Chalmers Conferences, 9th European Conference on Mathematical and Theoretical Biology

Immunology and epidemiology of hepatitis C virus: plenty of data for a nested model approach.
Fabio Luciani

Last modified: 2014-06-09



Hepatitis C virus (HCV) evolves rapidly to escape host selective pressures. It is know that innate immune responses mediated by NK cells, HCV-specific neutralizing antibodies (NAbs), as well as T cell responses are critical determinants of the outcome of infection. HCV can be naturally cleared in 30% of the infected people, thus offering the unique opportunity to study the mechanisms with which the host immune response successfully drive viral clearance, or unsuccessfully lead to chronic infections and hence to the larger spread of the disease. Recent data showed that both host and viral genetic factors strongly affect the outcome of the infection. For instance, viral genome mutations have been discovered to drive immune escape, while host genetic polymorphisms in the Interferon Lambda λ3 region predicts viral clearance. Furthermore, the highly polymorphic HLA genes also drive the probability of viral recognition and consequently the probability of T cell response specific against evolving variants.


Here I argue that the vast data set generated during the last decade, and the dichotomous outcome of this infection offer an ideal model to study how within host immunological and genetic factors affect the success of a viral infection, and ultimately its spread at the epidemiological level. These arguments can then be expanded to other rapidly mutating pathogens that cause chronic infection in humans. Here I will also review some experimental and theoretical work pointing towards this goal. I will present some phylogenetics analyses on viral sequences showing that viral genomes carry heritable traits, which affect disease outcome. Also, I will review recent experimental data convincingly showing the strong role played by host genetic factors in driving the success of the host immune response.


The lack of a vaccine for rapidly mutating viruses causing chronic infections (e.g. HIV, HCV) or their limited efficacy (Influenza) rely on the extreme and rapid adaptation dynamics of these viruses at both within and between host levels. Therefore, mathematical modeling that nest within-host into epidemiological models with the vast data available can now be developed to understand and eventually predict outcome of diseases.