Empirical Modeling of T cell Activation Predicts Interplay of Host Cytokines and Bacterial Indole

Adoptive transfer of anti-inflammatory FOXP3+ Tregs has gained attention as a new therapeutic strategy for auto-inflammatory disorders such as Inflammatory Bowel Disease. The isolated cells are conditioned in vitro to obtain a sufficient number of anti-inflammatory FOXP3+ Tregs that can be reintroduced into the patient to potentially reduce the pathologic inflammatory response. Previous evidence suggests that microbiota metabolites can potentially condition cells during the in vitro expansion/differentiation step. However, the number of combinations of cytokines and metabolites that can be varied is large, preventing a purely experimental investigation which would determine optimal cell therapeutic outcomes. To address this problem, a combined experimental and modeling approached is investigated here: an artificial neural network model was trained to predict the steady-state T cell population phenotype after differentiation with a variety of host cytokines and the microbial metabolite indole. This artificial neural network model was able to both reliably predict the phenotype of these T cell populations and also uncover unexpected conditions for optimal Treg differentiation that were subsequently verified experimentally.