Macrophytes mitigate Microcystis aeruginosa-induced fish appetite suppression via intestinal metabolite regulation
Bibliographic record
Abstract
Cyanobacterial blooms and aquatic macrophytes can affect the health, physiology, and behavior of freshwater fish. Changes in food intake can be a key indicator of stress in teleost fish, while changes in metabolite abundance in the gut can indicate a shift in metabolic priorities, including response to environmental stressors. Here, we exposed stone moroko (Pseudorasbora parva) to the cyanobacterium Microcystis aeruginosa and/or the macrophyte Ottelia acuminata and analyzed changes in fish health, appetite regulation, and intestinal metabolome after 96-h exposures. We found that O. acuminata treatment didn't change the tested indicators, while exposure to M. aeruginosa increased concentrations of appetite-inhibiting factors, such as CART and GLP-1, and decreased concentrations of stimulatory factors like orexin. Exploration of the metabolome following exposure revealed that the appetite-inhibiting influence of M. aeruginosa was positively correlated with key metabolites of lipid, amino acid, and cholesterol metabolism, especially those associated with bile acid synthesis and secretion. Further, the presence of O. acuminata decreased the adverse effects of M. aeruginosa among neuro-endocrine regulatory factors, which could be explained by altered regulation of intestinal amino acid metabolites. The deeper mechanism by which O. acuminata moderates the harmful effects of M. aeruginosa remains to be identified.
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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".