Bibliographic record
Abstract
Mammalian target of rapamycin (mTOR) is a protein kinase that integrates signals including stress and nutrient availability, to modulate the metabolic state of the cell. Interestingly, mTOR complex 1 (mTORC1) is specifically found on lysosomes where it regulates lysosome function, trafficking and morphology. However, little is known about how mTOR interfaces with lysosomal regulators, like the Rab7 GTPase, the canonical mediator of lysosome function, including biogenesis and trafficking to lysosomes to control processes such as antigen presentation and cell signaling. Rab7 can exist in two states. GDP-bound Rab7 is inactive but can be converted to GTP-bound Rab7, the catalytically active form of Rab7. Guanine nucleotide exchange factors (GEFs) are responsible for this conversion. Conversely, GTP-hydrolysis activating proteins (GAPs) facilitate Rab7 GTP hydrolysis, resulting in the conversion of GTP to GDP, rendering Rab7 inactive. We hypothesized that mTOR and Rab7 are functionally linked. Here we show that mTOR negatively controls Rab7. mTOR inhibition through the use of torin, EBSS (an amino acid deprived media) and A-769662 (an AMPK activator), increased levels of both total and active Rab7 (Rab7-GTP) on the lysosomal membrane. This was observed through fractionation, fluorescence microscopy and biochemical pull-down assays. This negative regulation is specific to mTOR, acting through TBC1D15, a Rab7 GAP. mTOR inhibition resulted in a significant reduction in levels of TBC1D15 via ubiquitination, which can be restored upon inhibition of the proteasome. Additionally, mTOR inhibition increased lysosomal proteolytic activity, and enhanced trafficking of material toward the lysosome. Our work suggests that under starvation conditions, trafficking to the lysosome is accelerated, re-establishing nutrient homeostasis. This research could provide a novel link between membrane regulation (Rab7) and metabolic stress sensors (mTOR), allowing insight into the pathogenesis of several metabolic diseases, including certain forms of cancer.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".