Comparative transcriptomic analyses to scrutinize the assumption that genotoxic PAHs exert effects via a common mode of action
Bibliographic record
Abstract
In this study, the accuracy of the assumption that genotoxic, carcinogenic polycyclic aromatic hydrocarbons (PAHs) act via similar mechanisms of action as benzo(a)pyrene (BaP), the reference PAH used in the human health risk assessment of PAH-containing complex mixtures, was investigated. Adult male Muta™Mouse were gavaged for 28 days with seven individual, genotoxic PAHs. Global gene expression profiles in forestomach, liver, and lung (target tissues of exposure) were determined at 3 days post-exposure. The results are compared with our previously published results from mice exposed to BaP via the same exposure regimen. Although all PAHs showed enhanced ethoxyresorufin-O-deethylase activity, DNA adduct formation, and lacZ mutant frequency in the lungs, the unsupervised cluster analysis of differentially expressed genes revealed that the transcriptional changes are both PAH- and tissue-specific, with lung showing the most response. Further bioinformatics-/pathway-based analysis revealed that all PAHs induce expression of genes associated with carcinogenic processes, including DNA damage response, immune/inflammatory response, or cell signaling processes; however, the type of pathways and the magnitude of change varied for each PAH and were not the same as those observed for BaP. Benchmark dose modeling showed transcriptomic data closely reflected the known tumor incidence for the individual PAHs in each tissue. Collectively, the results suggest that the underlying mechanisms of PAH-induced toxicity leading to tumorigenesis are tissue-specific and not the same for all PAHs; based on the tissue type considered, use of BaP as a reference chemical may overestimate or underestimate the carcinogenic potential of PAHs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".