Fragile X mental retardation protein regulates skeletal muscle stem cell activity by regulating the stability of Myf5 mRNA
Bibliographic record
Abstract
BACKGROUND: Regeneration of adult tissues relies on adult stem cells that are primed to enter a differentiation program, while typically remaining quiescent. In mouse skeletal muscle, these features are reconciled by multiple translational control mechanisms that ensure primed muscle stem cells (MuSCs) are not activated. In quiescent MuSCs, this concept is illustrated by reversible microRNA silencing of Myf5 translation, mediated by microRNA-31 and fragile X mental retardation protein (FMRP). METHODS: ) mice to support the role for FMRP in maintaining stem cell properties of the MuSC. We compare the activity of MuSCs in vivo after acute injury and engraftment, as well as ex vivo during culture. We use RNA immunoprecipitation and 3'UTR poly-adenine (poly(A)) length assays to assess the impact of FMRP on the stability of transcripts for myogenic regulatory factors. RESULTS: We show that RNA-binding FMRP is required to maintain the MuSC pool. More specifically, FMRP is required for stem cell properties of muscle stem cells, which include MuSC capacity to prime the myogenic program, their self-renewal, and their capacity to efficiently regenerate muscle. We provide evidence that FMRP regulation of MuSC activity occurs in part by the capacity of FMRP to directly bind Myf5 transcripts and impact rates of Myf5 deadenylation. CONCLUSIONS: Our results provide further evidence supporting a role for post-transcriptional silencing platforms by RNA-binding proteins in maintaining stemness properties of adult stem cells. In addition, deregulated MuSC activity in the absence of Fmr1 may have implications for fragile X syndrome, which is associated with muscle hypotonia during infancy.
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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) | 0.002 | 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".