REST/NRSF Preserves muscle stem cell identity by repressing alternate cell fate
Bibliographic record
Abstract
Cell fate and identity require timely activation of lineage-specific and concomitant repression of alternate-lineage genes. How this process is epigenetically encoded remains largely unknown. In skeletal muscle stem cells, the myogenic regulatory factors are well-established drivers of muscle gene activation but less is known about how non-muscle gene repression is achieved. Here, we show that the master epigenetic regulator, Repressor Element 1-Silencing Transcription factor (REST), also known as Neuron-Restrictive Silencer Factor (NRSF), is a key regulator of this process. We show that many non-lineage genes retain permissive chromatin state but are actively repressed by REST. Loss of functional REST in muscle stem cells and progenitors disrupts muscle specific epigenetic and transcriptional signatures, impairs differentiation, and triggers apoptosis in progenitor cells, leading to depletion of the stem cell pool. Consequently, REST-deficient skeletal muscle exhibits impaired regeneration and reduced myofiber growth postnatally. Collectively, our data suggests that REST plays a key role in safeguarding muscle stem cell identity by repressing multiple non-muscle lineage and developmentally regulated genes in adult mice. Here the authors show that REST/NRSF represses non-muscle lineage genes in muscle stem cells and progenitors, preserving their identity and differentiation capacity. Loss of REST disrupts gene silencing, impairs muscle regeneration, and leads to stem cell pool depletion.
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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".