A CRISPR/Cas9-based vector system enables the fast breeding of selection-marker-free canola with <i>Rcr1</i>-rendered clubroot resistance
Bibliographic record
Abstract
Breeding for disease resistance in major crops is of crucial importance for global food security and sustainability. However, common biotechnologies such as traditional transgenesis or genome editing do not provide an ideal solution, whereas transgenic crops free of selection markers such as cisgenic/intragenic crops might be suitable. In this study, after cloning and functional verification of the Rcr1 gene for resistance to clubroot (Plasmodiophora brassicae), we confirmed that the genes Rcr1, Rcr2, Rcr4, and CRa from Brassica rapa crops and the resistance gene from B. napus oilseed rape cv. 'Mendel' on chromosome A03 were identical in their coding regions. We also determined that Rcr1 has a wide distribution in Brassica breeding materials and renders potent resistance against multiple representative clubroot strains in Canada. We then modified a CRISPR/Cas9-based cisgenic vector system and found that it enabled the fast breeding of selection-marker-free transgenic crops with add-on traits, with selection-marker-free canola (B. napus) germplasms with Rcr1-rendered stable resistance to clubroot disease being successfully developed within 2 years. In the B. napus background, the intragenic vector system was able to remove unwanted residue sequences from the final product with high editing efficiency, and off-target mutations were not detected. Our study demonstrates the potential of applying this breeding strategy to other crops that can be transformed by Agrobacterium. Following the streamlined working procedure, intragenic germplasms can be developed within two generations, which could significantly reduce the breeding time and labor compared to traditional introgression whilst still achieving comparable or even better breeding results.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".