Identification of optimal target gene regions for hepatitis B virus genotyping by DNA sequencing

Genotyping Hepatitis B virus by Next-Generation Sequencing: Detection of Mixed Infections and Analysis of Sequence Conservation

Our aim was to develop an accurate, highly sensitive method for HBV genotype determination and detection of genotype mixtures. We examined the preS and 5' end of the HBV X gene (5X) regions of the HBV genome using next-generation sequencing (NGS). The 1852 haplotypes obtained were subjected to genotyping via the Distance-Based discrimination method (DB Rule) using two sets of 95 reference sequences of genotypes A-H. In clinical samples from 125 patients, the main genotypes were A, D, F and H in Caucasian, B and C in Asian and A and E in Sub-Saharan patients. Genotype mixtures were identified in 28 (22.40%) cases, and potential intergenotypic recombination was observed in 29 (23.20%) cases. Furthermore, we evaluated sequence conservation among haplotypes classified into genotypes A, C, D, and E by computing the information content. The preS haplotypes exhibited limited shared conserved regions, whereas the 5X haplotypes revealed two groups of conserved regions across the genotypes assessed. In conclusion, we developed an NGS-based HBV genotyping method utilizing the DB Rule for genotype classification. We identified two regions conserved across different genotypes at 5X, offering promising targets for RNA interference-based antiviral therapies.

Molecular epidemiology of hepatitis B virus in Paraguay

Hepatitis B virus (HBV) infection is a leading cause of severe chronic liver disease worldwide. The HBV epidemiology in Latin American countries is complex and the data is still scanty and fragmentary. The aim of this study was to investigate the distribution of HBV genotypes in Paraguay and to estimate the viral population dynamic and spread pattern of the main phylogenetic group. To this end, partial and complete genome sequences were obtained from 60 blood donor candidates and analysed by phylogenetic and Bayesian phylodynamic approaches. The phylogenetic analysis based on sequences of partial Polymerase/Pre-S1 overlapping region showed a predominance of the Native American subgenotype F4 (81.7%), the presence of the European subgenotypes A2 (1.7%) and D3 (8.3%), the African subgenotype A1 (3, 5%) and the Asian subgenotypes B2 (1.7%) and C2 (1.7%). The distribution of HBV genotypes was in accordance with the ethnic composition of the population. The phylogeographic analysis of subgenotype F4 complete genomes suggests that this lineage emerged and spread in the last 300 years. Paraguay was the most probable location of the common ancestor. The lineage diverged into two main clades and spread to neighbor regions, mainly Bolivia and Northwest Argentina, and Buenos Aires. The phylogeny showed a scanty geographical structure and a complex migratory pattern. In conclusion, the HBV genotypes circulating in Paraguay reflect the ethnic origin of the population. The distribution of genotypes and the phylogeographic reconstruction showed the impact of both global and local migrations in shaping the HBV molecular epidemiology in the region.

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.