Codon bias of influenza a viruses and their hosts

Keli C. Fancher, Wei Hu

.

DOI: 10.4236/ajmb.2011.13017                

Abstract

The Influenza A virus remains an annual and worldwide health concern due to its fast evolutionary rate. There are two major forms of Influenza evolution: drift, caused by mutations, and shift, which results from the exchange of genetic information between two gene segments. Using six synonymous codon usage bias indexes (GC content, ENC, SCUO, Codon Volatility, RSCU, and Odds Ratio), this study revealed the evolutionary drift patterns in the Influenza A viruses of avian, human, and swine origins as well as those of their hosts. We found that the variation of GC content across the 11 genes in Influenza A uniquely determines the viral origins in avian, human, and swine hosts. As was previously noticed in the Flaviviridae virus, a codon’s RSCU value of the Influenza viruses is positively correlated to the Odds Ratio of the dinucleotides contained within that codon. Additionally, the RSCU values of avian, human, and swine viruses and their corresponding hosts are similar, which is also true of Odds Ratio. Furthermore, the GC content, ENC, SCUO, and Codon Volatility are similar across the avian, human, and swine hosts; however, the RSCU and Odds Ratio of the hosts are distinct. Our findings expanded the knowledge on codon bias of Influenza viruses and their hosts.

Keywords

Influenza A; Evolutionary Drift; Codon Bias

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Smith, G., et al. (2009) Origins and evolutionary genomics of the 2009 swine-origin H1N1 influena A epidemic. Nature, 459, 1122-1125. doi:10.1038/nature08182
[2]	Dawood, F., et al. (2009) Emergence of a novel swine-origin influenza A (H1N1) virus in humans. New England Journal of Medicine, 360, 2605-2615. doi:10.1056/NEJMoa0903810
[3]	Zhou, T., Gu, W., Ma, J., Sun, X. and Lu, Z. (2005) Analysis of synonymous codon usage in H5N1 virus and other influenza A viruses. BioSystems, 81, 77-86. doi:10.1016/j.biosystems.2005.03.002
[4]	Suzuki, H., Brown, C. J., Forney, L. J. and Top, E. M. (2008) Comparison of correspondence analysis methods for synonymous codon usage in Bacteria. DNA Research, 15, 357-365. doi:10.1093/dnares/dsn028
[5]	Zeeberg, B. (2002) Shannon information theoretic computation of synonymous codon usage biases in coding regions of human and mouse genomes. Genome Research, 12, 944-955. doi:10.1101/gr.213402
[6]	Wan, X., Xu, D. and Zhou, J. (2003) A new informatics method for measuring synonymous codon usage bias. Intelligent Engineering Systems Through Artificial Neural Networks , 13, 1101-1108.
[7]	Wan, X., Xu, D. and Zhou, J. (2006) Codono: A new informatics method for measuring synonymous codon usage bias within and across genomes. International Journal of General Systems, 35, 109-125. doi:10.1080/03081070500502967
[8]	Wan, X.F., Xu, D., Kleinhofs, A. and Zhou, J. (2004) Quantitative relationship between synonymous codon usage bias and GC composition across unicellular genomes. BMC Evolutionary Biology, 4, 19. doi:10.1186/1471-2148-4-19
[9]	Plotkin, J. B. and Dushoff, J. (2003) Codon bias and frequency-dependent selection on the hemagglutinin epitopes of influenza A virus. Proceedings of the National Academy of Sciences of the United States of America, 100, 7152-7157. doi:10.1073/pnas.1132114100
[10]	Plotkin, J. B., Dushoff, J. and Fraser, H. B. (2004) Detecting selection using a single genome sequence of M. tuberculosis and P. falciparum. Nature, 428, 942-945. doi:10.1038/nature02458
[11]	Zhang, J. (2004) On the evolution of codon volatility. Genetics, 169, 495-501. doi:10.1534/genetics.104.034884
[12]	Burge, C., Campbell, A. M. and Karlin, S. (1992) Over- and under-representation of short oligonucleotides in DNA sequences. Proceedings of the National Academy of Sciences, 89, 1358-1362. doi:10.1073/pnas.89.4.1358
[13]	Arndt, P. F., Burge, C. B. and HWA, T. (2003) DNA Sequence evolution with neighbor-dependent mutation. Journal of Computational Biology, 10, 313-322. doi:10.1089/10665270360688039
[14]	Greenbaum, B. D., Levine, A. J., Bhanot, G. and Rabadan, R. (2008) Patterns of evolution and host gene mimicry in influenza and other RNA viruses. Plos Pathogens, 4, 1-9. doi:10.1371/journal.ppat.1000079
[15]	Lobo, F. P., Mota, B. E., Pena, S. D., Azevedo, V., Macedo, A. M., Tauch, A., et al. (2009) Virus-host coevolution: Common patterns of nucleotide motif usage in flaviviridae and their hosts. Plos One, 4. doi:10.1371/journal.pone.0006282
[16]	Bao, Y., Bolotov, P., Dernovoy, D., Kiryutin, B., Zas-lavsky, L., et al. (2008) The influenza virus resource at the national center for biotechnology information. Journal of Virology, 82, 596-601. doi:10.1128/JVI.02005-07
[17]	Katoh, K., Misawa, K., Kuma, K.-I. and Miyata, T. (2002) MAFFT: A novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Research , 30, 3059-3066. doi:10.1093/nar/gkf436
[18]	Pruitt, K. D., Tatusova, T. and Maglott, D. R. (2005) NCBI reference sequence (RefSeq): A curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Research, 33, 501-504. doi:10.1093/nar/gki025
[19]	Qiu, S., Bergero, R., Zeng, K. and Charlesworth, D. (2011) Patterns of codon usage bias in Silene latifolia. Molecular Biological Evolution, 28, 771-780. doi:10.1093/molbev/msq251
[20]	Wright, F. (1990) The effective number of codons used in a gene. Genetics, 87, 23-29.
[21]	Karlin, S., Doerfler, W. and Cardon, L. (1994) Why is CpG suppressed in the genomes of virtually all small eukaryotic viruses but not in those of large eukaryotic viruses? Journal of Virology, 68, 2889-2897.
[22]	Solovyov, A., Greenbaum, B., Palacios, G., Ian Lipkin, W. and Rabadan, R. (2010) Host dependent evolutionary patterns and the origin of 2009 H1N1 pandemic influenza. 3th Edition, PLos Currents, 3, 147.