The Anti-Atherogenic Properties of Sesamin are Mediated via Improved Macrophage Cholesterol Efflux Through PPARγ1-LXRα and MAPK Signaling
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Bibliographic record
Abstract
BACKGROUND: Foam cell formation resulting from disrupted macrophage cholesterol efflux, which is triggered by PPARγ1 and LXRα, is a hallmark of atherosclerosis. Sesamin and sesame oil exert anti-atherogenic effects in vivo. However, the exact molecular mechanisms underlying such effects are not fully understood. AIM: This study examines the potential effects of sesamin (0, 25, 50, 75, 100 μM) on PPARγ1 and LXRα expression and transcriptional activity as well as macrophage cholesterol efflux. METHODS: PPARγ1 and LXRα expression and transcriptional activity are assessed by luciferase reporter assays. Macrophage cholesterol efflux is evaluated by ApoAI-specific cholesterol efflux assays. RESULTS: The 50 μM, 75 μM, and 100 μM concentrations of sesamin up-regulated the expression of PPARγ1 (p<0.001, p<0.001, p<0.001, respectively) and LXRα (p=0.002, p<0.001, p<0.001, respectively) in a concentration-dependent manner. Moreover, 75 μM and 100 μM concentrations of sesamin led to 5.2-fold (p<0.001) and 6.0-fold (p<0.001) increases in PPAR transcriptional activity and 3.9-fold (p<0.001) and 4.2-fold (p<0.001) increases in LXR transcriptional activity, respectively, in a concentration- and time-dependent manner via MAPK signaling. Consistently, 50 μM, 75 μM, and 100 μM concentrations of sesamin improved macrophage cholesterol efflux by 2.7-fold (p<0.001), 4.2-fold (p<0.001), and 4.2-fold (p<0.001), respectively, via MAPK signaling. CONCLUSION: Our findings shed light on the molecular mechanism(s) underlying sesamins anti-atherogenic effects, which seem to be due, at least in part, to its ability to up-regulate PPARγ1 and LXRα expression and transcriptional activity, improving macrophage cholesterol efflux. We anticipate that sesamin may be used as a therapeutic agent for treating atherosclerosis.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 it