Evaluation of Factors Affecting <i>In Planta</i> Gene Editing Efficiency in Wheat (<i>Triticum aestivum</i> L.)
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Gene editing in polyploid crops still suffers from low efficiency, and further improvement is needed for its routine implementation in the modern breeding practice. Here we examined factors that affect the CRISPR/Cas9-mediated gene editing efficiency in allohexaploid wheat plants. We selected three guide RNAs (gRNAs) and evaluated the potential of using heat shock at the seedlings stage to increase editing efficiency in transgenic plants. Only one out of three gRNAs demonstrated significantly increased editing efficiency following heat shock treatment. We also examined the expression of DNA repair and replication gene orthologues in response to heat shock in wheat leaves. Misregulation of the chromatin remodelers following the heat shock treatment could potentially be involved in the increase of editing efficiency in wheat. Overall, the editing efficiency of gRNAs observed in our study correlated with predictive scores from the gRNA design tools. The editing rate of the top-ranked gRNAs could potentially be increased using heat treatment of the transgenic plants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".