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Anticipating time-dependent antigenic variants of influenza A (H3N2) viruses

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  • Anticipating time-dependent antigenic variants of influenza A (H3N2) viruses

    nfect Genet Evol. 2018 Nov 1. pii: S1567-1348(18)30841-4. doi: 10.1016/j.meegid.2018.10.028. [Epub ahead of print] Anticipating time-dependent antigenic variants of influenza A (H3N2) viruses.

    Adabor ES1.
    Author information

    Abstract

    Frequent variations in influenza vaccines are necessary to match antigenic variants which appear in influenza epidemics. Antigenic variants of influenza viruses result from frequent mutations in amino acid residues located on their hemagglutinin (HA) proteins. Knowledge of specific changes in these amino acids helps to characterize distinct antigenic variants. In this paper, statistical models are developed and used to investigate changes in amino acids which accompany antigenic variants of epidemiological importance. Amino acid sequences of the HA proteins of influenza A (H3N2) strains isolated from 1968 to 2015 were obtained. The sequences were aligned using Clustal Omega and the number of differences in amino acid residues located on annotated positions of antigenic sites of the HA protein between pairs of strains were determined. These were linked in the statistical models and used to assess the relationship between any pair of influenza strains. The results revealed that both antigenic similarity between strains and the amino acid changes are affected by the time of isolation of the strains. Furthermore, the models predicted that rates of changes in amino acids located on the antigenic sites ranged between 5% and 6% per site per year. The findings of the study suggest that time-dependent antigenic variants of influenza A (H3N2) strains may occur as they evolve. The study has the potential to greatly improve influenza surveillance in as much as it supports vaccine designs.


    KEYWORDS:

    Amino acids; Antigenic sites; Evolution; Influenza virus; Statistical models

    PMID: 30391719 DOI: 10.1016/j.meegid.2018.10.028

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