Unbiased reconstruction of a mammalian transcriptional network mediating pathogen responses

Science. 2009 Oct 9;326(5950):257-63. doi: 10.1126/science.1179050. Epub 2009 Sep 3.


Models of mammalian regulatory networks controlling gene expression have been inferred from genomic data but have largely not been validated. We present an unbiased strategy to systematically perturb candidate regulators and monitor cellular transcriptional responses. We applied this approach to derive regulatory networks that control the transcriptional response of mouse primary dendritic cells to pathogens. Our approach revealed the regulatory functions of 125 transcription factors, chromatin modifiers, and RNA binding proteins, which enabled the construction of a network model consisting of 24 core regulators and 76 fine-tuners that help to explain how pathogen-sensing pathways achieve specificity. This study establishes a broadly applicable, comprehensive, and unbiased approach to reveal the wiring and functions of a regulatory network controlling a major transcriptional response in primary mammalian cells.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Bacteria / immunology*
  • Chromatin Assembly and Disassembly
  • DNA, Single-Stranded / immunology
  • Dendritic Cells / immunology*
  • Dendritic Cells / metabolism*
  • Feedback, Physiological
  • Gene Expression Profiling
  • Gene Expression Regulation*
  • Gene Regulatory Networks*
  • Inflammation / immunology
  • Inflammation / metabolism*
  • Lipopeptides / immunology
  • Lipopolysaccharides / immunology
  • Mice
  • Mice, Inbred C57BL
  • Poly I-C / immunology
  • RNA-Binding Proteins / metabolism
  • Toll-Like Receptors / agonists
  • Transcription Factors / metabolism
  • Transcription, Genetic
  • Viruses / immunology*


  • DNA, Single-Stranded
  • Lipopeptides
  • Lipopolysaccharides
  • Pam(3)CSK(4) peptide
  • RNA-Binding Proteins
  • Toll-Like Receptors
  • Transcription Factors
  • Poly I-C

Associated data

  • GEO/GSE17721