Molecular Basis for the Regulation of Transcriptional Coactivator p300 in Myogenic Differentiation
Bibliographic record
Abstract
Skeletal myogenesis is a highly ordered process which specifically depends on the function of transcriptional coactivator p300. Previous studies have established that Akt/protein kinase B (PKB), a positive regulator of p300 in proliferating cells, is also important for proper skeletal muscle development. Nevertheless, it is not clear as to how the p300 is regulated by myogenic signaling events given that both p300 and Akt are involved in many cellular processes. Our studies revealed that the levels of p300 protein are temporally maintained in ligand-enhanced skeletal myocyte development. Interestingly, this maintenance of p300 protein is observed at the stage of myoblast differentiation, which coincides with an increase in Akt phosphorylation. Moreover, regulation of p300 during myoblast differentiation appears to be mediated by Akt signaling. Blunting of p300 impairs myogenic expression and myoblast differentiation. Thus, our data suggests a particular role for Akt in myoblast differentiation through interaction with p300. Our studies also establish the potential of exploiting p300 regulation and Akt activation to decipher the complex signaling cascades involved in skeletal muscle development.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".