Cistrome and Epicistrome Features Shape the Regulatory DNA Landscape

By Ronan C. O’Malley, S. Huang, Liang Song, M. Lewsey, Anna Bartlett, Joseph R. Nery, M. Galli, A. Gallavotti, Joseph R. Ecker. 2016

1,372 citations recorded by the source corpus.

SUMMARY The cistrome is the complete set of transcription factor (TF) binding sites (cis-elements) in an organism, while an epicistrome incorporates tissue-specific DNA chemical modifications and TF-specific chemical sensitivities into these binding profiles. Robust methods to construct comprehensive cistrome and epicistrome maps are critical for elucidating complex transcriptional networks that underlie growth, behavior, and disease. Here, we describe DNA affinity purification sequencing (DAP-seq), a high-throughput TF binding site discovery method that interrogates genomic DNA with in-vitro-expressed TFs. Using DAP-seq, we defined the Arabidopsis cistrome by resolving motifs and peaks for 529 TFs. Because genomic DNA used in DAP-seq retains 5-methylcytosines, we determined that >75% (248/327) of Arabidopsis TFs surveyed were methylation sensitive, a property that strongly impacts the epicistrome landscape. DAP-seq datasets also yielded insight into the biology and binding site architecture of numerous TFs, demonstrating the value of DAP-seq for cost-effective cistromic and epicistromic annotation in any organism.

Subfields: Molecular Biology, Plant Science

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Citations in the corpus

Impact of cytosine methylation on DNA binding specificities of human transcription factors (2017, neutral): “(63) studied are affected by this modification.”
Auxin Response Factors — output control in auxin biology (2017, background): “Some correlation seems to exist between the number of AuxRE in a promotor region and its auxin inducibility (Berendzen et al. , 2012; O’Malley et al. , 2016) .”
De novo Identification of DNA Modifications Enabled by Genome-Guided Nanopore Signal Processing (2017, background): “Such correspondences seem likely given a recent report that at least 70% of transcription factors in Arabidopsis thaliana have differential binding affinities at 5mC sites 32 .”
ePlant: Visualizing and Exploring Multiple Levels of Data for Hypothesis Generation in Plant Biology[OPEN] (2017, positive): “…the Biomolecular Interaction Network Database (Bader et al., 2003), high-density Arabidopsis protein microarrays (Popescu et al., 2007, 2009 et al., 2016), which the authors show to be quite similar to ChIP-seq data in terms of quality, while encompassing a far greater number of transcription…”
Engineering Quantitative Trait Variation for Crop Improvement by Genome Editing. (2017, background): “REAGENT or RESOURCE SOURCE IDENTIFIER Custom Perl script for off-target sites search This study NA SeqMan DNASTAR Lasergene http://www.dnastar.com CoGe Gevo tool (Lyons and Freeling, 2008) https://www.genomevolution.org/coge/ mVISTA (Mathelier et al., 2014) http://genome.lbl.gov/vista/mvista/about.s”
Mapping genome-wide transcription-factor binding sites using DAP-seq (2017, methodology): “We have successfully performed the DAP-seq method using Arabidopsis, maize 15 and human (unpublished) TFs.”
PlantRegMap: charting functional regulatory maps in plants (2019, background): “With TF binding motifs throughout the genome determined by experiments in plants (7,8) and in silico-mapped in 156 plants (6), directly scanning the TF binding motifs in the promoters of putative target genes is becoming a promising option.”
WRKY transcription factors: evolution, binding, and action (2019, positive): “The DAP-seq method interrogates genomic DNA with recombinant TF proteins for the discovery of TF binding sites (O’Malley et al. 2016).”
Epigenomic diversity in a global collection of Arabidopsis thaliana accessions (2016, positive): “Binding inhibition by methylation (O’Malley et al., 2016) was predicted to be stronger for TFs depleted at eQTL compared to those that are enriched for such loci (Figures 6D and 6E).”
JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework (2017, methodology): “The curated PFMs were derived from ChIP-seq (from ReMap (25) and (26– 30)), DAP-seq (31), SMiLE-seq (32), PBM (33) and HTSELEX (34) experiments.”
PlantTFDB 4.0: toward a central hub for transcription factors and regulatory interactions in plants (2016, methodology): “We firstly collected 1790 binding motifs derived from experiments from multiple databases (including PlantCistromeDB (20), CIS-BP (21), JASPAR (32), UniPROBE (33), TRANSFAC (public 7.0) (34)), literature (22) and motifs discovered from ChIPseq peaks using MEME-ChIP (35).”
JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework (2017, methodology): “The curated PFMs were derived from ChIP-seq (from ReMap (25) and (26– 30)), DAP-seq (31), SMiLE-seq (32), PBM (33) and HTSELEX (34) experiments.”