Peroxisome Proliferator-activated Receptor γ Ligands Differentially Modulate Muscle Cell Differentiation and MyoD Gene Expression via Peroxisome Proliferator-activated Receptor γ-dependent and -independent Pathways
Bibliographic record
Abstract
The effects of distinct classes of peroxisome proliferator-activated receptor gamma (PPARgamma) ligands on myogenesis and MyoD gene expression were examined in mouse skeletal muscle C2C12 myoblasts. Treatment of C2C12 cells with the PPARgamma ligand, 15-deoxy-Delta12,14-prostaglandin J2 (15d-PGJ2), repressed morphologically defined myogenesis and reduced endogenous mRNA levels of the myogenic differentiation markers MyoD, myogenin, and alpha-actin. In contrast, two synthetic PPARgamma ligands, L-805645 and ciglitazone, exhibited no effects. In transient transfection assays, 15d-PGJ2 specifically inhibited the expression of a MyoD promoter-luciferase reporter gene (MyoDLuc) in a cell type- and promoter-specific manner, indicating that 15d-PGJ2 functions in part by repressing MyoD gene transcription. The inhibition of MyoD gene expression by 15d-PGJ2 is mediated by the distal region of the MyoD gene promoter. PPARgamma on its own also inhibited MyoDLuc expression and further augmented the 15d-PGJ2 response. In contrast, L-805645 and ciglitazone did not inhibit MyoDLuc expression on their own but did so in the presence of ectopically expressed PPARgamma. Interestingly, a transdominant inhibitor of PPARgamma (hPPARgamma2Delta500) had no effect on the 15d-PGJ2-dependent repression of MyoDLuc expression but overcame L-805645/PPARgamma-dependent repression. Finally, saturating concentrations of L-805645, which did not affect myogenesis, failed to ablate 15d-PGJ2-mediated repression of the myogenic program. Thus, distinct PPARgamma ligands may repress MyoD gene expression through PPARgamma-dependent and -independent pathways, and 15d-PGJ2 can inhibit the myogenic program independent of its cognate receptor, PPARgamma.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".