Environmental RNA interference in two-spotted spider mite, Tetranychus urticae, reveals dsRNA processing requirements for efficient RNAi response
Bibliographic record
Abstract
Comprehensive understanding of pleiotropic roles of RNAi machinery highlighted the conserved chromosomal functions of RNA interference. The consequences of the evolutionary variation in the core RNAi pathway genes are mostly unknown, but may lead to the species-specific functions associated with gene silencing. The two-spotted spider mite, Tetranychus urticae, is a major polyphagous chelicerate pest capable of feeding on over 1100 plant species and developing resistance to pesticides used for its control. A well annotated genome, susceptibility to RNAi and economic importance, make T. urticae an excellent candidate for development of an RNAi protocol that enables high-throughput genetic screens and RNAi-based pest control. Here, we show that the length of the exogenous dsRNA critically determines its processivity and ability to induce RNAi in vivo. A combination of the long dsRNAs and the use of dye to trace the ingestion of dsRNA enabled the identification of genes involved in membrane transport and 26S proteasome degradation as sensitive RNAi targets. Our data demonstrate that environmental RNAi can be an efficient reverse genetics and pest control tool in T. urticae. In addition, the species-specific properties together with the variation in the components of the RNAi machinery make T. urticae a potent experimental system to study the evolution of RNAi pathways.
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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 | 0.000 |
| 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) | 0.000 | 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".