Dynamic regulatory network controlling TH17 cell differentiation

Research output: Contribution to journalArticlepeer-review


  • 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
  • Mark Bix
  • Dean Tantin
  • Hongkun Park
  • Vijay K Kuchroo
  • Aviv Regev

Colleges, School and Institutes


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 languageEnglish
Pages (from-to)461-8
Number of pages8
Issue number7446
Publication statusPublished - 25 Apr 2013


  • Animals, Antigens, CD95, 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