RNA Interference Is Enhanced by Knockdown of Double-Stranded RNases in the Yellow Fever Mosquito Aedes aegypti
Bibliographic record
Abstract
RNA interference (RNAi) techniques are being developed for a range of pest insect control technologies, including the sterile insect technique (SIT) and double-stranded RNA (dsRNA)-based insecticides. In SIT applications, where >99% of the released males should be sterile to meet industry standards, the efficiency of RNAi will need to be improved for many insect species if this technology is to be adopted. Endogenous dsRNases can impede dsRNA delivery in some insects, and, here, we investigated whether dsRNases in the midgut could limit RNAi efficacy in the mosquito Aedes aegypti. Ten putative dsRNases were identified in the Ae. aegypti genome, with two highly expressed in the midguts of larvae. Using an ex vivo assay, we observed that dsRNA was rapidly degraded within the mosquito larva’s gut. Double-stranded RNA targeting these two dsRNases, when fed to the larvae, effectively reduced gut dsRNase activity. When these dsRNase-specific dsRNAs were co-delivered with dsRNA targeting a cyan fluorescent protein (CFP) reporter gene, greater knockdown of CFP fluorescence was observed. These results suggest that inhibiting dsRNase activity could enable the implementation of RNAi-based mosquito control methods.
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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".