Amino Acid and Insulin Signaling via the mTOR/p70 S6 Kinase Pathway
Bibliographic record
Abstract
Amino acids have emerged as potent modulators of the mTOR/p70 S6 kinase pathway. The involvement of this pathway in the regulation of insulin-stimulated glucose transport was investigated in the present study. Acute exposure (1 h) to a balanced mixture of amino acids reduced insulin-stimulated glucose transport by as much as 55% in L6 muscle cells. The effect of amino acids was fully prevented by the specific mTOR inhibitor rapamycin. Time course analysis of insulin receptor substrate 1 (IRS-1)-associated phosphatidylinositol (PI) 3-kinase activity revealed that incubation with amino acids speeds up its time-dependent deactivation, leading to a dramatic suppression (-70%) of its activity after 30 min of insulin stimulation as compared with its maximal activation (5 min of stimulation). This accelerated deactivation of PI 3-kinase activity in amino acid-treated cells was associated with a concomitant and sustained increase in the phosphorylation of p70 S6 kinase. In marked contrast, inhibition of mTOR by rapamycin maintained PI 3-kinase maximally activated for up to 30 min. The marked inhibition of insulin-mediated PI 3-kinase activity by amino acids was linked to a rapamycin-sensitive increase in serine/threonine phosphorylation of IRS-1 and a decreased binding of the p85 subunit of PI 3-kinase to IRS-1. Furthermore, amino acids were required for the degradation of IRS-1 during long term insulin treatment. These results identify the mTOR/p70 S6 kinase signaling pathway as a novel modulator of insulin-stimulated glucose transport in skeletal muscle cells.
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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) | 0.001 | 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".