Mutual in vivo interactions between benzo[<i>a</i>]pyrene and tributyltin in brook trout (<i>Salvelinus fontinalis</i>)
Bibliographic record
Abstract
Abstract Tributyltin (TBT), an organometal used as an antifouling biocide, has been reported to inhibit cytochrome P450 (P450) 1A isozyme. Benzo[a]pyrene (BaP), a widespread carcinogenic polycyclic aromatic hydrocarbon, is both metabolized and bioac-tivated to carcinogenic BaP diol-epoxide (BPDE) metabolites primarily by hepatic P450 1A. Hence, TBT may inhibit the metabolism and bioactivation of BaP This study was therefore designed to examine the potential in vivo interactions between BaP and TBT in a model fish. Male brook trout (Salvelinus fontinalis) were given a single intraperitoneal injection of either BaP (10 mg/kg), TBT (10 mg/kg), or both in combination. After 48 h, blood, bile, and liver samples were collected and analyzed for a suite of biomarkers associated with P450 activity, BaP metabolism and bioactivation, and TBT metabolism. The results showed that TBT significantly (p &lt; 0.05; two-way analysis of variance) inhibited (a) the induction of hepatic P450 1A-mediated ethoxyresorufin O-deethylase (EROD) and P450-mediated 3-cyano-7-ethoxycoumarin-O-deethylase (CN-ECOD) activities by BaP, (b) the formation of biliary BaP metabolites, and (c) the formation of (+)-anti-BPDE-plasma albumin adducts as measured by high-performance liquid chromatography/fluorescence. Notably, TBT alone did not inhibit EROD activity but induced CN-ECOD activity (p &lt; 0.05). Gas chromatography/mass spectrometry analysis indicated that the combined BaP + TBT dose resulted in higher levels of dibutyltin metabolites in the bile (p &lt; 0.05). The present study supports the hypothesis that a single, high dose of TBT can antagonize the metabolism and bioactivation of BaP at least by inhibiting the induction of P4501A. On the other hand, BaP unexpectedly potentiated the metabolism of TBT, suggesting that hepatic isoforms other than P4501A may be responsible for TBT metabolism. Finally, this study supports the utility of a biomarker approach to screen potential xenobiotic interactions in aquatic organisms and to obtain mechanistic insights.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.003 | 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 teacher head, 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".