Evaluation of virus-induced gene silencing methods for forage legumes including alfalfa, sainfoin, and fenugreek
Bibliographic record
Abstract
Virus-induced gene silencing (VIGS) is a rapid reverse genetics tool that has been developed in a wide variety of plant species for assessing gene functions. However, while VIGS has been utilized successfully in the diploid model leguminous species Medicago truncatula (Gaertn.) (barrel medic), such a platform has yet to be established in forage legume crop species. Therefore, we evaluated the effectiveness of this method in forage legumes using a previously developed PEBV (pea early browning virus) system whereby a fragment of the pea (Pisum sativum L.) PHYTOENE DESATURASE (PDS) gene was transferred into a range of alfalfa (Medicago sativa L.), sainfoin (Onobrychis viciifolia Scop.), and fenugreek (Trigonella foenum-graecum L.) cultivars using leaf infiltration and apical meristem injection. Barrel medic was used as a positive control. Gene silencing was observed after 10–15 d through the presence of a leaf bleaching phenotype, and was confirmed using quantitative real-time RT-PCR. Silencing of PDS was achieved in a selection of cultivars in all species assessed, with the highest silencing efficiency apparent in fenugreek. The introduction of a highly homologous gene fragment from a heterologous plant species to target endogenous genes for transient VIGS-based silencing in a range of species of interest represents a potentially useful strategy for the rapid functional characterization of candidate genes in forages.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".