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Record W2172621350 · doi:10.1101/gad.265116.115

NLK phosphorylates Raptor to mediate stress-induced mTORC1 inhibition

2015· article· en· W2172621350 on OpenAlexfundno aff
Hai-Xin Yuan, Zhen Wang, Fa‐Xing Yu, Fulong Li, Ryan C. Russell, Jenna L. Jewell, Kun‐Liang Guan

Bibliographic record

VenueGenes & Development · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPI3K/AKT/mTOR signaling in cancer
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesNational Institutes of HealthNational Cancer InstituteCanadian Institutes of Health ResearchNational Natural Science Foundation of ChinaU.S. Department of Defense
KeywordsmTORC1Cell biologyBiologyPI3K/AKT/mTOR pathwaymTORC2Cell growthPhosphorylationSignal transductionRegulatorBiochemistryGene

Abstract

fetched live from OpenAlex

The mechanistic target of rapamycin (mTOR) is a central cell growth controller and forms two distinct complexes: mTORC1 and mTORC2. mTORC1 integrates a wide range of upstream signals, both positive and negative, to regulate cell growth. Although mTORC1 activation by positive signals, such as growth factors and nutrients, has been extensively investigated, the mechanism of mTORC1 regulation by stress signals is less understood. In this study, we identified the Nemo-like kinase (NLK) as an mTORC1 regulator in mediating the osmotic and oxidative stress signals. NLK inhibits mTORC1 lysosomal localization and thereby suppresses mTORC1 activation. Mechanistically, NLK phosphorylates Raptor on S863 to disrupt its interaction with the Rag GTPase, which is important for mTORC1 lysosomal recruitment. Cells with Nlk deletion or knock-in of the Raptor S863 phosphorylation mutants are defective in the rapid mTORC1 inhibition upon osmotic stress. Our study reveals a function of NLK in stress-induced mTORC1 modulation and the underlying biochemical mechanism of NLK in mTORC1 inhibition in stress response.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.136
Threshold uncertainty score0.928

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)0.0000.000

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 head0.027
GPT teacher head0.267
Teacher spread0.240 · 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 designBench or experimental
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

Citations56
Published2015
Admission routes1
Has abstractyes

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