Effects of Mixtures of Polychlorinated Biphenyls, Methylmercury, and Organochlorine Pesticides on Hepatic DNA Methylation in Prepubertal Female Sprague-Dawley Rats
Bibliographic record
Abstract
DNA methylation is one of the epigenetic mechanisms that regulates gene expression, chromosome structure, and stability. Our objective was to determine whether the DNA methylation system could be a target following in utero and postnatal exposure to human blood contaminants. Pregnant rats were dosed daily from gestation day 1 until postnatal day 21 with 2 dose levels of either organochlorine pesticides (OCP; 0.019 or 1.9 mg/kg/day), methylmercury chloride (MeHg; 0.02 or 2 mg/kg/day), polychlorinated biphenyls (PCBs; 0.011 or 1.1 mg/kg/day), or a mixture (Mix; 0.05, or 5 mg/kg/day) including all 3 groups of chemicals. Livers from 1 female offspring per litter were collected at postnatal day 29. Hepatic analysis revealed that the mRNA abundance for DNA methyltransferase (DNMT)-1, -3a, and -3b were significantly reduced by the high dose of PCB, that the high dose of MeHg also reduced mRNA levels for DNMT-1, and -3b, but that OCP had no significant effects compared with control. The high dose of PCB and Mix reduced the abundance of the universal methyl donor S-adenosylmethionine, and Mix also reduced global genome DNA methylation (5-methyl-deoxycytidine/5-methyl-deoxycytidine + deoxycytidine). The latter is consistent with pyrosequencing methylation analysis, revealing that the high-dose groups (except OCP) generally decreased the methylation of CpG sites (position -63 to -29) in the promoter of the tumor suppressor gene p16(INK4a). Overall, these hepatic results suggest that the DNA methylation system can be affected by exposure to high doses of blood contaminants, and that OCP is the least potent chemical group from the investigated mixtures.
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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) | 0.000 | 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".