Genomic Analysis of Influenza A Viruses, including Avian Flu (H5N1) Strains

• Published in: European Journal of Epidemiology
• Main Authors: Ahn, Insung, Jeong, Byeong-Jin, Bae, Se-Eun, Jung, Jin, Son, Hyeon S.
• Format: Text
• Language: English
• Published: Kluwer Academic Publishers 2006
• Subjects: Genetic Epidemiology; Avian flu
• Online Access: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7088104/; http://www.ncbi.nlm.nih.gov/pubmed/16858618; https://doi.org/10.1007/s10654-006-9031-z

This study was designed to conduct genomic analysis in two steps, such as the overall relative synonymous codon usage (RSCU) analysis of the five virus species in the orthomyxoviridae family, and more intensive pattern analysis of the four subtypes of influenza A virus (H1N1, H2N2, H3N2, and H5N1) which were isolated from human population. All the subtypes were categorized by their isolated regions, including Asia, Europe, and Africa, and most of the synonymous codon usage patterns were analyzed by correspondence analysis (CA). As a result, influenza A virus showed the lowest synonymous codon usage bias among the virus species of the orthomyxoviridae family, and influenza B and influenza C virus were followed, while suggesting that influenza A virus might have an advantage in transmitting across the species barrier due to their low codon usage bias. The ENC values of the host-specific HA and NA genes represented their different HA and NA types very well, and this reveals that each influenza A virus subtype uses different codon usage patterns as well as the amino acid compositions. In NP, PA and PB2 genes, most of the virus subtypes showed similar RSCU patterns except for H5N1 and H3N2 (A/HK/1774/1999) subtypes which were suspected to be transmitted across the species barrier, from avian and porcine species to human beings, respectively. This distinguishable synonymous codon usage patterns in non-human origin viruses might be useful in determining the origin of influenza A viruses in genomic levels as well as the serological tests. In this study, all the process, including extracting sequences from GenBank flat file and calculating codon usage values, was conducted by Java codes, and these bioinformatics-related methods may be useful in predicting the evolutionary patterns of pandemic viruses.