MétaCan
Menu
Back to cohort
Record W2033642648 · doi:10.1128/mcb.00384-06

Smad7 Promotes and Enhances Skeletal Muscle Differentiation

2006· article· en· W2033642648 on OpenAlexafffund
Helen D. Kollias, Robert L. Perry, Tetsuaki Miyake, Arif Aziz, John C. McDermott

Bibliographic record

VenueMolecular and Cellular Biology · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicTGF-β signaling in diseases
Canadian institutionsYork University
FundersCanadian Institutes of Health ResearchMuscular Dystrophy Association
KeywordsMyogenesisMyoDMyostatinMyoD ProteinBiologySkeletal muscleMyogeninPITX2MyocyteC2C12Cellular differentiationCell biologyTranscription factorEndocrinologyGeneticsHomeobox

Abstract

fetched live from OpenAlex

Transforming growth factor beta1 (TGF-beta1) and myostatin signaling, mediated by the same Smad downstream effectors, potently repress skeletal muscle cell differentiation. Smad7 inhibits these cytokine signaling pathways. The role of Smad7 during skeletal muscle cell differentiation was assessed. In these studies, we document that increased expression of Smad7 abrogates myostatin- but not TGF-beta1-mediated repression of myogenesis. Further, constitutive expression of exogenous Smad7 potently enhanced skeletal muscle differentiation and cellular hypertrophy. Conversely, targeting of endogenous Smad7 by small interfering RNA inhibited C2C12 muscle cell differentiation, indicating an essential role for Smad7 during myogenesis. Congruent with a role for Smad7 in myogenesis, we observed that the muscle regulatory factor (MyoD) binds to and transactivates the Smad7 proximal promoter region. Finally, we document that Smad7 directly interacts with MyoD and enhances MyoD transcriptional activity. Thus, Smad7 cooperates with MyoD, creating a positive loop to induce Smad7 expression and to promote MyoD driven myogenesis. Taken together, these data implicate Smad7 as a fundamental regulator of differentiation in skeletal 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 score0.020
Threshold uncertainty score0.631

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 head0.003
GPT teacher head0.203
Teacher spread0.200 · 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

Citations80
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueMolecular and Cellular BiologySame topicTGF-β signaling in diseasesFrench-language works237,207