Nucleic Acids ResearchGenomicsPeer-reviewed journal article
Androgen and glucocorticoid receptor direct distinct transcriptional programs by receptor-specific and shared DNA binding sites
Marina KulikiD, Melissa Bothe, Gözde Kibar, Alisa FuchsiD, Stefanie Schöne, Stefan PrekovićiD, Isabel Mayayo‐Peralta, Ho‐Ryun ChungiD, Wilbert T. ZwartiD, Christine HelseniD, Frank ClaessensiD, Sebastiaan H. MeijsingiD
Abstract
The glucocorticoid (GR) and androgen (AR) receptors execute unique functions in vivo, yet have nearly identical DNA binding specificities. To identify mechanisms that facilitate functional diversification among these transcription factor paralogs, we studied AR and GR in an equivalent cellular context. Analysis of chromatin and sequence features suggest that divergent binding, and corresponding gene regulation, are driven by different abilities of AR and GR to interact with relatively inaccessible chromatin. Divergent genomic binding patterns can also be the results of subtle differences in DNA binding preference between AR and GR. Furthermore, the sequence composition of large regions (>10 kb) surrounding selectively occupied binding sites differs significantly, indicating a role for the sequence environment in selectively guiding AR and GR to distinct binding sites. The comparison of binding sites that are shared between AR and GR shows that the specificity paradox can also be resolved by differences in the events that occur downstream of receptor binding. Specifically, we find that shared binding sites display receptor-specific enhancer activity, cofactor recruitment and changes in histone modifications. Genomic deletion of shared binding sites demonstrates their contribution to directing receptor-specific gene regulation. Together, these data suggest that differences in genomic occupancy as well as divergence in the events that occur downstream of receptor binding direct functional diversification among transcription factor paralogs.
- organism: human
- assay: ChIP-seq
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 43
- FWCI
- 2.27
- Field percentile
- top 10%
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