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Record W2107135074 · doi:10.1083/jcb.200709083

Megf10 regulates the progression of the satellite cell myogenic program

2007· article· en· W2107135074 on OpenAlexaff
Chet E. Holterman, Fabien Le Grand, Shihuan Kuang, Patrick Seale, Michael A. Rudnicki

Bibliographic record

VenueThe Journal of Cell Biology · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsMcMaster UniversityStem Cell NetworkUniversity of Ottawa
FundersNational Institute of Arthritis and Musculoskeletal and Skin Diseases
KeywordsBiologyMyogeninMyoDCell biologyMyogenic regulatory factorsNotch signaling pathwayMyocyteMyoD ProteinCellular differentiationC2C12MyogenesisCell growthPopulationGene silencingSignal transductionGenetics

Abstract

fetched live from OpenAlex

We identify here the multiple epidermal growth factor repeat transmembrane protein Megf10 as a quiescent satellite cell marker that is also expressed in skeletal myoblasts but not in differentiated myofibers. Retroviral expression of Megf10 in myoblasts results in enhanced proliferation and inhibited differentiation. Infected myoblasts that fail to differentiate undergo cell cycle arrest and can reenter the cell cycle upon serum restimulation. Moreover, experimental modulations of Megf10 alter the expression levels of Pax7 and the myogenic regulatory factors. In contrast, Megf10 silencing in activated satellite cells on individual fibers or in cultured myoblasts results in a dramatic reduction in the cell number, caused by myogenin activation and precocious differentiation as well as a depletion of the self-renewing Pax7+/MyoD- population. Additionally, Megf10 silencing in MyoD-/- myoblasts results in down-regulation of Notch signaling components. We conclude that Megf10 represents a novel transmembrane protein that impinges on Notch signaling to regulate the satellite cell population balance between proliferation and differentiation.

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)0.001
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 score0.031
Threshold uncertainty score0.208

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.006
GPT teacher head0.265
Teacher spread0.260 · 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

Citations99
Published2007
Admission routes1
Has abstractyes

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