A computational spatial whole-Cell model for hepatitis B viral infection and drug interactions

Abstract

Despite a vaccine, hepatitis B virus (HBV) remains a world-wide source of infections and deaths. We develop a whole-cell computational platform combining spatial and kinetic models describing the infection cycle of HBV in a hepatocyte host. We simulate key parts of the infection cycle with this whole-cell platform for 10 min of biological time, to predict infection progression, map out virus-host and virus-drug interactions. We find that starting from an established infection, decreasing the copy number of the viral envelope proteins shifts the dominant infection pathway from capsid secretion to re-importing the capsids into the nucleus, resulting in more nuclear-localized viral covalently closed circular DNA (cccDNA) and boosting transcription. This scenario can mimic the consequence of drugs designed to manipulate viral gene expression. Mutating capsid proteins facilitates capsid destabilization and disassembly at nuclear pore complexes, resulting in an increase in cccDNA copy number. However, excessive destabilization leads to premature cytoplasmic disassembly and does not increase the cccDNA counts. Finally, our simulations can predict the best drug dosage and its administration timing to reduce the cccDNA counts. Our adaptable computational platform can be parameterized to study other viruses and identify the most central viral pathways that can be targeted by drugs.