Bibliographic record
Abstract
mTORC1 plays a crucial role in protein synthesis and cell proliferation and growth. It is activated by growth factors and amino acids, including essential amino acids (EAAs), such as leucine; Leu enters cells via the Leu transporter LAT1-4F2hc (also known as SLC7A5-SLC3A2) and potentially via endocytosis. Here, we investigated the contribution of the different routes of Leu entry into cells to mTORC1 activation using pharmacological inhibitors and cells that lack LAT1 or dynamin-1, -2 and -3. Our results show that LAT1 is the major route of Leu entry into cells and mTORC1 activation (∼70%), whereas dynamin-dependent endocytosis and macropinocytosis contribute minimally to both (5-15%). However, macropinocytosis contributes significantly (∼40%) to activation of mTORC1 by other EAAs. Surprisingly, the dynamin inhibitors dynasore and Dyngo 4A, which minimally inhibited Leu uptake, abolished mTORC1 activation independently of dynamin. Instead, dynasore inhibited RagA binding to Raptor, reduced mTORC1 recruitment to the lysosome, and inhibited Akt activation and TSC2-S939 phosphorylation; this resulted in inhibition of Rheb and mTORC1 activity. Our results suggest that these commonly used inhibitors of dynamin and endocytosis are potent suppressors of mTORC1 activation via off-target effects and not via dynamin inhibition.This article has an associated First Person interview with the first author of the paper.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".