Mechanistic models coupled with sequencing data for the prediction of variant/cell type-specific IL-10 responses 2332592

Abstract Introduction IL-10 plays a central role in limiting inflammation by suppressing pro-inflammatory cytokine production, reducing antigen presentation, and supporting regulatory T cell survival, but is also known to produce immune stimulatory functions on immune cell types like CD8+ T cells. This pleiotropy could be the cause behind the limited success of IL-10-based therapies, as therapeutic effects depend strongly on cell type and dose. Methods In this work, we built mathematical models of the IL-10 pathway that capture the events from IL-10 dimerization to the binding to surface receptors until STAT1/STAT3 phosphorylation. Using these models, we want to give a mechanistic reasoning on how different, opposite responses can emerge in IL-10 signaling, centering ourselves in the IL-10 response at the levels of pSTAT phosphorylation and downstream transcriptional programs. Results Using Bayesian-based model selection, we provide a mechanistic explanation for disentangling pro- and anti-inflammatory differential responses in pSTAT1/pSTAT3 dose-response curves. On top of that, through correlations of cell-type-specific pSTAT model predictions with changes in gene expression, we identified a set of IL-10-responsive genes that reflect a cell-type-independent regulatory program. Repeating the same analysis with a curated list of IL-10 DEGs, we suggest IL-10 could be aggregating distinct transcriptional programs engaged at different signaling strengths, explaining dose-dependent IL-10 effects like CD8+ T cell proliferation at high IL-10 concentrations. Conclusion Our study highlights the potential of computational modeling as a powerful framework to refine our understanding of the IL-10 pathway and to begin disentangling the pleiotropic effects of IL-10 from pathway mechanisms to downstream transcriptional responses, with the future goal of supporting the rational development and clinical translation of more precise and effective IL-10-based therapies. Funding Source Spanish Ministry of Science, Innovation and Universities Topic Categories Computational and Systems Immunology (COMP)