Nanoparticle-Shielded dsRNA Delivery for Enhancing RNAi Efficiency in Cotton Spotted Bollworm Earias vittella (Lepidoptera: Nolidae)

纳米粒子屏蔽的 dsRNA 递送可增强棉斑铃虫 (Earias vittella) 的 RNAi 效率 (鳞翅目:棉铃虫科)

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作者:Shelja Sandal, Satnam Singh, Gulshan Bansal, Ramandeep Kaur, Kanakachari Mogilicherla, Suneet Pandher, Amit Roy, Gurmeet Kaur, Pankaj Rathore, Anu Kalia

Abstract

The spotted bollworm Earias vittella (Lepidoptera: Nolidae) is a polyphagous pest with enormous economic significance, primarily affecting cotton and okra. However, the lack of gene sequence information on this pest has a significant constraint on molecular investigations and the formulation of superior pest management strategies. An RNA-seq-based transcriptome study was conducted to alleviate such limitations, and de novo assembly was performed to obtain transcript sequences of this pest. Reference gene identification across E. vittella developmental stages and RNAi treatments were conducted using its sequence information, which resulted in identifying transcription elongation factor (TEF), V-type proton ATPase (V-ATPase), and Glyceraldehyde -3-phosphate dehydrogenase (GAPDH) as the most suitable reference genes for normalization in RT-qPCR-based gene expression studies. The present study also identified important developmental, RNAi pathway, and RNAi target genes and performed life-stage developmental expression analysis using RT-qPCR to select the optimal targets for RNAi. We found that naked dsRNA degradation in the E. vittella hemolymph is the primary reason for poor RNAi. A total of six genes including Juvenile hormone methyl transferase (JHAMT), Chitin synthase (CHS), Aminopeptidase (AMN), Cadherin (CAD), Alpha-amylase (AMY), and V-type proton ATPase (V-ATPase) were selected and knocked down significantly with three different nanoparticles encapsulated dsRNA conjugates, i.e., Chitosan-dsRNA, carbon quantum dots-dsRNA (CQD-dsRNA), and Lipofectamine-dsRNA conjugate. These results demonstrate that feeding nanoparticle-shielded dsRNA silences target genes and suggests that nanoparticle-based RNAi can efficiently manage this pest.

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