Germ-line mutations, DNA damage, and global hypermethylation in mice exposed to particulate air pollution in an urban/industrial location
Bibliographic record
Abstract
Particulate air pollution is widespread, yet we have little understanding of the long-term health implications associated with exposure. We investigated DNA damage, mutation, and methylation in gametes of male mice exposed to particulate air pollution in an industrial/urban environment. C57BL/CBA mice were exposed in situ to ambient air near two integrated steel mills and a major highway, alongside control mice breathing high-efficiency air particulate (HEPA) filtered ambient air. PCR analysis of an expanded simple tandem repeat (ESTR) locus revealed a 1.6-fold increase in sperm mutation frequency in mice exposed to ambient air for 10 wks, followed by a 6-wk break, compared with HEPA-filtered air, indicating that mutations were induced in spermatogonial stem cells. DNA collected after 3 or 10 wks of exposure did not exhibit increased mutation frequency. Bulky DNA adducts were below the detection threshold in testes samples, suggesting that DNA reactive chemicals do not reach the germ line and cause ESTR mutation. In contrast, DNA strand breaks were elevated at 3 and 10 wks, possibly resulting from oxidative stress arising from exposure to particles and associated airborne pollutants. Sperm DNA was hypermethylated in mice breathing ambient relative to HEPA-filtered air and this change persisted following removal from the environmental exposure. Increased germ-line DNA mutation frequencies may cause population-level changes in genetic composition and disease. Changes in methylation can have widespread repercussions for chromatin structure, gene expression and genome stability. Potential health effects warrant extensive further investigation.
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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 | 0.001 | 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 | 0.001 |
| 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".