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


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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    ABSTRACT: Genomic footprinting has emerged as an unbiased discovery method for transcription factor (TF) occupancy at cognate DNA in vivo. A basic premise of footprinting is that sequence-specific TF-DNA interactions are associated with localized resistance to nucleases, leaving observable signatures of cleavage within accessible chromatin. This phenomenon is interpreted to imply protection of the critical nucleotides by the stably bound protein factor. However, this model conflicts with previous reports of many TFs exchanging with specific binding sites in living cells on a timescale of seconds. We show that TFs with short DNA residence times have no footprints at bound motif elements. Moreover, the nuclease cleavage profile within a footprint originates from the DNA sequence in the factor-binding site, rather than from the protein occupying specific nucleotides. These findings suggest a revised understanding of TF footprinting and reveal limitations in comprehensive reconstruction of the TF regulatory network using this approach.
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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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    ABSTRACT: DNase I is an enzyme preferentially cleaving DNA in highly accessible regions. Recently, Next-Generation Sequencing has been applied to DNase I assays (DNase-seq) to obtain genome-wide maps of these accessible chromatin regions. With high-depth sequencing, DNase I cleavage sites can be identified with base-pair resolution, revealing the presence of protected regions (“footprints”), corresponding to bound molecules on the DNA. Integrating footprint positions close to transcription start sites with motif analysis can reveal the presence of regulatory interactions between specific transcription factors (TFs) and genes. However, this inference heavily relies on the accuracy of the footprint call and on the sequencing depth of the DNase-seq experiment. Using ENCODE data, we comprehensively evaluate the performances of two recent footprint callers (Wellington and DNaseR) and one metric (the Footprint Occupancy Score, or FOS), and assess the consequences of different footprint calls on the reconstruction of TF-TF regulatory networks. We rate Wellington as the method of choice among those tested: not only its predictions are the best in terms of accuracy, but also the properties of the inferred networks are robust against sequencing depth.
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