Denervation Causes Fiber Atrophy and Myosin Heavy Chain Co-Expression in Senescent Skeletal Muscle
Bibliographic record
Abstract
Although denervation has long been implicated in aging muscle, the degree to which it is causes the fiber atrophy seen in aging muscle is unknown. To address this question, we quantified motoneuron soma counts in the lumbar spinal cord using choline acetyl transferase immunhistochemistry and quantified the size of denervated versus innervated muscle fibers in the gastrocnemius muscle using the in situ expression of the denervation-specific sodium channel, Nav₁.₅, in young adult (YA) and senescent (SEN) rats. To gain insights into the mechanisms driving myofiber atrophy, we also examined the myofiber expression of the two primary ubiquitin ligases necessary for muscle atrophy (MAFbx, MuRF1). MN soma number in lumbar spinal cord declined 27% between YA (638±34 MNs×mm⁻¹) and SEN (469±13 MNs×mm⁻¹). Nav₁.₅ positive fibers (1548±70 μm²) were 35% smaller than Nav₁.₅ negative fibers (2367±78 μm²; P<0.05) in SEN muscle, whereas Nav₁.₅ negative fibers in SEN were only 7% smaller than fibers in YA (2553±33 μm²; P<0.05) where no Nav₁.₅ labeling was seen, suggesting denervation is the primary cause of aging myofiber atrophy. Nav₁.₅ positive fibers had higher levels of MAFbx and MuRF1 (P<0.05), consistent with involvement of the proteasome proteolytic pathway in the atrophy of denervated muscle fibers in aging muscle. In summary, our study provides the first quantitative assessment of the contribution of denervation to myofiber atrophy in aging muscle, suggesting it explains the majority of the atrophy we observed. This striking result suggests a renewed focus should be placed on denervation in seeking understanding of the causes of and treatments for aging muscle atrophy.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".