Sirt1 Inhibits Resistin Expression in Aortic Stenosis
Bibliographic record
Abstract
The development of human calcified aortic stenosis (AS) includes age-dependent processes that have been involved in atherosclerosis, such as infiltration of macrophages in aortic valves, which then promote production of many pro-inflammatory cytokines, including resistin. However, the molecular mechanisms contributing to these processes are not established. Since Sirt1 has been shown to modulate macrophage biology and inflammation, we examined its levels in human AS and tested its impact on resistin expression. Sirt1 mRNA (p = 0.01) and protein (p<0.05) levels were reduced in explanted valves from AS patients (n = 51) compared to those from control (n = 11) patients. Sirt1 mRNA levels were negatively associated with resistin mRNA levels quantified in AS valves (p = 0.02). Stimulation of Sirt1 by resveratrol or virus-driven overexpression robustly diminished resistin mRNA and protein expression in macrophages, whereas down-regulation of Sirt1 triggered a large increase in resistin expression. These effects were direct, as chromatin immunoprecipitation assays showed that Sirt1 physically interacted with the resistin promoter region at an AP-1 response element. Moreover, Sirt1 blocked c-jun-induced resistin transactivation in gene reporter assays. These findings demonstrate that, in calcified AS, levels of Sirt1 are reduced whereas those of resistin are increased within aortic valve leaflets. Our results also suggest that this loss of Sirt1 expression alleviates its inhibition of resistin transcription in macrophages. Although the overall contribution of this process to the underlying mechanisms for AS disease development remains unresolved, these observations suggest that modification of Sirt1 expression and/or activity could represent a novel approach against inflammation in AS.
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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) | 0.001 | 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) | 0.002 | 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".