The glucocorticoid receptor is a critical regulator of muscle satellite cell quiescence
Bibliographic record
Abstract
Abstract Glucocorticoids are powerful anti-inflammatory medications that are associated with muscle atrophy. The effect of glucocorticoids in myofibers is well-studied, yet the role of the glucocorticoid receptor (GR), the primary mediator of glucocorticoid transcriptional responses, and the impact of glucocorticoid signalling in muscle stem cells (MuSCs), the adult progenitors responsible for regeneration, remain unknown. We developed a conditional null mouse model to knock out glucocorticoid receptor (GR) expression in MuSCs (GR MuSC-/- ) and established that while GR is dispensable for muscle regeneration, it is a critical regulator of MuSC homeostasis. Loss of GR significantly increased cycling MuSCs as compared to controls in injury-naïve mice and on single EDL myofiber cultures, and as such, loss of GR in MuSCs leads to precocious activation and subsequent proliferation as compared to controls. Bulk RNA-sequencing from in situ fixed MuSCs from injury-naïve GR MuSC-/- muscle identified a gene signature consistent with cells that have exited quiescence and undergone activation, with evidence of sexual dimorphism. Using ATAC-seq and footprinting we identify putative GR targets that promote quiescence. Thus, we advance the GR as a previously unrecognized crucial transcriptional regulator of gene expression in MuSCs whose activity is highest in quiescent cells and is essential to maintain that state.
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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) | 0.003 | 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".