Mgmt deficiency alters the in vivo mutational spectrum of tissues exposed to the tobacco carcinogen 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK)
Bibliographic record
Abstract
It has been proposed that O(6)-methylguanine DNA methyltransferase (MGMT) gene silencing in premalignant lesions and cancers of the lung might result in the acquisition of a 'mutator' phenotype. Previously, however, we found that Mgmt(-/-) mouse DNA failed to show an increase in spontaneous mutations. We thus hypothesized that only during exposure to specific environmental carcinogens would the consequences of MGMT deficiency become evident. Metabolism of the tobacco-derived nitrosamine, 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) generates alkylating species that can react with the O(6) position of deoxyguanine, thereby yielding substrates for MGMT-mediated repair. To investigate how MGMT might regulate the mutational effects of NNK, Mgmt(-/-) mice were crossed with a lacI-based transgenic reporter line (Big Blue) thus enabling an assessment of the in vivo mutagenic effects of this agent. We observed the induction of a complex spectrum of NNK-dependent lacI mutations in both control and Mgmt(-/-) tissues, but only a trend in the mutant frequency increases that could be attributed to MGMT deficiency. The mutational spectra of NNK-treated Mgmt(-/-) lungs revealed an increase in the absolute number of G:C to A:T changes accompanied by a shift in these from CpG to GpG sites, consistent with an S(N)1 alkylation mechanism. In keeping with the high levels of MGMT expressed in the liver, more pronounced mutagenic effects and greater differences in O(6) position of deoxyguanosine adduct levels following NNK were observed in Mgmt(-/-) versus wild-type mice. Extrapolating to humans, MGMT-deficient cells would likely exhibit an increased mutational burden, but only following exposures to specific environmental mutagens such as NNK.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".