The Anti-Atherogenic Properties of Sesamin are Mediated via Improved Macrophage Cholesterol Efflux Through PPARγ1-LXRα and MAPK Signaling
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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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 | 0.000 | 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".