Epigenetic regulation of satellite cell fate during skeletal muscle regeneration
Bibliographic record
Abstract
In response to muscle injury, muscle stem cells integrate environmental cues in the damaged tissue to mediate regeneration. These environmental cues are tightly regulated to ensure expansion of muscle stem cell population to repair the damaged myofibers while allowing repopulation of the stem cell niche. These changes in muscle stem cell fate result from changes in gene expression that occur in response to cell signaling from the muscle environment.Integration of signals from the muscle environment leads to changes in gene expression through epigenetic mechanisms. Such mechanisms, including post-translational modification of chromatin and nucleosome repositioning, act to make specific gene loci more, or less, accessible to the transcriptional machinery. In youth, the muscle environment is ideally structured to allow for coordinated signaling that mediates efficient regeneration. Both age and disease alter the muscle environment such that the signaling pathways that shape the healthy muscle stem cell epigenome are altered. Altered epigenome reduces the efficiency of cell fate transitions required for muscle repair and contributes to muscle pathology. However, the reversible nature of epigenetic changes holds out potential for restoring cell fate potential to improve muscle repair in myopathies.In this review, we will describe the current knowledge of the mechanisms allowing muscle stem cell fate transitions during regeneration and how it is altered in muscle disease. In addition, we provide some examples of how epigenetics could be harnessed therapeutically to improve regeneration in various muscle pathologies.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".