TGFβ-Smad3 signaling restores cell-autonomous Srsf1-mediated splicing of fibronectin in aged skeletal muscle stem cells
Bibliographic record
Abstract
Loss of Fibronectin (FN) from the skeletal muscle stem cell (MuSC) niche represents a root cause of regenerative failure in aging. While FN has pleiotropic functions during healthy skeletal muscle regeneration, it remains unclear how aging affects its spatiotemporal specificity for MuSCs. Here, we demonstrate that activated MuSCs secrete an autoregulatory FN splice variant containing the EDB extra domain (EDB(+) FN), which is not expressed by accessory cells in the niche. EDB(+) FN splicing in MuSCs depends on serine/arginine-rich splicing factor 1 (Srsf1) whose promoter is controlled by Smad3. EDB(+) FN knockdown or downregulation in aging affects MuSC proliferation through aberrant integrin signaling and impairs skeletal muscle regeneration. During a defined regeneration interval in aged mice, Smad3 activation using transforming growth factor-beta 1 (TGFβ1) improves MuSC function and skeletal muscle repair by stimulating EDB(+) FN secretion. Altogether, we identify and characterize the TGFβ1-Smad3-Srsf1-EDB(+) FN pathway as a therapeutic target for age-associated regenerative failure.
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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".