Efficiency of RNA interference is improved by knockdown of dsRNA nucleases in tephritid fruit flies
Bibliographic record
Abstract
RNA interference (RNAi) in insects is routinely used to ascertain gene function, but also has potential as a technology to control pest species. For some insects, such as beetles, ingestion of small quantities of double-stranded RNA (dsRNA) is able to knock down a targeted gene's expression. However, in other species, ingestion of dsRNA can be ineffective owing to the presence of nucleases within the gut, which degrade dsRNA before it reaches target cells. In this study, we observed that nucleases within the gut of the Queensland fruit fly ( Bactrocera tryoni ) rapidly degrade dsRNA and reduce RNAi efficacy. By complexing dsRNA with liposomes within the adult insect's diet, RNAi-mediated knockdown of a melanin synthesis gene, yellow , was improved significantly, resulting in strong RNAi phenotypes. RNAi efficiency was also enhanced by feeding both larvae and adults for several days on dsRNAs that targeted two different dsRNase gene transcripts. Co-delivery of both dsRNase-specific dsRNAs and yellow dsRNA resulted in almost complete knockdown of the yellow transcripts. These findings show that the use of liposomes or co-feeding of nuclease-specific dsRNAs significantly improves RNAi inhibition of gene expression in B. tryoni and could be a useful strategy to improve RNAi-based control in other insect species.
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 | 0.000 | 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".