bioRxivCell BiologyPreprint
BRD4, Mediator, and Pol II form heterogeneous condensates with distinct transcriptional and acetylation-dependent states
Samuel ShoupiD, Alina SchaafiD, Kathrin Hertäg, Amy-Sue Sattler, Márton GellériiD, Fridolin KielischiD, Thomas SpeckiD, Sandra SchickiD
Abstract
Transcriptional condensates at super-enhancers are thought to concentrate BRD4, Mediator, and RNA polymerase II (Pol II) to promote gene activation, yet their compositional organization and regulation remain poorly understood. We developed a high-throughput live-cell phenomics platform based on endogenous fluorescent tagging of BRD4, MED14 (Mediator), and POLR2A (Pol II) to systematically quantify transcriptional condensate states across >1,000 chemical perturbations. Contrary to prevailing models of largely co-occupied assemblies, we find compositionally heterogenous condensate populations. In particular, BRD4-only spots emerged as a prominent class that is depleted of Mediator and Pol II, enriched at chromatin, and resistant to transcription initiation inhibition. Mechanistically, compound screening coupled to mechanism-of-action analysis identifies histone acetylation as a dominant regulatory axis for BRD4-only spots: Bromodomain and Extra-Terminal motif (BET) and histone acetyltransferase inhibition selectively deplete BRD4-only condensates, while histone deacetylase inhibition expands them. Together, these findings support a model in which acetylation-dependent BRD4 condensates define a distinct chromatin-associated regulatory state that is separable from canonical transcriptionally engaged condensates. More broadly, our work establishes condensate composition as a quantitative phenotype and provides a scalable framework for systematically dissecting the regulation of condensates across perturbations, cell types, and disease contexts.
- organism: human
- assay: fluorescence / live-cell microscopy
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- approach: deep learning
- approach: statistical model
- Citations
- 0
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