ABSTRACT A recent phylogenetic inference indicated that the 2009 pandemic H1N1 strains circulating from March 2009 to September 2009 could be divided into two closely related but distinct clusters. Cluster one contained most strains from Mexico, Texas, and California, and cluster two had most strains from New York, both of which were reported to co-circulate in all continents. The same study further revealed nine nucleotide changes in six gene segments of the new virus specific for the two clusters. In the current study, the informational spectrum method (ISM), a bioinformatics technique, was employed to study the receptor binding patterns of the two clusters. It discovered that while both groups shared the same primary human binding affinity, their secondary binding preferences were different. Cluster one favored swine binding as its secondary binding pattern, whereas cluster two mostly exhibited the binding specificity of A/South Carolina/1/18 (H1N1) (one of the 1918 flu pandemic strains) as its secondary binding pattern. Besides all the nine nucleotide changes found in the previous study, Random Forests were applied to uncover several new nucleotide polymorphisms in 10 genes of the strains between the two clusters, and several amino acid changes in the HA protein that might be accountable for the discrepancy of the secondary receptor binding patterns of the two clusters. Finally, entropy analysis was conducted to present a global view of gene sequence variations between the two clusters, which illustrated that cluster one had much higher genetic divergence than cluster two. Furthermore, it suggested a significant overall correspondence between the nucleotide positions of high importance in differentiating the two clusters and nucleotide positions of high entropy in cluster one.
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nullHu, W. (2010) Subtle differences in receptor binding specificity and gene sequences of the 2009 pandemic H1N1 influenza virus. Advances in Bioscience and Biotechnology, 1, 305-314. doi: 10.4236/abb.2010.14040.
 Hu, W. (2010) Novel host markers in the 2009 pandemic H1N1 influenza a virus. Journal of Biomedical Science and Engineering, 3(6), 584-601.
Garten, R.J., Davis, C,T,, Russell, C.A., Shu, B., Lindstrom, S., Balish, A., Sessions, W.M., Xu, X., at el. (2009) Antigenic and genetic characteristics of swine-origin 2009 A(H1N1) influenza viruses circulating in humans, Science, 325 (5937), 197-201.
Hu, W. (2010) Nucleotide host markers in the influenza a viruses. Journal of Biomedical Science and Engineering, 3, 684-699.
Veljkovic, V., Niman, H.L., Glisic, S., Veljkovic, N., Perovic, V. and Muller, C.P. (2009) Identification of hemagglutinin structural domain and polymorphisms which may modulate swine H1N1 interactions with human receptor, BMC Structural Biology, 9, 62.
Veljkovic, V., Veljkovic, N., Muller, C.P., Müller, S., Glisic, S., Perovic, V., K?hler, H., (2009) Characterization of conserved properties of hemagglutinin of H5N1 and human influenza viruses: possible consequences for therapy and infection control. BMC Structural Biology, 7, 9-21.
Cosic, I. (1997) The resonant recognition model of macromolecular bioreactivity, theory and application. Birkhauser Verlag, Berlin.
Hu, W. (2010) Identification of highly conserved domains in hemagglutinin associated with the receptor binding specificity of influenza viruses: 2009 H1N1, avian H5N1, and swine H1N2. Journal of Biomedical Science and Engineering, 3, 114-123.
Wei, C.J., Boyington, J.C., Dai, K., Houser, K.V., Pearce, M.B., Kong, W.P., Yang, Z.Y., Tumpey, T.M. and Nabel, G.J. (2010) Cross-neutralization of 1918 and 2009 influenza viruses: Role of glycans in viral evolution and vaccine design. Science Translational Medicine, 2, 24ra21.
Xu, R., Damian, C., Ekiert, J.C.K., Rong, H., James, E., Crowe, J., Ian, A.W. (2010) Structural basis of preexisting immunity to the 2009 H1N1 pandemic influenza virus. Science, 328(5976), 357-360.
Igarashi, M., Ito, K., Yoshida, R., Tomabechi, D., Kida, H. and Takada, A. (2010) Predicting the antigenic structure of the pandemic (H1N1) 2009 influenza virus hemagglutinin, PLoS One. 5(1), e8553.
Childs, R.A., Palma, A.S., Wharton, S., Matrosovich, T., Liu, Y., Chai, W., Campanero-Rhodes, M.A., Zhang, Y., Eickmann, M., Kiso, M., Hay, A., Matrosovich, M. and Feizi, T. (2009) Receptor-binding specificity of pandemic influenza A (H1N1) 2009 virus determined by carbohydrate microarray. Nat Biotechnol. 27(9), 797-799.
Yang, H., Carney, P. and James, S. (2010) Structure and receptor binding properties of a pandemic H1N1 virus hemagglutinin. PLoS Curr Influenza. 22, RRN1152.
Hu, W. (2010) Quantifying the effects of mutations on receptor binding specificity of influenza viruses. Journal of Biomedical Science and Engineering, 3, 227-240.
Shen, J., Ma, J., Wang, Q. (2009) evolutionary trends of a (H1N1) influenza virus hemagglutinin since 1918. PLoS One, 4(11), e7789.
Nelson, M., Spiro, D., Wentworth, D., Beck, E., Jiang, F. et al. (2009) The early diversification of influenza A/H1N1pdm. PLoS Curr Influenza, 3, RRN1126.
Fereidouni, S.R., Beer, M., Vahlenkamp, T. and Starick, E. (2009) Differentiation of two distinct clusters among currently circulating influenza A (H1N1) viruses. Euro Surveill, 14(46).
Valli, M.B., Meschi, S., Selleri, M., Zaccaro, P., Ippolito, G., Capobianchi, M.R. and Menzo, S. (2010) Evolutionary pattern of pandemic influenza (H1N1) 2009 virus in the late phases of the 2009 pandemic, PLoS Currents Influenza, 3, RRN1149.
Katoh, K., Kuma, K., Toh, H. and Miyata, T. (2005) MAFFT version 5: improvement in accuracy of multiple sequence alignment. Nucleic Acids Research, 33(2), 511- 518.
MacKay, D. (2003) Information theory, inference, and learning algorithms. Cambridge University Press, UK.
Breiman, L. (2001) Random forests. Machine Learning, 45(1), 5–32.
Díaz-Uriarte, R. and Alvarez de Andrés, S. (2006) Gene selection and classification of microarray data using random forest. BMC Bioinformatics, 7, 3.
Kellie, J.A. and Ryan V.K. (2008) Empirical characterization of random forest variable importance measures, Computational Statistics and Data Analysis, 52(4), 2249- 2260.
Reif, D.M.M., Alison, A.M., Brett, A.C., James, E.M., Jason, H. (2006) Feature selection using a random forests classifier for the integrated analysis of multiple data types, Proceedings of 2006 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology, CIBCB '06, Toronto, 2006, 1-8.
Pablo, M.G., Cesare, F., Franco, B. and Flavia, G. (2006) Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products. Chemometrics and Intelligent Laboratory Systems, 83(2), 83-90.
Bjoern, H.M., Kelm, B.M., Masuch, R., Himmelreich, U., Bachert, P., Petrich, W. and Hamprecht, F.A. (2009) A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data. BMC Bioinformatics, 10, 213.
Gao, D., Zhang, Y.X. and Zhao, Y.H. (2009) Random forest algorithm for classification of multi-wavelength data. Research in Astronomy and Astrophysics, 9(2), 220- 226.
Hu, W. (2009) Identifying predictive markers of chemosensitivity of breast cancer with random forests. Journal of Biomedical Science and Engineering, 3(1), 59-64.