CRISPR/Cas9-Mediated Trait Improvement in Kiwifruit: Current Progress and Future Directions
Bibliographic record
Abstract
Kiwifruit is a globally significant economic and nutritional crop, but its breeding faces numerous challenges, including the need for improvements in yield, quality, and stress resistance traits. In recent years, the application of CRISPR/Cas9 technology in crop genetic improvement has demonstrated immense potential, providing a novel technical pathway for optimizing kiwifruit traits. This review systematically summarizes the research progress on key traits and their genetic basis in kiwifruit, with a focus on the application of CRISPR/Cas9 technology in kiwifruit breeding, including functional studies of key trait-related genes and case studies on trait improvement through gene editing. The study explores the technical bottlenecks of CRISPR/Cas9 technology in kiwifruit, such as off-target effects, editing efficiency, and genetic stability, and summarizes methods for improving editing efficiency and the prospects of applying novel Cas variants. Additionally, this review integrates the latest achievements in multi-omics studies, elucidating the role of genomics, transcriptomics, and epigenomics data in precision gene editing and proposing strategies for integrating gene editing with traditional breeding approaches. This review provides a comprehensive theoretical foundation for the research and application of CRISPR/Cas9 technology in kiwifruit, offering practical guidance for developing high-quality kiwifruit varieties with enhanced market competitiveness.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".