Abstract

The genus Cucurbita comprises many popular vegetable and ornamental plants, including pumpkins, squashes, and gourds, that are highly valued in China as well as in many other countries. During a survey conducted in Zhejiang province, Southeast China in 2016, severe symptoms of viral infection were observed on Cucurbita maxima Duch. ex Lam. Diseased plants showed symptoms such as stunting, mosaicking, Shoe string, blistering, yellowing, leaf deformation, and fruit distortion. Approximately, 50% of Cucurbita crops produced in Jinhua were diseased, causing an estimated yield loss of 35%. In this study, we developed a method using all known virus genomes from the NCBI database as a reference to map small RNAs to develop a diagnostic tool that could be used to diagnose virus diseases of C. maxima. 25 leaf samples from different symptomatic plants and 25 leaf samples from non-symptomatic plants were collected from the experimental field of Jihua National Agricultural Technology Garden for pathogen identification. Small RNAs from each set of three symptomatic and non-symptomatic samples were extracted and sequenced by Illumina sequencing. Twenty-four different viruses were detected in total. However, the majority of the small RNAs were from Zucchini yellow mosaic virus (ZYMV), Watermelon mosaic virus (WMV), and Cucumber mosaic virus (CMV). Mixed infections of these three viruses were diagnosed in leaf samples from diseased plants and confirmed by reverse transcription PCR (RT-PCR) using primers specific to these three viruses. Crude sap extract from symptomatic leaf samples was mechanically inoculated back into healthy C. maxima plants growing under greenhouse conditions. Inoculated plants developed the same disease symptoms as those observed in the diseased plants and a mixed infection of ZYMV, WMV, and CMV was detected again by RT-PCR, thus fulfilling Koch’s postulates. The diagnostic method developed in this study involves fewer bioinformatics processes than other diagnostic methods, does not require complex settings for bioinformatics parameters, provides a high level of sensitivity to rapidly diagnose plant samples with symptoms of virus diseases and can be performed cheaply. This method therefore has the potential to be widely applied as a diagnostic tool for viruses that have genome information in the NCBI database.

Highlights

  • The Cucurbitaceae family is a large, genetically diverse group of plants that are highly valued as popular food crops, such as watermelon, zucchini, cucumber, and squashes, and as ornamental plants, such as gourds, in China and in many other countries (Sanjur et al, 2002)

  • We developed a method using all known virus genomes from the NCBI database as a reference to map small RNA without the need for sequence assembly, as is usually required using other virus diagnostic methods

  • We identified the newly emerged disease of Cucurbita in the Jinhua area of Zhejiang province in China as a mixed infection of three viruses: Zucchini yellow mosaic virus (ZYMV), Watermelon mosaic virus (WMV), and Cucumber mosaic virus (CMV)

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Summary

Introduction

The Cucurbitaceae family (cucurbits) is a large, genetically diverse group of plants that are highly valued as popular food crops, such as watermelon, zucchini, cucumber, and squashes, and as ornamental plants, such as gourds, in China and in many other countries (Sanjur et al, 2002). At least 28 species of virus have been reported to naturally infect cucurbit crops, mainly those that belong to the genera Crinivirus, Begomovirus, Ipomovirus, and Potyvirus. Some of these virus species are widely distributed and can cause severe crop losses (Lecoq & Desbiez, 2012; Abrahamian & Abou-Jawdah, 2014; Perotto et al, 2018). The occurrence of single, co-, and triple infections of cucurbit vegetables with Cucumber mosaic virus (CMV; a Cucumovirus), Watermelon mosaic virus (WMV; a Potyvirus), and Zucchini yellow mosaic virus (ZYMV; a Potyvirus) has been reported recently in Karanganyar, Central Java, Indonesia and coastal areas of Tanzania (Sydanmetsa & Mbanzibwa, 2016; Supyani et al, 2017)

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