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Networks of Genomic Co-occurrence Capture Characteristics of Human Influenza A (H3N2) Evolution

Overview
Journal Genome Res
Specialty Genetics
Date 2007 Nov 23
PMID 18032723
Citations 22
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Abstract

The recent availability of full genomic sequence data for a large number of human influenza A (H3N2) virus isolates over many years provides us an opportunity to analyze human influenza virus evolution by considering all gene segments simultaneously. However, such analysis requires development of new computational models that can capture the complex evolutionary features over the entire genome. By analyzing nucleotide co-occurrence over the entire genome of human H3N2 viruses, we have developed a network model to describe H3N2 virus evolutionary patterns and dynamics. The network model effectively captures the evolutionary antigenic features of H3N2 virus at the whole-genome level and accurately describes the complex evolutionary patterns between individual gene segments. Our analyses show that the co-occurring nucleotide modules apparently underpin the dynamics of human H3N2 evolution and that amino acid substitutions corresponding to nucleotide co-changes cluster preferentially in known antigenic regions of the viral HA. Therefore, our study demonstrates that nucleotide co-occurrence networks represent a powerful method for tracking influenza A virus evolution and that cooperative genomic interaction is a major force underlying influenza virus evolution.

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References
1.
Ferguson N, Galvani A, Bush R . Ecological and immunological determinants of influenza evolution. Nature. 2003; 422(6930):428-33. DOI: 10.1038/nature01509. View

2.
Watts D, Strogatz S . Collective dynamics of 'small-world' networks. Nature. 1998; 393(6684):440-2. DOI: 10.1038/30918. View

3.
Wiley D, Wilson I, Skehel J . Structural identification of the antibody-binding sites of Hong Kong influenza haemagglutinin and their involvement in antigenic variation. Nature. 1981; 289(5796):373-8. DOI: 10.1038/289373a0. View

4.
Fauci A . Race against time. Nature. 2005; 435(7041):423-4. DOI: 10.1038/435423a. View

5.
Parrish C, Kawaoka Y . The origins of new pandemic viruses: the acquisition of new host ranges by canine parvovirus and influenza A viruses. Annu Rev Microbiol. 2005; 59:553-86. DOI: 10.1146/annurev.micro.59.030804.121059. View