Development of Site-Specific CFTR Gene Integration Tools for Testing Gene Editing in Pig Cells
Bibliographic record
Abstract
Development of Site-Specific CFTR Gene Integration Tools for Testing Gene Editing in Pig Cells Liang Yang Master of Science Department of Laboratory Medicine and Pathobiology University of Toronto 2017 Abstract Cystic Fibrosis (CF) is a genetic disorder caused by autosomal recessive mutations in the Cystic Fibrosis Transmembrane conductance Regulator (CFTR) gene. In this study, we proposed a novel gene therapy strategy to integrate a transgene expression cassette into GGTA1 locus utilizing the precise genome cleavage capability of the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISPR associated protein (Cas) and the large packaging capacity of Helper Dependent Adenoviral vector (HD-Ad). Using a LacZ reporter gene expression cassette, we determined this novel strategy can achieve stable and sufficient integration in the pig IPEC-J2 cell line. In addition, the CFTR transgene mRNA and protein can be successfully detected post HD-Ad delivery. Future experiments include investigating the CFTR functional correction and the impact of enhancing homology directed repair (HDR), which is the major pathway we rely on for transgene integration, on integration efficiency and assess CFTR transgene functional corrections.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".