Dystrophin Expression in Muscle Following Gene Transfer with a Fully Deleted ("Gutted") Adenovirus Is Markedly Improved by Trans-Acting Adenoviral Gene Products
Bibliographic record
Abstract
Helper-dependent adenoviruses (HDAd) are Ad vectors lacking all or most viral genes. They hold great promise for gene therapy of diseases such as Duchenne muscular dystrophy (DMD), because they are less immunogenic than E1/E3-deleted Ad (first-generation Ad or FGAd) and can carry the full-length (Fl) dystrophin (dys) cDNA (12 kb). We have compared the transgene expression of a HDAd (HDAdCMVDysFl) and a FGAd (FGAdCMV-dys) in cell culture (HeLa, C2C12 myotubes) and in the muscle of mdx mice (the mouse model for DMD). Both vectors encoded dystrophin regulated by the same cytomegalovirus (CMV) promoter. We demonstrate that the amount of dystrophin expressed was significantly higher after gene transfer with FGAdCMV-dys compared to HDAdCMVDysFl both in vitro and in vivo. However, gene transfer with HDAdCMVDysFl in the presence of a FGAd resulted in a significant increase of dystrophin expression indicating that gene products synthesized by the FGAd increase, in trans, the amount of dystrophin produced. This enhancement occurred in cell culture and after gene transfer in the muscle of mdx mice and dystrophic golden retriever (GRMD) dogs, another animal model for DMD. The E4 region of Ad is required for the enhancement, because no increase of dystrophin expression from HDAdCMVDysFl was observed in the presence of an E1/E4-deleted Ad in vitro and in vivo. The characterization of these enhancing gene products followed by their inclusion into an HDAd may be required to produce sufficient dystrophin to mitigate the pathology of DMD by HDAd-mediated gene transfer.
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 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".