Loss of dystrophin expression in skeletal muscle is associated with senescence of macrophages and endothelial cells
Bibliographic record
Abstract
Cellular senescence is the irreversible arrest of normally dividing cells and is driven by cell cycle inhibitory proteins such as p16, p21, and p53. When cells enter senescence, they secrete a host of proinflammatory factors known as the senescence-associated secretory phenotype, which has deleterious effects on surrounding cells and tissues. Little is known of the role of senescence in Duchenne muscular dystrophy (DMD), the fatal X-linked neuromuscular disorder typified by chronic inflammation, extracellular matrix remodeling, and a progressive loss in muscle mass and function. Here, we demonstrate using C57- mdx (8-wk-old) and D2- mdx (4-wk-old and 8-wk-old) mice, two mouse models of DMD, that cells displaying canonical markers of senescence are found within the skeletal muscle. Eight-week-old D2- mdx mice, which display severe muscle pathology, had greater numbers of senescent cells associated with areas of inflammation, which were mostly Cdkn1a-positive macrophages, whereas in C57- mdx muscle, senescent populations were endothelial cells and macrophages localized to newly regenerated myofibers. Interestingly, this pattern was similar to cardiotoxin (CTX)-injured wild-type (WT) muscle, which experienced a transient senescent response. Dystrophic muscle demonstrated significant upregulations in senescence pathway genes [ Cdkn1a (p21), Cdkn2a (p16INK4A), and Trp53 (p53)], which correlated with the quantity of senescence-associated β-galactosidase (SA-β-Gal)-positive cells. These results highlight an underexplored role for cellular senescence in murine dystrophic muscle.
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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".