PHOTO: Hanno van Schalkwyk.

Vitis vinifera is host to more than 70 intracellular pathogens, of which 63 are viruses, making this woody crop the most susceptible to these pathogens (Martelli, 2014). The viruses that infect grapevine are devastating pathogens and contribute to an extensive decrease in fruit yield and plant vigour (Martelli & Boudon-Padieu, 2006). No means of natural resistance against these pathogens exist in grapevine, adding additional pressure to an already stressed industry (Oliver & Fuchs, 2011; Fisher et al., 2004; Yamamoto et al., 2000). By studying the relationships of viral populations and their host, we can develop a greater understanding of the aetiology of grapevine diseases.

Grapevine leafroll disease is the most economically important of the grapevine virus diseases and has been reported to contribute up to 60% loss in yield of fruit (Rayapati et al., 2008). The grapevine leafroll-associated viruses (GLRaVs) are the collective infectious agents commonly found in GLD-symptomatic grapevines, where Grapevine leafroll-associated virus 3 (GLRaV-3) (genus Ampelovirus, family Closteroviridae) is the primary causative agent. Eight variant groups of GLRaV-3 have been identified and their presence in diseased grapevines, as either single or mixed infections, varies from plant to plant (Maree et al., 2015; Chooi et al., 2013b; Farooq et al., 2012; Jooste et al., 2011; Sharma et al., 2011). Other viruses commonly infecting grapevine belong to the families Closteroviridae and Betaflexiviridae (Le Maguet et al., 2012). The interaction of GLRaVs within diseased grapevines, in effecting plant physiology and physical symptoms, is not well documented and further in-depth research is needed.

In the last decade, sequencing technology (determining the genetic code of an organism) made great advances, from sequencing a few thousand bases a day with Sanger sequencing, to literally billions of bases with the use of NGS platforms and the accompanying computer hardware and bioinformatics software. This rapid expansion avails new opportunities for the field of plant virology that many studies have utilised; to detect known viruses, for the discovery of new viruses and virus variants of known viruses, and to study virus genomes and populations. The application of this technology has great potential to change the way we view healthy plants and how we deal with the cross border movement of plant material.

The application of NGS in a metagenomic approach allows for genetic material to be taken directly from the environment and entire viral populations (viromes) present in such a sample to be identified and studied at one time, without requiring prior knowledge of the viral sequences present in the sample (Adams et al., 2009). This approach has been used in several studies for the detection of viral populations in grapevines (Espach et al., 2013; Al Rwahnih et al., 2011; Coetzee et al., 2010; Adams et al., 2009). A diverse population of mycoviruses (viruses infecting fungi) has also been detected in grapevine with this approach (Espach et al., 2013; Al Rwahnih et al., 2011). Research performed in the USA (Sharma et al., 2011; Fuchs et al., 2009; Martin et al., 2005), Chile (Fiore et al., 2008) and Turkey (Akbas et al., 2007) found mixed viral infections in single GLD-symptomatic plants to be a common occurrence.

In this study, the diversity of viruses within GLD-affected vines was determined using a metagenomic NGS approach. Double stranded RNA was extracted (Burger & Maree, 2015) from 17 samples of typical GLD-symptomatic plants and subjected to NGS at the ARC Biotechnology platform in Pretoria. The samples were a selection of four rootstocks, six white- and seven red-fruited cultivars that represented 10 different cultivars. NGS data was bioinformatically analysed (by incorporating aspects of computer science, statistics and mathematics) to identify known and novel viruses (Burger & Maree, 2015). Two types of bioinformatic analyses were performed: De novo assembly, where a new sequence was assembled without referencing it to an existing viral genome to detect known and novel viruses; and read-mapping, where the new sequence reads were mapped against existing viral genomes, was used to detect known viruses.

In the de novo assembly analysis, sequence reads from the NGS data sets were assembled into longer overlapping sequences (called contiguous sequences or contigs) using commercial software (CLC Genomics Workbench). The origins of these contigs were then identified using homology searches (BLAST) in international nucleotide databases (NCBI GenBank). In Figure 1 a combined view of the data from the 17 vines is presented. From Figure 1A it is clear that GLRaV-3 represents the greatest percentage of the data, which justifies its position as being the dominant causative agent of GLD. However, from Figure 1B several other viruses, such as GRSPaV, GVA, GVB and GVE, were also regularly detected, although not consistently with GLD. Most importantly, by using this approach new viruses can be detected as was reported by Coetzee et al., 2010 for GVE (survey in Winetech project GenUS11/2). In the current study a new vitivirus named Grapevine virus F (GVF) was detected in South Africa for the first time. The 31.8% “other viruses” in Figure 1B consisted mainly of mycoviruses that are believed to be infecting the endophytic fungal population of the plant.

FIGURE 1. Chart representations of the pooled viral population within sampled grapevines, established through de novo assemblies. A. Percentage distribution of GLRaV-3 variant groups, as the primary causative agents of grapevine leafroll disease. B. Viruses detected at lower frequencies whose percentage distribution comprises “Other” in A.

The 17 NGS datasets were also mapped to reference genomes of grapevine-infecting viruses, obtained from the NCBI GenBank database, in a read-mapping analysis using CLC Genomics Workbench. The average depth of coverage and the fraction of the reference virus genome covered for each sample were calculated and used to determine the possibility that the virus was present in a sample. The average coverage of each virus reference and the fraction of the reference covered give a good indication of the presence and titre of an identified virus. Results that display a high read count with a low fraction of reference covered indicate the mapping of reads to conserved regions of the reference sequence. This may be a possible indication of the presence of alternative isolate representatives of the viruses mapped. On the other hand, viruses detected with a high percentage of sequence covered and a low read count, as for viroids belonging to the family Pospiviroidae and certain Vitivirus infections, can be accounted to relatively small genomes, uniform replication across all open reading frames (ORFs), as well as better quality reads mapping to regions across the reference genomes. Table 1 summarises the results from the read mapping analysis with green-shaded cells highlighting virus genomes that were represented greater than 10% and regarded as positive for that particular virus.

In this study, the viromes of 17 GLD-symptomatic grapevines were determined. On average four viruses per sample were detected with the surprising result that rootstock and white-fruited cultivars hosted more diverse populations of viruses (Table 1). As expected, the most abundant virus was GLRaV-3, particularly GLRaV-3 variant group II representatives, supporting the concept that GLRaV-3 is the primary causative agent of GLD in South Africa (Pietersen, 2004). Through read-mapping it was determined that viruses from the family Closteroviridae form the greatest percentage of the total viral population, and members from the genus Vitivirus the second greatest contributors. In this study NGS was shown to be an effective technology to identify grapevine-infecting viruses and was capable of identifying a new isolate of GVF. Further research, with focus on viral populations and associations between virus species, needs to commence to broaden the understanding of GLD etiology and assist in reducing the negative effects of this devastating disease.

TABLE 1. Summary of the read mapping analysis with the percentage of the reference genome covered for each sample against viruses from the families Closteroviridae, Betaflexiviridae, Pospiviroidae and Endornaviridae. Samples with an average sequencing depth of ³10 and genome coverage ³10% were regarded as positive and are highlighted in green.

Summary

Vitis vinifera is the woody crop most susceptible to pathogens living inside the plant cells. Currently 70 such pathogens infect grapevine, of which 63 are of viral origin. Grapevine leafroll-associated virus 3 (GLRaV-3) is considered to be the primary causative agent of grapevine leafroll disease (GLD) globally. However, the exact complexity in terms of contributing pathogens to GLD is not completely understood. Unlike current virus detection methods like ELISA and PCR, next-generation sequencing (NGS) is capable of detecting known and novel viruses without prior knowledge of viral proteins or genome sequences, and when used in a metagenomic approach is able to detect viral populations within diseased vines. In this study, a total of 17 grapevine samples were subjected to NGS using an Illumina sequencing platform to determine the virome of GLD affected vines. NGS datasets were subjected to both read-mapping and de novo assembly to construct the viromes of samples. Contigs assembled de novo were analysed with BLAST against the NCBI nucleotide database and as expected GLRaV-3 was the best-represented virus, comprising 97.5% of the assembled contigs. Grapevine virus F (GVF) was detected for the first time in South African vineyards through the de novo assemblies. The data generated in this study supports the current hypothesis that GLRaV-3 is the primary causative agent of GLD.

 

Acknowledgements

The authors would like to thank Winetech (projects GenUS10/1 and GenUS11/3), the Technology and Human Resource Programme (THRIP) (Department of Trade and Industry) and the National Research Foundation (NRF) for financial assistance towards this research. Opinions expressed and conclusions arrived at, are those of the authors and are not necessarily to be attributed to the NRF.

 

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– For more information, contact Johan Burger at jtb@sun.ac.za.

 

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