Towards an understanding of the molecular basis of effective RNAi against a global insect pest, the whitefly Bemisia tabaci
Bibliographic record
Abstract
In planta RNAi against essential insect genes offers a promising route to control insect crop pests, but is constrained for many insect groups, notably phloem sap-feeding hemipterans, by poor RNAi efficacy. This study conducted on the phloem-feeding whitefly Bemisia tabaci reared on tomato plants investigated the causes of low RNAi efficacy and routes to ameliorate the problem. Experiments using tomato transgenic lines containing ds-GFP (green fluorescent protein) revealed that full-length dsRNA is phloem-mobile, ingested by the insects, and degraded in the insect. We identified B. tabaci homologs of nuclease genes (dsRNases) in other insects that degrade dsRNA, and demonstrated that degradation of ds-GFP in B. tabaci is suppressed by administration of dsRNA against these genes. dsRNA against the nuclease genes was co-administered with dsRNA against two insect genes, an aquaporin AQP1 and sucrase SUC1, that are predicted to protect B. tabaci against osmotic collapse. When dsRNA constructs for AQP1, SUC1, dsRNase1 and dsRNase2 were stacked, insect mortality was significantly elevated to 50% over 6 days on artificial diets. This effect was accompanied by significant reduction in gene expression of the target genes in surviving diet-fed insects. This study offers proof-of-principle that the efficacy of RNAi against insect pests can be enhanced by using dsRNA to suppress the activity of RNAi-suppressing nuclease genes, especially where multiple genes with related physiological function but different molecular function are targeted.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".