Characterization of ovary genes of beet webworm, Loxostege sticticalis, through de novo transcriptome analysis and its potential application in ovary grading

Abstract

AbstractThe beet webworm, Loxostege sticticalis (L.) (Lepidoptera: Crambidae), is an important insect pest that infests crops such as sugar beet, maize, and potato in Asia, Europe, and North America. The ovary grade is a reliable factor that can be used to forecast L. sticticalis outbreaks. However, forecasting outbreaks by dissecting female ovaries is tedious and generally impractical. Better methods for outbreak forecasting need to be developed. Molecular information on ovary transcriptomes potentially provides criteria for ovary grade. We constructed a library of female ovaries from L. sticticalis and sequenced the transcriptome with RNA‐Seq technology. We analyzed the gene expression profile with Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Clusters of Orthologous Groups (COG) for eukaryotic complete genome analysis. The RNA‐seq libraries generated over 62 640 808 clean reads, and a total of 22 628 genes were identified. Many genes related to oocyte meiosis, oocyte maturation, cell adhesion, and signal transduction were identified. Twenty‐eight oocyte meiosis‐ and oocyte maturation‐related genes were selected for quantitative real‐time (qRT) PCR and revealed differentially expressed genes among ovary grades (I–IV) of females. Of the 28 genes selected, 12 were highly expressed in one of the four ovary grades. These genes have potential use as molecular markers for ovary grading serve as a resource for forecasting population outbreaks of L. sticticalis.