Apabetalone downregulates factors and pathways associated with vascular calcification
Bibliographic record
Abstract
BACKGROUND AND AIMS: Apabetalone is an inhibitor of bromodomain and extraterminal (BET) proteins. In clinical trials, apabetalone reduced the incidence of major adverse cardiac events (MACE) in patients with cardiovascular disease and reduced circulating factors that promote vascular calcification (VC). Because VC contributes to MACE, effects of apabetalone on pro-calcific processes were examined. METHODS AND RESULTS: Apabetalone inhibited extracellular calcium deposition and opposed induction of transdifferentiation markers in human coronary artery vascular smooth muscle cells (VSMCs) under osteogenic culture conditions. Tissue-nonspecific alkaline phosphatase (TNAP) is a key contributor to VC, and apabetalone suppressed osteogenic induction of the mRNA, protein and enzyme activity. The liver is a major source of circulating TNAP, and apabetalone also downregulated TNAP expression in primary human hepatocytes. BRD4, a transcriptional regulator and target of apabetalone, has been linked to calcification. Osteogenic transdifferentiation of VSMCs resulted in disassembly of 100 BRD4-rich enhancers, with concomitant enlargement of remaining enhancers. Apabetalone reduced the size of BRD4-rich enhancers, consistent with disrupting BRD4 association with chromatin. 38 genes were uniquely associated with BRD4-rich enhancers in osteogenic conditions; 11 were previously associated with calcification. Apabetalone reduced levels of BRD4 on many of these enhancers, which correlated with decreased expression of the associated gene. Bioinformatics revealed BRD4 may cooperate with 7 specific transcription factors to promote transdifferentiation and calcification. CONCLUSIONS: Apabetalone counters transdifferentiation and calcification of VSMCs via an epigenetic mechanism involving specific transcription factors. The mechanistic findings, combined with evidence from clinical trials, support further development of apabetalone as a therapeutic for VC.
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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) | 0.001 | 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) | 0.002 | 0.000 |
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".