Zinc finger nuclease‐mediated targeting of multiple transgenes to an endogenous soybean genomic locus via non‐homologous end joining
Bibliographic record
Abstract
Summary Emerging genome editing technologies hold great promise for the improvement of agricultural crops. Several related genome editing methods currently in development utilize engineered, sequence‐specific endonucleases to generate DNA double strand breaks (DSBs) at user‐specified genomic loci. These DSBs subsequently result in small insertions/deletions (indels), base substitutions or incorporation of exogenous donor sequences at the target site, depending on the application. Targeted mutagenesis in soybean (Glycine max) via non‐homologous end joining (NHEJ)‐mediated repair of such DSBs has been previously demonstrated with multiple nucleases, as has homology‐directed repair (HDR)‐mediated integration of a single transgene into target endogenous soybean loci using CRISPR/Cas9. Here we report targeted integration of multiple transgenes into a single soybean locus using a zinc finger nuclease (ZFN). First, we demonstrate targeted integration of biolistically delivered DNA via either HDR or NHEJ to the FATTY ACID DESATURASE 2‐1a (FAD2‐1a) locus of embryogenic cells in tissue culture. We then describe ZFN‐ and NHEJ‐mediated, targeted integration of two different multigene donors to the FAD2‐1a locus of immature embryos. The largest donor delivered was 16.2 kb, carried four transgenes, and was successfully transmitted to T1 progeny of mature targeted plants obtained via somatic embryogenesis. The insertions in most plants with a targeted, 7.1 kb, NHEJ‐integrated donor were perfect or near‐perfect, demonstrating that NHEJ is a viable alternative to HDR for gene targeting in soybean. Taken together, these results show that ZFNs can be used to generate fertile transgenic soybean plants with NHEJ‐mediated targeted insertions of multigene donors at an endogenous genomic locus.
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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 | 0.001 |
| 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".