Dynamic regulatory network controlling TH 17 cell differentiation

Nir Yosef, Alex K. Shalek, Jellert T. Gaublomme, Hulin Jin, Youjin Lee, Amit Awasthi, Chuan Wu, Katarzyna Karwacz, Sheng Xiao, Marsela Jorgolli, David Gennert, Rahul Satija, Arvind Shakya, Diana Y. Lu, John J. Trombetta, Meenu R. Pillai, Peter J. Ratcliffe, Mathew L. Coleman, Mark Bix, Dean TantinHongkun Park, Vijay K. Kuchroo, Aviv Regev

Research output: Contribution to journalArticlepeer-review

Abstract

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 TH 17 cells, a proinflammatory T-cell subset that has been implicated in the pathogenesis of multiple autoimmune diseases. The TH 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 TH 17 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 TH 17 cell differentiation.

Original languageEnglish (US)
Pages (from-to)461-468
Number of pages8
JournalNature
Volume496
Issue number7446
DOIs
StatePublished - Apr 25 2013

ASJC Scopus subject areas

  • General

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