Effects of glutamine on signaling pathways involved in synthesis and degradation of skeletal muscle protein
Bibliographic record
Abstract
Patients with diabetes mellitus present muscle mass loss and muscle atrophy that activate signaling pathways of protein degradation, such as MURF‐1 and MAF‐bx. The aim of this study was to evaluate the effects of glutamine (Gln) supplementation on signaling pathways of protein synthesis (Akt, GSK3, p70s6K, 4E‐BP1, mTOR) and degradation (MuRF‐1 e MAF‐bx) in rat soleus muscles in vitro and in vivo, by using Western blotting. In the in vitro experiments, skeletal muscle cells were cultured in the absence and in the presence of Gln (4 and 16 mM) for seven days; whereas, in the in vivo experiments, streptozocin‐induced diabetic rats were supplemented with glutamine (1g/kg bw for 15 days. Protein synthesis was determined by measuring the incorporation of labeled leucine into muscle cells. In cultured skeletal muscle cells, Gln (at 4 and 16 mM) increased L‐[U‐14C]‐leucine incorporation, Akt, mTOR and GSK3 phosphorylation, as well as p70s6k protein content phosphorylation, and decreased 4E‐BP1 and MuRF‐1 expression. In soleus muscle from diabetic rats there was an increase of Akt, GSK3 and mTOR phosphorylation, followed by a reduction of 4E‐BP1, MuRF‐1 and MAF‐bx. In conclusion, Gln stimulated the signaling pathway of protein synthesis and inhibited that of protein degradation in skeletal muscle. Financial Support: Fapesp, Capes and CNPq
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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".