Regulatory Mechanisms Modulating the Expression of Cytochrome P450 1A1 Gene by Heavy Metals
Bibliographic record
Abstract
We recently demonstrated that heavy metals, Hg2+, Pb2+, and Cu2+ induced Cyp1a1 gene expression, yet the mechanisms involved remain unknown. To explore the molecular mechanisms involved in the modulation of Cyp1a1 by heavy metals, Hepa 1c1c7 cells were treated with the metals in the presence and absence of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), a potent Cyp1a1 inducer. Time-dependent effect study showed that all metals significantly induced the basal Cyp1a1 mRNA. This was apparent 3 h after treatment, and levels remained elevated for at least 24 h. At the inducible level, Hg2+ and Pb2+ further increased, while Cu2+ decreased, the TCDD-mediated induction of Cyp1a1 mRNA. The RNA synthesis inhibitor, actinomycin D, completely blocked the Cyp1a1 induction by heavy metals. The protein synthesis inhibitor, cycloheximide, and 26S proteasome inhibitor, carbobenzoxy-L-leucyl-L-leucyl-leucinal (MG-132), super-induced the metal-mediated induction of Cyp1a1 mRNA. In addition, all three metals induced aryl hydrocarbon receptor/xenobiotic-responsive element (AhR/XRE) binding, suggesting an AhR-dependent mechanism. Cyp1a1 mRNA and protein decay experiments showed that the three metals did not significantly affect the half-life of mRNA; however, they significantly decreased the degradation rate of its protein, implying a posttranslational regulation of the Cyp1a1 by the heavy metals. A significant decrease in TCDD-mediated induction of Cyp1a1 activity associated with an increase in HO-1 mRNA and a decrease in cellular heme content was observed after all metals treatment. This suggests that heme degradation plays a role in reducing Cyp1a1 activity. This is the first demonstration that heavy metals can directly induce Cyp1a1 gene expression in an AhR-dependent manner through transcriptional and posttranslational mechanisms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".