Dissecting the DNA binding landscape and gene regulatory network of p63 and p53

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Abstract

The transcription factor p53 is the best-known tumor suppressor, but its sibling p63 is a master regulator of epidermis development and a key oncogenic driver in squamous cell carcinomas (SCC). Despite multiple gene expression studies becoming available, the limited overlap of reported p63-dependent genes has made it difficult to decipher the p63 gene regulatory network. Particularly, analyses of p63 response elements differed substantially among the studies. To address this intricate data situation, we provide an integrated resource that enables assessing the p63-dependent regulation of any human gene of interest. We use a novel iterative de novo motif search approach in conjunction with extensive ChIP-seq data to achieve a precise global distinction between p53 and p63 binding sites, recognition motifs, and potential co-factors. We integrate these data with enhancer:gene associations to predict p63 target genes and identify those that are commonly de-regulated in SCC representing candidates for prognosis and therapeutic interventions.

Data availability

All data generated or analysed during this study are included in the manuscript and supporting files.

The following previously published data sets were used

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Article and author information

Author details

Konstantin Riege

Computational Biology, Leibniz Institute on Aging - Fritz Lipmann Institute (FLI), Jena, Germany

Competing interests
The authors declare that no competing interests exist.
Helene Kretzmer

Genome Regulation, Max Planck Institute for Molecular Genetics, Berlin, Germany

Competing interests
The authors declare that no competing interests exist.
Arne Sahm

Computational Biology Group, Leibniz Institute on Aging - Fritz Lipmann Institute (FLI), Jena, Germany

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0002-7330-1790
Simon S McDade

Queen's University Belfast, Belfast, United Kingdom

Competing interests
The authors declare that no competing interests exist.
Steve Hoffmann

Computational Biology Group, Leibniz Institute on Aging - Fritz Lipmann Institute (FLI), Jena, Germany

Competing interests
The authors declare that no competing interests exist.
Martin Fischer

Computational Biology, Leibniz Institute on Aging - Fritz Lipmann Institute (FLI), Jena, Germany

For correspondence
Martin.Fischer@leibniz-fli.de

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0002-3429-1876

Funding

Deutsche Forschungsgemeinschaft (FI 1993/2-1)

Martin Fischer

Bundesministerium für Bildung und Forschung (031L016D)

Steve Hoffmann

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.