A chronic increase in physical activity inhibits fed-state mTOR/S6K1 signaling and reduces IRS-1 serine phosphorylation in rat skeletal muscle
Bibliographic record
Abstract
A chronic increase in physical activity and (or) endurance training can improve insulin sensitivity in insulin-resistant skeletal muscle. Cellular mechanisms responsible for the development of insulin resistance are unclear, though one proposed mechanism is that nutrient overload chronically increases available energy, over-activating the mammalian target of rapamycin (mTOR) and ribosomal S6 kinase 1 (S6K1) signaling pathway leading to increased phosphorylation of serine residues on insulin receptor substrate-1 (IRS-1). The objective of this study was to determine if increased physical activity would inhibit mTOR/S6K1 signaling and reduce IRS-1 serine phosphorylation in rat skeletal muscle. Soleus muscle was collected from fed male Sprague-Dawley sedentary rats (Inactive) and rats with free access to running wheels for 9 weeks (Active). Immunoblotting methods were used to measure phosphorylation status of mTOR, S6K1, IRS-1, and PKB/Akt (protein kinase B/AKT), and total abundance of proteins associated with the mTOR pathway. Muscle citrate synthase activity and plasma insulin and glucose concentrations were measured. Phosphorylation of mTOR (Ser2448), S6K1 (Thr389), and IRS-1 (Ser636-639) was reduced in Active rats (p<0.05). Total protein abundance of mTOR, S6K1, IRS-1, 4E-BP1, eEF2, PKB/Akt and AMPKalpha, and phosphorylation of PKB/Akt were unaffected (p>0.05). Total SKAR protein, a downstream target of S6K1, and citrate synthase activity increased in Active rats (p<0.05), though plasma insulin and glucose levels were unchanged (p>0.05). Reduced mTOR/S6K1 signaling during chronic increases in physical activity may play an important regulatory role in the serine phosphorylation of IRS-1, which should be examined as a potential mechanism for attenuation of insulin resistance associated with increased IRS-1 serine phosphorylation.
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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 | 0.001 |
| 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".