Hormonal modulation of the heat shock response: insights from fish with divergent cortisol stress responses
Bibliographic record
Abstract
Acute temperature stress in animals results in increases in heat shock proteins (HSPs) and stress hormones. There is evidence that stress hormones influence the magnitude of the heat shock response; however, their role is equivocal. To determine whether and how stress hormones may affect the heat shock response, we capitalized on two lines of rainbow trout specifically bred for their high (HR) and low (LR) cortisol response to stress. We predicted that LR fish, with a low cortisol but high catecholamine response to stress, would induce higher levels of HSPs after acute heat stress than HR trout. We found that HR fish have significantly higher increases in both catecholamines and cortisol compared with LR fish, and LR fish had no appreciable stress hormone response to heat shock. This unexpected finding prevented further interpretation of the hormonal modulation of the heat shock response but provided insight into stress-coping styles and environmental stress. HR fish also had a significantly greater and faster heat shock response and less oxidative protein damage than LR fish. Despite these clear differences in the physiological and cellular responses to heat shock, there were no differences in the thermal tolerance of HR and LR fish. Our results support the hypothesis that responsiveness to environmental change underpins the physiological differences in stress-coping styles. Here, we demonstrate that the heat shock response is a distinguishing feature of the HR and LR lines and suggest that it may have been coselected with the hormonal responses to stress.
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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".