Immune aging impairs muscle regeneration via macrophage-derived anti-oxidant selenoprotein P
Bibliographic record
Abstract
Abstract Muscle regeneration is impaired in the aged organism, due to both intrinsic defects of muscle stem cells (MuSCs) and alterations of their environmental niche. However, the latter has still been poorly explored. Here, we compared and analyzed the time course of the various cell types constituting the MuSC niche during muscle generation in young and old mice. Aging altered the amplification of all niche cells with particularly prominent phenotypes in macrophages that impaired the resolution of inflammation in the old regenerating muscle. RNAsequencing of FACs-isolated MuSCs and non-myogenic niche cells during regeneration uncovered specific profiles and kinetics of genes and molecular pathways differentially regulated in old versus young regenerating muscle, indicating that each cell type responded to aging in a specific manner. Through this, we discovered that macrophages have a strong signature of aging with altered the activation of Selenoprotein P (Sepp1) expression in macrophages during the resolution of inflammation in regenerating muscle. Macrophage-specific deletion of Sepp1 gene was sufficient to impair the acquisition of the repair inflammatory profile, perturbed the support of macrophages to MuSCs in vitro and in vivo , and to cause inefficient skeletal muscle regeneration. When transplanted in aged mice, bone marrow from young WT mice, but not Sepp1 KOs, restored muscle regeneration to youthful levels. Altogether this work provides a unique resource to study the aging of the MuSC niche, reveals that aging of niche cells is asynchronous and establishes impaired macrophage dynamics/polarization and the anti-oxidant Selenoprotein P expression as drivers of age-related decline of muscle regeneration. Teaser: Cell profiling reveals asynchronicity of aging in the muscle stem cell niche and age-dependent macrophage/stem cell interactions through anti-oxidant selenoprotein P
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".