Unveiling the molecular responses of human lung cells to retene: Transcriptomics insights and implications for toxicity
Bibliographic record
Abstract
While retene (RET) ecotoxicity has been studied, its molecular mechanisms remain unclear despite its prevalence in forest fires. This study investigated RET's effects in BEAS-2B cells using whole transcriptome (RNA-seq) analysis, cell viability, ATP level, cell adhesion, cell migration, and cell invasiveness endpoints after 24 h and 72 h of exposure. RNA-seq revealed dynamic transcriptional changes, including dysregulated long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and pseudogenes. Pathway analysis implicated disrupted fatty acid metabolism and mitochondrial function, suggesting energy imbalance. RET induced hormesis effects, with low doses stimulating cell proliferation and increase in ATP levels. Altered cytoskeletal and extracellular matrix genes likely drove enhanced adhesion, migration, and invasiveness. Wound healing and transwell assays suggested RET may promote epithelial-mesenchymal transition-like functional phenotype (EMT). The present findings suggested metabolic adaptation and transcriptional regulation as key to RET's effects on proliferation and invasiveness, revealing complex cellular responses to environmental stressors with implications for PAH-linked respiratory diseases and carcinogenesis.
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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 | 0.001 | 0.001 |
| 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".