Advancing crop disease resistance through genome editing: a promising approach for enhancing agricultural production
Bibliographic record
Abstract
Modern agriculture has encountered several challenges in achieving constant yield stability especially due to disease outbreaks and lack of long-term disease-resistant crop cultivars. In the past, disease outbreaks in economically important crops had a major impact on food security and the economy. On the other hand climate-driven emergence of new pathovars or changes in their host specificity further poses a serious threat to sustainable agriculture. At present, chemical-based control strategies are frequently used to control microbial pathogens and pests, but they have detrimental impact on the environment and also resulted in the development of resistant phyto-pathogens. As a replacement, cultivating engineered disease-resistant crops can help to minimize the negative impact of regular pesticides on agriculture and the environment. Although traditional breeding and genetic engineering have been instrumental in crop disease improvement but they have certain limitations such as labour intensity, time consumption, and low efficiency. In this regard, genome editing has emerged as one of the potential tools for improving disease resistance in crops by targeting multiple traits with more accuracy and efficiency. For instance, genome editing techniques, such as CRISPR/Cas9, CRISPR/Cas13, base editing, TALENs, ZFNs, and meganucleases, have proved successful in improving disease resistance in crops through targeted mutagenesis, gene knockouts, knockdowns, modifications, and activation of target genes. CRISPR/Cas9 is unique among these techniques because of its remarkable efficacy, low risk of off-target repercussions, and ease of use. Some primary targets for developing CRISPR-mediated disease-resistant crops are host-susceptibility genes (the S gene method), resistance genes (R genes) and pathogen genetic material that prevents their development, broad-spectrum disease resistance. The use of genome editing methods has the potential to notably ameliorate crop disease resistance and transform agricultural practices in the future. This review highlights the impact of phyto-pathogens on agricultural productivity. Next, we discussed the tools for improving disease resistance while focusing on genome editing. We provided an update on the accomplishments of genome editing, and its potential to improve crop disease resistance against bacterial, fungal and viral pathogens in different crop systems. Finally, we highlighted the future challenges of genome editing in different crop systems for enhancing disease resistance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".