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Record W2755382968 · doi:10.14814/phy2.13395

Depletion of the mRNA translation initiation inhibitor, programmed cell death protein 4 (PDCD4), impairs L6 myotube formation

2017· article· en· W2755382968 on OpenAlexafffund
Naomi Maeda, Abdikarim Abdullahi, Brendan Beatty, Zameer Dhanani, Olasunkanmi A. J. Adegoke

Bibliographic record

VenuePhysiological Reports · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaYork University
KeywordsmTORC1P70-S6 Kinase 1Programmed cell deathCell biologyPoly ADP ribose polymeraseUbiquitin ligaseBiologyMyogenesisApoptosisMyocyteC2C12Protein degradationDownregulation and upregulationChemistryMolecular biologySignal transductionPI3K/AKT/mTOR pathwayUbiquitinBiochemistryPolymeraseDNAGene

Abstract

fetched live from OpenAlex

The mechanistic (mammalian) target of rapamycin complex 1 (mTORC1) signaling is vital for optimal muscle mass and function. Although the significance of mTORC1 in stimulating muscle growth is unequivocal, evidence in support of its role during muscle regeneration is less clear. Here, we showed that the abundance (protein and mRNA) of the mTORC1/S6K1 substrate, programmed cell death protein 4 (PDCD4), is upregulated at the onset of differentiation of L6 and C2C12 cells. The increase in PDCD4 was not associated with any changes in S6K1 activation, but the abundance of beta transducing repeat-containing protein (β-TrCP), the ubiquitin ligase that targets PDCD4 for degradation, increased. Myoblasts lacking PDCD4 showed impaired myotube formation and had markedly low levels of MHC-1. Analysis of poly (ADP-ribose) Polymerase (PARP), caspase 7 and caspase 3 indicated reduced apoptosis in PDCD4-deficient cells. Our data demonstrate a role for PDCD4 in muscle cell formation and suggest that interventions that target this protein may hold promise for managing conditions associated with impaired myotube formation.

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 score0.343
Threshold uncertainty score0.377

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 head0.030
GPT teacher head0.269
Teacher spread0.239 · 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

Citations7
Published2017
Admission routes2
Has abstractyes

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