MeDReaders: a database for transcription factors that bind to methylated DNA

Abstract Understanding the molecular principles governing interactions between transcription factors (TFs) and DNA targets is one of the main subjects for transcriptional regulation. Recently, emerging evidence demonstrated that some TFs could bind to DNA motifs containing highly methylated CpGs both in vitro and in vivo. Identification of such TFs and elucidation of their physiological roles now become an important stepping-stone toward understanding the mechanisms underlying the methylation-mediated biological processes, which have crucial implications for human disease and disease development. Hence, we constructed a database, named as MeDReaders, to collect information about methylated DNA binding activities. A total of 731 TFs, which could bind to methylated DNA sequences, were manually curated in human and mouse studies reported in the literature. In silico approaches were applied to predict methylated and unmethylated motifs of 292 TFs by integrating whole genome bisulfite sequencing (WGBS) and ChIP-Seq datasets in six human cell lines and one mouse cell line extracted from ENCODE and GEO database. MeDReaders database will provide a comprehensive resource for further studies and aid related experiment designs. The database implemented unified access for users to most TFs involved in such methylation-associated binding actives. The website is available at http://medreader.org/.

[1]  S. Tweedie,et al.  The methyl-CpG binding domain and the evolving role of DNA methylation in animals. , 2003, Trends in genetics : TIG.

[2]  Jing Zhang,et al.  MethBank: a database integrating next-generation sequencing single-base-resolution DNA methylation programming data , 2014, Nucleic Acids Res..

[3]  R. Chatterjee,et al.  CpG methylation of half-CRE sequences creates C/EBPα binding sites that activate some tissue-specific genes , 2010, Proceedings of the National Academy of Sciences.

[4]  D. Schübeler,et al.  Impact of cytosine methylation on DNA binding specificities of human transcription factors , 2017, Science.

[5]  Hui Liu,et al.  AnimalTFDB: a comprehensive animal transcription factor database , 2011, Nucleic Acids Res..

[6]  Felix Krueger,et al.  Allele-specific binding of ZFP57 in the epigenetic regulation of imprinted and non-imprinted monoallelic expression , 2015, Genome Biology.

[7]  Zhi Xie,et al.  MethSMRT: an integrative database for DNA N6-methyladenine and N4-methylcytosine generated by single-molecular real-time sequencing , 2016, Nucleic Acids Res..

[8]  Éric Renault,et al.  MethDB - a public database for DNA methylation data , 2001, Nucleic Acids Res..

[9]  A. H. Smits,et al.  Dynamic Readers for 5-(Hydroxy)Methylcytosine and Its Oxidized Derivatives , 2013, Cell.

[10]  Tae-Wook Kang,et al.  MENT: methylation and expression database of normal and tumor tissues. , 2013, Gene.

[11]  Felix Krueger,et al.  Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications , 2011, Bioinform..

[12]  Jiajie Zhang,et al.  MethyCancer: the database of human DNA methylation and cancer , 2007, Nucleic Acids Res..

[13]  Ge Gao,et al.  PlantTFDB 4.0: toward a central hub for transcription factors and regulatory interactions in plants , 2016, Nucleic Acids Res..

[14]  S. Balasubramanian,et al.  A screen for hydroxymethylcytosine and formylcytosine binding proteins suggests functions in transcription and chromatin regulation , 2013, Genome Biology.

[15]  A. Bird,et al.  The p120 catenin partner Kaiso is a DNA methylation-dependent transcriptional repressor. , 2001, Genes & development.

[16]  Christoph Bock,et al.  Sequential ChIP-bisulfite sequencing enables direct genome-scale investigation of chromatin and DNA methylation cross-talk , 2012, Genome research.

[17]  Lin Yang,et al.  TFBSshape: a motif database for DNA shape features of transcription factor binding sites , 2013, Nucleic Acids Res..

[18]  A. Bird,et al.  Epigenetic regulation of gene expression: how the genome integrates intrinsic and environmental signals , 2003, Nature Genetics.

[19]  Michael Hackenberg,et al.  NGSmethDB: a database for next-generation sequencing single-cytosine-resolution DNA methylation data , 2010, Nucleic Acids Res..

[20]  T. Hughes,et al.  CG methylated microarrays identify a novel methylated sequence bound by the CEBPB|ATF4 heterodimer that is active in vivo , 2013, Genome research.

[21]  Mikael Bodén,et al.  MEME Suite: tools for motif discovery and searching , 2009, Nucleic Acids Res..

[22]  J. Qian,et al.  DNA methylation presents distinct binding sites for human transcription factors , 2013, eLife.

[23]  Xin Chen,et al.  TRANSFAC: an integrated system for gene expression regulation , 2000, Nucleic Acids Res..

[24]  Qing Yang,et al.  ITFP: an integrated platform of mammalian transcription factors , 2008, Bioinform..

[25]  Peter J. Bickel,et al.  Measuring reproducibility of high-throughput experiments , 2011, 1110.4705.

[26]  Wyeth W. Wasserman,et al.  JASPAR: an open-access database for eukaryotic transcription factor binding profiles , 2004, Nucleic Acids Res..

[27]  Jiang Qian,et al.  Transcription factors as readers and effectors of DNA methylation , 2016, Nature Reviews Genetics.

[28]  Martha L. Bulyk,et al.  UniPROBE: an online database of protein binding microarray data on protein–DNA interactions , 2008, Nucleic Acids Res..

[29]  D. Trono,et al.  In Embryonic Stem Cells, ZFP57/KAP1 Recognize a Methylated Hexanucleotide to Affect Chromatin and DNA Methylation of Imprinting Control Regions , 2011, Molecular cell.

[30]  S. Robson,et al.  Nucleosome-Interacting Proteins Regulated by DNA and Histone Methylation , 2010, Cell.