Effects of sgRNAs, Promoters, and Explants on the Gene Editing Efficiency of the CRISPR/Cas9 System in Chinese Kale
Bibliographic record
Abstract
The CRISPR/Cas9 system is extensively used for plant gene editing. This study developed an efficient CRISPR/Cas9 system for Chinese kale using multiple sgRNAs and two promoters to create various CRISPR/Cas9 vectors. These vectors targeted BoaZDS and BoaCRTISO in Chinese kale protoplasts and cotyledons. Transient transformation of Chinese kale protoplasts was assessed for editing efficiency at three BoaZDS sites. Notably, sgRNA: Z2 achieved the highest efficiency (90%). Efficiency reached 100% when two sgRNAs targeted BoaZDS with a deletion of a large fragment (576 bp) between them. However, simultaneous targeting of BoaZDS and BoaCRTISO yielded lower efficiency. Transformation of cotyledons led to Chinese kale mutants with albino phenotypes for boazds mutants and orange-mottled phenotypes for boacrtiso mutants. The mutation efficiency of 35S-CRISPR/Cas9 (92.59%) exceeded YAO-CRISPR/Cas9 (70.97%) in protoplasts, and YAO-CRISPR/Cas9 (96.49%) surpassed 35S-CRISPR/Cas9 (58%) in cotyledons. These findings introduce a strategy for enhancing CRISPR/Cas9 editing efficiency in Chinese kale.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".