Glucocorticoid receptor activation reduces food intake independent of hyperglycemia in zebrafish
Bibliographic record
Abstract
Chronic cortisol exposure suppresses food intake in fish, but the central mechanism(s) involved in appetite regulation are unclear. Stress and the associated increase in cortisol levels increase hepatic gluconeogenesis, leading to hyperglycemia. As hyperglycemia causes a reduction in food intake, we tested the hypothesis that cortisol-induced hyperglycemia suppresses feeding in zebrafish (Danio rerio). We first established that stress-independent hyperglycemia suppressed food intake, and this corresponded with a reduction in the phosphorylation of the nutrient sensor, AMP-activated protein kinase (AMPK) in the brain. Chronic cortisol exposure also led to hyperglycemia and reduced food intake, but the mechanisms were distinct. In cortisol-exposed fish, there were no changes in brain glucose uptake or AMPK phosphorylation. Also, the phosphorylation of Akt and mTOR was reduced along with an increase in redd1, suggesting an enhanced capacity for proteolysis. Loss of the glucocorticoid receptor did not rescue cortisol-mediated feeding suppression but did increase glucose uptake and abolished the changes seen in mTOR phosphorylation and redd1 transcript abundance. Taken together, our results indicate that GR activation enhances brain proteolysis, and the associated amino acids levels, and not hyperglycemia, maybe a key mediator of the feeding suppression in response to chronic cortisol stimulation in zebrafish.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".