Regulation of mTORC1 and mTORC2 Complex Assembly by Phosphatidic Acid: Competition with Rapamycin
Bibliographic record
Abstract
mTOR, the mammalian target of rapamycin, is a critical node for control of cell growth and survival and has widely been implicated in cancer survival signals. mTOR exists in two complexes: mTORC1 and mTORC2. Phospholipase D (PLD) and its metabolite phosphatidic acid (PA) have been implicated in the regulation of mTOR; however, their role has been controversial. We report here that suppression of PLD prevents phosphorylation of the mTORC1 substrate S6 kinase (S6K) at Thr389 and the mTORC2 substrate Akt at Ser473. Suppression of PLD also blocked insulin-stimulated Akt phosphorylation at Ser473 and the mTORC2-dependent phosphorylation of PRAS40. Importantly, PA was required for the association of mTOR with Raptor to form mTORC1 and that of mTOR with Rictor to form mTORC2. The effect of PA was competitive with rapamycin-with much higher concentrations of rapamycin needed to compete with the PA-mTORC2 interaction than with PA-mTORC1. Suppressing PA production substantially increased the sensitivity of mTORC2 to rapamycin. Data provided here demonstrate a PA requirement for the stabilization of both mTORC1 and mTORC2 complexes and reveal a mechanism for the inhibitory effect of rapamycin on mTOR. This study also suggests that by suppressing PLD activity, mTORC2 could be targeted therapeutically with rapamycin.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".