Efficient Multi-Sites Genome Editing and Plant Regeneration via Somatic Embryogenesis in Picea glauca
Bibliographic record
Abstract
Conifers are the world's major source of timber and pulpwood and have great economic and ecological value. Currently, little research on the application of CRISPR/Cas9, the commonly used genome-editing tool in angiosperms, has been reported in coniferous species. An efficient CRISPR/Cas9 system based on somatic embryogenesis (SEis) suitable for conifers could benefit both fundamental and applied research in these species. In this study, the SpCas9 gene was optimized based on codon bias in white spruce, and a spruce U6 promoter was cloned and function-validated for use in a conifer specific CRISPR/Cas9 toolbox, i.e., PgCas9/PaU6 . With this toolbox, a genome-editing vector was constructed to target the DXS1 gene of white spruce. By Agrobacterium -mediated transformation, the genome-editing vector was then transferred into embryogenic tissue of white spruce. Three resistant embryogenic tissues were obtained and used for regenerating plants via SEis. Albino somatic embryo (SE) plants with mutations in DXS1 were obtained in all of the three events, and the ratios of the homozygous and biallelic mutants in the 18 albino mutants detected were 22.2% in both cases. Green plants with mutations in DXS1 were also produced, and the ratios of the DXS1 mutants to the total green plants were 7.9, 28, and 13.5%, respectively, among the three events. Since 22.7% of the total 44 mutants were edited at both of the target sites 1 and 2, the CRISPR/Cas9 toolbox in this research could be used for multi-sites genome editing. More than 2,000 SE plants were regenerated in vitro after genome editing, and part of them showed differences in plant development. Both chimerism and mosaicism were found in the SE plants of white spruce after genome editing with the CRISPR/Cas9 toolbox. The conifer-specific CRISPR/Cas9 system developed in this research could be valuable in gene function research and trait improvement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".