Decreased Nuclear Receptor Activity Mediates Down‐ Regulation of Drug Metabolizing Enzymes in Chronic Kidney Disease Through Epigenetic Modulation.
Bibliographic record
Abstract
Objective To determine the mechanism of hepatic drug metabolizing enzyme down‐regulation in chronic kidney disease (CKD). Methods Hepatic CYP3A1, CYP3A2 and CYP2C11 mRNA expression were determined in rats with surgically induced CKD. Chromatin Immunoprecipitation (ChIP) was performed to determine nuclear receptor and epigenetic mediated differences in the promoter region of these enzymes. Results Hepatic CYP3A and CYP2C11 mRNA expression was significantly decreased in CKD rats compared to controls (P<0.05). RNA polymerase II binding to the CYP3A and CYP2C11 promoter regions was decreased in CKD rats (P<0.05). ChIP also revealed a decreased PXR binding to the CYP3A2 promoter in CKD rats (P<0.05). HNF4α binding to the CYP3A and CYP2C11 promoter regions was also decreased compared to controls (P<0.05). The decrease in PXR and HNF4α binding was concurrent with diminished histone 4 acetylation in the CYP3A2 promoter locus for nuclear receptor activation. Conclusions We demonstrate a novel mechanism of drug metabolizing enzyme regulation in CKD. Our results show that decreased CYP3A and CYP2C11 mRNA expression is secondary to decreased PXR and HNF4α binding as a result of histone modulation in CKD. This research is supported by the Natural Sciences and Engineering Research Council.
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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".