Genome ResearchSystems BiologyPeer-reviewed journal article
SelexGLM differentiates androgen and glucocorticoid receptor DNA-binding preference over an extended binding site
Liyang ZhangiD, Gabriella D. MartiniiD, H. Tomas RubeiD, Judith F. Kribelbauer-SwietekiD, Chaitanya RastogiiD, Vincent FitzPatrick, Jon C. D. HoutmaniD, Harmen J. BussemakeriD, Miles A. PufalliD
Abstract
The DNA-binding interfaces of the androgen (AR) and glucocorticoid (GR) receptors are virtually identical, yet these transcription factors share only about a third of their genomic binding sites and regulate similarly distinct sets of target genes. To address this paradox, we determined the intrinsic specificities of the AR and GR DNA binding domains using a refined version of SELEX-seq. We developed an algorithm, SelexGLM, that quantifies binding specificity over a large (31 bp) binding-site by iteratively fitting a feature-based generalized linear model to SELEX probe counts. This analysis revealed that the DNA binding preferences of AR and GR homodimers differ significantly, both within and outside the 15bp core binding site. The relative preference between the two factors can be tuned over a wide range by changing the DNA sequence, with AR more sensitive to sequence changes than GR. The specificity of AR extends to the regions flanking the core 15bp site, where isothermal calorimetry measurements reveal that affinity is augmented by enthalpy-driven readout of poly-A sequences associated with narrowed minor groove width. We conclude that the increased specificity of AR is correlated with more enthalpy-driven binding than GR. The binding models help explain differences in AR and GR genomic binding, and provide a biophysical rationale for how promiscuous binding by GR allows functional substitution for AR in some castration-resistant prostate cancers.
- assay: DNA methylation
- assay: ChIP-seq
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: variant effect prediction
- research focus: protein structure prediction
- approach: statistical model
- Citations
- 39
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