Improvements of TKC Technology Accelerate Isolation of Transgene-Free CRISPR/Cas9-Edited Rice Plants
Bibliographic record
Abstract
Elimination of the CRISPR/Cas9 constructs in edited plants is a prerequisite for assessing genetic stability, conducting phenotypic characterization, and applying for commercialization of the plants. However, removal of the CRISPR/Cas9 transgenes by genetic segregation and by backcross is laborious and time consuming. We previously reported the development of the transgene killer CRISPR (TKC) technology that uses a pair of suicide genes to trigger self-elimination of the transgenes without compromising gene editing efficiency. The TKC technology enables isolation of transgene-free CRISPR-edited plants within a single generation, greatly accelerating crop improvements. Here, we presented two new TKC vectors that show great efficiency in both editing the target gene and in undergoing self-elimination of the transgenes. The new vectors replaced the CaMV35S promoter used in our previous TKC vector with two rice promoters to drive one of the suicide genes, providing advantages over our previous TKC vector under certain conditions. The vectors reported here offered more options and flexibility to conduct gene editing experiments in rice.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".