Helper-Dependent Adenoviral Vectors Containing Modified Fiber for Improved Transduction of Developing and Mature Muscle Cells
Bibliographic record
Abstract
Adenoviruses (Ads) have shown great utility as vectors for the delivery of genes to mammalian cells, partly because of their ability to infect a wide range of different cell types independent of the replicative state of the cell. However, Ads do not transduce mature muscle efficiently because of low levels of the natural viral primary receptor, the coxsackie virus and adenovirus receptor, on the surface of adult muscle cells. In this study, we have addressed whether incorporation of polylysine [p(K)] or arginine-glycine-aspartic acid (RGD) placed in the H-I loop of the adenoviral fiber protein can improve helper-dependent Ad vector (hdAd) transduction of mature muscle cells. We show that incorporation of the p(K) motif into the fiber of early region 1 (E1)-deleted Ad results in enhanced transduction of undifferentiated and differentiated C2C12 cells relative to a virus, containing a wild-type fiber (12- and 21-fold enhancement, respectively). Incorporation of the RGD motif resulted in only a 60-70% increase in transduction efficiency in these cells. The two fiber modifications were then incorporated into helper viruses for use in the Cre-lox system for generating hdAd, and the resulting retargeted Ad vectors, which encoded the beta-galactosidase reporter gene (beta-Gal), demonstrated enhanced transduction of C2C12 cells in culture. Although hdAdpK also showed enhanced infection of mature mouse muscle in vivo, hdAdRGD did not. All hdAd vectors elicited only minor anti-Ad immune responses, compared with an E1-deleted control vector, but each vector elicited strong anti-beta-Gal immunoreactivity. Our results demonstrate that hdAd with modified cell tropism can be generated efficiently and, in the case of polylysine-modified hdAd, can lead to improved transduction of adult muscle cells in vivo.
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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 | 0.000 | 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".