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Updated: Jan 10, 2026

Double-stranded RNA Oral Delivery Methods to Induce RNA Interference in Phloem and Plant-sap-feeding Hemipteran Insects
Published on: May 4, 2018
Insect double-stranded RNA-binding proteins: structure, function and RNAi efficiency
1Instytut Biologii, Uniwersytet Opolski, Oleska 22, 45-052 Opole, Poland.
Abstract:
Double-stranded RNA-binding proteins (dsRBPs) are essential cofactors in insect RNA interference (RNAi), where they stabilize and process double-stranded RNA and guide small RNAs into effector complexes. While Dicer and Argonaute proteins constitute the catalytic core of RNAi, dsRBPs such as R2D2, Loquacious, and Staufen, as well as lineage-specific variants like StaufenC, determine the efficiency, specificity, and systemic spread of silencing signals. Comparative analyses across Diptera, Coleoptera, Lepidoptera, and Hemiptera reveal pronounced differences in the presence, expression, and functional specialization of these proteins, which correlate with order-specific variability in RNAi responsiveness. Coleopterans exhibit robust systemic RNAi supported by high expression of dsRBPs and SID-1-like transporters, whereas lepidopterans and hemipterans display restricted dsRBP activity, elevated nuclease degradation, and limited systemic transport, resulting in inconsistent RNAi outcomes. Structural and biochemical studies have clarified the roles of dsRBPs in Dicer-mediated processing and RISC assembly, highlighting isoform-specific mechanisms in Loquacious and cooperative interactions between R2D2 and Dicer-2. Emerging evidence also implicates lineage-specific proteins, such as StaufenC in beetles, in enhancing oral RNAi. From an applied perspective, dsRBPs represent promising molecular determinants and biomarkers of RNAi susceptibility, offering entry points for improving host-induced and spray-induced gene silencing strategies. This review provides an integrated framework for understanding the molecular causes of RNAi variability and for developing targeted approaches to enhance the reliability of RNAi-based pest management.
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