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.

Reference

S. Steinmeyer, D.P. Howsmon, R.C. Alaniz, J. Hahn, and A. Jayaraman. "Empirical Modeling of T cell Activation Predicts Interplay of Host Cytokines and Bacterial Indole"

Biotechnology & Bioengineering 114, No. 11, pp. 2660-2667 (2017)