Alanine alters the carbohydrate metabolism of rainbow trout: glucose flux and cell signaling
Bibliographic record
Abstract
In rainbow trout, dietary carbohydrates are poorly metabolized compared with other macronutrients. One prevalent hypothesis suggests that high dietary amino acid levels could contribute to the poor utilization of carbohydrates in trout. In mammals, alanine is considered an important gluconeogenic precursor, but has recently been found to stimulate AMP-activated protein kinase (AMPK) to reduce glucose levels. In trout, the effect of alanine on glucose flux is unknown. The goal of this study was to determine the effects of 4 h exogenous alanine infusion on glucose metabolism in rainbow trout. Glucose flux, and the rate of glucose appearance (Ra) and disposal (Rd) were measured in vivo. Key glycolytic and gluconeogenic enzyme expression and activity, and cell signaling molecules relevant to glucose metabolism were assessed in the liver and muscle. The results show that alanine inhibits glucose Ra (from 13.2±2.5 to 7.3±1.6 μmol kg-1 min-1) and Rd (from 13.2±2.5 to 7.4±1.5 μmol kg-1 min-1) and the slight mismatch between Ra and Rd caused a reduction in glycemia, similar to the effects of insulin in trout. The reduction in glucose Rd can be partially explained by a reduction in glut4b expression in red muscle. In contrast to mammals, trout alanine-dependent glucose-lowering effects did not involve hepatic AMPK activation, suggesting a different mechanistic basis. Interestingly, protein kinase B (AKT) activation increased only in muscle, similar to effects observed in insulin-infused trout. We speculate that alanine-dependent effects were probably mediated through stimulation of insulin secretion, which could indirectly promote alanine oxidation to provide the needed energy.
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".