TGFβ signaling curbs cell fusion and muscle regeneration
Bibliographic record
Abstract
Summary Fusion of muscle progenitor cells is necessary for skeletal muscle development and repair. Cell fusion is a multistep process involving cell migration, adhesion, membrane remodeling and actin-nucleation pathways to generate multinucleated myotubes. While the cellular and molecular mechanisms promoting muscle cell fusion have been intensely investigated in recent years, molecular brakes restraining cell–cell fusion events to control syncytia formation have remained elusive. Here, we show that transforming growth factor beta (TGFβ) signaling is active in adult muscle cells throughout the fusion process and reduce muscle cell fusion independently of the differentiation step. In contrast, inhibition of TGFβ signaling enhances cell fusion and promotes branching between myotubes. Pharmacological modulation of the pathway in vivo perturbs muscle regeneration after injury. Exogenous addition of TGFβ protein results in a loss of muscle function while inhibition of the TGFβ pathway induces the formation of giant myofibres. Transcriptome analyses and functional assays revealed that TGFβ acts on actin dynamics and reduce cell spreading through modulation of actin-based protrusions. Together our results reveal a signaling pathway that limits mammalian myoblast fusion and add a new level of understanding to the molecular regulation of myogenesis.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".