Improvement of Rice Seed Storability by Regulating Lipid Metabolism Using a CRISPR/Cas9 System
Bibliographic record
Abstract
Long-term storage of rice grain is critical for global food security, yet rice is inherently susceptible to deterioration during storage. Herein, rice seed storability was improved by targeting three key enzyme genes in the lipid metabolism pathway via CRISPR/Cas9 technology, and the mechanism underlying this was analyzed by an untargeted lipidomic approach. Our findings demonstrate that the significantly inferior seed storability in the Yu–Zhen–Xiang (YZX) cultivar compared with the Xi–Li–Gong–Mi (XLGM) cultivar arises from accelerated lipid catabolism and reactive oxygen species (ROS) overproduction. Moreover, a fad2-1 / lox3 / pldα1 triple mutant in the YZX background was rapidly generated by FMPKC systems, and the mutant exhibited lower fatty acid accumulation and reduced ROS content, along with improved grain quality and nutritional value after accelerated aging. Lipidomic analysis indicated that diminished lipid hydrolysis and peroxidation collectively accounted for enhanced storability of the flp mutant. Collectively, this study establishes a robust strategy for rapidly and significantly improving rice aging tolerance, with potential applicability to other cereal crops for addressing critical challenges of grain storage.
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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".