Dissection of cell-cell communication networks in chronic viral infection by kinetic immune-cell monitoring and mathematical modeling

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The mammalian immune system depends on the coordinated action of many heterogeneous cells, which together tightly control immune responses. While ineffective responses to pathogens cause immediate risks for the whole organism, the same is true for overreactions of the immune system, which can induce directly lethal septic shock as well as chronic allergies or autoimmune diseases with life-long consequences for patients. The last decades brought about an enormous knowledge on immune-regulatory pathways, and identified cell-to-cell communication networks consisting of more than 40 cytokine species and a growing number of immune cell subtypes. Accumulating evidence suggests that these cell types and regulatory networks evolve under conditions of chronic inflammation, towards a well-controlled persistent state of inflammation. However, a quantitative understanding of these processes is only beginning to emerge, and the effects of drugs targeting immune cell communication networks (‘biological therapies’ such as TNF alpha blockers) remain incompletely understood.Here, we propose an in-depth, interdisciplinary study of T cell differentiation, proliferation and cell-to-cell communication in mice infected with lymphocytic choriomeningitis virus (LCMV). That well-established model system for both acute and chronic inflammation offers the opportunity to study T cell phenotypes and cell-cell communication networks in vivo, in the acute phase (~days 4 to 8 after infection), the acute-to-chronic transition phase (~days 10-20 after infection) and over long time-intervals of persistent inflammation (more than 50 days). Single-cell phenotyping experiments (single-cell RNA-sequencing and multicolor flow cytometry) along with quantitative data analysis and mathematical modeling will quantify and rationalize the evolution of cell-cell communication networks in chronic inflammation. Here, the data-based models will allow for functional assessment of control circuits such as feedback and feedforward loops, and will enable systematic in silico perturbation studies simulating the effects of drugs. Results from data analysis and modeling will be tested within the study by functional in vivo assays using already established technology. Taken together, the proposed study will use state-of-the art single-cell and data analysis techniques, aiming for a deeper, quantitative understanding of immune responses in chronic inflammation. Such insights will likely contribute to the development and optimization of targeted combination therapies for autoimmune diseases.