The <scp>AMPK</scp> allosteric activator <scp>MK</scp> ‐8722 improves the histology and spliceopathy in myotonic dystrophy type 1 ( <scp>DM1</scp> ) skeletal muscle
Bibliographic record
Abstract
Abstract Multiple signaling pathways have been reported to be altered in Myotonic Dystrophy type 1 (DM1) skeletal muscle, contributing to pathogenicity. In particular, previous work established that AMPK signaling, a key sensor of energy metabolism, is repressed in DM1 mouse muscle and that activating AMPK through exercise and/or with pharmacological activators is beneficial for the DM1 muscle phenotype. Here, we explored the effects of a newer, more specific allosteric AMPK activator acting directly on AMPK. We treated male and female HSA LR mice for 1 and 4 weeks with a daily injection of the allosteric activator MK‐8722, the AMP mimetic AICAR, or vehicle. Our results show that 1 and 4 weeks of treatment with MK‐8722 improves alternative splicing toward wild‐type levels in male and female HSA LR muscle. However, the effects of MK‐8722 were more modest compared to AICAR. In contrast, 4 weeks of treatment with MK‐8722 improved muscle histology to a greater extent than AICAR. As expected with AMPK activation, 4 weeks of treatment with MK‐8722 and AICAR promoted the expression of slower, more oxidative fibers. Finally, acute injections of MK‐8722 and AICAR triggered the rapid and transient increase in phospho‐AMPK in muscle. However, the peak of AMPK phosphorylation was lower with MK‐8722 compared to AICAR, thereby explaining the more modest effects of AMPK allosteric activation. Altogether, our data demonstrate that chronic activation of AMPK with specific pharmacological activators is beneficial for the DM1 muscle. They further indicate that at least a portion of the beneficial effects seen following the administration of these drugs occurs through AMPK.
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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 | 0.000 | 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) | 0.002 | 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".