Myeongji Cho, Hayeon Kim, Mikyeong Je and Hyeon S. Son* Pages 1 - 11 ( 11 )
Background: Persistent high-risk genital human papillomavirus (HPV) infection is a major cause of cervical cancer in women. The products of the viral transforming genes E6 and E7 in the high-risk HPVs are known to be similar in their amino acid composition and structure. We performed a comparative analysis of codon usage patterns in the E6 and E7 genes of HPVs.
Methods: The E6 and E7 gene sequences of eight HPV subtypes were analyzed to determine their nucleotide composition, relative synonymous codon usage (RSCU), effective number of codons (ENC), neutrality, genetic variability, selection pressure, and codon adaptation index (CAI). Additionally, a correspondence analysis (CoA) was performed.
Results: The analysis to determine the effects of differences in composition on the codon usage patterns revealed that there may be usage bias for ‘A’ nucleotides. This was consistent with the results of the RSCU analysis, which demonstrated that the selection of A/T-rich patterns and the preference for A/T-ended codons in HPVs are influenced by compositional constraints. Moreover, the results reveal that selection pressure plays an important role in the CoA results for the RSCU values, Tajima’s D tests, and neutrality tests.
Conclusion: The results of this study are consistent with previous findings that most papillomavirus genes are under purifying selection pressure, which limits changes to the encoded proteins. Natural selection and mutation pressures resulting in changes in the nucleotide composition and codon usage bias in the two tumor genes of HPV act differently during the evolution of the HPV subtype; thus, throughout the viral life cycle, HPV can constantly evolve to adapt to a new environment.
Human papillomavirus, E6, E7, codon usage pattern, RSCU, natural selection
Laboratory of Computational Biology & Bioinformatics, Graduate School of Public Health, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Department of Biomedical Laboratory Science, Kyungdong University, 815 Gyeonhwon-ro, Munmak, Wonju, Gangwondo, 24695, Laboratory of Computational Biology & Bioinformatics, Graduate School of Public Health, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Laboratory of Computational Biology & Bioinformatics, Graduate School of Public Health, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826