Identification of Novel A2/C2 Inter-Genotype Recombinants of Hepatitis B Virus from a Korean Chronic Patient Co-Infected with Both Genotype A2 and C2

Genetic diversity of hepatitis B virus in Yunnan, China: identification of novel subgenotype C17, an intergenotypic B/I recombinant, and B/C recombinants

Yunnan is considered to be a geographical hotspot for the introduction, mutation and recombination of several viruses in China. However, there are limited data regarding the genotypic profiles of hepatitis B virus (HBV) in this region. In this study, we characterized 206 HBV strains isolated from chronic hepatitis B patients in Yunnan, China. Initial genotyping based on 1.5 kb sequences revealed that genotype C was the most prevalent at 52.4 % (108/206), followed by genotype B at 30.6 % (63/206) and unclassified genotypes at 17.0 % (35/206). To characterize the 35 unclassified strains, 32 complete HBV genomes were amplified and analysed; 17 isolates were classified within a known subgenotype, 8 were classified as B/C recombinants, 1 was classified as a B/I recombinant and 6 constituted a potentially novel C subgenotype that we designated as C17, based on the characteristics of a monophyletic cluster, >4 % genetic distances, no significant evidence of recombination and no epidemiological link among individuals. Thus, multiple subgenotypes - namely B1, B2, B4, C1, C2, C3, C4, C8 and C17 - and two distinct intergenotypic recombinants exist in Yunnan, China, highlighting the complex and diverse distribution pattern of HBV genotypic profiles.

Naturally occurring hepatitis B virus reverse transcriptase mutations related to potential antiviral drug resistance and liver disease progression

The annual number of deaths caused by hepatitis B virus (HBV)-related disease, including cirrhosis and hepatocellular carcinoma (HCC), is estimated as 887000. The reported prevalence of HBV reverse transcriptase (RT) mutation prior to treatment is varied and the impact of preexisting mutations on the treatment of naïve patients remains controversial, and primarily depends on geographic factors, HBV genotypes, HBeAg serostatus, HBV viral loads, disease progression, intergenotypic recombination and co-infection with HIV. Different sensitivity of detection methodology used could also affect their prevalence results. Several genotype-dependent HBV RT positions that can affect the emergence of drug resistance have also been reported. Eight mutations in RT (rtL80I, rtD134N, rtN139K/T/H, rtY141F, rtM204I/V, rtF221Y, rtI224V, and rtM309K) are significantly associated with HCC progression. HBeAg-negative status, low viral load, and genotype C infection are significantly related to a higher frequency and prevalence of preexisting RT mutations. Preexisting mutations are most frequently found in the A-B interdomain of RT which overlaps with the HBsAg “a” determinant region, mutations of which can lead to simultaneous viral immune escape. In conclusion, the presence of baseline RT mutations can affect drug treatment outcomes and disease progression in HBV-infected populations via modulation of viral fitness and host-immune responses.

Deep sequencing of HBV pre-S region reveals high heterogeneity of HBV genotypes and associations of word pattern frequencies with HCC

Hepatitis B virus (HBV) infection is a common problem in the world, especially in China. More than 60–80% of hepatocellular carcinoma (HCC) cases can be attributed to HBV infection in high HBV prevalent regions. Although traditional Sanger sequencing has been extensively used to investigate HBV sequences, NGS is becoming more commonly used. Further, it is unknown whether word pattern frequencies of HBV reads by Next Generation Sequencing (NGS) can be used to investigate HBV genotypes and predict HCC status. In this study, we used NGS to sequence the pre-S region of the HBV sequence of 94 HCC patients and 45 chronic HBV (CHB) infected individuals. Word pattern frequencies among the sequence data of all individuals were calculated and compared using the Manhattan distance. The individuals were grouped using principal coordinate analysis (PCoA) and hierarchical clustering. Word pattern frequencies were also used to build prediction models for HCC status using both K-nearest neighbors (KNN) and support vector machine (SVM). We showed the extremely high power of analyzing HBV sequences using word patterns. Our key findings include that the first principal coordinate of the PCoA analysis was highly associated with the fraction of genotype B (or C) sequences and the second principal coordinate was significantly associated with the probability of having HCC. Hierarchical clustering first groups the individuals according to their major genotypes followed by their HCC status. Using cross-validation, high area under the receiver operational characteristic curve (AUC) of around 0.88 for KNN and 0.92 for SVM were obtained. In the independent data set of 46 HCC patients and 31 CHB individuals, a good AUC score of 0.77 was obtained using SVM. It was further shown that 3000 reads for each individual can yield stable prediction results for SVM. Thus, another key finding is that word patterns can be used to predict HCC status with high accuracy. Therefore, our study shows clearly that word pattern frequencies of HBV sequences contain much information about the composition of different HBV genotypes and the HCC status of an individual.

HBV infection can lead to many liver complications including hepatocellular carcinoma (HCC), one of the most common liver cancers in China. High-throughput sequencing technologies have recently been used to study the genotype sequence compositions of HBV infected individuals and to distinguish chronic HBV (CHB) infection from HCC. We used NGS to sequence the pre-S region of a large number of CHB and HCC individuals and designed novel word pattern based approaches to analyze the data. We have several surprising key findings. First, most HBV infected individuals contained mixtures of genotypes B and C sequences. Second, multi-dimensional scaling (MDS) analysis of the data showed that the first principal coordinate was closely associated with the fraction of genotype B (or C) sequences and the second principal coordinate was highly associated with the probability of HCC. Third, we also designed K-nearest neighbors (KNN) and support vector machine (SVM) based classifiers for CHB and HCC with high prediction accuracy. The results were validated in an independent data set.