Dataset Information

Distinct distribution pattern of hepatitis B virus genotype C and D in liver tissue and serum of dual genotype infected liver cirrhosis and hepatocellular carcinoma patients.

ABSTRACT:

Aims

The impact of co-infection of several hepatitis B virus (HBV) genotypes on the clinical outcome remains controversial. This study has for the first time investigated the distribution of HBV genotypes in the serum and in the intrahepatic tissue of liver cirrhotic (LC) and hepatocellular carcinoma (HCC) patients from India. In addition, the genotype-genotype interplay and plausible mechanism of development of HCC has also been explored.

Methods

The assessment of HBV genotypes was performed by nested PCR using either surface or HBx specific primers from both the circulating virus in the serum and replicative virus that includes covalently closed circular DNA (cccDNA) and relaxed circular DNA (rcDNA) of HBV from the intrahepatic tissue. The integrated virus within the host chromosome was genotyped by Alu-PCR method. Each PCR products were cloned and sequences of five randomly selected clones were subsequently analysed.

Results

HBV/genotype D was detected in the serum of all LC and HCC patients whereas the sequences of the replicative HBV DNA (cccDNA and rcDNA) from the intrahepatic tissue of the same patients revealed the presence of both HBV/genotype C and D. The sequences of the integrated viruses exhibited the solo presence of HBV/genotype C in the majority of LC and HCC tissues while both HBV/genotype C and D clones were found in few patients in which HBV/genotype C was predominated. Moreover, compared to HBV/genotype D, genotype C had higher propensity to generate double strand breaks, ER stress and reactive oxygen species and it had also showed higher cellular homologous-recombination efficiency that engendered more chromosomal rearrangements, which ultimately led to development of HCC.

Conclusions

Our study highlights the necessity of routine analysis of HBV genotype from the liver tissue of each chronic HBV infected patient in clinical practice to understand the disease prognosis and also to select therapeutic strategy.

SUBMITTER: Datta S

PROVIDER: S-EPMC4102524 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Publications

Distinct distribution pattern of hepatitis B virus genotype C and D in liver tissue and serum of dual genotype infected liver cirrhosis and hepatocellular carcinoma patients.

Datta Somenath S Roychoudhury Shrabasti S Ghosh Alip A Dasgupta Debanjali D Ghosh Amit A Chakraborty Bidhan Chandra BC Ray Sukanta S Gupta Subash S Santra Amal Kumar AK Datta Simanti S Das Kausik K Dhali Gopal Krishna GK Chowdhury Abhijit A Banerjee Soma S

PloS one 20140717 7

<h4>Aims</h4>The impact of co-infection of several hepatitis B virus (HBV) genotypes on the clinical outcome remains controversial. This study has for the first time investigated the distribution of HBV genotypes in the serum and in the intrahepatic tissue of liver cirrhotic (LC) and hepatocellular carcinoma (HCC) patients from India. In addition, the genotype-genotype interplay and plausible mechanism of development of HCC has also been explored.<h4>Methods</h4>The assessment of HBV genotypes w ...[more]

PMID: 25032957

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Project description:Chronic hepatitis B virus (CHB) infection is one of the primary risk factors associated with the development of hepatocellular carcinoma (HCC). Despite having been extensively studied, diagnosing early-stage HCC remains challenging, and diagnosed patients have a poor (3-5%) survival rate. Identifying new approaches to detect changes in the serum metabolic profiles of patients with CHB and liver cirrhosis (LC) may provide a valuable approach to better detect HCC at an early stage when it is still amenable to treatment, thereby improving patient prognosis and survival. In the present study, we, therefore, employed a liquid chromatography-mass spectrometry (LC-MS)-based approach to evaluate the serum metabolic profiles of 30 CHB patients, 29 LC patients, and 30 HCC patients. We then employed appropriate statistical methods to identify those metabolites that were best able to distinguish HCC cases from LC and CHB controls. A mass-based database was then used to putatively identify these metabolites. We then confirmed the identities of a subset of these metabolites through comparisons with the MS/MS fragmentation patterns and retention times of reference standards. The serum samples were then reanalyzed to quantify the levels of these selected metabolites and of other metabolites that have previously been identified as potential HCC biomarkers. Through this approach, we observed clear differences in the metabolite profiles of the CHB, LC, and HCC patient groups in both positive- and negative-ion modes. We found that the levels of taurodeoxy cholic acid (TCA) and 1,2-diacyl-3-β-d-galactosyl-sn-glycerol rose with the progression from CHB to LC to HCC, whereas levels of 5-hydroxy-6E,8Z,11Z,14Z,17Z-eicosapentaenoic acid, and glycyrrhizic acid were gradually reduced with liver disease progression in these groups. The ROC analysis showed that taurodeoxy cholic acid (TCA), 1,2-diacyl-3-β-d-galactosyl-sn-glycerol, 5-hydroxy-6E,8Z,11Z,14Z,17Z-eicosapentaenoic acid, and glycyrrhizic acid had a diagnosis performance with liver disease progression. These four metabolites have a significant correlation with alpha fetal protein (AFP) level and age. Our results highlight novel metabolic biomarkers that have the potential to be used for differentiating between CHB, LC, and HCC patients, thereby facilitating the identification and treatment of patients with early-stage HCC.

Project description:Characterizing the metabolic changes pertaining to hepatocellular carcinoma (HCC) in patients with liver cirrhosis is believed to contribute towards early detection, treatment, and understanding of the molecular mechanisms of HCC. In this study, we compare metabolite levels in sera of 78 HCC cases with 184 cirrhotic controls by using ultra performance liquid chromatography coupled with a hybrid quadrupole time-of-flight mass spectrometry (UPLC-QTOF MS). Following data preprocessing, the most relevant ions in distinguishing HCC cases from patients with cirrhosis are selected by parametric and non-parametric statistical methods. Putative metabolite identifications for these ions are obtained through mass-based database search. Verification of the identities of selected metabolites is conducted by comparing their MS/MS fragmentation patterns and retention time with those from authentic compounds. Quantitation of these metabolites is performed in a subset of the serum samples (10 HCC and 10 cirrhosis) using isotope dilution by selected reaction monitoring (SRM) on triple quadrupole linear ion trap (QqQLIT) and triple quadrupole (QqQ) mass spectrometers. The results of this analysis confirm that metabolites involved in sphingolipid metabolism and phospholipid catabolism such as sphingosine-1-phosphate (S-1-P) and lysophosphatidylcholine (lysoPC 17:0) are up-regulated in sera of HCC vs. those with liver cirrhosis. Down-regulated metabolites include those involved in bile acid biosynthesis (specifically cholesterol metabolism) such as glycochenodeoxycholic acid 3-sulfate (3-sulfo-GCDCA), glycocholic acid (GCA), glycodeoxycholic acid (GDCA), taurocholic acid (TCA), and taurochenodeoxycholate (TCDCA). These results provide useful insights into HCC biomarker discovery utilizing metabolomics as an efficient and cost-effective platform. Our work shows that metabolomic profiling is a promising tool to identify candidate metabolic biomarkers for early detection of HCC cases in high risk population of cirrhotic patients.

Dataset Information

Distinct distribution pattern of hepatitis B virus genotype C and D in liver tissue and serum of dual genotype infected liver cirrhosis and hepatocellular carcinoma patients.

Aims

Methods

Results

Conclusions

Publications

Distinct distribution pattern of hepatitis B virus genotype C and D in liver tissue and serum of dual genotype infected liver cirrhosis and hepatocellular carcinoma patients.

Similar Datasets

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