Oxidized low-density lipoprotein and peroxisome-proliferator-activated receptor α down-regulate platelet-activating-factor receptor expression in human macrophages
Bibliographic record
Abstract
Regulation of the expression of platelet-activating factor (PAF) receptor by atherogenic lipoproteins might contribute to atherogenesis. We show that progressive oxidation of low-density lipoprotein (LDL) gradually inhibits PAF receptor expression on the macrophage cell surface. We tested the effect of oxidized LDL (oxLDL) on PAF receptor expression in human monocytes that do not contain peroxisome-proliferator-activated receptor gamma (PPARgamma), a nuclear receptor activated by oxLDL. OxLDL decreased by 50% (P < or = 0.001) and by 29% (P < or = 0.05) the binding of PAF and the expression of PAF receptor mRNA respectively. Next we demonstrated that progressive oxidation of LDLs significantly activated PPARalpha-dependent transcription in transfected mouse aortic endothelial cells. Finally we demonstrated, in mature macrophages, that fenofibrate (20 microM), a specific PPARalpha agonist, but not the specific PPARgamma agonist BRL49653 (20 nM), significantly decreased both PAF binding and PAF receptor mRNA expression, by 65% and 40% (P < or = 0.001) respectively. Additionally, another PPARalpha agonist, Wy14,643, decreased PAF receptor promoter activity by 70% (P < or = 0.05) in transfected THP-1 cells, suggesting the involvement of the proximal promoter region (-980 to -500) containing a series of four nuclear factor (NF)-kappaB motifs. Thus PPARalpha might be involved in the down-regulation of PAF receptor gene expression by oxLDLs in human monocytes/macrophages. The oxidation of one or more lipid components of LDLs might result in the formation of natural activators of PPARalpha. It is hypothesized that such activators might modulate inflammation and apoptosis upon atherogenesis by decreasing the expression of PAF receptor.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".