Unbiased Reconstruction of a Mammalian Transcriptional Network Mediating Pathogen Responses

Peeking at Pathogen Response Networks Networks controlling gene expression serve as key decision-making circuits in cells, but the regulatory networks that control dynamic and specific gene expression responses to stimuli are often not well understood. This is particularly true for immune dendritic cells (DCs), which respond to pathogens by mounting elaborate transcriptional responses, and are centrally involved in infectious diseases, autoimmunity, and vaccines. Amit et al. (p. 257, published online 3 September) explored the transcriptional response of dendritic cells to specific classes of pathogens. The transcriptional subnetworks responsible for mammalian dendritic cell responses to different pathogens were identified, and the function of 100 regulators clarified. Inflammatory and antiviral programs in dendritic cells are controlled and tuned by a network of regulators. 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.

[1]  Joshua M. Korn,et al.  The plasticity of dendritic cell responses to pathogens and their components. , 2001, Science.

[2]  Govinda Rao,et al.  IRF3 mediates a TLR3/TLR4-specific antiviral gene program. , 2002, Immunity.

[3]  A. Luster.  The role of chemokines in linking innate and adaptive immunity. , 2002, Current opinion in immunology.

[4]  Amos Tanay,et al.  Minreg: Inferring an active regulator set , 2002, ISMB.

[5]  S. Mangan,et al.  Structure and function of the feed-forward loop network motif , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[6]  D. Pe’er,et al.  Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data , 2003, Nature Genetics.

[7]  S. Rafii,et al.  Splitting vessels: Keeping lymph apart from blood , 2003, Nature Medicine.

[8]  C. Coban,et al.  Interferon-α induction through Toll-like receptors involves a direct interaction of IRF7 with MyD88 and TRAF6 , 2004, Nature Immunology.

[9]  Pablo Tamayo,et al.  Metagenes and molecular pattern discovery using matrix factorization , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[10]  T. Decker,et al.  The Yin and Yang of type I interferon activity in bacterial infection , 2005, Nature Reviews Immunology.

[11]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[12]  T. Ideker,et al.  Supporting Online Material for A Systems Approach to Mapping DNA Damage Response Pathways , 2006 .

[13]  C. Allis,et al.  Mouse Polycomb Proteins Bind Differentially to Methylated Histone H3 and RNA and Are Enriched in Facultative Heterochromatin , 2006, Molecular and Cellular Biology.

[14]  A. Regev,et al.  Distinct physiological states of Plasmodium falciparum in malaria-infected patients , 2007, Nature.

[15]  U. Alon.  Network motifs: theory and experimental approaches , 2007, Nature Reviews Genetics.

[16]  Masahide Takahashi,et al.  Polycomb protein Cbx4 promotes SUMO modification of de novo DNA methyltransferase Dnmt3a. , 2007, The Biochemical journal.

[17]  Patrick J. Killion,et al.  Genetic reconstruction of a functional transcriptional regulatory network , 2007, Nature Genetics.

[18]  Eytan Domany,et al.  A module of negative feedback regulators defines growth factor signaling , 2007, Nature Genetics.

[19]  S. Akira,et al.  TRAM couples endocytosis of Toll-like receptor 4 to the induction of interferon-β , 2008, Nature Immunology.

[20]  N. Friedman,et al.  Structure and function of a transcriptional network activated by the MAPK Hog1 , 2008, Nature Genetics.

[21]  Jennifer L. Osborn,et al.  Direct multiplexed measurement of gene expression with color-coded probe pairs , 2008, Nature Biotechnology.

[22]  Bin Li,et al.  Uncovering a Macrophage Transcriptional Program by Integrating Evidence from Motif Scanning and Expression Dynamics , 2008, PLoS Comput. Biol..

[23]  C. Glass,et al.  Induced ncRNAs Allosterically Modify RNA Binding Proteins in cis to Inhibit Transcription , 2008, Nature.

[24]  David A. Drubin,et al.  Learning a Prior on Regulatory Potential from eQTL Data , 2009, PLoS genetics.

[25]  E. Davidson,et al.  The evolution of hierarchical gene regulatory networks , 2009, Nature Reviews Genetics.

[26]  Martin S. Taylor,et al.  The transcriptional network that controls growth arrest and differentiation in a human myeloid leukemia cell line , 2009, Nature Genetics.

[27]  Shizuo Akira,et al.  The roles of TLRs, RLRs and NLRs in pathogen recognition. , 2009, International immunology.

[28]  Aviv Regev,et al.  Transcriptional Regulatory Circuits: Predicting Numbers from Alphabets , 2009, Science.

[29]  Alistair G. Rust,et al.  Role of the transcription factor C/EBPδ in a regulatory circuit that discriminates between transient and persistent Toll-like receptor 4-induced signals , 2009, Nature Immunology.