UGT1A1 polymorphisms are important determinants of dietary carcinogen detoxification in the liver†‡
Bibliographic record
Abstract
PhIP (2-amino-1-methyl-6-phenylimidazo[4,5-f]pyridine), the most abundant heterocyclic amine in diet, is involved in the etiology of cancer. PhIP and its carcinogenic metabolite N-hydroxy-PhIP (N-OH-PhIP) are extensively conjugated by UDP-glucuronosyltransferase (UGTs) with wide variability. This study aimed to determine the genetic influence of UGTs on the hepatic detoxification of this carcinogen. The formation of N-OH-PhIP glucuronides was studied in 48 human liver samples by mass spectrometry. Liver samples were genotyped for common polymorphisms and correlated with UGT protein levels and N-OH-PhIP glucuronidation activities. The formation of four different N-OH-PhIP glucuronide metabolites was observed in all livers. The major metabolite was N-OH-PhIP-N(2)-glucuronide (N(2)G), which is the primary metabolite found in human urine, and showed a high interindividual variability (up to 28-fold). Using an heterologous expression system, the bilirubin-conjugating UGT1A1 enzyme was identified among all known UGTs (n = 16) as the predominant enzyme involved. The significant correlation between UGT1A1 protein content and formation of N(2)G (Rs = 0.87; P < .0001) suggests a critical role for UGT1A1 in the hepatic metabolism of this carcinogen. UGT1A1 expression was strongly determined by the presence of the common promoter polymorphisms, UGT1A1*28 (TATA box polymorphism) (P = .0031), -3156G/A (P = .0006) and -3279G/T (P = .0017), and rates of N(2)G were indeed correlated with these polymorphisms (P < .05), whether analyzed individually or in combination (haplotypes). In conclusion, UGT1A1 polymorphisms modulate the hepatic metabolism of the carcinogenic intermediate of PhIP and may determine the level of its exposure and potentially influence the risk of cancer through dietary exposure to HCAs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".