ArticleBMC bioinformatics2018
Antigenic cartography of H1N1 influenza viruses using sequence-based antigenic distance calculation.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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37 citing papers in PubMed, 62 citations in OpenAlex.
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- Mapping SARS-CoV-2 antigenic relationships and serological responses.Science (New York, N.Y.) · 2023Article
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- Development of PREDAC-H1pdm to model the antigenic evolution of influenza A/(H1N1) pdm09 viruses.Virologica Sinica · 2023Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundThe ease at which influenza virus sequence data can be used to estimate antigenic relationships between strains and the existence of databases containing sequence data for hundreds of thousands influenza strains make sequence-based antigenic distance estimates an attractive approach to researchers. Antigenic mismatch between circulating strains and vaccine strains results in significantly decreased vaccine effectiveness. Furthermore, antigenic relatedness between the vaccine strain and the strains an individual was originally primed with can affect the cross-reactivity of the antibody response. Thus, understanding the antigenic relationships between influenza viruses that have circulated is important to both vaccinologists and immunologists.
resultsHere we develop a method of mapping antigenic relationships between influenza virus stains using a sequence-based antigenic distance approach (SBM). We used a modified version of the p-all-epitope sequence-based antigenic distance calculation, which determines the antigenic relatedness between strains using influenza hemagglutinin (HA) genetic coding sequence data and provide experimental validation of the p-all-epitope calculation. We calculated the antigenic distance between 4838 H1N1 viruses isolated from infected humans between 1918 and 2016. We demonstrate, for the first time, that sequence-based antigenic distances of H1N1 Influenza viruses can be accurately represented in 2-dimenstional antigenic cartography using classic multidimensional scaling. Additionally, the model correctly predicted decreases in cross-reactive antibody levels with 87% accuracy and was highly reproducible with even when small numbers of sequences were used.
conclusionThis work provides a highly accurate and precise bioinformatics tool that can be used to assess immune risk as well as design optimized vaccination strategies. SBM accurately estimated the antigenic relationship between strains using HA sequence data. Antigenic maps of H1N1 virus strains reveal that strains cluster antigenically similar to what has been reported for H3N2 viruses. Furthermore, we demonstrated that genetic variation differs across antigenic sites and discuss the implications.
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