An optimised CRISPR/Cas9 protocol to create targeted mutations in homoeologous genes and an efficient genotyping protocol to identify edited events in wheat
Bibliographic record
Abstract
Abstract Background Targeted genome editing using the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9 system has been applied in a large number of plant species. Using a gene-specific single guide RNA (sgRNA) and the CRISPR/Cas9 system, small editing events such as deletions of few bases can be obtained. However larger deletions are required for some applications. In addition, identification and characterization of edited events can be challenging in plants with complex genomes, such as wheat. Results In this study, we used the CRISPR/Cas9 system and developed a protocol that yielded high number of large deletions employing a pair of co-expressed sgRNA to target the same gene. The protocol was validated by targeting three genes, TaABCC6 , TaNFXL1 and TansLTP9.4 in a wheat protoplast assay. Deletions of sequences located between the two sgRNA in each gene were the most frequent editing events observed for two of the three genes. A comparative assessment of editing frequencies between a codon-optimized Cas9 for expression in algae, crCas9, and a plant codon-optimized Cas9, pcoCas9, showed more consistent results with the vector expressing pcoCas9. Editing of TaNFXL1 by co-expression of sgRNA pair was investigated in transgenic wheat plants. Given the ploidy of bread wheat, a rapid, robust and inexpensive genotyping protocol was also adapted for hexaploid genomes and shown to be a useful tool to identify homoeolog-specific editing events in wheat. Conclusions Co-expressed pairs of sgRNA targeting single genes in conjunction with the CRISPR/Cas9 system produced large deletions in wheat. In addition, a genotyping protocol to identify editing events in homoeologs of TaNFXL1 was successfully adapted.
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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 | 0.000 |
| 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 | 0.002 |
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".