Breaking boundaries: Pan BETi disrupt 3D chromatin structure, BD2-selective BETi are strictly epigenetic transcriptional regulators
Bibliographic record
Abstract
BACKGROUND: Bromodomain and extraterminal proteins (BETs) are more than just epigenetic regulators of transcription. Here we highlight a new role for the BET protein BRD4 in the maintenance of higher order chromatin structure at Topologically Associating Domain Boundaries (TADBs). BD2-selective and pan (non-selective) BET inhibitors (BETi) differentially support chromatin structure, selectively affecting transcription and cell viability. METHODS: Using RNA-seq and BRD4 ChIP-seq, the differential effect of BETi treatment on the transcriptome and BRD4 chromatin occupancy of human aortic endothelial cells from diabetic patients (dHAECs) stimulated with TNFα was evaluated. Chromatin decondensation and DNA fragmentation was assessed by immunofluorescence imaging and quantification. Key dHAEC findings were verified in proliferating monocyte-like THP-1 cells using real time-PCR, BRD4 co-immunoprecipitation studies, western blots, proliferation and apoptosis assays. FINDINGS: We discovered that 1) BRD4 co-localizes with Ying-Yang 1 (YY1) at TADBs, critical chromatin structure complexes proximal to many DNA repair genes. 2) BD2-selective BETi enrich BRD4/YY1 associations, while pan-BETi do not. 3) Failure to support chromatin structures through BRD4/YY1 enrichment inhibits DNA repair gene transcription, which induces DNA damage responses, and causes widespread chromatin decondensation, DNA fragmentation, and apoptosis. 4) BD2-selective BETi maintain high order chromatin structure and cell viability, while reducing deleterious pro-inflammatory transcription. INTERPRETATION: BRD4 plays a previously unrecognized role at TADBs. BETi differentially impact TADB stability. Our results provide translational insight for the development of BETi as therapeutics for a range of diseases including CVD, chronic kidney disease, cancer, and COVID-19.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".