关键词: 小菜蛾, 玫烟色棒束孢, 免疫应答, 转录组, RNA-seq, 表达谱, 生物信息学

Abstract: 【Aim】 This study aims to explore the immune-related genes and immune recognition regulatory network of Plutella xylostella against the entomopathogenic fungus Isaria fumosorosea, so to further investigate the innate immune mechanisms of P. xylostella to I. fumosorosea. 【Methods】 We analyzed the transcriptome of I. fumosorosea infected and uninfected (the control) 4th instar larvae of P. xylostella at 12 h after treatment using high-throughput single-end RNA-sequencing (RNA-seq). The differentially expressed genes and their functions, classifications and signaling pathways were analyzed using bioinformatic tools. 【Results】 The numbers of 12 346 987 and 12 315 210 clean reads were obtained for the infected and uninfected libraries, respectively. Among the two RNA-seq libraries, 60.93% and 61.26% of reads could be mapped to reference genome with a perfect match proportion of 32.15% and 32.73%, respectively. A total of 351 differentially expressed unigenes (DEUs) were identified between infected and uninfected larvae. Among them, 275 DEUs were up-regulated while 76 DEUs down-regulated. In total, there were 156 DEUs putatively involved in immune responses. A Blast2GO analysis indicated that 102 differentially expressed transcripts were able to be assigned to 46 Gene Ontology (GO) terms. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis mapped 132 DEUs to 13 pathways (P<0.05).【Conclusion】 Most of the DEUs are putatively linked to innate immune recognition and regulation. These infection-responsive DEUs are enriched in various immune processes, such as energy metabolism, disease and defense response. The findings generated from this study provide a bioinformatic database for searching immune-related genes, and also benefit the future investigations of the innate immune mechanisms of P. xylostella against the entomopathogenic fungus I. fumosorosea.

Key words: Plutella xylostella; Isaria fumosorosea, immune response, transcriptome, RNA-seq, expression profile, bioinformatics