ERK1/2 signaling induces skeletal muscle slow fiber-type switching and reduces muscular dystrophy disease severity
Bibliographic record
Abstract
Mitogen-activated protein kinase (MAPK) signaling consists of an array of successively acting kinases. The extracellular signal-regulated kinases 1/2 (ERK1/2) are major components of the greater MAPK cascade that transduce growth factor signaling at the cell membrane. Here we investigated ERK1/2 signaling in skeletal muscle homeostasis and disease. Using mouse genetics, we observed that the muscle-specific expression of a constitutively active MEK1 mutant promotes greater ERK1/2 signaling that mediates fiber-type switching to a slow, oxidative phenotype with type I myosin heavy chain expression. Using a conditional and temporally regulated Cre strategy as well as Mapk1 (ERK2) and Mapk3 (ERK1) genetically targeted mice, MEK1-ERK2 signaling was shown to underlie this fast-to-slow fiber type switching in adult skeletal muscle as well as during development. Physiologic assessment of these activated MEK1-ERK1/2 mice showed enhanced metabolic activity and oxygen consumption with greater muscle fatigue resistance. Moreover, induction of MEK1-ERK1/2 signaling increased dystrophin and utrophin protein expression in a mouse model of limb-girdle muscle dystrophy and protected myofibers from damage. In summary, sustained MEK1-ERK1/2 activity in skeletal muscle produces a fast-to-slow fiber-type switch that protects from muscular dystrophy, suggesting a therapeutic approach to enhance the metabolic effectiveness of muscle and protect from dystrophic disease.
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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) | 0.002 | 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 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".