Article

Circuitry and Dynamics of Human Transcription Factor Regulatory Networks

Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.
Cell (Impact Factor: 32.24). 09/2012; 150(6):1274-86. DOI: 10.1016/j.cell.2012.04.040
Source: PubMed

ABSTRACT

The combinatorial cross-regulation of hundreds of sequence-specific transcription factors (TFs) defines a regulatory network that underlies cellular identity and function. Here we use genome-wide maps of in vivo DNaseI footprints to assemble an extensive core human regulatory network comprising connections among 475 sequence-specific TFs and to analyze the dynamics of these connections across 41 diverse cell and tissue types. We find that human TF networks are highly cell selective and are driven by cohorts of factors that include regulators with previously unrecognized roles in control of cellular identity. Moreover, we identify many widely expressed factors that impact transcriptional regulatory networks in a cell-selective manner. Strikingly, in spite of their inherent diversity, all cell-type regulatory networks independently converge on a common architecture that closely resembles the topology of living neuronal networks. Together, our results provide an extensive description of the circuitry, dynamics, and organizing principles of the human TF regulatory network.

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Available from: Alex Reynolds, Dec 18, 2013
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    • "Integration of open chromatin data such as that from FAIRE-seq and DNase-seq with RNA-seq has mostly been limited to verifying the expression status of genes that overlap a region of interest[150]. DNase-seq can be used for genome-wide footprinting of DNA-binding factors, and this in combination with the actual expression of genes can be used to infer active transcriptional networks[150]. "
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    • "DGF was originally applied to the yeast genome, which allows a more accurate cleavage profile because of its small genome size (Hesselberth et al., 2009; Chen et al., 2010). Subsequently, computational detection of footprint candidates has been performed on the mitochondrial genome and the entire human genome (Mercer et al., 2011; Neph et al., 2012a, 2012b; Piper et al., 2013). However, the algorithms used in previous studies are either inefficient for mammalian genomes , or not publicly available, leaving the general community without proper computational tools. "
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    • "After evaluating the performance of the footprint callers, we assessed the impact of different sets of footprints on downstream analyses. In particular, we reconstructed the network of TF-TF interactions following the protocol described in Neph et al. (2012b) (see Extended Methods for a detailed description in Supplementary Material). We repeated the procedure for DGF data from (1) the K562 cell line (myelogenous leukemia) used in the previous section, (2) the SkMC cell line and (3) the HepG2 (liver hepatocellular carcinoma) cell line. "
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