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Record W2002712538 · doi:10.4161/sgtp.19422

Rags connect mTOR and autophagy

2012· article· en· W2002712538 on OpenAlexaff
Masashi Narita, Ken Inoki

Bibliographic record

VenueSmall GTPases · 2012
Typearticle
Languageen
FieldMedicine
TopicAutophagy in Disease and Therapy
Canadian institutionsInstitute of Cancer Research
FundersCancer Research UK
KeywordsAutophagyPI3K/AKT/mTOR pathwayCell biologyBiologyRPTORLysosomeCatabolismRegulatormTORC1Mechanistic target of rapamycinCompartment (ship)AnabolismAmino acidmTORC2ULK1Signal transductionBiochemistryKinaseProtein kinase AMetabolismApoptosisGeneEnzyme

Abstract

fetched live from OpenAlex

mTOR, the master regulator of protein metabolism, is activated by growth factor signaling, amino acids and other nutrients. Emerging evidence indicates that an unexpected physical association between mTOR and lysosomes plays a critical role in amino acid mediated mTOR activation. Rag GTPases, together with a multi-protein complex called Ragulator, mediate amino acid-mediated mTOR recruitment to the lysosome surface where mTOR becomes activated. Furthermore, mTOR is also recruited to a unique cytoplasmic compartment composed of autolysosomes, which is observed in oncogenic Ras-induced senescent (RIS) cells. Formation of this TOR-autophagy spatial coupling compartment (TASCC) seems to allow activation of mTOR and autophagy in a mutually reinforcing manner. Proper formation of the TASCC also requires active Rag proteins. Interestingly, inhibition of activity of Rag proteins also suppresses acute induction of secretory protein synthesis during RIS. Thus, the TASCC provides evidence for the functional relevance of the Rag-mediated association between lysosomes and mTOR, and provides a mechanism for the simultaneous activation of anabolic and catabolic processes.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.133
Threshold uncertainty score0.815

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.028
GPT teacher head0.275
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2012
Admission routes1
Has abstractyes

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