five

Dissecting protein domain variability in the core RNA interference machinery of five insect orders

收藏
Taylor & Francis Group2024-03-21 更新2026-04-16 收录
下载链接:
https://tandf.figshare.com/articles/dataset/Dissecting_protein_domain_variability_in_the_core_rna_interference_machinery_of_five_insect_orders/13513478/2
下载链接
链接失效反馈
官方服务:
资源简介:
RNA interference (RNAi)-mediated gene silencing can be used to control specific insect pest populations. Unfortunately, the variable efficiency in the knockdown levels of target genes has narrowed the applicability of this technology to a few species. Here, we examine the current state of knowledge regarding the miRNA (micro RNA) and siRNA (small interfering RNA) pathways in insects and investigate the structural variability at key protein domains of the RNAi machinery. Our goal was to correlate domain variability with mechanisms affecting the gene silencing efficiency. To this end, the protein domains of 168 insect species, encompassing the orders Coleoptera, Diptera, Hemiptera, Hymenoptera, and Lepidoptera, were analysed using our pipeline, which takes advantage of meticulous structure-based sequence alignments. We used phylogenetic inference and the evolutionary rate coefficient (<i>K</i>) to outline the variability across domain regions and surfaces. Our results show that four domains, namely dsrm, Helicase, PAZ and Ribonuclease III, are the main contributors of protein variability in the RNAi machinery across different insect orders. We discuss the potential roles of these domains in regulating RNAi-mediated gene silencing and the role of loop regions in fine-tuning RNAi efficiency. Additionally, we identified several order-specific singularities which indicate that lepidopterans have evolved differently from other insect orders, possibly due to constant coevolution with plants and viruses. In conclusion, our results highlight several variability hotspots that deserve further investigation in order to improve the application of RNAi technology in the control of insect pests.
提供机构:
Barbosa, Joao Alexandre R. G; de Macedo, Leonardo Lima Pepino; Morgante, Carolina Vianna; Togawa, Roberto Coiti; Danchin, Etienne G. J.; Faheem, Muhammad; Arraes, Fabricio Barbosa Monteiro; Moreira, Valdeir Junio Vaz; Vasquez, Daniel D. Noriega; Grossi-de-Sa, Maria Fatima; Melo, Bruno Paes; Martins-de-Sa, Diogo
创建时间:
2021-01-18
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作