Smad7 Promotes and Enhances Skeletal Muscle Differentiation
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".