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Record W2804014398 · doi:10.1016/j.celrep.2018.04.058

Loss of a Negative Regulator of mTORC1 Induces Aerobic Glycolysis and Altered Fiber Composition in Skeletal Muscle

2018· article· en· W2804014398 on OpenAlexaff
Paul A. Dutchak, Sandi Jo Estill, Abigail A. Plec, Xiaozheng Zhao, Chendong Yang, Jun Chen, Bookyung Ko, Ralph J. DeBerardinis, Yonghao Yu, Benjamin P. Tu

Bibliographic record

VenueCell Reports · 2018
Typearticle
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsUniversité Laval
FundersNational Institute of General Medical SciencesNational Cancer InstituteNational Institutes of HealthCancer Prevention and Research Institute of TexasWelch Foundation
KeywordsmTORC1GlycolysisBiologyOxidative phosphorylationSkeletal muscleCell biologyRegulatorMitochondrionCitric acid cycleBiochemistryMetabolismEndocrinologySignal transductionPI3K/AKT/mTOR pathwayGene

Abstract

fetched live from OpenAlex

The conserved GATOR1 complex consisting of NPRL2-NPRL3-DEPDC5 inhibits mammalian target of rapamycin complex 1 (mTORC1) in response to amino acid insufficiency. Here, we show that loss of NPRL2 and GATOR1 function in skeletal muscle causes constitutive activation of mTORC1 signaling in the fed and fasted states. Muscle fibers of NPRL2 knockout animals are significantly larger and show altered fiber-type composition, with more fast-twitch glycolytic and fewer slow-twitch oxidative fibers. NPRL2 muscle knockout mice also have altered running behavior and enhanced glucose tolerance. Furthermore, loss of NPRL2 induces aerobic glycolysis and suppresses glucose entry into the TCA cycle. Such chronic activation of mTORC1 leads to compensatory increases in anaplerotic pathways to replenish TCA intermediates that are consumed for biosynthetic purposes. These phenotypes reveal a fundamental role for the GATOR1 complex in the homeostatic regulation of mitochondrial functions (biosynthesis versus ATP) to mediate carbohydrate utilization in muscle.

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 score0.203
Threshold uncertainty score0.336

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 head0.012
GPT teacher head0.258
Teacher spread0.247 · 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

Citations50
Published2018
Admission routes1
Has abstractyes

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