Development of cassava common mosaic virus-based vector for protein expression and gene editing in cassava
Bibliographic record
Abstract
BACKGROUND: Plant virus vectors designed for virus-mediated protein overexpression (VOX), virus-induced gene silencing (VIGS), and genome editing (VIGE) provide rapid and cost-effective tools for functional genomics studies, biotechnology applications and genome modification in plants. We previously reported that a cassava common mosaic virus (CsCMV, genus Potexvirus)-based VIGS vector was used for rapid gene function analysis in cassava. However, there are no VOX and VIGE vectors available in cassava. RESULTS: In this study, we developed an efficient VOX vector (CsCMV2-NC) for cassava by modifying the CsCMV-based VIGS vector. Specifically, the length of the duplicated putative subgenomic promoter (SGP1) of the CsCMV CP gene was increased to improve heterologous protein expression in cassava plants. The modified CsCMV2-NC-based VOX vector was engineered to express genes encoding green fluorescent protein (GFP), bacterial phytoene synthase (crtB), and Xanthomonas axonopodis pv. manihotis (Xam) type III effector XopAO1 for viral infection tracking, carotenoid biofortification and Xam virulence effector identification in cassava. In addition, we used CsCMV2-NC to deliver single guide RNAs (gMePDS1/2) targeting two loci of the cassava phytoene desaturase gene (MePDS) in Cas9-overexpressing transgenic cassava lines. The CsCMV-gMePDS1/2 efficiently induced deletion mutations of the targeted MePDS with the albino phenotypes in systemically infected cassava leaves. CONCLUSIONS: Our results provide a useful tool for rapid and efficient heterologous protein expression and guide RNA delivery in cassava. This expands the potential applications of CsCMV-based vector in gene function studies, biotechnology research, and precision breeding for cassava.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".