A highly efficient CRISPR-Cas9-based gene editing system in oat ( <i>Avena sativa</i> )
Bibliographic record
Abstract
Abstract Cultivated oat ( Avena sativa ) is an emerging cereal for healthy lives owing to its unique characteristics, such as high β-glucan and oil content, distinctive fatty acid composition, and gluten-free nature. The recent unravelling of the 12.5 Gb hexaploid oat genome underlined breeding barriers caused by ancestral translocations and inversions, leading to recombination suppression and pseudo-linkage further hindering conventional trait introgression. Over the past decade, the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-Cas9 system has been extensively used for crop improvement and functional genomics in all other cereals except oats. Its large repetitive genome with three sub-genomes, lack of efficient transformation, recalcitrant nature, and complex molecular screening due to gene redundancy have been major obstacles to gene editing success in oat. We report the first successful CRISPR-Cas9-based gene editing in oat in three genes — AsTLP8, AsVRN3 and AsVRN3D with gene-editing efficiency of up to 41.1%. The gene-edited plants for all the genes carried deletions and/or one base insertion. Further analysis of VRN3 T 1 and T 2 mutants revealed bent leaves in heterozygous knockouts (AACCdD), while an extended vegetative growth phase was seen in the T 1 homozygous and biallelic mutants (aaccdd), accentuating the important role of VRN3 in oat development. We are confident that this highly efficient oat gene editing system will pave the way for a deeper molecular understanding of this healthy cereal, deciphering oat’s functional genomics, and creating genetic diversity at the cold spots of recombination in oat.
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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".