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Bioinformatics Advance Access originally published online on August 25, 2009
Bioinformatics 2009 25(18):2309-2317; doi:10.1093/bioinformatics/btp423
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© The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Using a mutual information-based site transition network to map the genetic evolution of influenza A/H3N2 virus

Zhen Xia 1, Gulei Jin 1, Jun Zhu 1 and Ruhong Zhou 1,2,3,*

1Institute of Bioinformatics, Zhejiang University, Hangzhou 310027, P. R. China, 2Computational Biology Center, IBM Thomas J Watson Research Center, Yorktown Heights, NY 10598 and 3Department of Chemistry, Columbia University, New York, NY 10027, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: Mapping the antigenic and genetic evolution pathways of influenza A is of critical importance in the vaccine development and drug design of influenza virus. In this article, we have analyzed more than 4000 A/H3N2 hemagglutinin (HA) sequences from 1968 to 2008 to model the evolutionary path of the influenza virus, which allows us to predict its future potential drifts with specific mutations.

Results: The mutual information (MI) method was used to design a site transition network (STN) for each amino acid site in the A/H3N2 HA sequence. The STN network indicates that most of the dynamic interactions are positioned around the epitopes and the receptor binding domain regions, with strong preferences in both the mutation sites and amino acid types being mutated to. The network also shows that antigenic changes accumulate over time, with occasional large changes due to multiple co-occurring mutations at antigenic sites. Furthermore, the cluster analysis by subdividing the STN into several subnetworks reveals a more detailed view about the features of the antigenic change: the characteristic inner sites and the connecting inter-subnetwork sites are both responsible for the drifts. A novel five-step prediction algorithm based on the STN shows a reasonable accuracy in reproducing historical HA mutations. For example, our method can reproduce the 2003–2004 A/H3N2 mutations with ~70% accuracy. The method also predicts seven possible mutations for the next antigenic drift in the coming 2009–2010 season. The STN approach also agrees well with the phylogenetic tree and antigenic maps based on HA inhibition assays.

Availability: All code and data are available at http://ibi.zju.edu.cn/birdflu/

Contact: ruhongz{at}us.ibm.com

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: John Quackenbush

Received on December 18, 2008; revised on June 19, 2009; accepted on July 7, 2009

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