Diversity of transgene integration and gene-editing events in wheat (Triticum aestivum L.) transgenic plants generated using Agrobacterium-mediated transformation
Bibliographic record
Abstract
Improvement in agronomic traits in crops through gene editing (GE) relies on efficient transformation protocols for delivering the CRISPR/Cas9-coded transgenes. Recently, a few embryogenesis-related genes have been described, the co-delivery of which significantly increases the transformation efficiency with reduced genotype-dependency. Here, we characterized the transgenic and GE events in wheat (cv. Fielder) when transformed with GROWTH-REGULATING FACTOR 4 ( GRF4 ) and its cofactor GRF-INTERACTING FACTOR 1 ( GIF1 ) chimeric gene. Transformation efficiency in our experiments ranged from 22% to 68%, and the editing events were faithfully propagated into the following generation. Both low- and high-copy-number integration events were recovered in the T 0 population with various levels of integrity of the left and right T-DNA borders. We also generated a population of wheat plants with 10 different gRNAs targeting 30 loci in the genome. A comparison of the epigenetic profiles at the target sites and editing efficiency revealed a significant positive correlation between chromatin accessibility and mutagenesis rate. Overall, the preliminary screening of transgene quality and GE events in the T 0 population of plants regenerated through the co-delivery of GRF–GIF can allow for the propagation of the best candidates for further phenotypic analysis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".