The rapid activation of protein synthesis by growth hormone requires signaling through mTOR
Bibliographic record
Abstract
An important function of growth hormone (GH) is to promote cell and tissue growth, and a key component of these effects is the stimulation of protein synthesis. In this study, we demonstrate that, in H4IIE hepatoma cells, GH acutely activated protein synthesis through signaling via the mammalian target of rapamycin (mTOR) and specifically through the rapamycin-sensitive mTOR complex 1 (mTORC1). GH treatment enhanced the phosphorylation of two targets of mTOR signaling, 4E-BP1 and ribosomal protein S6. Phosphorylation of S6 and 4E-BP1 was maximal at 30-45 min and 10-20 min after GH stimulation, respectively. Both proteins modulate components of the translational machinery. The GH-induced phosphorylation of 4E-BP1 led to its dissociation from eIF4E and increased binding of eIF4E to eIF4G to form (active) eIF4F complexes. The ability of GH to stimulate the phosphorylation of S6 and 4E-BP1 was blocked by rapamycin. GH also led to the dephosphorylation of a third translational component linked to mTORC1, the elongation factor eEF2. Its regulation followed complex biphasic kinetics, both phases of which required mTOR signaling. GH rapidly activated both the MAP kinase (ERK) and PI 3-kinase pathways. Signaling through PI 3-kinase alone was, however, sufficient to activate the downstream mTORC1 pathway. Consistent with this, GH increased the phosphorylation of TSC2, an upstream regulator of mTORC1, at sites that are targets for Akt/PKB. Finally, the activation of overall protein synthesis by GH in H4IIE cells was essentially completely inhibited by wortmannin or rapamycin. These results demonstrate for the first time that mTORC1 plays a major role in the rapid activation of protein synthesis by GH.
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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".