Genome editing in wheat microspores and haploid embryos mediated by delivery of ZFN proteins and cell‐penetrating peptide complexes
Bibliographic record
Abstract
Recent advances in genome engineering technologies based on designed endonucleases (DE) allow specific and predictable alterations in plant genomes to generate value-added traits in crops of choice. The EXZACT Precision technology, based on zinc finger nucleases (ZFN), has been successfully used in the past for introduction of precise mutations and transgenes to generate novel and desired phenotypes in several crop species. Current methods for delivering ZFNs into plant cells are based on traditional genetic transformation methods that result in stable integration of the nuclease in the genome. Here, we describe for the first time, an alternative ZFN delivery method where plant cells are transfected with ZFN protein that eliminates the need for stable nuclease genomic integration and allows generation of edited, but not transgenic cells or tissues. For this study, we designed ZFNs targeting the wheat IPK1 locus, purified active ZFN protein from bacterial cultures, complexed with cell-penetrating peptides (CPP) and directly transfected the complex into either wheat microspores or embryos. NGS analysis of ZFN-treated material showed targeted edits at the IPK1 locus in independent experiments. This is the first description of plant microspore genome editing by a ZFN when delivered as a protein complexed with CPP.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".