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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.

Original publication

DOI

10.1038/nature11981

Type

Journal article

Journal

Nature

Publication Date

25/04/2013

Volume

496

Pages

461 - 468

Keywords

Animals, Cell Differentiation, Cells, Cultured, DNA, Forkhead Transcription Factors, Gene Knockdown Techniques, Gene Regulatory Networks, Genome, Interferon-gamma, Interleukin-2, Mice, Mice, Inbred C57BL, Nanowires, Neoplasm Proteins, Nuclear Proteins, RNA, Messenger, Reproducibility of Results, Silicon, Th17 Cells, Time Factors, Trans-Activators, Transcription Factors, Transcription, Genetic, fas Receptor