A rapid and efficient method for uniform gene expression using the barley stripe mosaic virus
Bibliographic record
Abstract
The barley stripe mosaic virus (BSMV) has become a popular vector to study gene function in cereals. However, studies have been limited to gene silencing in leaves of barley or wheat. In addition, the method produces high variability between different leaves and plants. To overcome these limitations, we explored the potential of modifying the inoculation protocol for BSMV gene overexpression. An improved light, oxygen or voltage-sensing (iLOV) domain-based fluorescent protein was used as a reporter of gene expression to monitor the infection and spread of BSMV. Tobacco (Nicotiana benthamiana) leaves were infected via agroinfiltration and the leaves were homogenized to extract the BSMV particles and inoculate wheat tissues using the traditional leaf abrasion method or by incubation during seed imbibition in a Petri dish. Compared to the leaf abrasion method, the seed imbibition method resulted in a high and uniform detection of iLOV in both roots and leaves of different wheat cultivars and other monocot and dicot species within 7 days after germination. The progression of viral infection via the imbibition method as measured by the expression of iLOV was more stable in different organs and tissues and is transmissible to the next generation. Our results show that BSMV can be used as a vector for the expression of small genes such as iLOV in wheat roots and leaves. The inoculation by seed imbibition allows genes to be expressed rapidly and uniformly in wheat and different monocot and dicot species compared to the traditional leaf abrasion method. It also produces high successful transformation as early as 7 days post infection allowing gene function studies during the first generation of infected plants. Furthermore, the method is simple, rapid, and inexpensive compared to the production of transgenic plants.
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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 | 0.001 | 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".