Brain-derived Neurotrophic Factor Regulates Satellite Cell Differentiation and Skeltal Muscle Regeneration
Bibliographic record
Abstract
In adult skeletal muscle, brain-derived neurotrophic factor (BDNF) is expressed in myogenic progenitors known as satellite cells. To functionally address the role of BDNF in muscle satellite cells and regeneration in vivo, we generated a mouse in which BDNF is specifically depleted from skeletal muscle cells. For comparative purposes, and to determine the specific role of muscle-derived BDNF, we also examined muscles of the complete BDNF(-/-) mouse. In both models, expression of the satellite cell marker Pax7 was significantly decreased. Furthermore, proliferation and differentiation of primary myoblasts was abnormal, exhibiting delayed induction of several markers of differentiation as well as decreased myotube size. Treatment with exogenous BDNF protein was sufficient to rescue normal gene expression and myotube size. Because satellite cells are responsible for postnatal growth and repair of skeletal muscle, we next examined whether regenerative capacity was compromised. After injury, BDNF-depleted muscle showed delayed expression of several molecular markers of regeneration, as well as delayed appearance of newly regenerated fibers. Recovery of wild-type BDNF levels was sufficient to restore normal regeneration. Together, these findings suggest that BDNF plays an important role in regulating satellite cell function and regeneration in vivo, particularly during early stages.
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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) | 0.001 | 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".