Chronic AMPK activation induces beneficial phenotypic adaptations in mdx mouse skeletal muscle
Bibliographic record
Abstract
A therapeutic approach for Duchenne muscular dystrophy (DMD) is to upregulate utrophin levels in skeletal muscle in an effort to compensate for the lack of dystrophin. We have previously hypothesized that promotion of the slow, oxidative myogenic program, which triggers utrophin upregulation, can attenuate the dystrophic pathology in mdx animals, the murine model of DMD. Indeed, treatment of mdx mice with the PPARδ activator GW501516 shifted muscle phenotype and ameliorated the disease pathology (Miura et al. Hum Mol Genet. 18:4640–49, 2009). Treatment of healthy mice with the AMPK activator AICAR enhances oxidative capacity and triggers a fast-to-slow fiber-type transition. Our purpose was to evaluate the effects of chronic AICAR administration on muscle gene expression and the dystrophic pathology in mdx mice. AICAR mitigated muscle pseudo-hypertrophy and attenuated central nucleation. Furthermore, we observed an elevation in mitochondrial enzyme activity, an increase in MHC IIa-positive fibers, and slower twitch contraction kinetics in the EDL muscle. Utrophin and PGC-1α proteins were augmented in response to AICAR, concomitant with a reduction in RIP140. Exercise increased utrophin A, PGC-1α, and PPARδ mRNAs. This effect was attenuated with AICAR. Our data suggest that AICAR-evoked muscle plasticity results in beneficial phenotypic adaptations in mdx mice. Supported by MDA (USA), CIHR and NSERC.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".