Endothelial Cell mTOR Complex-2 Regulates Sprouting Angiogenesis
Bibliographic record
Abstract
Tumor neovascularization is targeted by inhibition of vascular endothelial growth factor (VEGF) or the receptor to prevent tumor growth, but drug resistance to angiogenesis inhibition limits clinical efficacy. Inhibition of the phosphoinositide 3 kinase pathway intermediate, mammalian target of rapamycin (mTOR), also inhibits tumor growth and may prevent escape from VEGF receptor inhibitors. mTOR is assembled into two separate multi-molecular complexes, mTORC1 and mTORC2. The direct effect of mTORC2 inhibition on the endothelium and tumor angiogenesis is poorly defined. We used pharmacological inhibitors and RNA interference to determine the function of mTORC2 versus Akt1 and mTORC1 in human endothelial cells (EC). Angiogenic sprouting, EC migration, cytoskeleton re-organization, and signaling events regulating matrix adhesion were studied. Sustained inactivation of mTORC1 activity up-regulated mTORC2-dependent Akt1 activation. In turn, ECs exposed to mTORC1-inhibition were resistant to apoptosis and hyper-responsive to renal cell carcinoma (RCC)-stimulated angiogenesis after relief of the inhibition. Conversely, mTORC1/2 dual inhibition or selective mTORC2 inactivation inhibited angiogenesis in response to RCC cells and VEGF. mTORC2-inactivation decreased EC migration more than Akt1- or mTORC1-inactivation. Mechanistically, mTORC2 inactivation robustly suppressed VEGF-stimulated EC actin polymerization, and inhibited focal adhesion formation and activation of focal adhesion kinase, independent of Akt1. Endothelial mTORC2 regulates angiogenesis, in part by regulation of EC focal adhesion kinase activity, matrix adhesion, and cytoskeletal remodeling, independent of Akt/mTORC1.
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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 | 0.001 | 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".