Deciphering per- and polyfluoroalkyl substances mode of action: comparative gene expression analysis in human liver spheroids
Bibliographic record
Abstract
Understanding the mechanisms by which environmental chemicals cause toxicity is necessary for effective human health risk assessment. High-throughput transcriptomics (HTTr) can be used to inform risk assessment on toxicological mechanisms, hazards, and potencies. We applied HTTr to elucidate the molecular mechanisms by which per- and polyfluoroalkyl substances (PFAS) cause liver perturbations. We contrasted transcriptomic profiles of PFOA, PFBS, PFOS, and PFDS against transcriptomic profiles from established liver-toxic and non-toxic reference compounds, alongside peroxisome proliferator-activated receptors (PPARs) agonists. Our analysis was conducted on metabolically competent 3-D human liver spheroids produced from primary cells from 10 donors. Pathway analysis showed that PFOS and PFDS perturb many of the same pathways as the known liver-toxic compounds in the spheroids, and that the cholesterol biosynthesis pathways are significantly affected by exposure to these compounds. PFOA alters lipid metabolism-related pathways but its expression profile does not closely match reference compounds. PFBS upregulates many degradation-related pathways and targets many of the same pathways as the PPAR agonists and acetaminophen. Our transcriptional analysis does not support the claim that these PFAS are DNA-damaging in this model. A multidimensional scaling (MDS) analysis revealed that PFOS, PFOA, and PFDS cluster together in the same multidimensional space as liver-damaging compounds, whereas PFBS clusters more closely with the non-liver-damaging compounds. Benchmark concentration-response modeling predicts that all the PFAS are bioactive in the liver. Overall, our results show that these PFAS produce unique transcriptional changes but also alter pathways associated with established liver-toxic chemicals in this liver spheroid model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".