Oral Delivery of Double-Stranded RNA in Larvae of the Yellow Fever Mosquito,<i>Aedes aegypti:</i>Implications for Pest Mosquito Control
Bibliographic record
Abstract
RNA interference has already proven itself to be a highly versatile molecular biology tool for understanding gene function in a limited number of insect species, but its widespread use in other species will be dependent on the development of easier methods of double-stranded RNA (dsRNA) delivery. This study demonstrates that RNA interference can be induced in the mosquito Aedes aegypti L. (Diptera: Culicidae) simply by soaking larvae in a solution of dsRNA for two hours. The mRNA transcripts for β-tubulin, chitin synthase-1 and -2, and heat shock protein 83 were reduced between 30 and 50% three days post-dsRNA treatment. The dsRNA was mixed with a visible dye to identify those individuals that fed on the dsRNA, and based on an absence of RNA interference in those individuals that contained no dye within their guts, the primary route of entry of dsRNA is likely through the gut epithelium. RNA interference was systemic in the insects, inducing measurable knock down of gene expression in tissues beyond the gut. Silencing of the β-tubulin and chitin synthase-1 genes resulted in reduced growth and/or mortality of the larvae, demonstrating the utility of dsRNA as a potential mosquito larvicide. Silencing of chitin synthase-2 did not induce mortality in the larvae, and silencing of heat shock protein 83 only induced mortality in the insects if they were subsequently subjected to a heat stress. Drosophila melanogaster Meigen (Diptera: Drosophilidae) larvae were also soaked in dsRNA designed to specifically target either their own β-tubulin gene, or that of A. aegypti, and significant mortality was only seen in larvae treated with dsRNA targeting their own gene, which suggests that dsRNA pesticides could be designed to be species-limited.
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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) | 0.001 | 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".