Maintenance of the Undifferentiated State in Myogenic Progenitor Cells by TGFβ Signaling is Smad Independent and Requires MEK Activation
Bibliographic record
Abstract
Transforming growth factor β (TGFβ) is a pluripotent cytokine and regulates a myriad of biological processes. It has been established that TGFβ potently inhibits skeletal muscle differentiation; however, the molecular mechanism is not clearly defined. Previously, we reported that inhibition of the TGFβ canonical pathway by an inhibitory Smad, Smad7, does not reverse this effect on differentiation, suggesting that activation of receptor Smads (R-Smads) by TGFβ is not responsible for repression of myogenesis. In addition, pharmacological blockade of Smad3 activation by TGFβ did not reverse TGFβ’s inhibitory effect on myogenesis. In considering other pathways, we observed that TGFβ potently activates MEK/ERK, and a pharmacological inhibitor of MEK reversed TGFβ’s inhibitory effect on myogenesis, as indicated by a myogenin promoter-reporter gene, sarcomeric myosin heavy chain accumulation, and phenotypic myotube formation. Furthermore, we found that c-Jun, a known potent repressor of myogenesis, which is coincidently also a down-stream target of MEK/ERK signaling, was phosphorylated and accumulates in the nucleus in response to TGFβ activation. Taken together, these observations support a model in which TGFβ activates a MEK/ERK/c-Jun pathway to repress skeletal myogenesis, maintaining the pluripotent undifferentiated state in myogenic progenitors.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".