Cell Cycle Stage and DNA Repair Pathway Influence CRISPR/Cas9 Gene Editing Efficiency in Porcine Embryos
Bibliographic record
Abstract
CRISPR/Cas9 technology is a powerful tool used for genome manipulation in different cell types and species. However, as with all new technologies, it still requires improvements. Different factors can affect CRISPR/Cas efficiency in zygotes, which influence the total cost and complexity for creating large-animal models for research. This study evaluated the importance of zygote cell cycle stage between early-injection (within 6 h post activation/fertilization) versus late-injection (14-16 h post activation/fertilization) when the CRISPR/Cas9 components were injected and the inhibition of the homologous recombination (HR) pathway of DNA repair on gene editing, embryo survival and development on embryos produced by fertilization, sperm injection, somatic cell nuclear transfer, and parthenogenetic activation technologies. Injections at the late cell cycle stage decreased embryo survival (measured as the proportion of unlysed embryos) and blastocyst formation (68.2%; 19.3%) compared to early-stage injection (86.3%; 28.8%). However, gene editing was higher in blastocysts from late-(73.8%) vs. early-(63.8%) injected zygotes. Inhibition of the HR repair pathway increased gene editing efficiency by 15.6% in blastocysts from early-injected zygotes without compromising embryo development. Our finding shows that injection at the early cell cycle stage along with HR inhibition improves both zygote viability and gene editing rate in pig blastocysts.
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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) | 0.000 | 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 | 0.000 |
| 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".