Genomic sequence analysis and field detection of mume-associated luteovirus in apricot trees

【Objective】In Aksu Prefecture, Xinjiang, symptoms including chlorosis, ring spots, and marginal yellowing were observed on apricot tree leaves, which are suspected to be caused by viral infection. At present, the occurrence and epidemic patterns of this virus in the field remain unclear, making it difficult to develop targeted prevention and control strategies and provide effective field management guidance. According to field investigations, leaf symptoms are not obvious in the early stage of this disease, so the optimal window for prevention and control is often missed. Therefore, this study aimed to obtain the complete genomic sequence of Mume- associated luteovirus (MaLV), analyze its genomic structural characteristics, and perform sequence identity and phylogenetic analysis to clarify its genetic and evolutionary relationships. Meanwhile, molecular detection technology can be applied to achieve early diagnosis and dynamic monitoring of viral diseases in apricot trees. By clarifying the dynamic patterns of field infection of this virus, we can determine the critical period for disease control.【Methods】 Apricot tree leaves exhibiting symptoms such as chlorosis, ring spots, and marginal yellowing were used as experimental materials. Through high-throughput sequencing, RT-PCR and rapid amplification of cDNA ends (RACE) techniques, specific primers were designed using Primer Premier 6.0 and Snap Gene software. The whole genome sequence of the Xinjiang isolate of Mume-associated luteovirus was cloned; Based on the obtained whole genome sequence, we designed primers with high specificity. Cherry luteovirus A, plum pox virus, apple rust fruit virus, apple mosaic virus, and peach-associated luteovirus were used as materials for primer specificity verification, and the specificity was detected using the primers (MaLV-7F/7R). After measuring the concentration of cDNA from leaf samples infected with Mume-associated luteovirus using an ultraviolet spectrophotometer, the cDNA was diluted to test the sensitivity of the primers. We then established a molecular detection method by optimizing the reaction system, and applied it to dynamic field tracking and detection of Mume-associated luteovirus.【Results】The results showed that the complete genome sequence of a Mume-associated luteovirus isolate (designated MaLV-XH-1) was successfully obtained. The full length of this sequence is 5753 nt, and it contains 6 open reading frames (ORFs). The nucleotide sequence identity between this isolate and the four reported Mume-associated luteovirus isolates ranged from 92.10% to 96.92%. We performed recombination analysis on all five full- length MaLV genomic sequences using seven methods: RDP, GENECONV, BOOTSCAN, MACCHI, CHIMAERA, SISCAN and 3SEQ, and no recombination events were detected. Phylogenetic analysis showed that the isolates obtained in this study are genetically most closely related to the Chinese isolate (GenBank No: OR523245.1), with a nucleotide homology of 96.92%. When the initial cDNA of collected viral disease samples was diluted 105 - fold, the target band could still be detected, while no bands were observed in other crops, confirming that the primers have high specificity and sensitivity. Dynamic field tracking of Mume-associated luteovirus conducted via the established molecular detection technology showed that as plant reproduction progresses, the field detection rate of this virus presents a continuous increasing trend and peaks in July and August. This is associated with the latent infection symptoms of this virus observed in field investigations. The investigation results obtained during the peak period when symptoms become obvious in the later stage are consistent with this conclusion.【Conclusion】In this study, we obtained the complete genome sequence of MaLV, and clarified the evolutionary relationship between the isolated Mume-associated luteovirus and other known isolates of this virus. The established molecular detection technology enables rapid and accurate detection and identification of Mume-associated luteovirus. Through dynamic tracking and detection of field infections, we determined that mid-to-late May is the optimal time for prevention and control of Mume-associated luteovirus.