Stress-Immune-Growth Interactions: Cortisol Modulates Suppressors of Cytokine Signaling and JAK/STAT Pathway in Rainbow Trout Liver
Bibliographic record
Abstract
Chronic stress is a major factor in the poor growth and immune performance of salmonids in aquaculture. However, the molecular mechanisms linking stress effects to growth and immune dysfunction is poorly understood. The suppressors of cytokine signaling (SOCS), a family of genes involved in the inhibition of JAK/STAT pathway, negatively regulates growth hormone and cytokine signaling, but their role in fish is unclear. Here we tested the hypothesis that cortisol modulation of SOCS gene expression is a key molecular mechanism leading to growth and immune suppression in response to stress in fish. Exposure of rainbow trout (Oncorhynchus mykiss) liver slices to cortisol, mimicking stress level, upregulated SOCS-1 and SOCS-2 mRNA abundance and this response was abolished by the glucocorticoid receptor antagonist mifepristone. Bioinformatics analysis confirmed the presence of putative glucocorticoid response elements in rainbow trout SOCS-1 and SOCS-2 promoters. Prior cortisol treatment suppressed acute growth hormone (GH)-stimulated IGF-1 mRNA abundance in trout liver and this involved a reduction in STAT5 phosphorylation and lower total JAK2 protein expression. Prior cortisol treatment also suppressed lipopolysaccharide (LPS)-induced IL-6 but not IL-8 transcript levels; the former but not the latter cytokine expression is via JAK/STAT phosphorylation. LPS treatment reduced GH signaling, but this was associated with the downregulation of GH receptors and not due to the upregulation of SOCS transcript levels by this endotoxin. Collectively, our results suggest that upregulation of SOCS-1 and SOCS-2 transcript levels by cortisol, and the associated reduction in JAK/STAT signaling pathway, may be a novel mechanism leading to growth reduction and immune suppression during stress in trout.
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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) | 0.002 | 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".