2,3,7,8 Tetrachlorodibenzo-p-dioxin-induced RNA abundance changes identify Ackr3, Col18a1, Cyb5a and Glud1 as candidate mediators of toxicity
Bibliographic record
Abstract
2,3,7,8 Tetrachlorodibenzo-p-dioxin (TCDD) is an aromatic, long-lived environmental contaminant. While the pathogenesis of TCDD-induced toxicity is poorly understood, it has been shown that the aryl hydrocarbon receptor (AHR) is required. However, the specific transcriptomic changes that lead to toxic outcomes have not yet been identified. We previously identified a panel of 33 genes that respond to TCDD treatment in two TCDD-sensitive rodent species. To identify genes involved in the onset of hepatic toxicity, we explored 25 of these in-depth using liver from two rat strains: the TCDD-resistant Han/Wistar (H/W) and the TCDD-sensitive Long-Evans (L-E). Time course and dose-response analyses of mRNA abundance following TCDD insult indicate that eight genes are similarly regulated in livers of both strains of rat, suggesting that they are not central to the severe L-E-specific TCDD-induced toxicities. The remaining 17 genes exhibited various divergent mRNA abundances between L-E and H/W strains after TCDD treatment. Several genes displayed a biphasic response where the initial response to TCDD treatment was followed by a secondary response, usually of larger magnitude in L-E liver. This secondary response was most often an exaggeration of the original TCDD-induced response. Only cytochrome b5 type A (microsomal) (Cyb5a) had equivalent TCDD sensitivity to the prototypic AHR-responsive cytochrome P450, family 1, subfamily a, polypeptide 1 (Cyp1a1), while six genes were less sensitive. Four genes showed an early inter-strain difference that was sustained throughout most of the time course (atypical chemokine receptor 3 (Ackr3), collagen, type XVIII, alpha 1 (Col18a1), Cyb5a and glutamate dehydrogenase 1 (Glud1)), and of those genes examined in this study, are most likely to represent genes involved in the pathogenesis of TCDD-induced hepatotoxicity in L-E rats.
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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".