Abstract 10591: Epigenetic BET Reader Inhibitor Apabetalone (RVX-208) Counters Proinflammatory Aortic Gene Expression in a Diet Induced Obesity Mouse Model
Bibliographic record
Abstract
Introduction: Obesity increases the risk of type 2 diabetes (DM2) and cardiovascular disease (CVD), due to associated insulin resistance, dyslipidemia, high blood pressure and chronic inflammation. Bromodomain and extraterminal (BET) proteins such as BRD4 enhance the expression of proinflammatory genes by recruiting transcription factors to promoters and enhancers. Inhibition of BET binding to chromatin leads to anti-inflammatory and anti-atherogenic effects in models of DM2 and CVD. Hypothesis: BET inhibition with apabetalone, a clinical-stage small molecule, reduces vascular inflammation in a mouse model of diet-induced obesity. Methods: From 8 to 30 weeks of age, C57 BL/6J mice were fed a high-fat (HFD) or low-fat diet (LFD). Mice received apabetalone at 150 mg/kg b.i.d between 14 and 30 weeks. Gene expression was analyzed post necropsy in the aorta by PCR, nCounter® Inflammation Panel and Ingenuity® Pathway Analysis. Human aortic endothelial cells (HAECs) treated with TNFα and apabetalone were analyzed by BRD4 ChIP-seq and RNA-seq to assess BRD4 chromatin occupancy and transcriptional changes. Results: Profiling of 254 genes in the aorta showed an upregulation of 27 inflammatory genes in HFD-fed mice as compared to LFD (p<0.05), including transcription factors Rela (22%), Hif1a (25%) and Tcf4 (44%), involved in inflammation, hypoxia and cell growth, respectively. Other upregulated genes mapped to cytokine, cytoskeleton, coagulation and complement pathways. Apabetalone reduced aortic mRNA expression of transcription factors Rela (12%), Nfkb1 (22%) and Tcf4 (15%), chemokines Ccl2 (47%), Ccl7 (49%) and Ccl8 (69%), leukocyte receptors Ccr2 (64%) and Itgam (29%) and endothelial receptors Sele (64%) and Icam1 (36%). Bioinformatics predicted enhanced signaling by TNFα in the HFD vs. LFD aorta, which was countered by apabetalone. In HAECs, apabetalone lowered gene expression and BRD4 binding to Rela , Hif1a and Tcf4 genes and prevented TNFα-mediated BRD4 accumulation in proximity of Ccl2 , Sele and Icam1 genes. Conclusions: HFD induces vascular inflammation in mice. Apabetalone treatment diminishes this proinflammatory phenotype, providing mechanistic insight into how BET inhibitors may reduce CVD risk in DM2 patients.
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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) | 0.001 | 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) | 0.004 | 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".