Overexpression of Follistatin in Human Myoblasts Increases Their Proliferation and Differentiation, and Improves the Graft Success in SCID Mice
Bibliographic record
Abstract
Duchenne muscular dystrophy is caused by the absence of functional dystrophin, leading to the myofiber membrane instability and progressive muscle atrophy. Myoblast transplantation in dystrophic muscles is a potential therapy, as it permits the long-term restoration of dystrophin expression in transplanted muscles. However, the success of this approach is limited by the short period of muscle repair following myoblast transplantation. Myostatin, a powerful inhibitor of muscle growth, is involved in terminating the period of muscle repair following injury by reducing myoblast proliferation and differentiation. Follistatin forms a complex with myostatin, preventing its interaction with its receptor and thus blocking the myostatin signal. Here, we used a lentivirus to overexpress the follistatin protein in normal myoblasts to block the myostatin signaling. We measured the potential of transduced myoblasts to proliferate and to form multinucleated myotubes in vitro. And finally, we considered the engraftment success of those transduced myoblasts in comparison with control cells in vivo within SCID mice TA muscle. Our results first confirmed the overexpression of follistatin into lentivirus transduced myoblasts, and second showed that the overexpression of the follistatin in normal human myoblasts improved in vitro their proliferation rate by about 1.5-fold after 96 h and also their differentiation rate by about 1.6- and 1.8-fold, respectively, in the absence and in the presence of recombinant myostatin. Finally, our data demonstrated that the engraftment of human normal myoblasts overexpressing the follistatin protein into SCID mouse muscles was enhanced by twofold.
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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 | 0.001 | 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) | 0.001 | 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".