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Record W2153931999 · doi:10.1002/jcp.22320

PKCε regulates contraction‐stimulated GLUT4 traffic in skeletal muscle cells

2010· article· en· W2153931999 on OpenAlexafffund
Wenyan Niu, Philip J. Bilan, Jun-Na Yu, Jing Gao, Shlomit Boguslavsky, Jonathan D. Schertzer, Zhi Yao, Amira Klip

Bibliographic record

VenueJournal of Cellular Physiology · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism, Diabetes, and Cancer
Canadian institutionsHospital for Sick Children
FundersCanadian Institutes of Health Research
KeywordsProtein kinase CGLUT4Cell biologyCarbacholMyogenesisChemistryC2C12MyocyteMolecular biologySignal transductionBiologyGlucose transporterEndocrinologyBiochemistryReceptorInsulin

Abstract

fetched live from OpenAlex

The signaling pathways that stimulate glucose uptake in response to muscle contraction are not well defined. Recently, we showed that carbachol, an acetylcholine analog, stimulates contraction of C2C12 myotube cultures and the rapid arrival of myc-epitope tagged GLUT4 glucose transporters at the cell surface. Here, we explore a role for protein kinase C (PKC) in regulating GLUT4 traffic. Cell surface carbachol-induced GLUT4myc levels were partly inhibited by the conventional/novel PKC inhibitors GF-109203X, Gö6983, and Ro-31-8425 but not by the conventional PKC inhibitor Gö6976. C2C12 myotubes expressed several novel isoforms of PKC mRNA with PKCδ and PKCε in greater abundance. Carbachol stimulated phosphorylation of PKC isoforms and translocation of PKCδ and PKCε to membranes within 5 min. However, only a peptidic inhibitor of PKCε translocation (myristoylated-EAVSLKPT), but not one of PKCδ (myristoylated-SFNSYELGSL), prevented the GLUT4myc response to carbachol. Significant participation of PKCε in the carbachol-induced gain of GLUT4myc at the surface of C2C12 myotubes was further supported through siRNA-mediated PKCε protein knockdown. These findings support a role for novel PKC isoforms, especially PKCε, in contraction-stimulated GLUT4 traffic in muscle cells.

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 score0.187
Threshold uncertainty score0.641

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 head0.004
GPT teacher head0.219
Teacher spread0.214 · 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

Citations26
Published2010
Admission routes2
Has abstractyes

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