RNAi-based reverse genetics in the chelicerate model Tetranychus urticae: A comparative analysis of five methods for gene silencing
Bibliographic record
Abstract
RNA interference (RNAi) can be used for the protection against agricultural pests through the silencing of genes required for pest fitness. To assess the potential of RNAi approaches in the two-spotted spider mite, Tetranychus urticae, we compared 5 methods for the delivery of double-stranded RNA (dsRNA). These methods include mite feeding on either (i) leaves floating on a dsRNA solution, (ii) dsRNA-expressing plants, (iii) artificial diet supplemented with dsRNA, or (iv) dsRNA-coated leaves, and (v) mite soaking in a dsRNA solution. In all cases, the gene targeted for method validation was the Vacuolar-type H+-ATPase (TuVATPase), encoding a constitutively expressed ATP-driven proton pump located in the membrane. Down-regulation of TuVATPase increased mortality and/or reduced fecundity in all methods, but with variable efficiency. The most efficient methods for dsRNA delivery were direct soaking of mites in the dsRNA solution and mite feeding on dsRNA-coated leaves that mimics dsRNA application as a sprayable pesticide. Both resulted in a dark-body phenotype not observed in mites treated with a control dsRNA. Although with lower efficiency, dsRNA designed for TuVATPase silencing and expressed in transgenic Arabidopsis plants impacted the fitness of mites feeding on these plants. RNAi may thus be a valuable strategy to control spider mite populations, either as a sprayable pesticide or through transgenic crops. This comparative methodological study focusing on the induction of RNAi-based gene silencing in T. urticae paves the way for reverse genetics approaches in this model chelicerate system and prepares large-scale systematic RNAi screens as a first step towards the development of specific RNA-based pesticides. Such alternative molecules may help control spider mites that cause significant damages to crops and ornamental plant species, as well as other chelicerates detrimental to agriculture and health.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".