Increased survival of muscle stem cells lacking the <i>MyoD</i> gene after transplantation into regenerating skeletal muscle
Bibliographic record
Abstract
MyoD is a myogenic master transcription factor that plays an essential role in muscle satellite cell (muscle stem cell) differentiation. To further investigate the function of MyoD in satellite cells, we examined the transplantation of satellite cell-derived myoblasts lacking the MyoD gene into regenerating skeletal muscle. After injection into injured muscle, MyoD(-/-) myoblasts engrafted with significantly higher efficiency compared with wild-type myoblasts. In addition, MyoD(-/-) myoblast-derived satellite cells were detected underneath the basal lamina of muscle fibers, indicating the self-renewal property of MyoD(-/-) myoblasts. To gain insights into MyoD gene deficiency in muscle stem cells, we investigated the pathways regulated by MyoD by GeneChip microarray analysis of gene expression in wild-type and MyoD(-/-) myoblasts. MyoD deficiency led to down-regulation of many muscle-specific genes and up-regulation of some stem cell markers. Importantly, in MyoD(-/-) myoblasts, many antiapoptotic genes were up-regulated, whereas genes known to execute apoptosis were down-regulated. Consistent with these gene expression profiles, MyoD(-/-) myoblasts were revealed to possess remarkable resistance to apoptosis and increased survival compared with wild-type myoblasts. Forced expression of MyoD or the proapoptotic protein Puma increased cell death in MyoD(-/-) myoblasts. Therefore, MyoD(-/-) myoblasts may preserve stem cell characteristics, including their resistance to apoptosis, expression of stem cell markers, and efficient engraftment and contribution to satellite cells after transplantation. Furthermore, our data offer evidence for improved therapeutic stem cell transplantation for muscular dystrophy, in which suppression of MyoD in myogenic progenitors would be beneficial to therapy by providing a selective advantage for the expansion of stem cells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".