Responses of the Proteome and Metabolome in Livers of Zebrafish Exposed Chronically to Environmentally Relevant Concentrations of Microcystin-LR
Bibliographic record
Abstract
In this study, for the first time, changes in expressions of proteins and profiles of metabolites in liver of the small, freshwater fish (zebrafish) were investigated after long-term exposure to environmentally relevant concentrations of microcystin-LR (MC-LR). Male zebrafish were exposed via water to 1 or 10 μg MC-LR/L for 90 days, and iTRAQ-based proteomics and 1 H NMR-based metabolomics were employed. Histopathological observations showed that MC-LR caused damage to liver, and the effects were more pronounced in fish exposed to 10 μg MC-LR/L. Metabolomic analysis also showed alterations of hepatic function, which included changes in a number of metabolic pathways, including small molecules involved in energy, glucose, lipids, and amino acids metabolism. Concentrations of lactate were significantly greater in individuals exposed to MC-LR than in unexposed controls. This indicated a shift toward anaerobic metabolism, which was confirmed by impaired respiration in mitochondria. Proteomics revealed that MC-LR significantly influenced multiple proteins, including those involved in folding of proteins and metabolism. Endoplasmic reticulum stress contributed to disturbance of metabolism of lipids in liver of zebrafish exposed to MC-LR. Identification of proteins and metabolites in liver of zebrafish responsive to MC-LR provides insights into mechanisms of chronic toxicity of MCs.
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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".