Mechanisms linked to differences in the mutagenic potential of 1,3-dinitropyrene and 1,8-dinitropyrene
Bibliographic record
Abstract
This study explores and characterizes the toxicity of two closely related carcinogenic dinitro-pyrenes (DNPs), 1,3-DNP and 1,8-DNP, in human bronchial epithelial BEAS-2B cells and mouse hepatoma Hepa1c1c7 cells. Neither 1,3-DNP nor 1,8-DNP (3–30 μM) induced cell death in BEAS-2B cells. In Hepa1c1c7 cells only 1,3-DNP (10–30 μM) induced a mixture of apoptotic and necrotic cell death after 24 h. Both compounds increased the level of reactive oxygen species (ROS) in BEAS-2B as measured by CM-H2DCFDA-fluorescence. A corresponding increase in oxidative damage to DNA was revealed by the formamidopyrimidine-DNA glycosylase (fpg)-modified comet assay. Without fpg, DNP-induced DNA damage detected by the comet assay was only found in Hepa1c1c7 cells. Only 1,8-DNP formed DNA adduct measured by 32P-postlabelling. In Hepa1c1c cells, 1,8-DNP induced phosphorylation of H2AX (γH2AX) and p53 at a lower concentration than 1,3-DNP and there was no direct correlation between DNA damage/DNA damage response (DR) and induced cytotoxicity. On the other hand, 1,3-DNP-induced apoptosis was inhibited by pifithrin-α, an inhibitor of p53 transcriptional activity. Furthermore, 1,3-DNP triggered an unfolded protein response (UPR), as measured by an increased expression of CHOP, ATF4 and XBP1. Thus, other types of damage possibly linked to endoplasmic reticulum (ER)-stress and/or UPR could be involved in the induced apoptosis. Our results suggest that the stronger carcinogenic potency of 1,8-DNP compared to 1,3-DNP is linked to its higher genotoxic effects. This in combination with its lower potency to induce cell death may increase the probability of causing mutations.
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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 | 0.001 | 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".