Computational Model-based Dynamics of the Innate and Adaptive Immune Response Systems During Hepatitis C Virus Infection

Abstract

Hepatitis C Virus (HCV) is the main cause of liver infection and damage in the Humans. In response to HCV infection, the host immune system recruits multiple cell types, diverse variety of cytokine mediators and interacting signalling networks to neutralize the infection. The improper regulation of the host immune system may lead to chronic HCV infection. Quite often it is challenging to understand the both qualitative along with quantitative dynamics of this highly integrated system. To have a holistic view of this system, systems biology provides an efficient alternative modelling approach where large populations of interacting components of a system could be modelled and simulated. Integrating such models with knowledge from experimental virology and immunology further helps in order to analyse such highly multifaceted immune regulatory networks activated during response to HCV. Formal modelling approaches are employed here to present a simplified formalization of the highly dynamic HCV infection system, the replication cycle of the virus and the host immune responses at the cellular level. First, a Baseline model of the innate immune response system is designed which reflects the normal behaviour of the system, prior to any infection. The innate model includes TLR3/RIG1 mediated pathways, RNase L pathway, role of miR-122, interferons and interferon stimulated genes (ISGs). The Baseline model is then extended to formulate infection models such as the Effective innate immune response model and Failed innate immune response model. These models comprise of the viral entities such as viral RNA, structural and non-structural proteins and their inhibitory effects. Abstract 2 In addition to innate immune response models, the distinct components of the adaptive immune response system are also modelled and presented as Effective adaptive immune response model and the Failed adaptive immune response model, these models depict the adaptive immune system behaviour during HCV clearance and chronic infection, respectively. The adaptive immune models include cellular responses such as Natural Killer cells, T-regulatory cells, CD4+ & CD8+ cells, B-cells, cytotoxic T-lymphocytes, macrophages and several cytokine mediators. This is followed by, in silico experiments based on the targeted manipulation/interventions by immunomodulatory treatment of HCV infection, which comprises of PEGylated interferon-α (IFN-α) and ribavirin (RBV). Accordingly, a Treatment response model is generated revealing the NK cells, T regulatory cells, IL-10, IL-21, IL-12, IL-2 entities to be the most critical determinants of treatment response. More specific experiments are designed to compare and suggest potential immunomodulatory therapeutic interventions in combination with current standard of care therapy for HCV. The proposed interventions include IL-21 treatment, blocking of inhibitory receptors on T-cells such as Tim-3 and exogenous anti-IL-10 antibody treatment. The relative results showed that these treatments have differential effect on the expression levels of cellular and cytokines entities of the immune response. Notably, IL-21 enhances the expression of NK cells, Cytotoxic T lymphocytes and CD4+ T cells and hence restore the host immune potential. Rene’ Thomas formalism was applied to build and analyse a Biological Regulatory Network (BRN) to further highlight the intrinsic regulations amongst the cellular and cytokine components of the HCV induced immune system identified in Abstract 3 the previous part of the study. The constructed qualitative network, comprises of NS5A protein of HCV, the Natural Killer (NK) and T regulatory (Tregs) cells along with cytokines such as IFN-γ, IL-10, IL-12. From the network simulation it is observed that IL-12 is constitutively overexpressed during the pathogenic state which warrants for further experimental studies to elucidate its role in promoting chronic infection. In conclusion, we believe that the study attempted to reduce the noisy biological data and captures a holistic view of the regulations amongst HCV induced immune response systems. The described methodology can be easily reproduced and can be extended to other viral infections in a formal, automated and expressive manner.