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    Competition between DNA methylation and transcription factors determines binding of NRF1 (2015)
    Nature Publishing Group (NPG)
    Details
    Publication Date: 2015-12-18
    Description: Eukaryotic transcription factors (TFs) are key determinants of gene activity, yet they bind only a fraction of their corresponding DNA sequence motifs in any given cell type. Chromatin has the potential to restrict accessibility of binding sites; however, in which context chromatin states are instructive for TF binding remains mainly unknown. To explore the contribution of DNA methylation to constrained TF binding, we mapped DNase-I-hypersensitive sites in murine stem cells in the presence and absence of DNA methylation. Methylation-restricted sites are enriched for TF motifs containing CpGs, especially for those of NRF1. In fact, the TF NRF1 occupies several thousand additional sites in the unmethylated genome, resulting in increased transcription. Restoring de novo methyltransferase activity initiates remethylation at these sites and outcompetes NRF1 binding. This suggests that binding of DNA-methylation-sensitive TFs relies on additional determinants to induce local hypomethylation. In support of this model, removal of neighbouring motifs in cis or of a TF in trans causes local hypermethylation and subsequent loss of NRF1 binding. This competition between DNA methylation and TFs in vivo reveals a case of cooperativity between TFs that acts indirectly via DNA methylation. Methylation removal by methylation-insensitive factors enables occupancy of methylation-sensitive factors, a principle that rationalizes hypomethylation of regulatory regions.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Domcke, Silvia -- Bardet, Anais Flore -- Adrian Ginno, Paul -- Hartl, Dominik -- Burger, Lukas -- Schubeler, Dirk -- England -- Nature. 2015 Dec 24;528(7583):575-9. doi: 10.1038/nature16462. Epub 2015 Dec 16.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, CH 4058 Basel, Switzerland. ; University of Basel, Faculty of Sciences, Petersplatz 1, CH 4003 Basel, Switzerland. ; Swiss Institute of Bioinformatics, Maulbeerstrasse 66, CH 4058 Basel, Switzerland.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26675734" target="_blank"〉PubMed〈/a〉
    Keywords: Animals ; *Binding, Competitive ; Cells, Cultured ; Chromatin/chemistry/genetics/*metabolism ; *DNA Methylation ; Deoxyribonuclease I/metabolism ; Genome/genetics ; Humans ; Mice ; Mouse Embryonic Stem Cells/metabolism ; Nuclear Respiratory Factor 1/*metabolism ; Protein Binding ; Transcription Factors/*metabolism
    Print ISSN: 0028-0836
    Electronic ISSN: 1476-4687
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
    Published by Nature Publishing Group (NPG)
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  • 2
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    Identification of transcription factor binding sites from ChIP-seq data at high resolution (2013)
    Oxford University Press
    Details
    Publication Date: 2013-10-19
    Description: Motivation:  Chromatin immunoprecipitation coupled to next-generation sequencing (ChIP-seq) is widely used to study the in vivo binding sites of transcription factors (TFs) and their regulatory targets. Recent improvements to ChIP-seq, such as increased resolution, promise deeper insights into transcriptional regulation, yet require novel computational tools to fully leverage their advantages. Results:  To this aim, we have developed peakzilla, which can identify closely spaced TF binding sites at high resolution (i.e. resolves individual binding sites even if spaced closely), as we demonstrate using semisynthetic datasets, performing ChIP-seq for the TF Twist in Drosophila embryos with different experimental fragment sizes, and analyzing ChIP-exo datasets. We show that the increased resolution reached by peakzilla is highly relevant, as closely spaced Twist binding sites are strongly enriched in transcriptional enhancers, suggesting a signature to discriminate functional from abundant non-functional or neutral TF binding. Peakzilla is easy to use, as it estimates all the necessary parameters from the data and is freely available. Availability and implementation:  The peakzilla program is available from https://github.com/steinmann/peakzilla or http://www.starklab.org/data/peakzilla/ . Contact:   stark@starklab.org Supplementary information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
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