Nucleofection of muscle-derived stem cells and myoblasts with ϕC31 integrase: stable expression of a full-length-dystrophin fusion gene by human myoblasts
Bibliographic record
Abstract
Ex vivo gene therapy offers a potential treatment for Duchenne muscular dystrophy by transfection of the dystrophin gene into the patient's own myogenic precursor cells, followed by transplantation. We used nucleofection to introduce DNA plasmids coding for enhanced green fluorescent protein (eGFP) or eGFP-dystrophin fusion protein and the phage phiC31 integrase into myogenic cells and to integrate these genes into a limited number of sites in the genome. Using a plasmid expressing eGFP, we transfected 50% of a mouse muscle-derived stem cell line and 60% of normal human myoblasts. Co-nucleofection of a plasmid expressing the phiC31 integrase and an eGFP expression plasmid containing an attB sequence produced 15 times more frequent stable expression, because of site-specific integration of the transgene. Co-nucleofection of the phiC31 integrase plasmid and a large plasmid containing the attB sequence and the gene for an eGFP-full-length dystrophin fusion protein produced fluorescent human myoblasts that were able to form more intensely fluorescent myotubes after 1 month of culture. A nonviral approach combining nucleofection and the phiC31 integrase may eventually permit safe autotransplantation of genetically modified cells to patients.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".