Nature CommunicationsGenomicsPeer-reviewed journal article
Decoding the Epigenetics and Chromatin Loop Dynamics of Androgen Receptor-Mediated Transcription
Umut Berkay AltıntaşiD, Ji-Heui SeoiD, Claudia GiambartolomeiiD, Doğancan ÖzturaniD, Brad J. FortunatoiD, Geoffrey M. NelsoniD, Seth Raphael GoldmaniD, Karen AdelmaniD, Faraz HachiD, Matthew L. FreedmaniD, Nathan A. LackiD
Abstract
Androgen receptor (AR)-mediated transcription plays a critical role in normal prostate development and prostate cancer growth. AR drives gene expression by binding to thousands of cis-regulatory elements (CRE) that loop to hundreds of target promoters. With multiple CREs interacting with a single promoter, it remains unclear how individual AR bound CREs contribute to gene expression. To characterize the involvement of these CREs, we investigated the AR-driven epigenetic and chromosomal chromatin looping changes. We collected a kinetic multiomic dataset comprised of steady-state mRNA, chromatin accessibility, transcription factor binding, histone modifications, chromatin looping, and nascent RNA. Using an integrated regulatory network, we found that AR binding induces sequential changes in the epigenetic features at CREs, independent of gene expression. Further, we showed that binding of AR does not result in a substantial rewiring of chromatin loops, but instead increases the contact frequency of pre-existing loops to target promoters. Our results show that gene expression strongly correlates to the changes in contact frequency. We then proposed and experimentally validated an unbalanced multi-enhancer model where the impact on gene expression of AR-bound enhancers is heterogeneous, and is proportional to their contact frequency with target gene promoters. Overall, these findings provide new insight into AR-mediated gene expression upon acute androgen simulation and develop a mechanistic framework to investigate nuclear receptor mediated perturbations.
- organism: human
- assay: bulk RNA-seq
- assay: ChIP-seq
- assay: DNA methylation
- assay: Hi-C
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 21
- FWCI
- 2.85
- Field percentile
- top 7%
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