Optimization of a Protocol for Launching Grapevine Infection with the Biologically Active cDNA Clones of a Virus
Bibliographic record
Abstract
, designated as grapevine leafroll-associated viruses (GLRaVs). However, the specific etiological role of any of these GLRaVs in GLRD has not been demonstrated. Even though GLRaV-3 is considered the chief GLRD agent, little is known about the molecular, cellular, and pathological properties of this virus. Such a knowledge gap is due to multiple factors, including the unavailability of biologically active virus cDNA clones and the lack of reliable experimental systems for launching grapevine infection using such clones. In this work, we tested four methods for inoculating tissue-cultured grapevine plantlets with cDNA clones of GLRaV-3: (i) vacuum agro-infiltration; (ii) agro-pricking; (iii) agro-drenching; and (iv) agro-injection. We showed that vacuum agro-infiltration was the most effective of these methods. Furthermore, we examined the impacts of different experimental conditions on the survival and infectivity rate of grapevines after infiltration. To verify the infectivity rate for different treatments, we used RT-PCR, RT-qPCR, and Western blotting. We found that humidity plays a critical role in the survival of plantlets after agro-infiltration and that the use of RNA silencing suppressor and dormancy treatment both had strong effects on the infection rates. To our knowledge, the experimental protocol reported herein is the most effective system for launching the infection of grapevine using cDNA clones of grapevine viruses featuring up to a 70% infection rate. This system has strong potential to facilitate grapevine virology research including the fulfillment of Koch's postulates for GLRD and other major virus diseases as well as identifying the molecular, cellular, and pathological properties of GLRaVs and, potentially, other important grapevine viruses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".