Intergenic subset organization within a set of geographically-defined viral sequences from the 2009 H1N1 influenza A pandemic

William A. Thompson, Joel K. Weltman

Department of Medicine, Alpert/Brown University School of Medicine, Providence, USA.
Division of Applied Mathematics and Center for Computational Molecular Biology, Brown University, Providence, USA.

DOI: 10.4236/ajmb.2012.21004                

Abstract

We report a bioinformatic analysis of the datasets of sequences of all ten genes from the 2009 H1N1 influenza A pandemic in the state of Wisconsin. The gene with the greatest summed information entropy was found to be the hemagglutinin (HA) gene. Based upon the viral ID identifier of the HA gene sequence, the sequences of all of the genes were sorted into two subsets, depending upon whether the nucleotide occupying the position of maximum entropy, position 658 of the HA sequence, was either A or U. It was found that the information entropy (H) distributions of subsets differed significantly from each other, from H distributions of randomly generated subsets and from the H distributions of the complete datasets of each gene. Mutual information (MI) values facilitated identification of nine nucleotide positions, distributed over seven of the influenza genes, at which the nucleotide subsets were disjoint, or almost disjoint. Nucleotide frequencies at these nine positions were used to compute mutual information values that subsequently served as weighting factors for edges in a graph net-work. Seven of the nucleotide positions in the graph network are sites of synonymous mutations. Three of these sites of synonymous mutation are within a single gene, the M1 gene, which occupied the position of greatest graph centrality. It is proposed that these bioinformatic and network graph results may reflect alterations in M1-mediated viral packaging and exteriorization, known to be susceptible to synonymous mutations.

Keywords

Influenza A; H1N1; Bioinformatics; Genes; Pandemic; Epidemic; Information Entropy; MutualInFormation; Graph; Network; Centrality; Subsets

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	World Health Organization Global Influenza Programme, 2011. http://www.who.int/csr/disease/influenza/en/
[2]	Truelove, S.A., Chitnis, A.S., Heffernan, R.T., Karon, A.E., Haupt, T.E. and Davis, J.P. (2011) Comparison of patients hospitalized with pandemic 2009 influenza A (H1N1) virus infection during the first two pandemic waves in Wisconsin. Journal of Infectious Diseases, 203, 828-37. doi:10.1093/infdis/jiq117
[3]	Bao, Y., Bolotov, P., Dernovoy, D., Kiryutin, B., Zaslavsky, L., Tatusova, T., Ostell, J. and Lipman, D. (2008). The Influenza Virus Resource at the National Center for Biotechnology Information. Journal of Virology, 82, 596-601. doi:10.1128/JVI.02005-07
[4]	Python Programming Language, Official Website. http://www.python.org
[5]	SciPy.org, Scientific Tools for Python. http://www.scipy.org
[6]	Python Plotting. http://matplotlib.sourceforge.net
[7]	Hagberg, A.A., Schult, D.A. and Swart, P.J. (2008) Exploring network structure, dynamics, and function using NetworkX. In: Varoquaux, G., Vaught, T. and Millman, J., Eds., Proceedings of 7th Python in Science Conference (SciPy2008), 11-15.
[8]	Shannon, C.E. (1948) A mathematical theory of communication. The Bell System Technical Journal, 27, 379-423, 623-656.
[9]	Hausser, J. and Strimmer, K. (2009) Entropy inference and the James-Stein estimator, with application to nonlinear gene association networks. The Journal of Machine Learning Research, 10, 1469-1484.
[10]	Thompson, W.A., Martwick, A. and Weltman, J.K. (2009) Decimative Multiplication of Entropy Arrays, with Application to Influenza. Entropy, 11, 351-359. doi:10.3390/e11030351
[11]	Cover, T.M. and Thomas, J.A. (1991) Elements of information theory. Chapter 2: Entropy, relative entropy and mutual information. Wiley, New York, 16-33.
[12]	Bonacich, P. and Lloyd, P. (2001) Eigenvector-like measures of centrality for asymmetric relations. Social Networks, 23, 191-201. doi:10.1016/S0378-8733(01)00038-7
[13]	Thompson, W.A., Fan, S. and Weltman, J.K. (2008). Information Entropy of Influenza A Segment 7. Entropy, 10, 736-744. doi:10.3390/e10040736
[14]	Murti, K.G., Brown, P.S., Bean Jr., W.J. and Webster, R.G. (1992) Composition of the helical internal components of influenza virus as revealed by immunogold labeling/electron microscopy. Virology, 186, 294-299. doi:10.1016/0042-6822(92)90084-3
[15]	Tchatalbachev S., Flick, R. and Hobom, G. (2001) The packaging signal of influenza viral RNA molecules. RNA, 7, 979-989. doi:10.1017/S1355838201002424
[16]	Hutchinson, E.C., Curran, M.D., Read, E.K., Gog, J.R. and Digard, P. (2008) Mutational Analysis of cis-Acting RNA Signals in Segment 7 of Influenza A Virus. Journal of Virology, 82, 11869-11879. doi:10.1128/JVI.01634-08