Computational modelling of hepatitis C virus evolution and applications

Abstract

This thesis provides a computational framework, using statistical models rooted in statistical physics and population genetics, for studying hepatitis C virus (HCV) evolution. Three main problems are addressed. The first contribution explores why HCV subtype 1b is associated with a higher chronicity rate and more severe infection outcomes than subtype 1a. Focusing on the envelope protein 2 (E2) protein, the primary target of antibodies against HCV, our analysis suggests that the higher chronicity rate of 1b may be attributed to lower evolutionary constraints, enabling 1b viruses to more easily escape antibody responses. The second contribution studies the role of interactions between E2 and the other envelope protein, E1. Our results suggest that these interactions are crucial in mediating viral fitness, and that E1 may assist in evasion from E2-specific immune responses. The third contribution investigates how evolutionary factors are associated with the emergence of drug resistant mutations (DRMs) in the HCV nonstructural protein 3 (NS3), one of the major targets of drugs against HCV. Our results suggest that epistasis, a phenomenon in which the phenotypic effect of a mutation at one position depends on mutations elsewhere in the protein sequence, is a significant determinant in the evolution of DRMs, and that accounting for epistasis is important for designing future HCV NS3-targeting drugs. Overall, these findings may aid the development of an effective vaccine and provide insight into the evolutionary determinants of drug resistance.

Date of Award	2023
Original language	English
Awarding Institution	The Hong Kong University of Science and Technology
Supervisor	Ross MURCH (Supervisor), Matthew Robert MCKAY (Supervisor) & Ahmed Abdul QUADEER (Supervisor)