Transcriptional Profiling of Dibenzo[<i>def,p</i>]chrysene-induced Spleen Atrophy Provides Mechanistic Insights into its Immunotoxicity in MutaMouse
Bibliographic record
Abstract
Dibenzo[def,p]chrysene (DBC) is the most carcinogenic polycyclic aromatic hydrocarbon (PAH) examined to date. We investigated the immunotoxicity of DBC, manifested as spleen atrophy, following acute exposure of adult MutaMouse males by oral gavage. Mice were exposed to 0, 2.0, 6.2, or 20.0 mg DBC /kg-bw per day, for 3 days. Genotoxic endpoints (DBC-DNA adducts and lacZ mutant frequency in spleen and bone marrow, and red blood cell micronucleus frequency) and global gene expression changes were measured. All of the genotoxicity measures increased in a dose-dependent manner in spleen and bone marrow. Gene expression analysis showed that DBC activates p53 signaling pathways related to cellular growth and proliferation, which was evident even at the low dose. Strikingly, the expression profiles of DBC exposed mouse spleens were highly inversely correlated with the expression profiles of the only published toxicogenomics dataset of enlarged mouse spleen. This analysis suggested a central role for Bnip3l, a pro-apoptotic protein involved in negative regulation of erythroid maturation. RT-PCR confirmed expression changes in several genes related to apoptosis, iron metabolism, and aryl hydrocarbon receptor signaling that are regulated in the opposite direction during spleen atrophy versus benzo[a]pyrene-mediated splenomegaly. In addition, benchmark dose modeling of toxicogenomics data yielded toxicity estimates that are very close to traditional toxicity endpoints. This work illustrates the power of toxicogenomics to reveal rich mechanistic information for immunotoxic compounds and its ability to provide information that is quantitatively similar to that derived from standard toxicity methods in health risk assessment.
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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".