High-throughput pseudovirus neutralisation maps the antigenic landscape of influenza A/H1N1 virusesResearch in context

Summary: Background: The continuous mutations in the haemagglutinin gene in A/H1N1 influenza viruses drives antigenic drift, necessitating frequent vaccine updates. Pseudovirus systems, with their flexible HA protein presentation and high-throughput luciferase-based reporter assays, provide a versatile approach for mapping the antigenic landscape of A/H1N1 viruses. Methods: This study constructed a pseudovirus library comprising 123 representative strains and 21 vaccine strains to analyse the antigenic evolution of human A/H1N1 from 1918 to 2023. Additionally, a machine learning-based antigenicity evaluation model, PN-AgEvaH1, was developed using pseudovirus neutralisation assay data. Findings: The pseudovirus neutralisation experiments identified eight antigenic clusters, each associated with distinct epidemiological characteristics. All vaccine strains were found to align closely with the prevalent circulating strains of their respective years. Key amino acid residues contributing to pdm09 antigenic clusters were also identified. Furthermore, the PN-AgEvaH1 model achieved a high antigenicity evaluation accuracy with a receiver operating characteristic area under the curve (ROC AUC) score of 0.990, providing a reliable tool for characterising A/H1N1 antigenic clusters and guiding vaccine selection. Interpretation: The pseudovirus neutralisation (PN) assay demonstrated high accuracy in identifying antigenic matches between vaccine and circulating strains, highlighting its value in vaccine strain selection. This study represents the large-scale antigenicity evaluation of human A/H1N1 viruses using the PN assay and underscores its potential as a complementary or alternative approach to the HI assay in both experimental and computational applications. Funding: This project was financially supported by a grant from Chinese Academy of Medical Sciences and Technology Innovation Project in Medicine and Health (project Nos. 2022-I2M-3-001 and 2021-I2M-1-061).