CRISPR/Cas9 gene editing in legume crops: Opportunities and challenges
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Legumes are an excellent source of proteins and health‐promoting phytochemicals. Recognizing their importance in human nutrition and sustainable agricultural production, significant efforts are currently being made to accelerate genetic gain related to yield, stress resilience, and nutritional quality. Recent increases in genomic resources for multiple legume crops have laid a solid foundation for application of transformative breeding technologies such as genomic selection and genome editing for crop improvement. In this review, we focus on the recent plant‐specific advances in CRISPR/Cas9‐based gene editing technology and discuss the challenges and opportunities to harnessing this innovative technology for targeted improvement of traits in legume crops. Gene‐editing methods have been successfully established for soybean, cowpea, chickpea, and model legumes such as Medicago truncatula and Lotus japonicus . However, the recalcitrance of other legumes to in vitro gene transfer and regeneration has posed a serious challenge to application of gene editing. We discuss various modifications to in vitro culture methods, in terms of the choice of explant, media composition, and DNA delivery and gene‐editing detection methods that can potentially improve the rate of transformation and regeneration of whole plant in legume crops. Although gene‐editing technology can bring enormous benefits to legume breeding, regulatory hurdles are a cause for serious concern. We compare the regulatory environments existing in the European Union and the United States of America. A favorable regulatory framework and public acceptance are important factors in realizing CRISPR's potential benefits to global food security.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it