Delineating the relationship between immune system aging and myogenesis in muscle repair
Bibliographic record
Abstract
Recruited immune cells play a critical role in muscle repair, in part by interacting with local stem cell populations to regulate muscle regeneration. How aging affects their communication during myogenesis is unclear. Here, we investigate how aging impacts the cellular function of these two cell types after muscle injury during normal aging or after immune rejuvenation using a young to old (Y-O) or old to old (O-O) bone marrow (BM) transplant model. We found that skeletal muscle from old mice (20 months) exhibited elevated basal inflammation and possessed fewer satellite cells compared with young mice (3 months). After cardiotoxin muscle injury (CTX), old mice exhibited a blunted inflammatory response compared with young mice and enhanced M2 macrophage recruitment and IL-10 expression. Temporal immune and cytokine responses of old mice were partially restored to a young phenotype following reconstitution with young cells (Y-O chimeras). Improved immune responses in Y-O chimeras were associated with greater satellite cell proliferation compared with O-O chimeras. To identify how immune cell aging affects myoblast function, conditioned media (CM) from activated young or old macrophages was applied to cultured C2C12 myoblasts. CM from young macrophages inhibited myogenesis while CM from old macrophages reduced proliferation. These functional differences coincided with age-related differences in macrophage cytokine expression. Together, this study examines the infiltration and proliferation of immune cells and satellite cells after injury in the context of aging and, using BM chimeras, demonstrates that young immune cells retain cell autonomy in an old host to increase satellite cell proliferation.
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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) | 0.000 | 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".