Prolactin activates mammalian target-of-rapamycin through phosphatidylinositol 3-kinase and stimulates phosphorylation of p70S6K and 4E-binding protein-1 in lymphoma cells
Bibliographic record
Abstract
Mitogens activate the mammalian target-of-rapamycin (mTOR) pathway through phosphatidylinositol 3-kinase (PI3K). The activated mTOR kinase phosphorylates/ activates ribosomal protein S6 kinase (p70S6K) and phosphorylates/inactivates eukaryotic initiation factor 4E-binding protein-1 (4E-BP1), resulting in the initiation of translation and cell-cycle progression. The prolactin receptor signaling cascade has been implicated in crosstalk with the mTOR pathway, but whether prolactin (PRL) directly activates mTOR is not known. This study showed that PRL stimulated the phosphorylation of mTOR, p70S6K, Akt, and Jak2 kinases in a dose- and time-dependent manner in PRL-dependent rat Nb2 lymphoma cells. PRL-stimulated phosphorylation of mTOR was detected as early as 10 min, closely following the phosphorylation of Akt (upstream of mTOR), but preceding that of the downstream p70S6K. PRL activation of mTOR was inhibited by rapamycin (mTOR inhibitor), LY249002, and wortmannin (P13K inhibitors), but not by AG490 (Jak2 inhibitor), indicating that it was mediated by the P13K/Akt, but not Jak2, pathway. PRL also stimulated phosphorylation of 4E-BP1 in Nb2 cells. PRL-induced phosphorylation of p70S6K and 4E-BP1 was inhibited by rapamycin, but not by okadaic acid (inhibitor of protein phosphatase, PP2A). PRL induced a transient interaction between p70S6K and the catalytic subunit of PP2A (PP2Ac) in 1 and 2 h, whereas a PP2Ac-4E-BP1 complex was constitutively present in quiescent and PRL-treated Nb2 cells. These results suggested that p70S6K and 4E-BP1 were substrates of PP2A and the inhibition of mTOR promoted their dephosphorylation by PP2A. In summary, PRL-stimulated phosphorylation of mTOR is mediated by PI3K. PRL-activated mTOR may phosphorylate p70S6K and 4E-BP1 by restraining PP2A.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".