Identification of Immuno-regulatory modules and optimal treatment strategies for eliciting effector functions against different diseases

Abstract

The immune system protects it from various infectious disease and cancer. However, regulations governing suppression of T-cells under varied antigenic challenges remains elusive through experimental approaches. In order to unveil the regulatory mechanisms underlying immune-suppression during Cancer and Leishmaniasis, we have used various mathematical and computational approaches, to identify the regulatory modules of the immunological network and design novel treatment strategies. Manual reconstruction of T-cell pathway and Boolean Modelling was used to gain a holistic understanding of the co-receptor mediated pathways. In silico knock-out analysis revealed minimal combination of proteins (TCR:CD3, CRAC and OX40) that are absolutely essential to achieve sustained T-cell proliferation and activation of effector functions. Co-receptor molecules CD27 and LTBR were identified to play major role in the regulation of Interleukin expression during antigenic challenges. For Cutaneous Leishmaniasis, signalling routes regulating the switching of T-cell responses from healing T H1 to non-healing T H2 response, were identified using Logical Steady State Analysis. Novel targets for eliciting robust anti-Leishmania immune response are also proposed through this study. For the study of Visceral Leishmaniasis, a putative host-pathogen interactome between Leishmania donovani and Human has been predicted using Interlog and Domain mapping strategies. Network analysis revealed key signalling routes mediating the host pathogen interaction. A novel combination of protein targets (UBC+1433Z+HS90A) has also been identified which governs the host immune response, parasite survival strategies and visceralization of the infection during Visceral Leishmaniasis. To study the tumor-immune interaction, an ODE based model has been developed related to the Seed Soil hypothesis of tumor development. Model analysis revealed the role of Cancer Stem Cell differentiation pattern on the development o drug resistance. Three novel feedback regulations governing tumor progression, resistance and relapse have been proposed in the study. The model has been further used to propose improvised combinatorial treatment protocol that shows promising results in suppressing resistant tumor for better Cancer remission.