New perspective on the natural course of chronic HBV infection
Yong-Yuan Zhang1,*(),Ke-Qin Hu2,Zhongping Duan3
1. HBVtech, LLC, 13901 Lullaby RD, Germantown, MD 20874, USA 2. Division of Gastroenterology and Hepatology, University of California, Irvine, School of Medicine, Orange, CA 92868, USA 3. You An Hospital, Capital Medical University, Beijing 100069, China
Chronic hepatitis B virus (HBV) infection is a significant threat to public health and an enormous burden on society. Mechanisms responsible for chronic HBV infection remain poorly understood. A better understanding of the natural course of chronic HBV infection may shed new light on the mechanisms underlying this disease and help in designing new antiviral strategies. Natural course of chronic HBV infection is conventionally viewed as an uninterrupted process that is usually marked by HBV e antigen (HBeAg) seroconversion or characterized by different phases associated with assumed host responses to HBV infection. However, none of these descriptions captures or highlights the core events that determine the natural course of chronic HBV infection. In this review, we briefly present the current knowledge on this subject and explain the significance and implication of events that occur during infection. A pre-core mutant becomes predominant in the viral population following elimination of the wild-type virus in duck hepatitis B virus-chronically infected animals. The coupled events in which first there is viral clearance that clears wild-type virus and then there is the reinfection of wild-type virus cleared livers with mutant virus are highly relevant to understanding of the natural course of chronic HBV infection under both treated and untreated conditions. In our new perspective, a general natural course of chronic HBV infection comprises cycles of viral clearance and reinfection, and such cycles prolong the chronic HBV infection course. Reviewing published data on the natural course of chronic HBV infection can reduce the possibility of missing important points in the initial data interpretation.
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