Sulfur-Containing Heterocyclic Aromatic Hydrocarbons Alter Estrogen Metabolism and Cause DNA Damage and Apoptosis in Granulosa Cells
Bibliographic record
Abstract
The expansion of the Alberta Oil Sands Region (AOSR) has increased the deposition of petroleum-derived chemicals into the surrounding environment. Among these, polycyclic aromatic compounds (PACs), including sulfur-containing heterocyclic hydrocarbons, have been detected in exposed local wildlife, yet the reproductive toxicity and genotoxicity of this suite of PACs remain largely unexplored. This study examined the effects of dibenzothiophene (DBT) and its alkylated congener, 2,4,7-trimethyldibenzothiophene (2,4,7-DBT), on estradiol (E2) synthesis and metabolism in granulosa cells (SIGCs). Cells were exposed to DBT or 2,4,7-DBT for 24 h at concentrations detected in AOSR wildlife tissues (0, 0.1, 1 and 10 nM). We measured the gene expression of markers involved in E2 synthesis, signaling and metabolism, E2 output via ELISA and E2 metabolite production via HPLC-MS/MS. Exposure to 2,4,7-DBT, but not DBT, shifted E2 metabolism towards 4-OHE2, a genotoxic E2 metabolite. DNA damage was assessed by γH2Ax expression, alongside DNA repair (Parp1) and survival markers (pAKT). Interestingly, both DBT and 2,4,7-DBT increased DNA damage and triggered apoptosis via a caspase-independent mechanism. Given the critical role of granulosa cells in steroidogenesis and fertility, these findings highlight the endocrine-disruptive effects of sulfur-containing heterocyclic PACs and their potential to compromise reproductive health in exposed mammals.
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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".