A teleost in vitro reporter gene assay to screen for agonists of the peroxisome proliferator-activated receptors
Bibliographic record
Abstract
Several contaminants detected in aquatic ecosystems are agonists of peroxisome proliferator-activated receptors (PPARs). Peroxisome proliferator-activated receptors interact with the retinoid X receptor (RXR) to activate the transcription of genes that control a variety of physiological functions. We cloned and sequenced partial cDNA fragments of rainbow trout (Oncorhynchus mykiss) PPARalpha and PPARbeta from rainbow trout (rt) gill-W1 cells, a cell line derived from rainbow trout gills; predicted amino acid identities are 77% and 82% compared with their respective human homologs and 83 to 88% and 91 to 98% identical to fish homologs. A reporter gene assay was developed by transfecting rt-gill-W1 cells with a reporter gene construct containing the peroxisome proliferator response element (PPRE) of the rat liver 3-ketoacyl-CoA thiolase B (TB) gene, which drives luciferase expression. Agonists of both PPARalpha (WY14,643 and gemfibrozil) and PPARbeta (bezafibrate) induced luciferase activity, while rosiglitazone, a PPARgamma agonist, was not effective. The fibrate drug, bezafibrate increased luciferase activity in a dose-dependent manner, but addition of 50 nM 9-cis-retinoic acid to the transfected rt-gill-W1 cell culture maximized the sensitivity of the assay so that bezafibrate could be detected at concentrations as low as 6 nM. Extracts from treated domestic wastewater containing fibrate drugs induced luciferase activity in the transfected gill cells. This in vitro reporter gene assay shows promise as a rapid and sensitive technique for screening environmental samples for PPAR-active substances.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".