Activation of PPARδ stimulates utrophin A expression in skeletal muscle cells
Bibliographic record
Abstract
A potential therapeutic strategy to treat Duchenne Muscular Dystrophy (DMD) involves upregulation of utrophin in muscle fibers of patients. Our recent studies demonstrated that utrophin A is regulated by mechanisms that also promote expression of the slow myofiber program (PNAS 100: –6, 2003; Hum Mol Genet. 13: –88, 2004). Since activation of PPARδ in muscle causes a fiber type shift towards the slower, more oxidative phenotype, we initiated studies to determine whether PPARδ activation also increases utrophin expression. Treatment of C2C12 muscle cells with GW501516 , a PPARδ agonist, caused a 50–100% increase in utrophin A mRNA levels. Examination of the human utrophin A promoter region revealed the presence of a conserved PPAR response element (PPRE). GW501516 treatment of C2C12 cells transfected with a utrophin A promoter‐reporter construct induced a 50–100% increase in reporter activity. This effect was abrogated by deletion of the PPRE. Treatment of utrophin A promoter reporter transgenic mice with GW501516 also stimulated utrophin A promoter activity. In mdx mice, PPARδ was found to be higher in edl and soleus muscles compared to wild type control mice. Based on these results, activation of PPARδ, which promotes utrophin A expression, appears as a promising therapeutic intervention to counteract the devastating effects of DMD. Funded by MDA.
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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) | 0.004 | 0.002 |
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".