Development of a FLP/frt System for Generating Helper-Dependent Adenoviral Vectors
Bibliographic record
Abstract
Helper-dependent (HD) adenoviral vectors devoid of all viral coding sequences have a large cloning capacity and have been reported to provide long-term transgene expression in vivo with negligible toxicity, making them attractive vectors for gene therapy. Currently, the most efficient means of generating HD vectors involves co-infecting 293 cells expressing Cre with the HD vector and a helper virus bearing a packaging signal flanked by loxP sites. Cre-mediated excision of the packaging signal renders the helper virus genome unpackageable but still able to replicate and to provide helper functions for HD vector propagation. HD vector titer is increased by serial co-infections. Typically, helper virus contamination is < or =1% pre- and < or =0.1% postpurification by CsCl banding. While these contamination levels are low, further reduction is desirable. Alternative methods of selection against the helper virus may achieve this goal, especially when combined with Cre/loxP. We describe the development of a system for generating HD vectors based on site-specific recombination between frt sites catalyzed by FLP recombinase and show by direct comparison that the FLP/frt and Cre/loxP systems are equivalent with respect to HD vector amplification efficiency and helper virus contamination levels. Availability of a second recombinase system for HD vector production will enhance the utility and flexibility of HD vectors.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".