Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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A high throughput in vivo protein-DNA mapping approach reveals principles of dynamic gene regulation in mammals (RNA-Seq)

ABSTRACT: Dynamic binding of transcription factors to DNA elements specifies gene expression and cell fate, in both normal physiology and disease. To date, our understanding of mammalian gene regulation has been hampered by the difficulty of directly measuring in vivo binding of large numbers of transcription factors to DNA. Here, we develop a high-throughput indexed Chromatin ImmunoPrecipitation (iChIP) method coupled to massively parallel sequencing to systematically map protein-DNA interactions. We apply iChIP to reconstruct the physical regulatory landscape of a mammalian cell, by building genome-wide binding maps for 29 transcription factors (TFs) and chromatin marks at four time points following stimulation of primary dendritic cells (DCs) with pathogen components. Using over 180,000 TF-DNA interactions in these maps, we derive an initial dynamic physical model of a mammalian cell regulatory network. Our data demonstrates that transcription factors vary substantially in their binding dynamics, genomic localization, number of binding events, and degree of interaction with other factors. Further, many of the TF-DNA interactions at stimulus-activated genes are established during differentiation and maintained in a poised state. Functionally, the TFs are organized in a hierarchy of different types: Cell differentiation factors bind most of the genes and remain largely unchanged during the stimulation. A second set of TFs bind already in the un-stimulated and preferentially target induced genes. A third set consists of TF that bind mainly after the stimuli and target specific gene functions. Together these factors determine the magnitude and timing of stimulus induced gene expression. Our method, which allowed us to map routinely temporal binding profiles of dozens of TFs, provides a foundation for future understanding of the mammalian regulatory code. A study of dynamic binding of transcription factors in an immune cell following pathogen stimulation

ORGANISM(S): Mus musculus

SUBMITTER: Manuel Garber

PROVIDER: E-GEOD-36103 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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A high-throughput chromatin immunoprecipitation approach reveals principles of dynamic gene regulation in mammals.

Garber Manuel M Yosef Nir N Goren Alon A Raychowdhury Raktima R Thielke Anne A Guttman Mitchell M Robinson James J Minie Brian B Chevrier Nicolas N Itzhaki Zohar Z Blecher-Gonen Ronnie R Bornstein Chamutal C Amann-Zalcenstein Daniela D Weiner Assaf A Friedrich Dennis D Meldrim James J Ram Oren O Cheng Christine C Gnirke Andreas A Fisher Sheila S Friedman Nir N Wong Bang B Bernstein Bradley E BE Nusbaum Chad C Hacohen Nir N Regev Aviv A Amit Ido I

Molecular cell 20120830 5

Understanding the principles governing mammalian gene regulation has been hampered by the difficulty in measuring in vivo binding dynamics of large numbers of transcription factors (TF) to DNA. Here, we develop a high-throughput Chromatin ImmunoPrecipitation (HT-ChIP) method to systematically map protein-DNA interactions. HT-ChIP was applied to define the dynamics of DNA binding by 25 TFs and 4 chromatin marks at 4 time-points following pathogen stimulus of dendritic cells. Analyzing over 180,00 ...[more]

PMID: 22940246

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Project description:DNA sequence is a major determinant of the binding specificity of transcription factors (TFs) for their genomic targets. However, eukaryotic cells often express, at the same time, TFs with highly similar DNA binding motifs but distinct in vivo targets. Currently, it is not well understood how TFs with seemingly identical DNA motifs achieve unique specificities in vivo. Here, we used custom protein binding microarrays to analyze TF specificity for putative binding sites in their genomic sequence context. Using yeast TFs Cbf1 and Tye7 as our case study, we found that binding sites of these bHLH TFs (i.e., E-boxes) are bound differently in vitro and in vivo, depending on their genomic context. Computational analyses suggest that nucleotides outside E-box binding sites contribute to specificity by influencing the 3D structure of DNA binding sites. Thus, local shape of target sites might play a widespread role in achieving regulatory specificity within TF families. Three protein binding microarray (PBM) experiments of Saccharomyces cerevisiae transcription factors were performed. Briefly, the PBMs involved binding GST-tagged yeast transcription factors Cbf1 and Tye7 to double-stranded 44K Agilent microarrays in order to determine their binding specificity for putative DNA binding sites in native genomic context. Briefly, we represent three categories of 30-bp genomic sequences: 1) ChIP-chip bound probes, 2) ChIP-chip unbound probes, and 3) negative control probes. ChIP-chip bound probes corresponded to genomic regions bound in vivo by Cbf1 or Tye7 (ChIP-chip P < 0.005 in rich medium (YPD) (Harbison et al., Nature 2004, PMID 15343339)) contained at least two consecutive 8-mers with universal PBM E-score > 0.35 (Zhu et al., Genome Research 2009, PMID 19158363). All putative binding sites occurred at the same position within the probes on the array. M-bM-^@M-^\ChIP-chip unboundM-bM-^@M-^] probes corresponded to genomic regions with ChIP-chip P > 0.5 and at least two consecutive 8-mers at a more stringent universal PBM E-score threshold of 0.4. Negative control probes corresponded to S. cerevisiae intergenic regions with a maximum 8-mer E-score < 0.3. We also designed probes that contain, within constant flanking regions, all 10-bp sequences that occur within the M-bM-^@M-^\ChIP-chip boundM-bM-^@M-^] probes and contain the E-box CACGTG, but are flanked by synthetic rather than native genomic sequence. Each DNA sequence represented on the array is present in 4 replicate spots. We report the PBM signal intensity for each spot. The PBM protocol is described in Berger et al., Nature Biotechnology 2006 (PMID 16998473).

Project description:Accurate predictions of the DNA binding specificities of transcription factors (TFs) are necessary for understanding gene regulatory mechanisms. Traditionally, predictive models are built based on nucleotide sequence features. Here, we employed three- dimensional DNA shape information obtained on a high-throughput basis to integrate intuitive DNA structural features into the modeling of TF binding specificities using support vector regression. We performed quantitative predictions of DNA binding specificities, using the DREAM5 dataset for 65 mouse TFs and genomic-context protein binding microarray data for three human basic helix-loop-helix TFs. DNA shape-augmented models compared favorably with sequence-based models for these predictions. Although both k-mer and DNA shape features encoded the interdependencies between nucleotide positions of the binding site, using DNA shape features reduced the dimensionality of the feature space compared to k-mer use. Finally, analyzing the weights of DNA shape-augmented models uncovered TF family- specific structural readout mechanisms that were not obvious from the nucleotide sequence. Three genomic-context protein binding microarray (gcPBM) experiments of human transcription factors were performed. Briefly, the gcPBMs involved binding his-tagged transcription factors c-Myc, Max, and Mad1(Mxd1) to double-stranded 180K Agilent microarrays in order to determine their binding specificity for putative DNA binding sites in native genomic context. Briefly, we represent three categories of 36-bp sequences: 1) bound probes, 2) unbound probes (or negative controls), and 3) test probes. Bound probes corresponded to genomic regions bound in vivo by c-Myc, Max, or Mad2 (ChIP-seq P < 10^(-10) in HeLaS3 or K562 celld (ENCODE)) that contain at least two consecutive 8-mers with universal PBM E-score > 0.4 (Munteanu and Gordan, LNCS 2013). All putative binding sites occur at the same position within the probes on the array. M-bM-^@M-^\UnboundM-bM-^@M-^] probes corresponded to genomic regions with ChIP-seq P < 10^(-10) and a maximum 8-mer E-score < 0.2. We also designed test probes that contain, within constant flanking regions, all nnCACGTGnn 10-mers and 18 nnnCACGTGnnn 12-mers (where n = A, C, G, or T). Each DNA sequence represented on the array is present in 6 replicate spots. We report the gcPBM signal intensity for each spot. The PBM protocol is described in Berger et al., Nature Biotechnology 2006 (PMID 16998473).

Dataset Information

A high throughput in vivo protein-DNA mapping approach reveals principles of dynamic gene regulation in mammals (RNA-Seq)

Publications

A high-throughput chromatin immunoprecipitation approach reveals principles of dynamic gene regulation in mammals.

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