Expression patterns and action analysis of genes associated with inflammatory responses during rat liver regeneration

Influence of age and HBeAg status on the correlation between HBV DNA and hepatic inflammation and fibrosis in chronic hepatitis B patients

AIMS

The purpose of this study was to examine the correlation between serum HBV DNA level and the severity of hepatic inflammation and fibrosis in patients with chronic hepatitis B.

METHODS

Liver function, serological markers of HBV and serum HBV DNA quantification were assayed in 215 CHB patients who also underwent liver biopsy. Liver pathology was regarded as the evaluation criteria to evaluate the correlation between serum HBV DNA level and the severity of liver inflammation and fibrosis.

RESULTS

Of the 215 patients, 136 were HBeAg-positive and 79 were HBeAg-negative; 134 patients had mild hepatic inflammation and fibrosis and 81 had significant hepatic inflammation and fibrosis in pathological diagnosis. We found that positive correlation between the severity of hepatic inflammation and fibrosis and serum HBV DNA level was only present in HBeAg-negative patients but not HBeAg-positive patients (P < 0.001). Patients' age was a key factor influencing the correlation between serum HBV DNA level and the severity of hepatic inflammation and fibrosis. the cutoff values to predict the severity of hepatic inflammation and fibrosis were 33.5 and 36 years, respectively. Using 35 years as the cutoff value, positive correlation between serum HBV DNA level and hepatic inflammation and fibrosis was observed in both HBeAg-positive and HBeAg-negative patients aged ≥ 35 years (P < 0.05), however no correlation was observed in HBeAg-positive patients aged <35 years.

CONCLUSIONS

There was positive correlation between serum HBV DNA level and hepatic inflammation and fibrosis in both HBeAg-positive and HBeAg-negative patients' aged ≥ 35 years, but in patients aged <35 years, positive correlation was only observed in HBeAg-negative patients.

Alterations in DNA repair gene expression and their possible regulation in rat-liver regeneration

Rapidly proliferating tissue may require enhanced DNA repair capacity in order to avoid fixation of promutagenic DNA lesions to mutations. Partial hepatectomy (PH) triggers cell proliferation during liver regeneration (LR). However, little is known on how DNA repair genes change and how they are regulated at the transcriptional level during LR. In the present study, the Rat Genome 230 2.0 array was used to detect the expression profiles of DNA repair genes during LR, and differential expression of selected genes was confirmed by real-time RT-PCR. 69 DNA repair genes were found to be associated with LR, more than half of which distributed in a cluster characterized by a gradual increase at 24-72h and then returning to normal. The expression of base excision repair- and transcription-coupled repair-related genes was enhanced in the early and intermediate phases of LR, whereas the expression of genes related to HR, NHEJ and DNA cross-link repair, as well as DNA polymerases and related accessory factors, and editing or processing nucleases, were mainly enhanced in the intermediate phase. The expression changes of genes in DNA damage response were complicated throughout the whole LR. Our data also suggest that the expression of most DNA repair genes may be regulated by the cell cycle during LR.

Studying liver regeneration by means of molecular biology: how far we are in interpreting the findings?

Liver regeneration in mammals is a unique phenomenon attracting scientific interest for decades. It is a valuable model for basic biology research of cell cycle control as well as for clinically oriented studies of wide and heterogeneous group of liver diseases. This article provides a concise review of current knowledge about the liver regeneration, focusing mainly on rat partial hepatectomy model. The three main recognized phases of the regenerative response are described. The article also summarizes history of molecular biology approaches to the topic and finally comments on obstacles in interpreting the data obtained from large scale microarray-based gene expression analyses.