The roles of different porcine cytochrome P450 enzymes and cytochrome b5A in skatole metabolism
Bibliographic record
Abstract
Boar taint is the unfavourable odour and taste from pork fat, which results in part from the accumulation of skatole (3-methylindole, 3MI). The key enzymes in skatole metabolism are thought to be cytochrome P450 2E1 (CYP2E1) and cytochrome 2A (CYP2A); however, the cytochrome P450 (CYP450) isoform responsible for the production of the metabolite 6-hydroxy-3-methylindole (6-OH-3MI, 6-hydroxyskatole), which is thought to be involved in the clearance of skatole, has not been established conclusively. The aim of this study was to characterize the role of porcine CYP450s in skatole metabolism by expressing them individually in the human embryonic kidney HEK293-FT cell line. This system eliminates the problems of the lack of specificity of antibodies, inhibitors and substrates for CYP450 isoforms in the pig, and contributions of any other CYP450s that would be present. The results show that pig CYP1A1, CYP2A19, CYP2C33v4, CYP2C49, CYP2E1 and CYP3A and human CYP2E1 (hCYP2E1) are all capable of producing the major skatole metabolite 3-methyloxyindole (3MOI), as well as indole-3-carbinol (I3C), 5-hydroxy-3-methylindole (5-OH-3MI), 6-OH-3MI, 2-aminoacetophenone (2AAP) and 3-hydroxy-3-methyloxindole. CYP2A19 produced the highest amount of the physiologically important metabolite 6-OH-3MI, followed by porcine CYP2E1 and CYP2C49; CYP2A19 also produced more 6-OH-3MI than hCYP2E1. Co-transfection with CYB5A increased the production of skatole metabolites by some of the CYP450s, suggesting that CYB5A plays an important role in the metabolism of skatole. We also show the utility of this expression system to check the specificity of selected substrates and antibodies for porcine CYP450s. Further information regarding the abundance of different CYP450 isoforms is required to fully understand their contribution to skatole metabolism in vivo in the pig.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".