Molecular mechanisms of the stress-induced regulation of the inflammatory response in fish
Bibliographic record
Abstract
Stressors in the environment of aquatic organisms can profoundly affect their immune system. The stress response in fish involves the activation of the hypothalamus-pituitary-interrenal (HPI) axis, leading to the release of several stress hormones, among them glucocorticoids, such as cortisol, which bind and activate corticosteroid receptors, namely the glucocorticoid receptor (GR) and mineralocorticoid receptor (MR). These receptors are highly expressed on immune cells, thereby allowing stress to have a potent effect that is classically considered to suppress immune function. In this review, we highlight the conserved structure and function of GR and MR among vertebrates and describe their role in modulating inflammation by regulating the expression of pro-inflammatory and anti-inflammatory genes. In particular, the involvement of MR during inflammation is reviewed, which in many studies has been shown to be immune-enhancing. In recent years, the use of zebrafish as a model organism has opened up new possibilities to study the effects of stress on inflammation, making it possible to investigate knockout lines for MR and/or GR, in combination with transgenic models with fluorescently labeled leukocyte subpopulations that enable the visualization and manipulation of these immune cells. The potential roles of other hormones of the HPI axis, such as corticotrophin-releasing hormone (Crh) and adrenocorticotropic hormone (Acth), in immune modulation are also discussed. Overall, this review highlights the need for further research to elucidate the specific roles of GR, MR and other stress hormones in regulating immune function in fish. Understanding these mechanisms will contribute to improving fish health and advancing our knowledge of stress signalling.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".