Quantitative relationships between<i>lacZ</i>mutant frequency and DNA adduct frequency in Muta™Mouse tissues and cultured cells exposed to 3-nitrobenzanthrone
Bibliographic record
Abstract
The frequency of stable DNA adducts in a target tissue can be used to assess biologically effective dose; however, the utility of the metric in a risk assessment context depends on the likelihood that the DNA damage will be manifested as mutation. Previously, we employed the Muta™Mouse system to examine the induction of lacZ mutants and DNA adducts following exposure to the well-studied mutagenic carcinogen 3-nitrobenzanthrone (3-NBA). In this follow-up work, we examined the empirical relationships between total adduct frequency and mutant frequency (MF) in tissues and cultured cells following acute 3-NBA exposure. The results show a significant induction of DNA damage and lacZ mutants in liver, colon and bone marrow, as well as FE1 pulmonary epithelial cells. In contrast, lung and small intestine samples had low, but significantly elevated adduct levels, with no significant increases in lacZ MF. Additional analyses showed a significant relationship between the mutagenic efficiency of total adducts, measured as the slope of the relationships between MF and total adduct frequency, and tissue-specific mitotic index (MI). The lack of mutation response in lung, in contrast to the high in vitro MF in FE-1 lung cells, is likely related to the 100-fold difference in MI. The lack of small intestine mutagenic response may be related to limited metabolic capacity, differences in DNA repair, and /or chemically induced apoptosis that has been observed for other potent mutagens. The results indicate that interpretation of adduct frequency values in a risk assessment context can be improved by considering the MI of the target tissue; however, more generalised interpretation is hampered by tissue-specific variations in metabolic capacity and damage processing. The work provides a proof of principle regarding the use of the Muta™Mouse system to critically examine the health risks associated with tissue-specific adduct loads.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".