Jul 2026· Pest Management Science· 1 citation· 65 references
Medicine
TL;DR
The presence of core RNAi machinery, together with transcript reduction and associated mortality, highlights the potential of RNAi-based strategies for CSW management, with αCOP and Rop as promising candidates for future RNAi-based control.
Abstract
Background
The cabbage seedpod weevil (CSW) (Ceutorhynchus obstrictus) is a major pest of oilseed rape (Brassica napus). Current management depends heavily on broad-spectrum synthetic insecticides due to limited effectiveness of alternative methods. However, this reliance is becoming unsustainable as regulatory withdrawals reduce available active ingredients and resistance diminishes their efficacy, highlighting the need for environmentally friendly selective, and durable control strategies. RNA interference (RNAi) offers such potential; however, its development in non-model pests is constrained by lack of molecular resources. To address this limitation, we constructed transcriptome of CSW, characterized core RNAi pathway genes, and identified candidate RNAi targets, which show that RNAi can efficiently reduce transcript levels and induce mortality in CSW.
Results
The CSW transcriptome enabled identification of RNAi pathway-related genes and candidate target genes (αCOP, ncm, Rop, RpII140, dre4, Spt5, and Spt6) involved in essential cellular processes. All targets exhibited significant transcript reduction (> 70%) accompanied by increased mortality in adult CSW following double-stranded RNA (dsRNA) delivery via microinjection, although the magnitude of the survival response varied among genes. Silencing of αCOP and Rop produced the strongest reductions in survival, with 14.0% and 23.3% survival at 15 days post-injection, respectively, versus 76.0% in double-stranded green fluorescent protein (dsGFP) controls.
By addressing current limitations and improving methodologies, RNAi can become a promising and sustainable method for controlling CSW populations and outlines future directions for advancing RNAi-based pest management.
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