Identification of four mechanisms of toxicity for per- and polyfluoroalkyl substances through transcriptomic profiling in human liver spheroids exposed to 24 PFAS
Bibliographic record
Abstract
Per- and polyfluoroalkyl substances (PFAS) are persistent and widespread contaminants. Epidemiological effects of PFAS include increased serum cholesterol, decreased immune response to vaccination and disease, and increased incidence of cancer; however, PFAS modes of action remain unclear. Herein, we analyzed gene expression data from human liver spheroids that were exposed to several concentrations of 24 different PFAS. Benchmark concentration (BMC) response modeling was used to identify the 250 lowest gene BMCs for each PFAS. Hierarchical clustering analysis revealed 4 functionally diverse gene sets. Each gene set was affected by a distinct group of PFAS, whereas individual PFAS were usually part of more than 1 PFAS group. The biological roles of these gene sets relate to: (1) cholesterol biogenesis and cholesterol clearance (downregulated by 7 fluorocarbon or longer PFAS), putatively through discordance of cholesterol sensing by SCAP and LXR due to membrane integration of PFAS; (2) lipolysis (upregulated by 8 carbon or shorter PFAS); (3) innate immunity (downregulated by most PFAS); and (4) adaptive immunity (downregulated by sulfonate-type PFAS). The distinctions between the 4 PFAS groups suggest that PFAS can act through at least 4 independent mechanisms. The molecular characteristics of each PFAS group may be useful for understanding the molecular interactions leading to their effect on gene expression. Inclusion of some PFAS congeners in more than one PFAS group suggests that individual PFAS can act through multiple unrelated molecular interactions. This transcriptomic analysis offers a major advancement to the understanding of the molecular mechanisms underlying the effects of PFAS exposure and provides guidance for future work that may strengthen links between PFAS exposure and their proposed effects on human health.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".