Microcystis aeruginosa decreased fish appetite via inducing intestinal inflammation
Bibliographic record
Abstract
Microcystis aeruginosa is a dominant and widely distributed cyanobacterium that plays a major role in the formation and recurrence of harmful algal blooms in lakes. While it is known to cause intestinal inflammation and appetite dysregulation in fish, the relationship between these effects and their underlying molecular mechanisms remains poorly understood. This study explored the impact of M. aeruginosa-induced intestinal inflammation on fish appetite, with a focus on the underlying molecular pathways, using Pseudorasbora parva exposed to M. aeruginosa for 96 h. We found a significant increase in inflammatory cells in the intestinal tract, accompanied by notable changes in glycerophospholipid and tryptophan metabolism, pathways associated with inflammation and appetite regulation. Key inflammatory markers interleukin-6 (IL-6), interleukin-8 (IL-8), interleukin-1 beta (IL-1β), Nuclear factor-kappaB (NF-κB), and tumor necrosis factor-alpha (TNF-α) were significantly elevated, while orexigenic factors orexins, ghrelin, acetylcholine (Ach), and dopamine (DA) were markedly reduced. In contrast, anorexigenic factors 5-Hydroxytryptamine (5-HT), gamma-aminobutyric acid (GABA), proopiomelanocortin (POMC), and glucagon-like peptide-1 (GLP-1) showed significant increases. Correlation analysis revealed that inflammatory markers were negatively correlated with orexigenic factors and positively correlated with anorexigenic factors. These findings indicate that M. aeruginosa-induced intestinal inflammation is a potential mechanism underlying reduced appetite in fish. This study provides novel insights into the toxic effects of M. aeruginosa on fish and offers a new perspective on the mechanisms by which it suppresses fish appetite, contributing to the broader understanding of the ecological and physiological impacts of cyanobacterial blooms.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".