Probing into the Effects of Grapevine Leafroll-Associated Viruses on the Physiology, Fruit Quality and Gene Expression of Grapes
Bibliographic record
Abstract
Grapevine leafroll is one of the most widespread and highly destructive grapevine diseases that is responsible for great economic losses to the grape and wine industries throughout the world. Six distinct viruses have been implicated in this disease complex. They belong to three genera, all in the family Closteroviridae. For the sake of convenience, these viruses are named as grapevine leafroll-associated viruses (GLRaV-1, -2, -3, -4, -7, and -13). However, their etiological role in the disease has yet to be established. Furthermore, how infections with each GLRaV induce the characteristic disease symptoms remains unresolved. Here, we first provide a brief overview on each of these GLRaVs with a focus on genome structure, expression strategies and gene functions, where available. We then provide a review on the effects of GLRaV infection on the physiology, fruit quality, fruit chemical composition, and gene expression of grapevine based on the limited information so far reported in the literature. We outline key methodologies that have been used to study how GLRaV infections alter gene expression in the grapevine host at the transcriptomic level. Finally, we present a working model as an initial attempt to explain how infections with GLRaVs lead to the characteristic symptoms of grapevine leafroll disease: leaf discoloration and downward rolling. It is our hope that this review will serve as a starting point for grapevine virology and the related research community to tackle this vastly important and yet virtually uncharted territory in virus-host interactions involving woody and perennial fruit crops.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".