Tyrosine phosphorylation of DEPTOR functions as a molecular switch to activate mTOR signaling
Bibliographic record
Abstract
Metabolic dysfunction is a major driver of tumorigenesis. The serine/threonine kinase mechanistic target of rapamycin (mTOR) constitutes a key central regulator of metabolic pathways promoting cancer cell proliferation and survival. mTOR activity is regulated by metabolic sensors as well as by numerous factors comprising the phosphatase and tensin homolog/PI3K/AKT canonical pathway, which are often mutated in cancer. However, some cancers displaying constitutively active mTOR do not carry alterations within this canonical pathway, suggesting alternative modes of mTOR regulation. Since DEPTOR, an endogenous inhibitor of mTOR, was previously found to modulate both mTOR complexes 1 and 2, we investigated the different post-translational modification that could affect its inhibitory function. We found that tyrosine (Tyr) 289 phosphorylation of DEPTOR impairs its interaction with mTOR, leading to increased mTOR activation. Using proximity biotinylation assays, we identified SYK (spleen tyrosine kinase) as a kinase involved in DEPTOR Tyr 289 phosphorylation in an ephrin (erythropoietin-producing hepatocellular carcinoma) receptor-dependent manner. Altogether, our work reveals that phosphorylation of Tyr 289 of DEPTOR represents a novel molecular switch involved in the regulation of both mTOR complex 1 and mTOR complex 2.
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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".