Regulation of liver fatty acid binding protein expression by clofibrate in hepatoma cells
Bibliographic record
Abstract
Peroxisome proliferator-activated receptor (PPAR) agonists such as clofibrate are known to affect liver fatty acid binding protein (L-FABP) levels, which in turn influence hepatocellular oxidant status. The mechanism of clofibrate's modulation of L-FABP levels is not clear. In this study we used clofibrate (PPARα agonist), MK886 (PPARα antagonist), and GW9662 (PPARγ antagonist) in determining the regulating mechanism of L-FABP expression and its antioxidant activity in CRL-1548 hepatoma cells. Antioxidant activity was assessed by determining intracellular reactive oxygen species (ROS) using dichlorofluorescein (DCF) fluorescence. The effect of clofibrate on cytosolic activity of the intracellular antioxidant enzymes was also assessed. RT-PCR and mRNA stability assay showed that clofibrate treatment enhanced L-FABP mRNA stability, which resulted in increased L-FABP levels. A nuclear run-off assay and RT-PCR measurements of L-FABP mRNA revealed that clofibrate increased the L-FABP gene transcription rate. The increased L-FABP was associated with reduced cytosolic ROS. Levels of superoxide dismutase, glutathione peroxidase, and catalase were not affected by clofibrate treatment. L-FABP siRNA knockdown studies showed that a reduction in L-FABP expression was associated with increased DCF fluorescence. We conclude that clofibrate enhanced L-FABP gene transcription and mRNA stability, thus affecting L-FABP expression and ultimately cellular antioxidant activity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".