Abnormal Ovarian DNA Methylation Programming during Gonad Maturation in Wild Contaminated Fish
Bibliographic record
Abstract
There is increasing evidence that pollutants may cause diseases via epigenetic modifications. Epigenetic mechanisms such as DNA methylation participate in the regulation of gene transcription. Surprisingly, epigenetics research is still limited in ecotoxicology. In this study, we investigated whether chronic exposure to contaminants experienced by wild female fish (Anguilla anguilla) throughout their juvenile phase can affect the DNA methylation status of their oocytes during gonad maturation. Thus, fish were sampled in two locations presenting a low or a high contamination level. Then, fish were transferred to the laboratory and artificially matured. Before hormonal treatment, the DNA methylation levels of the genes encoding for the aromatase and the receptor of the follicle stimulating hormone were higher in contaminated fish than in fish from the clean site. For the hormone receptor, this hypermethylation was positively correlated with the contamination level of fish and was associated with a decrease in its transcription level. In addition, whereas gonad growth was associated with an increase in DNA methylation in fish from the clean site, no changes were observed in contaminated fish in response to hormonal treatment. Finally, a higher gonad growth was observed in fish from the reference site in comparison to contaminated fish.
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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".