1H-Nuclear Magnetic Resonance Metabolomics Analysis of <i>Arabidopsis thaliana</i> Exposed to Perfluorooctanoic Acid and Perfluoroctanesulfonic Acid
Bibliographic record
Abstract
Abstract Perfluorinated alkyl substances (PFAS) are ubiquitous environmental contaminants that are widely used in consumer products and fire suppression foams. The presence of PFAS in ground and surface water can create a route for PFAS to enter the soil, exposing ecosystems (including agroecosystems), where they will move through the food web via biomagnification. The toxicity of PFAS to plants, particularly in agricultural ecosystems, is of emerging concern due to the application of biosolids that are often contaminated with PFAS. Nevertheless, due to the low concentrations of PFAS in most agricultural soils, the direct impact of PFAS on plant health is not well understood. We used 1H-nuclear magnetic resonance (NMR) metabolomics to explore the effects of exposure of two key PFAS, perfluorooctanoic acid and perfluorooctanesulfonic acid, on Arabidopsis thaliana, a model organism. We found that Arabidopsis exhibited an accumulation of multiple metabolites, including soluble sugars (glucose and sucrose), multiple amino acids, and tri-carboxylic acid (TCA) cycle intermediates, suggesting that PFAS exposure impacts the metabolism of plants by causing an accumulation of stress-related amino acids and soluble sugars that drives increased activity of the TCA cycle. The present study shows that 1H-NMR metabolomics is a viable tool for investigating changes in the metabolic profile of plants exposed to PFAS and can be used to illuminate the stress response of plants in a high-throughput, nonbiased manner. Environ Toxicol Chem 2023;42:663–672. © 2022 SETAC
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".