Regulation of 3‐methylindole metabolism by nuclear receptors
Bibliographic record
Abstract
Regulation of 3‐methylindole metabolism by nuclear receptors 3‐methylindole (3MI) is a known pneumotoxin that is found in naturally in fecal matter and is a major component of tobacco smoke; it is also a major component of boar taint, the accumulation of malodorous compounds in pigs that negatively impacts meat quality. We studied the effects transactivation of the constitutive androstane receptor (CAR), pregnane X receptor (PXR), and farnesoid X receptor (FXR) on gene expression and the metabolism of 3MI in porcine hepatocytes. Real‐time PCR was used to determine the expression of key genes in agonist treated porcine hepatocytes, while the production of major 3MI metabolites over time was quantified by HPLC. FXR transactivation significantly (p<0.05) increased the expression of CYP2E1, which is crucial to 3MI metabolism, 1.29 (1.19,1.40) fold over the untreated controls. PXR transactivation increased CYP2A19 expression 1.24 (1.14, 1.36) and CYP2C49 expression 4.51 (3.64, 5.57) fold; both of these enzymes have been shown to play a role in 3MI metabolism in pigs. FXR transactivation stimulated the formation of 6‐hydroxy‐3‐methylindole, a metabolite crucial in the 3MI excretion pathway, by 1.86 fold over the untreated controls. CAR and PXR transactivation did not affect overall 3MI metabolism or the formation of major metabolites. FXR may thus play an important role in regulating the excretion of 3MI, thus helping prevent toxicity, as well as improving meat quality in boars.
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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 | 0.001 |
| 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) | 0.002 | 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".