Assessing the in vitro effects of priority Arctic contaminants using cell-based transcriptomics
Bibliographic record
Abstract
Persistent organic pollutants (POPs) are a diverse group of chemicals that resist degradation, bioaccumulate in food webs, and pose risks to human health. Despite international regulation, both legacy and emerging POPs remain widely detected in the environment and human populations. To support the transition toward new approach methodologies (NAMs) that minimize animal use, this study employed human cell–based transcriptomics to investigate the molecular and concentration-dependent effects of POPs. Using human liver (HepG2) and adrenal (H295R) cell models, transcriptional responses to multiple contaminant classes, including organophosphate and organochlorine pesticides, perfluorooctane sulfonic acid, polychlorinated biphenyls (PCB), and polybrominated diphenyl ethers (PBDE), were evaluated. Cytotoxicity assessment, differential gene expression analysis, benchmark dose (BMD) modeling, and biological pathway analysis were integrated to derive transcriptomic points of departure (tPODs) and characterize molecular responses. Concentration-dependent transcriptional changes revealed distinct molecular signatures across chemicals, reflecting diverse modes of action. Benchmark dose modeling of transcriptomic data yielded tPODs that were generally within an order of magnitude of reported apical effect concentrations, demonstrating the relevance of these molecular thresholds for risk assessment. Comparative analysis of PCB-138 between the two cell types indicated cell-specific transcriptional responses, emphasizing the importance of tissue context in evaluating POP toxicity. Overall, this work demonstrates the value of in vitro transcriptomic profiling for mechanistic and quantitative toxicity assessment of POPs. Integrating gene expression and dose–response modeling approaches provides an animal-free framework for defining biologically meaningful toxicity thresholds and advancing next-generation chemical 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".