A Novel and Simple Method for Rapid Generation of Recombinant Porcine Adenoviral Vectors for Transgene Expression
Bibliographic record
Abstract
Many human (different serotypes) and nonhuman adenovirus vectors are being used for gene delivery. However, the current system for isolating recombinant adenoviral vectors is either time-consuming or expensive, especially for the generation of recombinant non-human adenoviral vectors. We herein report a new and simple cloning approach for the rapid generation of a porcine adenovirus (PAdV-3) vector which shows promise for gene transfer to human cells and evasion of human adenovirus type 5 (HAdV-5) immunity. Based on the final cloning plasmid, pFPAV3-CcdB-Cm, and our modified SLiCE strategy (SLiCE cloning and lethal CcdB screening), the process for generating recombinant PAdV-3 plasmids required only one step in 3 days, with a cloning efficiency as high as 620 ± 49.56 clones/ng and zero background (100% accuracy). The recombinant PAdV-3 plasmids could be successfully rescued in porcine retinal pigment epithelium cells (VR1BL), which constitutively express the HAdV-5 E1 and PAdV-3 E1B 55k genes, and the foreign genes were highly expressed at 24 h after transduction into swine testicle (ST) cells. In conclusion, this strategy for generating recombinant PAdV-3 vectors based on our modified SLiCE cloning system was rapid and cost-efficient, which could be used as universal cloning method for modification the other regions of PAdV-3 genome as well as other adenoviral genomes.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 0.002 |
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".