1H NMR metabolomics of Eisenia fetida responses after sub-lethal exposure to perfluorooctanoic acid and perfluorooctane sulfonate
Bibliographic record
Abstract
Environmental context Perfluoroalkyl acids are persistent environmental contaminants that are also found in soils. We use a metabolomics approach based on nuclear magnetic resonance analyses to investigate the responses of earthworms to exposure to sub-lethal levels of two perfluoroalkyl acids. The results indicate that this metabolomics approach is able to delineate the toxic mode of action of contaminants present at sub-lethal levels. Abstract Metabolomics entails the analysis of endogenous metabolites within organisms exposed to an external stressor such as an environmental contaminant. We utilised 1H NMR-based metabolomics to elucidate sub-lethal toxic mechanisms of Eisenia fetida earthworms after exposure to perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS). Earthworms were exposed to a range of concentrations of PFOA (6.25 to 50 µg cm–2) and PFOS (3.125 to 25 µg cm–2) by contact tests for 2 days. Earthworm tissues were extracted using a mixture of chloroform, methanol and water, and the polar fraction was analysed by 1H NMR spectroscopy. NMR-based metabolomic analysis revealed heightened E. fetida toxic responses with higher PFOA and PFOS exposure concentrations. Principal component analysis (PCA) exhibited significant separation between control and exposed earthworms along PC1 for all PFOA and PFOS exposure concentrations. Leucine, arginine, glutamate, maltose and adenosine triphosphate (ATP) are potential indicators of PFOA and PFOS exposure as these metabolite concentrations fluctuated with exposure. Our data also indicate that PFOA and PFOS exposure may increase fatty acid oxidation and interrupt ATP synthesis due to a disruption in the inner mitochondrial membrane structure. NMR-based metabolomics has promise as an insightful tool for elucidating the environmental toxicology of sub-lethal contaminant exposure.
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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) | 0.000 | 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 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".