Abstract: 【Aim】 The purpose of this study is to investigate the molecular characteristics and expression patterns of two circadian clock genes Gmper and Gmtim in the oriental fruit moth, Grapholita molesta, and to explore their regulatory roles in the emergence rhythm, so as to provide potential new targets for controlling G. molesta. 【Methods】 Based on the transcriptome data of G. molesta, the full-length cDNAs of two circadian clock genes Gmper and Gmtim were cloned by PCR. The expression levels of these two genes in the head, thorax, abdomen and leg of adults and their daily expression patterns in the pupal head were determined by RT-qPCR. Then the regulatory roles of Gmper and Gmtim in the emergence rhythm of G. molesta were explored by RNAi with siRNAs. 【Results】 The full-length cDNAs of Gmper (GenBank accession no.: MN862636) and Gmtim (GenBank accession no.: MN862637) were cloned from G. molesta. The open reading frame of Gmper is 2 862 bp in length, encoding 953 amino acids with two PAS domains and a PAC domain. The open reading frame of Gmtim is 3 048 bp in length, encoding 1 015 amino acids. The expression levels of Gmper and Gmtim in heads were higher than those in other tissues of both male and female adults. In pupal head, the expression levels of the two genes were significantly higher in the scotophase than in the photophase. The emergence rhythm changed after RNAi of both Gmper and Gmtim, and G. molesta in the RNAi group showed a more dispersed emergence pattern than that in the control group, and the amount of emerged adults during the emergence peak period decreased after RNAi. 【Conclusion】 Both Gmper and Gmtim in G. molesta show differential expression patterns among different tissues and between day and night, and they may play important regulatory roles in the emergence rhythm of G. molesta. The results provide clues to the development of monitoring and control technologies for G. molesta based on behavioral rhythm regulation.

Key words: Grapholita molesta, circadian clock gene; per; tim, RNAi, emergence rhythm