Dynamic regulatory network controlling TH17 cell differentiation

Biology Biology
Genomics Genomics
Immunology Immunology
Alex K. Shalek Alex K. Shalek

Yosef et al.▾ Yosef, N. *, Shalek, A.K.*, Gaublomme, J.T. *, Jin, H., Lee, Y., Awasthi, A., Wu, C., Karwacz, K., Xiao, S., Jorgolli, M., Gennert, D., Satija, R., Shakya, A., Lu, D.Y., Trombetta, J.J., Pillai, M., Ratcliffe, P.J., Coleman, M.L., Bix, M., Tantin, D., Hongkun Park, H., Kuchroo, V.K., and Regev, A.

Nature , Volume 496

March, 2013


Despite their importance, the molecular circuits that control the differentiation of naive T cells remain largely unknown. Recent studies that reconstructed regulatory networks in mammalian cells have focused on short-term responses and relied on perturbation-based approaches that cannot be readily applied to primary T cells. Here we combine transcriptional profiling at high temporal resolution, novel computational algorithms, and innovative nanowirebased perturbation tools to systematically derive and experimentally validate a model of the dynamic regulatory network that controls the differentiation of mouse TH17 cells, a proinflammatory T-cell subset that has been implicated in the pathogenesis of multiple autoimmune diseases. The TH17 transcriptional network consists of two self-reinforcing, but mutually antagonistic, modules, with 12 novel regulators, the coupled action of which may be essential for maintaining the balance between TH17 and other CD4+ T-cell subsets. Our study identifies and validates 39 regulatory factors, embeds them within a comprehensive temporal network and reveals its organizational principles; it also highlights novel drug targets for controlling TH17 cell differentiation.