Comparison of hepatic in vitro metabolism of the pyrrolizidine alkaloid senecionine in sheep and cattle
Bibliographic record
Abstract
OBJECTIVE: To compare hepatic metabolism of pyrrolizidine alkaloids (PAs) between sheep and cattle and elucidate the protective mechanism of sheep. SAMPLE POPULATION: Liver microsomes and cytosol from 8 sheep and 8 cattle. PROCEDURE: The PA senecionine, senecionine N-oxide (nontoxic metabolite) and 6,7-dihydro-7-hydroxy-1-hydroxymethyl-5H-pyrrolizine (DHP; toxic metabolite) were measured in microsomal incubations. The kcat (turnover number) was determined for DHP and N-oxide formation. Chemical and immunochemical inhibitors were used to assess the role of cytochrome P450s, flavin-containing monooxygenases (FMOs), and carboxylesterases in senecionine metabolism. The CYP3A, CYP2B, and FMO concentrations and activities were determined, in addition to the role of glutathione (GSH) in senecionine metabolism. RESULTS: DHP concentration did not differ between species. Sheep formed more N-oxide, had higher N-oxide kcat, and metabolized senecionine faster than cattle. The P450 concentrations and isoforms had a large influence on DHP formation, whereas FMOs had a large influence on N-oxide formation. In cattle, CYP3A played a larger role in DHP formation than in sheep. FMO activity was greater in sheep than in cattle. Addition of GSH to in vitro microsomal incubations decreased DHP formation; addition of cytosol decreased N-oxide formation. CONCLUSIONS AND CLINICAL RELEVANCE: Hepatic metabolism differences alone do not account for the variation in susceptibility seen between these species. Rather, increased ruminal metabolism in sheep appears to be an important protective mechanism, with hepatic enzymes providing a secondary means to degrade any PAs that are absorbed from the rumen.
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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".