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Nature DOI:10.1038/nature11981

Dynamic regulatory network controlling T<sub>H</sub>17 cell differentiation.

Publication TypeJournal Article
Year of Publication2013
AuthorsYosef, N, Shalek, AK, Gaublomme, JT, Jin, H, Lee, Y, Awasthi, A, Wu, C, Karwacz, K, Xiao, S, Jorgolli, M, Gennert, D, Satija, R, Shakya, A, Lu, DY, Trombetta, JJ, Pillai, MR, Ratcliffe, PJ, Coleman, ML, Bix, M, Tantin, D, Park, H, Kuchroo, VK, Regev, A
Date Published2013/03/06

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 nanowire-based 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.