Foxtail mosaic virus: A tool for gene function analysis in maize and other monocots
Bibliographic record
Abstract
Many plant viruses have been engineered into vectors for use in functional genomics studies, expression of heterologous proteins, and, most recently, gene editing applications. The use of viral vectors overcomes bottlenecks associated with mutagenesis and transgenesis approaches often implemented for analysis of gene function. There are several engineered viruses that are demonstrated or suggested to be useful in maize through proof-of-concept studies. However, foxtail mosaic virus (FoMV), which has a relatively broad host range, is emerging as a particularly useful virus for gene function studies in maize and other monocot crop or weed species. A few clones of FoMV have been independently engineered, and they have different features and capabilities for virus-induced gene silencing (VIGS) and virus-mediated overexpression (VOX) of proteins. In addition, FoMV can be used to deliver functional guide RNAs in maize and other plants expressing the Cas9 protein, demonstrating its potential utility in virus-induced gene editing applications. There is a growing number of studies in which FoMV vectors are being applied for VIGS or VOX in maize and the vast majority of these are related to maize-microbe interactions. In this review, we highlight the biology and engineering of FoMV as well as its applications in maize-microbe interactions and more broadly in the context of the monocot functional genomics toolbox.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".