Combination of cell signaling molecules can facilitate MYOD1-mediated myogenic transdifferentiation of pig fibroblasts
Bibliographic record
Abstract
BACKGROUND: Myogenic transdifferentiation can be accomplished through ectopic MYOD1 expression, which is facilitated by various signaling pathways associated with myogenesis. In this study, we attempted to transdifferentiate pig embryonic fibroblasts (PEFs) myogenically into skeletal muscle through overexpression of the pig MYOD1 gene and modulation of the FGF, TGF-β, WNT, and cAMP signaling pathways. RESULTS: The MYOD1 overexpression vector was constructed based on comparative sequence analysis, demonstrating that pig MYOD1 has evolutionarily conserved domains across various species. Although forced MYOD1 expression through these vectors triggered the expression of endogenous muscle markers, transdifferentiated muscle cells from fibroblasts were not observed. Therefore, various signaling molecules, including FGF2, SB431542, CHIR99021, and forskolin, along with MYOD1 overexpression were applied to enhance the myogenic reprogramming. The modified conditions led to the derivation of myotubes and activation of muscle markers in PEFs, as determined by qPCR and immunostaining. Notably, a sarcomere-like structure was observed, indicating that terminally differentiated skeletal muscle could be obtained from transdifferentiated cells. CONCLUSIONS: In summary, we established a protocol for reprogramming MYOD1-overexpressing PEFs into the mature skeletal muscle using signaling molecules. Our myogenic reprogramming can be used as a cell source for muscle disease models in regenerative medicine and the production of cultured meat in cellular agriculture.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".