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Record W2061777219 · doi:10.1242/jcs.00525

Inhibition of p38 MAPK signaling promotes late stages of myogenesis

2003· article· en· W2061777219 on OpenAlexaff
Andrea D. Weston, Arthur V. Sampaio, Alan G. Ridgeway, T. Michael Underhill

Bibliographic record

VenueJournal of Cell Science · 2003
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsWestern University
Fundersnot available
KeywordsMyogenesisBiologyCell biologyLimb budMyocytep38 mitogen-activated protein kinasesC2C12ChondrogenesisMyosinMesenchymeMAPK/ERK pathwayMyoDMesenchymal stem cellKinaseEmbryo

Abstract

fetched live from OpenAlex

Signaling through the p38 mitogen-activated protein kinases (MAPKs) is essential for cartilage formation in primary cultures of limb mesenchyme. Here we show that, concurrent with a decrease in chondrogenesis, inhibition of p38 in limb bud cultures dramatically promotes muscle development. Specifically, treatment of primary limb bud cultures with p38 inhibitors increases the expression of myogenic markers and causes a striking increase in formation of myotubes, which were detected using antibodies specific for myosin heavy chain. These results are surprising in that they contrast with several previous reports describing a requirement for p38 during myogenesis. Nonetheless, the enhanced myogenesis leads to the formation of an extensive network of contractile myofibers, and this enhanced myogenesis can be conferred upon myogenic cells from clonal populations, such as G8 or C2C12 cells, if they are co-cultured with the limb mesenchymal cells. We provide evidence for the maintenance and rapid organization of existing, somitic-derived limb myoblasts in response to p38 inhibitors. These findings imply a novel and unexpected role for p38 MAPK inhibition in myogenesis and highlight the importance of the limb bud microenvironment in promoting the progression of limb myoblasts.

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 score0.001
Threshold uncertainty score0.161

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 studies0.0000.000
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 head0.008
GPT teacher head0.237
Teacher spread0.228 · 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

Citations54
Published2003
Admission routes1
Has abstractyes

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