Transcriptomic responses to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) in liver: Comparison of rat and mouse
Bibliographic record
Abstract
BACKGROUND: Mouse and rat models are mainstays in pharmacology, toxicology and drug development -- but differences between strains and between species complicate data interpretation and application to human health. Dioxin-like polyhalogenated aromatic hydrocarbons represent a major class of environmentally and economically relevant toxicants. In mammals dioxin exposure leads to a broad spectrum of adverse affects, including hepatotoxicity of varying severity. Several studies have shown that dioxins extensively alter hepatic mRNA levels. Surprisingly, though, analysis of a limited portion of the transcriptome revealed that rat and mouse responses diverge greatly (Boverhof et al. Toxicol Sci 94:398-416, 2006). RESULTS: We employed oligonucleotide arrays to compare the response of 8,125 rat and mouse orthologs. We confirmed that there is limited inter-species overlap in dioxin-responsive genes. Rat-specific and mouse-specific genes are enriched for specific functional groups which differ between species, conceivably accounting for species-specificities in liver histopathology. While no evidence for the involvement of copy-number variation was found, extensive inter-species variation in the transcriptional-regulatory network was identified; Nr2f1 and Fos emerged as candidates to explain species-specific and species-independent responses, respectively. CONCLUSION: Our results suggest that a small core of genes is responsible for mediating the similar features of dioxin hepatotoxicity in rats and mice but non-overlapping pathways are simultaneously at play to result in distinctive histopathological outcomes. The extreme divergence between mouse and rat transcriptomic responses appears to reflect divergent transcriptional-regulatory networks. Taken together, these data suggest that both rat and mouse models should be used to screen the acute hepatotoxic effects of drugs and toxic compounds.
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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 | 0.001 | 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".