Journal
NATURE
Volume 496, Issue 7446, Pages 461-+Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/nature11981
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Funding
- NHGRI [1P50HG006193-01]
- NIH Pioneer Awards [5DP1OD003893-03, DP1OD003958-01]
- NIH [NS 30843, NS045937, AI073748, AI45757]
- National MS Society [RG2571]
- HHMI
- Klarman Cell Observatory
- British Heart Foundation [RG/11/1/28684] Funding Source: researchfish
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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 T(H)17 cells, a proinflammatory T-cell subset that has been implicated in the pathogenesis of multiple autoimmune diseases. The T(H)17 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 T(H)17 and other CD41 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 T(H)17 cell differentiation.
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