Glucagon regulation of carbohydrate metabolism in rainbow trout: <i>in vivo</i> glucose fluxes and gene expression
Bibliographic record
Abstract
Glucagon increases fish glycemia, but how it affects glucose fluxes in vivo has never been characterized. The goal of this study was to test the hypothesis that glucagon stimulates hepatic glucose production (Ra) and inhibits disposal (Rd) of rainbow trout. Changes in the mRNA abundance of key proteins involved in glycolysis, gluconeogenesis, and glycogen breakdown were also monitored. Results show that glucagon increases glycemia (+38%) by causing a temporary mismatch between Ra and Rd before both fluxes converge below baseline (-17%). A novel aspect of the regulation of trout gluconeogenesis is also demonstrated: the completely different effects of glucagon on the expression of three Pepck isoforms (stimulation of pck1, inhibition of pck2a, and no response of pck2b). Glycogen phosphorylase was modulated differently among tissues, and muscle upregulated pygb and downregulated pygm. Glucagon failed to activate the cAMP-dependent protein kinase or FoxO1 signalling cascades. We conclude that trout hyperglycemia results from the combination of two responses: (i) an increase in Ra glucose induced by the stimulation of gluconeogenesis through transcriptional activation of pck1 (and possibly glycogen phosphorylase), and (ii) a decrease in Rd glucose via inhibition of glycogen synthase and glycolysis. The observed decrease in glucose fluxes after 4 h of glucagon administration may be caused by a counterregulatory response of insulin, potentially linked to the decrease in pygm transcript abundance. Overall, however, these integrated effects of glucagon only lead to modest changes in glucose fluxes that partly explain why trout seem to be unable to control glycemia very tightly.
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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".