A Roadmap to the Structure-Related Metabolism Pathways of Per- and Polyfluoroalkyl Substances in the Early Life Stages of Zebrafish ( <i>Danio rerio</i> )
Bibliographic record
Abstract
Background: Thousands of per- and polyfluoroalkyl substances (PFAS) with diverse structures have been detected in the ambient environment. Apart from a few well-studied PFAS, the structure-related toxicokinetics of a broader set of PFAS remain unclear. Objectives: To understand the toxicokinetics of PFAS, we attempted to characterize the metabolism pathways of 74 structurally diverse PFAS samples from the U.S. Environmental Protection Agency’s PFAS screening library. Methods: Using the early life stages of zebrafish (Danio rerio) as a model, we determined the bioconcentration factors and phenotypic toxicities of 74 PFAS. Then, we applied high-resolution mass spectrometry–based nontargeted analysis to identify metabolites of PFAS in zebrafish larvae after 5 d of exposure by incorporating retention time and mass spectra. In vitro enzymatic activity experiments with human recombinant liver carboxylesterase (hCES1) were employed to validate the structure-related hydrolysis of 11 selected PFAS. Results: Our findings identified five structural categories of PFAS prone to metabolism. The metabolism pathways of PFAS were highly related to their structures as exemplified by fluorotelomer alcohols that the predominance of β-oxidation or taurine conjugation pathways were primarily determined by the number of hydrocarbons. Hydrolysis was identified as a major metabolism pathway for diverse PFAS, and perfluoroalkyl carboxamides showed the highest in vivo hydrolysis rates, followed by carboxyesters and sulfonamides. The hydrolysis of PFAS was verified with recombinant hCES1, with strong substrate preferences toward perfluoroalkyl carboxamides. Conclusions: We suggest that the roadmap of the structure-related metabolism pathways of PFAS established in this study would provide a starting point to inform the potential health risks of other PFAS. https://doi.org/10.1289/EHP7169
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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 | 0.000 |
| 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) | 0.001 | 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 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".