Oxoguanine glycosylase 1 (OGG1) protects cells from DNA double‐strand break damage following methylmercury (MeHg) exposure
Bibliographic record
Abstract
MeHg is a potent neurotoxin, teratogen and probable carcinogen, but the underlying mechanisms remain unclear. Although MeHg causes several types of DNA damage, the toxicological consequences of this macromolecular damage are unknown. MeHg also enhances oxidative stress, which can cause various oxidative DNA lesions that are primarily repaired by OGG1. Thus, we investigated the potential protective role of OGG1 in MeHg‐induced DNA damage using OGG1‐deficient ( Ogg1 −/− ) murine embryonic fibroblasts ( MEFs ) with impaired capacity to repair oxidatively damaged DNA. The response of wild‐type ( WT ) and Ogg1 −/− MEFs to low micromolar concentrations of MeHg was compared by measuring clonogenic efficiency, cell cycle arrest and generation of DNA double‐strand breaks. Ogg1 −/− cells exhibited greater sensitivity to MeHg than WT controls as measured by the clonogenic assay. Both WT and Ogg1 −/− cells underwent cell cycle arrest when exposed to MeHg; however, double‐strand break induction was exacerbated in Ogg1 −/− cells compared to WT controls. Thus, impaired DNA repair capacity enhances cellular sensitivity to MeHg, indicating that the genotoxic properties of MeHg may contribute to its neurotoxic effects, and that an individual's response to DNA damage may be a determinant of risk. Support: CIHR doctoral scholarship (SLO); Cancer Research Society & Province of Ontario Early Researcher Award (JPM); CIHR (PGW).
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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 | 0.000 |
| 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".