Efficacy and Site-Specificity of Adenoviral Vector Integration Mediated by the Phage φC31 Integrase
Bibliographic record
Abstract
Adenoviral vectors deleted of all their viral genes (helper-dependent [HD]) are efficient gene-transfer vehicles. Because transgene expression is rapidly lost in actively dividing cells, we investigated the feasibility of using phage φC31 integrase (φC31-Int) to integrate an HD carrying an attB site and the puromycin resistance gene into human cells (HeLa) and murine myoblasts (C2C12) by co-infection with a second HD-expressing φC31-Int. Because the HD genome is linear, we also investigated whether its circularization, through expression of Cre using a third HD, affects integration. Efficacy and specificity were determined by scoring the number of puromycin-resistant colonies and by sequencing integration sites. Unexpectedly, circularization of HD was unnecessary and it even reduced the integration efficacy. The maximum integration efficacy achieved was 0.5% in HeLa cells and 0.1% in C2C12 myoblasts. Up to 76% of the integration events occurred at pseudo attP sites and previously characterized hotspots were found. A small (two- to three-fold) increase in the number of γ-H2AX positive foci, accompanied by no noticeable change in γ-H2AX expression, indicated the low genotoxicity of φC31-Int. In conclusion, integration of HD mediated by φC31-Int is an attractive alternative to engineer cells, because it permits site-specific integration of large DNA fragments with low genotoxicity. Robert and colleagues investigate the feasibility of using phage φC31 integrase to integrate a helper-dependent (HD) adenoviral vector into mammalian cells. Using this approach, the authors are able to achieve an integration efficacy of 0.5% and 0.12% in HeLa cells and C2C12 myoblasts, respectively. Moreover, they report that up to 76% of the integration events occur at pseudo attP sites and previously characterized hot spots.
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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 | 0.001 |
| 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 | 0.001 | 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".