Environmental Contaminant Mixtures at Ambient Concentrations Invoke a Metabolic Stress Response in Goldfish Not Predicted from Exposure to Individual Compounds Alone
Bibliographic record
Abstract
Environmental contaminants from wastewater and industrial or agricultural areas are known to have adverse effects on development, reproduction, and metabolism. However, reliable assessment of environmental contaminant impact at low (i.e., ambient) concentrations using genomics and transcriptomics approaches has proven challenging. A goldfish model was used to investigate the effects of aquatic pollutant exposure in vivo by means of quantitative nuclear magnetic resonance metabolomics in multiple organs to elucidate a system-wide response. Animals were exposed to 4,4'-isopropylidenediphenol (Bisphenol-A, BPA), di-(2-ethylhexyl)-phthalate (DEHP), and nonylphenol (NP). Metabolite-specific spectral analysis combined with pathway-driven bioinformatics indicated changes in energy and lipid metabolism in liver following exposure to individual contaminants and a tertiary mixture. A dissimilar response in testis exposed to DEHP and mixture indicates disrupted AMPK and cAMP signaling. Uniquely, our observations (1) suggest that exposure to a contaminant mixture is characterized by a stress response not predicted from exposure to individual contaminants, even in the absence of other phenotypic features and (2) demonstrate the sensitivity of metabolomics in risk-assessment of environmental toxicant mixtures at ambient concentrations by detecting early stage metabolic dysregulation. These findings have general applicability in the assessment of "benign" compound mixtures in environmental and pharmaceutical development.
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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".