds of Abs for inclusion in a library of human mAbs that recognize antigenic sites of the A(H1N1)pdm09 HA molecule, and mAbs have been characterized and mapped to antigenic sites in the HA. Work continues to add to this panel of reagents to generate a comprehensive library of mAbs that map the antigenic sites of contemporary A(H1N1)pdm09 influenza viruses.