Curcumin prevents and reverses murine cardiac hypertrophy
Post-publication record
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Bibliographic record
Abstract
Chromatin remodeling, particularly histone acetylation, plays a critical role in the progression of pathological cardiac hypertrophy and heart failure. We hypothesized that curcumin, a natural polyphenolic compound abundant in the spice turmeric and a known suppressor of histone acetylation, would suppress cardiac hypertrophy through the disruption of p300 histone acetyltransferase-dependent (p300-HAT-dependent) transcriptional activation. We tested this hypothesis using primary cultured rat cardiac myocytes and fibroblasts as well as two well-established mouse models of cardiac hypertrophy. Curcumin blocked phenylephrin-induced (PE-induced) cardiac hypertrophy in vitro in a dose-dependent manner. Furthermore, curcumin both prevented and reversed mouse cardiac hypertrophy induced by aortic banding (AB) and PE infusion, as assessed by heart weight/BW and lung weight/BW ratios, echocardiographic parameters, and gene expression of hypertrophic markers. Further investigation demonstrated that curcumin abrogated histone acetylation, GATA4 acetylation, and DNA-binding activity through blocking p300-HAT activity. Curcumin also blocked AB-induced inflammation and fibrosis through disrupting p300-HAT-dependent signaling pathways. Our results indicate that curcumin has the potential to protect against cardiac hypertrophy, inflammation, and fibrosis through suppression of p300-HAT activity and downstream GATA4, NF-kappaB, and TGF-beta-Smad signaling pathways.
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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 | 0.001 | 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".