Virus‐induced gene silencing as a tool for functional studies in <i>Cleome violacea</i>
Bibliographic record
Abstract
Premise Cleomaceae is emerging as a promising family to investigate a wide range of phenomena, such as C4 photosynthesis and floral diversity. However, functional techniques are lacking for elucidating this diversity. Herein, we establish virus‐induced gene silencing (VIGS) as a method of generating functional data for Cleome violacea, bolstering Cleomaceae as a model system. Methods We leveraged the sister relationship of Cleomaceae and Brassicaceae by using constructs readily available for Arabidopsis thaliana to provide initial information about the feasibility of VIGS in C. violacea. We then developed endogenous constructs to optimize VIGS efficiency and viability for fruit development. Results PHYTOENE DESATURASE was successfully downregulated in C. violacea using both heterologous and endogenous constructs. The endogenous construct had the highest degree of downregulation, with many plants displaying strong photobleaching. FRUITFULL‐treated plants were also successfully downregulated, with a high rate of survival but less effective silencing; only a small percentage of survivors showed a strong phenotype. Discussion Our optimized VIGS protocol in C. violacea enables functional gene analyses at different developmental stages. Additionally, C. violacea is amenable to heterologous knockdown, which suggests that a first pass using non‐endogenous constructs is a possible route to test additional species of Cleomaceae.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".