<i>Trans</i>-resveratrol modulates the catalytic activity and mRNA expression of the procarcinogen-activating human cytochrome P450 1B1
Bibliographic record
Abstract
The present study was performed to determine if trans-resveratrol (3,5,4'-trihydroxy-trans-stilbene) modulates the catalytic activity and gene expression of cytochrome P450 1B1 (CYP1B1). In vitro, trans-resveratrol decreased human recombinant CYP1B1-catalyzed 7-ethoxyresorufin O-dealkylation activity, with an IC50value of 1.4 ± 0.2 µM (mean ± SEM). Enzyme kinetic analysis indicated that trans-resveratrol inhibited CYP1B1 enzyme activity by a mixed-type inhibition and the apparent Kiwas 0.75 ± 0.06 µM. To determine if trans-resveratrol modulates constitutive CYP1B1 gene expression, cultured MCF-7 human breast carcinoma cells were treated with trans-resveratrol. As indicated by RT-PCR analysis, treatment of MCF-7 cells with 10 µM trans-resveratrol decreased relative CYP1B1 mRNA levels after 5 h, but not after 1.5 or 3 h, of exposure. trans-Resveratrol treatment at 5, 7.5, 10, or 20 µM for 5 h produced a concentration-dependent decrease in CYP1B1 mRNA levels. The extent of suppression was ~50% at 20 µM concentration. The suppressive effect was not a consequence of a toxic response to the compound as assessed by a cell proliferation assay. Overall, our novel finding that trans-resveratrol inhibits the catalytic activity and suppresses the constitutive gene expression of CYP1B1 leads to the possibility that this nutraceutical confers protection against toxicity and carcinogenicity induced by compounds that undergo CYP1B1-catalyzed bioactivation.Key words: cytochrome P450, CYP1B1, 7-ethoxyresorufin, nutraceutical, trans-resveratrol.
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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 | 0.000 |
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".