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| Establishment and Application of a SYBR Green Ⅰ Real-time Fluorescent RT-PCR for Detecting Shallot latent virus on Shallot (Allium cepa var. aggregatum) |
| LIU Yue1,2,*, LIU Jian-Qing1,*, ZHANG Chun-Yu1, SU Ying1, LI Xiao-Yu1,**, WANG Yong-Zhi1** |
1 Jilin Academy of Agricultural Sciences, Gongzhuling 136100, China;
2 College of Life Sciences, Jilin Agricultural University, Changchun 130118, China |
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Abstract Shallot latent virus (SLV) is one of the major viruses that damage shallot (Allium cepa var. aggregatum) and other Allium crops and prevails in many Allium-growing regions. To develop a SYBR Green Ⅰ real-time fluorescent RT-PCR assay for the sensitive detection of SLV, a pair of specific primers were designed and synthesized based on the conserved region of SLV coat protein (CP) gene registered in GenBank and a series of optimization including primers concentration and annealing temperature were performed. Ct values were linear with the logarithm of the template concentration from standard curve of cDNA. The amplification efficiency was 94.529% and the correlation coefficient was 0.999 6. There was no crossing reaction with Shallot yellow stripe virus (SYSV), Onion yellow dwarf virus (OYDV) and Shallot virus X (SVX). The lowest detection limit was 10-7 dilution fold, which was 1 000 times higher than that of routine RT-PCR. The coefficients of variation of intra-assay and inter-assay were 0.02%~0.63% and 0.03%~1.24%, respectively, indicating the excellent stability of the method. The SYBR Green Ⅰ real-time fluorescent RT-PCR was used to determine the content of SLV in shallot leaves and bulbs and to detect 50 samples from suspected infectious shallot. The results showed that the content of SLV in leaves was significantly higher than that in bulbs, and 46 out of 50 samples were detected to be positive by this assay, and the detection rate was 6% higher than that of conventional RT-PCR. Therefore, the SYBR Green Ⅰ real-time fluorescent RT-PCR method for detection of SLV provides technical support for the rapid detection of SLV on shallot.
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Received: 13 January 2022
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Corresponding Authors:
** lxyzsx@163.com; yzwang@126.com
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| About author:: * These authors contributed equally to this work |
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