Xenobiotics Produce Distinct Metabolomic Responses in Zebrafish Larvae (<i>Danio rerio</i>)
Bibliographic record
Abstract
Sensitive and quantitative protocols for characterizing low-dose effects are needed to meet the demands of 21st century chemical hazard assessment. To test the hypothesis that xenobiotic exposure at environmentally relevant concentrations produces specific biochemical fingerprints in organisms, metabolomic perturbations in zebrafish (Danio rerio) embryo/larvae were measured following 24 h exposures to 13 individual chemicals covering a wide range of contaminant classes. Measured metabolites (208 in total) included amino acids, biogenic amines, fatty acids, bile acids, sugars, and lipids. The 96-120 h post-fertilization developmental stage was the most appropriate model for detecting xenobiotic-induced metabolomic perturbations. Metabolomic fingerprints were largely chemical- and dose-specific and were reproducible in multiple exposures over a 16-month period. Furthermore, chemical-specific responses were detected in the presence of an effluent matrix; importantly, in the absence of morphological response. In addition to improving sensitivity for detecting biological responses to low-level xenobiotic exposures, these data can aid the classification of novel contaminants based on the similarity of metabolomic responses to well-characterized "model" compounds. This approach is clearly of use for rapid, sensitive, and specific analyses of chemical effect on organisms, and can supplement existing methods, such as the Zebrafish Embryo Toxicity assay (OECD TG236), with molecular-level information.
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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".