CRISPR/Cas‐Mediated Gene Editing in Plant Immunity and Its Potential for the Future Development of Fungal, Oomycete, and Bacterial Pathogen‐Resistant Pulse Crops
Bibliographic record
Abstract
Pulses provide myriad health benefits and are advantageous in an environmental context as a result of their leguminous nature. However, phytopathogenic fungi, oomycetes and bacteria pose a substantial threat to pulse production, at times leading to crop failure. Unfortunately, existing disease management strategies often provide insufficient control, and there is a clear need for the development of new pulse cultivars with durable and broad-spectrum disease resistance. CRISPR/Cas-mediated gene editing has proven its potential for rapidly enhancing disease resistance in many plant species. However, this tool has only very recently been applied in pulse species, and never in the context of plant immunity. In this review, we examine the recent successful utilization of this technology in pulse species for proof-of-concept or the improvement of other traits. In addition, we consider various genes that have been edited in other plant species to reduce susceptibility to pathogens, and discuss current knowledge regarding their roles in pulses. Given the functional conservation of the selected genes across diverse plant species, there is a high likelihood that their editing would elicit similar effects in non-oilseed grain legumes, thus providing a suite of potential targets for CRISPR/Cas-mediated gene editing to promote pulse crop productivity in coming years.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".