Journal of Proteomics & Bioinformatics
Open Access
ISSN: 0974-276X
ISSN: 0974-276X
Zhichao Lian, Qi Ning Tian, Yang Liu, Valeria Cento, Romina Salpini, Carlo Federico Perno, Valentina Svicher, Gang Chen, Cong Li and Jing Zhang
Background: With advancements in technology, a number of Hepatitis B virus (HBV) infections, where viral DNA is present in the liver or plasma, without the concomitant detection of HBsAg in plasma have been reported, and have been termed occult Hepatitis B infections (OBI). Unfortunately, the etiology and pathogenesis of OBI remain elusive to date, and the genetic characteristics of HBV sequence that lead to the development of OBI are still poorly understood.
Methods: 358 genotype-C (330 chronically infected patients and 28 occult infected patients) and 107 genotype-D (83 chronically infected patients and 24 occult infected patients) HBV Reverse Transcriptase (RT) amino acid sequences were collected. In addition to greedy search, a novel statistical approach, Bayesian Variable Partition Model is applied to pinpoint those positions, where amino acid mutations collaboratively discriminate OBI samples from chronically infected samples, in genotype-C and genotype-D, respectively.
Results: Several discriminate and correlated positions were found in genotype-C (high-order position combinations listed in tables) and genotype-D (positions 126+138, 129+131 and 138+139) respectively. By comparing amino acid distributions in these positions between genotype-C and genotype-D, six position combinations were reported to have obvious different amino acid distributions in these two HBV genotypes.
Conclusions: This paper furthers the understanding of the correlation between HBV sequence mutations and the differences of OBI in two HBV genotypes, by studying mutations in HBV RT amino acid sequences. Different from other traditional methods, the Bayesian-based method is able to analyze high-order combinations of positions.