An atlas of combinatorial transcriptional regulation in mouse and man

By Timothy Ravasi, Harukazu Suzuki, Carlo Vittorio Cannistraci, S. Katayama, Vladimir B. Bajic, Kai Tan, Altuna Akalin, S. Schmeier, M. Kanamori-Katayama, Nicolas Bertin and 36 more. Cell, 2010

688 citations recorded by the source corpus.

SUMMARY Combinatorial interactions among transcription factors are critical to directing tissue-specific gene expression. To build a global atlas of these combinations, we have screened for physical interactions among the majority of human and mouse DNA-binding transcription factors (TFs). The complete networks contain 762 human and 877 mouse interactions. Analysis of the networks reveals that highly connected TFs are broadly expressed across tissues, and that roughly half of the measured interactions are conserved between mouse and human. The data highlight the importance of TF combinations for determining cell fate, and they lead to the identification of a SMAD3/FLI1 complex expressed during development of immunity. The availability of large TF combinatorial networks in both human and mouse will provide many opportunities to study gene regulation, tissue differentiation, and mammalian evolution.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/816f5583b5f9ff7bd761ebdd66c443e5e485d88d

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Citations in the corpus

Sequence features and chromatin structure around the genomic regions bound by 119 human transcription factors (2012, background): “We then compared the pairs we discovered with experimentally detected pairs reported in a mammalian two-hybrid study (Ravasi et al. 2010) and in the BIOGRID database (Stark et al.”
EPD and EPDnew, high-quality promoter resources in the next-generation sequencing era (2012, background): “The EPD-supplied tracks show experimental TSS sites from DBTSS7 (14) and FANTOM4 (26), chromatin marks from (7) and DNA methylome data from (8).”
AnimalTFDB: a comprehensive animal transcription factor database (2011, methodology): “The protein–protein interaction information was parsed from BioGRID (15), HPRD (16) and An atlas of human and mouse TF interactions (17) databases.”
Integrative approaches for finding modular structure in biological networks (2013, background): “…have similarly uncovered composite modules in signalling and regulatory networks, primarily through combined evaluation of protein– DNA interactions (specifically, TF–target interactions) and protein–protein interactions 11,59,152,153 , or by additionally including genetic interactions 151 .”
Architecture of the human regulatory network derived from ENCODE data (2012, methodology): “We calculated the average expression levels of transcription factors across 34 tissues 26 ; highly connected factors tend to be highly expressed.”
RegNetwork: an integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse (2015, methodology): “More specifically, for transcriptional regulatory relationships, we first compile a list of TFs for human and mouse, respectively, from FANTOM (23), UniProt (41), TRANSFAC (16) and JASPAR (17).”
TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions (2017, background): “(3) and a list of mouse gene names and their synonyms derived from the NCBI Gene database (4), which were further filtered for the Consensus coding sequences (CCDS) database (5).”
Protein Networks as Logic Functions in Development and Cancer (2011, methodology): “To provide a complementary protein network, we downloaded a set of 5227 physical interactions measured among pairs of human transcription factors, many of which have been recently reported using the mammalian two hybrid system [15].”
DEEP: a general computational framework for predicting enhancers (2014, background): “In addition, strong dependency on sequence motifs may be problematic, since simultaneous binding of protein complexes to combinations of different motifs cannot be easily captured (20).”
Genome-wide analysis of ETS-family DNA-binding in vitro and in vivo (2010, neutral): “ELK1 or ELK4 can bind DNA together with SRF (Dalton and Treisman, 1992; Cooper et al, 2007; Boros et al, 2009), FLI1 associates with SMAD3 (Ravasi et al, 2010) and ETS1 can bind to composite sites with PAX5 (Garvie et al, 2001) and RUNX1 (Hollenhorst et al, 2007, 2009).”
TRRUST: a reference database of human transcriptional regulatory interactions (2015, methodology): “The list of TF genes was derived from Ravasi et al. 15 , which is based on the manual curation of several sources: i) the TRANSFAC database; ii) genes annotated by the Gene Ontology (GO) term ‘transcription factor’; iii) genes that contain the word ‘transcription’ in the Entrez description field;…”