Metabolomic-Based Comparison of <i>Daphnia magna</i> and Japanese Medaka Responses After Exposure to Acetaminophen, Diclofenac, and Ibuprofen
Bibliographic record
Abstract
Pharmaceuticals are found in aquatic environments due to their widespread use and environmental persistence. To date, a range of impairments to aquatic organisms has been reported with exposure to pharmaceuticals; however, further comparisons of their impacts across different species on the molecular level are needed. In the present study, the crustacean Daphnia magna and the freshwater fish Japanese medaka, common model organisms in aquatic toxicity, were exposed for 48 h to the common analgesics acetaminophen (ACT), diclofenac (DCF), and ibuprofen (IBU) at sublethal concentrations. A targeted metabolomic-based approach, using liquid chromatography-tandem mass spectrometry to quantify polar metabolites from individual daphnids and fish was used. Multivariate analyses and metabolite changes identified differences in the metabolite profile for D. magna and medaka, with more metabolic perturbations for D. magna. Pathway analyses uncovered disruptions to pathways associated with protein synthesis and amino acid metabolism with D. magna exposure to all three analgesics. In contrast, medaka exposure resulted in disrupted pathways with DCF only and not ACT and IBU. Overall, the observed perturbations in the biochemistry of both organisms were different and consistent with assessments using other endpoints reporting that D. magna is more sensitive to pollutants than medaka in short-term studies. Our findings demonstrate that molecular-level responses to analgesic exposure can reflect observations of other endpoints, such as immobilization and mortality. Thus, environmental metabolomics can be a valuable tool for selecting sentinel species for the biomonitoring of freshwater ecosystems while also uncovering mechanistic information. Environ Toxicol Chem 2024;43:1339-1351. © 2024 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of 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".