Myf6/MRF4 is a Myogenic Niche Regulator Required for the Maintenance of the Muscle Stem Cell Pool
Bibliographic record
Abstract
Abstract In metazoans, skeletal muscle evolved to contract and produce force. However, recent experimental evidence suggests that skeletal muscle has also acquired endocrine functions and produces a vast array of myokines. Using ChIP-Seq and gene expression analyses of myogenic factors, we show that Myf6/MRF4 transcriptionally regulates a broad spectrum of myokines and muscle-secreted proteins, including ligands for downstream activation of key signaling pathways such as EGFR, STAT3 and VEGFR. Homozygous deletion of Myf6 causes a significant reduction in the ability of muscle to produce key myokines such as EGF, VEGFA and LIF. Consequently, although Myf6 knockout mice are born with a normal muscle stem cell compartment, they undergo progressive reduction in their stem cell pool during postnatal life. Mechanistically, muscle stem cells from the Myf6 knockout animals show defects in activation of EGFR and STAT3 signaling, upregulate the p38 MAP kinase pathway and spontaneously break from quiescence. Exogenous application of recombinant EGF and LIF rescue the defects in the muscle stem cell pool of Myf6 knockout animals. Finally, skeletal muscles of mice lacking Myf6 have a significantly reduced ability to sustain donor-engrafted muscle stem cells. Taken together, our data uncovers a novel role for Myf6 in regulating the expression of niche factors and myokines to maintain the skeletal muscle stem cell pool in adult mice.
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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 | 0.001 |
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".