A CRISPR/Cas9-Based System with Controllable Auto-Excision Feature Serving Cisgenic Plant Breeding and Beyond
Bibliographic record
Abstract
Transgenic or genetically modified crops have great potential in modern agriculture but still suffer from heavy regulations worldwide due to biosafety concerns. As a promising alternative route, cisgenic crops have received higher public acceptance and better reviews by governing authorities. To serve the purpose of cisgenic plant breeding, we have developed a CRISPR/Cas9-based vector system, which is capable of delivering target gene-of-interest (GOI) into recipient plants while removing undesired genetic traces in the plants. The new system features a controllable auto-excision feature, which is realized by a core design of embedded multi-clonal sequence and the use of inducible promoters controlling the expression of Cas9 nuclease. In the current proof-of-concept study in Arabidopsis thaliana (L.) Heynh., we have successfully incorporated a GOI into the plant and removed the selection marker and CRISPR/Cas9 components from the final product. Following the designed workflow, we have demonstrated that novel cisgenic plant germplasms with desired traits could be developed within one to two generations. Further characterizations of the vector system have shown that heat treatment at 37 °C could significantly improve the editing efficiency (up to 100%), and no off-target mutations were identified in the Arabidopsis background. This novel vector system is the first CRISPR/Cas9-based genome editing tool for cisgenic plant breeding and should prove powerful for other similar applications in the bright future of precision molecular breeding.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".