Differential effects of silver nanoparticles on DNA damage and DNA repair gene expression in Ogg1-deficient and wild type mice
Bibliographic record
Abstract
Due to extensive use in consumer goods, it is important to understand the genotoxicity of silver nanoparticles (AgNPs) and identify susceptible populations. 8-Oxoguanine DNA glycosylase 1 (OGG1) excises 8-oxo-7,8-dihydro-2-deoxyguanine (8-oxoG), a pro-mutagenic lesion induced by oxidative stress. To understand whether defects in OGG1 is a possible genetic factor increasing an individual's susceptibly to AgNPs, we determined DNA damage, genome rearrangements, and expression of DNA repair genes in Ogg1-deficient and wild type mice exposed orally to 4 mg/kg of citrate-coated AgNPs over a period of 7 d. DNA damage was examined at 3 and 7 d of exposure and 7 and 14 d post-exposure. AgNPs induced 8-oxoG, double strand breaks (DSBs), chromosomal damage, and DNA deletions in both genotypes. However, 8-oxoG was induced earlier in Ogg1-deficient mice and 8-oxoG levels were higher after 7-d treatment and persisted longer after exposure termination. AgNPs downregulated DNA glycosylases Ogg1, Neil1, and Neil2 in wild type mice, but upregulated Myh, Neil1, and Neil2 glycosylases in Ogg1-deficient mice. Neil1 and Neil2 can repair 8-oxoG. Thus, AgNP-mediated downregulation of DNA glycosylases in wild type mice may contribute to genotoxicity, while upregulation thereof in Ogg1-deficient mice could serve as an adaptive response to AgNP-induced DNA damage. However, our data show that Ogg1 is indispensable for the efficient repair of AgNP-induced damage. In summary, citrate-coated AgNPs are genotoxic in both genotypes and Ogg1 deficiency exacerbates the effect. These data suggest that humans with genetic polymorphisms and mutations in OGG1 may have increased susceptibility to AgNP-mediated DNA damage.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".