Aryl hydrocarbon receptor-dependence of dioxin’s effects on constitutive mouse hepatic cytochromes P450 and growth hormone signaling components
Bibliographic record
Abstract
The aryl hydrocarbon receptor (AHR) has physiological roles in the absence of exposure to exogenous ligands, and mediates adaptive and toxic responses to the environmental pollutant 2,3,7,8-tetracholorodibenzo-p-dioxin (TCDD). A readily metabolized AHR agonist, 3-methylcholanthrene, disrupts the expression of mouse hepatic growth hormone (GH) signaling components and suppresses cytochrome P450 2D9 (Cyp2d9), a male-specific gene controlled by pulsatile GH via signal transducer and activator of transcription 5b (STAT5b). Using TCDD as an essentially nonmetabolized AHR agonist, and Ahr (-/-) mice as the preferred model to determine the AHR-dependence of biological responses, we now show that 2 mouse hepatic STAT5b target genes, Cyp2d9, and major urinary protein 2 (Mup2), are suppressed by TCDD in an AHR-dependent manner. TCDD also decreased hepatic mRNA levels for GH receptor, Janus kinase 2, and STAT5a/b with AHR-dependence. Without inducing selected hepatic inflammatory markers, TCDD caused AHR-dependent induction of Cyp1a1 and NADPH-cytochrome P450 oxidoreductase (Por) and suppression of Cyp3a11. In vehicle-treated mice, basal mRNA levels for CYP2D9, CYP3A11, POR, serum amyloid protein P, and MUP2 were influenced by Ahr genetic status. We conclude that AHR activation per se leads to dysregulation of hepatic GH signaling components and suppression of some, but not all, STAT5b target genes.
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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 | 0.001 |
| 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".