The Mineralocorticoid Receptor Functions as a Key Glucose Regulator in the Skeletal Muscle of Zebrafish
Bibliographic record
Abstract
Glucocorticoids (GCs) are essential for maintaining energy homeostasis as part of the adaptive stress response. Most work to date has characterized the metabolic role of GCs via the activation of the glucocorticoid receptor (nr3c1; GR), which is activated under high GC conditions. However, GCs also bind to the mineralocorticoid receptor (nr3c2; MR), a high-affinity corticosteroid receptor active under basal GC conditions. Despite the expression of MR in skeletal muscles, almost nothing is known about its physiological role. Here we tested the hypothesis that the MR promotes anabolic processes during resting cortisol levels and curtails the catabolic actions of the GR during high (stressed) levels of cortisol. To determine the effect of MR, a zebrafish line with a ubiquitous MR knockout (MRca402/ca402) was utilized. The GR was activated in the same group by chronically treating fish with exogenous cortisol. In the muscle, MR primarily promoted nutrient storage, and restricted energy substrate mobilization under resting conditions, whereas GR activation resulted in increased nutrient utilization. Interestingly, MR loss improved GR-driven metabolic flexibility, suggesting that the activation state of these receptors is a key determinant of skeletal muscle ability to switch fuel sources. To determine if the anabolism-promoting nature of MR was due to an interaction with insulin, fish were co-injected with insulin and the fluorescent glucose analogue 2-NBDG. A loss of MR abolished insulin-stimulated glucose uptake in the skeletal muscle. Taken together, we postulate that MR acts as a key modulator of glucose metabolism in the musculature during basal and stress conditions.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".