Defining Molecular Sensors to Assess Long-Term Effects of Pesticides on Carcinogenesis
Bibliographic record
Abstract
The abundance of dioxins and dioxin-like pollutants has massively increased in the environment due to human activity. These chemicals are particularly persistent and accumulate in the food chain, which raises major concerns regarding long-term exposure to human health. Most dioxin-like pollutants activate the aryl hydrocarbon receptor (AhR) transcription factor, which regulates xenobiotic metabolism enzymes that belong to the cytochrome P450 1A family (that includes CYP1A1 and CYP1B1). Importantly, a crosstalk exists between estrogen receptor α (ERα) and AhR. More specifically, ERα represses the expression of the CYP1A1 gene, which encodes an enzyme that converts 17β-estradiol into 2-hydroxyestradiol. However, (ERα) does not repress the CYP1B1 gene, which encodes an enzyme that converts 17β-estradiol into 4-hydroxyestradiol, one of the most genotoxic estrogen metabolites. In this review, we discuss how chronic exposure to xenobiotic chemicals, such as pesticides, might affect the expression of genes regulated by the AhR-ERα crosstalk. Here, we focus on recent advances in the understanding of molecular mechanisms that mediate this crosstalk repression, and particularly on how ERα represses the AhR target gene CYP1A1, and could subsequently promote breast cancer. Finally, we propose that genes implicated in this crosstalk could constitute important biomarkers to assess long-term effects of pesticides on human health.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".