Resveratrol Targeting of Carcinogen-Induced Brain Endothelial Cell Inflammation Biomarkers MMP-9 and COX-2 is Sirt1-Independent
Bibliographic record
Abstract
The occurrence of a functional relationship between the release of metalloproteinases (MMPs) and the expression of cyclooxygenase (COX)-2, two inducible pro-inflammatory biomarkers with important pro-angiogenic effects, has recently been inferred. While brain endothelial cells play an essential role as structural and functional components of the blood-brain barrier (BBB), increased BBB breakdown is thought to be linked to neuroinflammation. Chemopreventive mechanisms targeting both MMPs and COX-2 however remain poorly investigated. In this study, we evaluated the pharmacological targeting of Sirt1 by the diet-derived and antiinflammatory polyphenol resveratrol. Total RNA, cell lysates, and conditioned culture media from human brain microvascular endothelial cells (HBMEC) were analyzed using qRT-PCR, immunoblotting, and zymography respectively. Tissue scan microarray analysis of grade I-IV brain tumours cDNA revealed increased gene expression of Sirt-1 from grade I-III but surprisingly not in grade IV brain tumours. HBMEC were treated with a combination of resveratrol and phorbol 12-myristate 13-acetate (PMA), a carcinogen known to increase MMP-9 and COX-2 through NF-κB. We found that resveratrol efficiently reversed the PMA-induced MMP-9 secretion and COX-2 expression. Gene silencing of Sirt1, a critical modulator of angiogenesis and putative target of resveratrol, did not lead to significant reversal of MMP-9 and COX-2 inhibition. Decreased resveratrol inhibitory potential of carcinogen-induced IκB phosphorylation in siSirt1-transfected HBMEC was however observed. Our results suggest that resveratrol may prevent BBB disruption during neuroinflammation by inhibiting MMP-9 and COX-2 and act as a pharmacological NF-κB signal transduction inhibitor independent of Sirt1.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".