Bibliographic record
Abstract
We thank Dietrich and Geier for their comments and the opportunity for a deeper discussion of our work. We presented clear evidence that: (1) in human liver microsomes N-OH-PhIP is metabolized into glucuronides predominantly by UGT1A1, and (2) polymorphisms in this gene influence the carcinogen metabolism.1 We agree that no conclusions can be extrapolated regarding the general detoxification of carcinogens, and additional studies evidently are needed to extend our data to the in vivo situation regarding PhIP. Yet, we believe our study presented relevant information for genetic epidemiological studies aimed at identifying genes affecting an individual's susceptibility though dietary exposure, which was the primary aim of our study. We agree that changes in protein expression and activity at different levels of any of the components involved in the biotransformation and transport of PhIP are likely to influence exposure to N-OH-PhIP. In Fig. 1 of their letter, Dietrich and Geier draw our attention to the fact that an altered UGT1A-dependent conjugation of N-OH-PhIP would largely be compensated by other enzymes, such as UGT2 or sulfotransferases. However, this is supported by data obtained in rodents. Indeed, as elegantly demonstrated by our colleagues and others, sulfation is the major N-OH-PhIP–conjugating reaction occurring in rats,2, 3 instead of glucuronidation in humans.4-6 Consequently, alterations of UGT1A in rodents would only affect a small fraction of N-OH-PhIP metabolism, and this loss will be easily compensated by SULT enzymes.3 In addition, whereas UGT2 may play a significant role in N-OH-PhIP glucuronidation in rats,3 we and others1, 7 demonstrated that human UGT2 is not reactive with this substrate, and therefore cannot compensate for a loss of UGT1A. Thus, we emphasize the fact that mechanisms illustrated in Fig. 1 in the letter by Dietrich and Geier are valid for rodents only. After the publication of our work1 Malfatti et al. reported2 that an impaired UGT1A pathway leads to lower DNA adduct formation in the colon of rats. Lower formation of N-OH-PhIP glucuronides in the liver has the potential to decrease the exposure of the colon to N-OH-PhIP-N3-G. In fact, N-OH-PhIP-N3-G can be hydrolysed to N-OH-PhIP by bacterial β-glucuronidases and converted locally to reactive metabolites.2 As a result and consistent with our hypothesis, detoxification by UGT1A in the liver, a predominant site for PhIP metabolism via N3 glucuronidation in addition to N2G formation in humans,4 would be likely to influence the exposure to carcinogens in the intestine and the colon. Finally, we did not intend to confirm the work by Peters et al.8 but rather to concede our hypothesis. We agree that it is complex to compare the level of glucuronidation determined with human liver microsomes to the concentration of glucuronides found in human urine. However, despite the limited statistical power of Peters et al.'s study, it cannot be ignored that UGT1A1 polymorphisms were found to alter the mutagenicity of urinary dietary carcinogens in healthy volunteers. This observation strongly supports the hypothesis that in humans UGT genetic alteration influences the carcinogenic potential of PhIP exposure. Chantal Guillemette*, Hugo Girard*, * Canada Research Chair in Pharmacogenomics, Laval University – Pharmacy, Quebec, Canada.
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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 | 0.004 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.013 | 0.022 |
| Insufficient payload (model declined to judge) | 0.060 | 0.042 |
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".