Control of Plant Viral Diseases by CRISPR/Cas9: Resistance Mechanisms, Strategies and Challenges in Food Crops
Bibliographic record
Abstract
Protecting food crops from viral pathogens is a significant challenge for agriculture. An integral approach to genome-editing, known as CRISPR/Cas9 (clustered regularly interspaced short palindromic repeats and CRISPR associated protein 9), is used to produce virus-resistant cultivars. The CRISPR/Cas9 tool is an essential part of modern plant breeding due to its attractive features. Advances in plant breeding programs due to the incorporation of Cas9 have enabled the development of cultivars with heritable resistance to plant viruses. The resistance to viral DNA and RNA is generally provided using the Cas9 endonuclease and sgRNAs (single-guide RNAs) complex, targeting particular virus and host plant genomes by interrupting the viral cleavage or altering the plant host genome, thus reducing the replication ability of the virus. In this review, the CRISPR/Cas9 system and its application to staple food crops resistance against several destructive plant viruses are briefly described. We outline the key findings of recent Cas9 applications, including enhanced virus resistance, genetic mechanisms, research strategies, and challenges in economically important and globally cultivated food crop species. The research outcome of this emerging molecular technology can extend the development of agriculture and food security. We also describe the information gaps and address the unanswered concerns relating to plant viral resistance mediated by CRISPR/Cas9.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".