Genetic Mechanism of Cassava Disease Resistance: From Traditional Breeding to CRISPR/Cas Application
Bibliographic record
Abstract
The escalating threat of plant diseases to cassava ( Manihot esculenta ) production necessitates innovative strategies for developing disease-resistant varieties. Traditional breeding has been instrumental in enhancing cassava's resistance to various pathogens, but it is often limited by the complexity of genetic traits and the lengthy timeframes required. The advent of CRISPR/Cas genome editing technology has revolutionized the field of plant breeding by enabling precise modifications of plant genomes. This systematic review provides a comprehensive analysis of the genetic mechanisms underlying cassava disease resistance and the transition from conventional breeding techniques to the cutting-edge CRISPR/Cas applications. We examine the current state of knowledge on plant-pathogen interactions in cassava and discuss how CRISPR/Cas-mediated genome editing has been employed to disrupt these interactions by targeting susceptibility factors within the plant genome. Furthermore, we explore the advancements in genome editing tools, such as base editing and prime editing, that have broadened the scope of generating disease-resistant cassava varieties. The review also highlights the potential of CRISPR/Cas9 in enhancing disease resistance through multiplexed gene editing and trait stacking, which is particularly relevant for complex traits like disease resistance. By synthesizing insights from recent developments in CRISPR/Cas applications across various crops, we aim to provide a roadmap for future research and the development of cassava varieties with improved resistance to a spectrum of diseases, thereby contributing to global food security.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".