PPAR<b><i>α</i></b>: A Master Regulator of Bilirubin Homeostasis
Bibliographic record
Abstract
Hypolipidemic fibrates activate the peroxisome proliferator-activated receptor (PPAR) α to modulate lipid oxidation and metabolism. The present study aimed at evaluating how 3 PPARα agonists, namely, fenofibrate, gemfibrozil, and Wy14,643, affect bilirubin synthesis and metabolism. Human umbilical vein epithelial cells (HUVEC) and coronary artery smooth muscle cells (CASMC) were cultured in the absence or presence of the 3 activators, and mRNA, protein, and/or activity levels of the bilirubin synthesizing heme oxygenase- (HO-) 1 and biliverdin reductase (BVR) enzymes were determined. Human hepatocytes (HH) and HepG2 cells sustained similar treatments, except that the expression of the bilirubin conjugating UDP-glucuronosyltransferase (UGT) 1A1 enzyme and multidrug resistance-associated protein (MRP) 2 transporter was analyzed. In HUVECs, gemfibrozil, fenofibrate, and Wy14,643 upregulated HO-1 mRNA expression without affecting BVR. Wy14,643 and fenofibrate also caused HO-1 protein accumulation, while gemfibrozil and fenofibrate favored the secretion of bilirubin in cell media. Similar positive regulations were also observed with the 3 PPARα ligands in CASMCs where HO-1 mRNA and protein levels were increased. In HH and HepG2 cells, both UGT1A1 and MRP2 transcripts were also accumulating. These observations indicate that PPARα ligands activate bilirubin synthesis in vascular cells and metabolism in liver cells. The clinical implications of these regulatory events are discussed.
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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".