Growth hormone transgenesis disrupts immune function in muscle of coho salmon ( <i>Oncorhynchus kisutch</i> ) impacting cross-talk with growth systems
Bibliographic record
Abstract
ABSTRACT The suppression of growth during infection should facilitate resource allocation towards effective immune function. Work supporting this hypothesis has been recently reported in teleosts, demonstrating immune-responsive regulation of the insulin-like growth factor (IGF) system - a key endocrine growth pathway that acts downstream of growth hormone (GH). Skeletal muscle is the main target for growth and energetic storage in fish, yet little is known about how growth is regulated in this tissue during an immune response. We addressed this knowledge gap by characterizing muscle immune responses in size-matched coho salmon (Oncorhynchus kisutch) achieving different growth rates. We compared a wild-type strain with two GH transgenic groups achieving either maximal or highly-suppressed growth – an experimental design that separates GH’s direct effects from its influence on growth rate. Fish were sampled 30h post-injection with PBS (control) or mimics of bacterial (peptidoglycan) or viral (Poly:IC) infection. We quantified the mRNA level expression of genes from the GH, GH receptor (GHR), IGF hormone, IGF1 receptor (IGF-1R) and IGF binding protein (IGFBP) families, along with marker genes for muscle growth and host defence genes involved in inflammatory or antiviral responses. We provide strong evidence for dampened immunity in the GH transgenics compared to wild-type animals. Strikingly, the muscle of GH transgenics achieving rapid growth showed no detectable antiviral response, coupled with evidence of a constitutive inflammatory state. GH and IGF system gene expression was also strongly altered by GH transgenesis and fast growth, both for baseline expression levels and responses to immune stimulation. Overall, our findings demonstrate that GH transgenesis disrupts normal immune function and growth-immune cross-talk in muscle, with implications for the health and welfare of farmed salmon.
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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".