dsRNA as a novel tool to fight Verticillium diseases - from basics to future applications
Bibliographic record
Abstract
Verticillium infections affect a wide range of plant hosts and cause considerable losses for economically relevant crops like cotton, tomatoes, oilseed rape, and many others. The lifestyle of this soil-borne fungal pathogen further complicates the management strategies and is currently limited to cultural practices such as crop rotation and the use of resistant cultivars, aimed to reduce the presence of disease causing microsclerotia resting in the soil. V. longisporum is the latest characterized species with a nearly diploid genome and a narrower host range – in comparison to the better studied species, namely V. dahliae and V. albo-atrum – is a major threat to oilseed rape production in Europe and Canada. The lack of reliable management strategies led to the interest in exploring RNA interference (RNAi) based alternatives, using double-stranded (ds)RNA to target virulence genes identified from the closely related species V. dahliae. The dissertation covers the confirmation of RNAi machinery activity and targeted gene silencing using 450-500 bp (ds)RNA, followed by a hydroponic based infection assay development and in-vitro growth assay in 96-well-plates for scalability. Both assays were used to assess the plant protection potential for the selected gene targets, formulation development for stabilizing (ds)RNA, and (ds)RNA detection assay after spray application. The selected gene targets demonstrated variable effects on growth and virulence, resulting in different in-vitro growth patterns and disease severity after (ds)RNA addition. The results highlighted the necessity of gene target selection framework and revealed the challenges facing this approach to achieve a prolonged plant protection on a larger scale.
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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".