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A significant number of new members of the genus Vitivirus have been identified recently, mainly due to the advent of high-throughput sequencing (HTS). Grapevine virus I (GVI), which was identified in New Zealand in 2018, is one of these viruses. RNAseq HTS analysis of a Greek grapevine (cv. Daphnia), revealed the presence of a GVI-like isolate (D2-1/19). Sequence analysis confirmed the classification of D2-1/19 as GVI. However, both sequence and phylogenetic data exhibited high levels of variability between D2-1/19 and the previously characterized GVI isolates. This study provides the full-length sequence of a divergent GVI isolate, adding knowledge to the limited information available about this recently identified virus.

Grapevine Pinot gris virus (GPGV) is a widely distributed grapevine pathogen that has been associated to the grapevine leaf mottling and deformation disease. With the aim of better understanding the disease epidemiology and providing efficient control strategies a specific and quantitative duplex TaqMan real-time RT-PCR assay has been developed. This method has allowed reliable quantitation of the GPGV titer ranging from 30 up to 3 x 108 transcript copies, with a detection limit of 70 viral copies in plant material. The assay targets a grapevine internal control that reduces the occurrence of false negative results, thus increasing the diagnostic sensitivity of the technique. Viral isolates both associated and non-associated to symptoms from Greece, Slovakia and Spain have been successfully detected. The method has also been applied to the absolute quantitation of GPGV in its putative transmission vector Colomerus vitis. Moreover, the viral titer present in single mites has been determined. In addition, in the current study a new polymorphism in the GPGV genome responsible for a shorter movement protein has been found. A phylogenetic study based on this genomic region has shown a high variability among Spanish isolates and points to a different evolutionary origin of this new polymorphism. The methodology here developed opens new possibilities for basic and epidemiological studies as well as for the establishment of efficient control strategies.

Grapevine leafroll-associated virus 3 (GLRaV-3) has a worldwide distribution and is the most economically important virus that causes grapevine leafroll disease. Reliable, sensitive, and specific methods are required for the detection of the pathogen in order to assure the production of healthy plant material and control of the disease. Although different serological and nucleic acid-based methods have been developed for the detection of GLRaV-3, diagnostic parameters have not been established, and there is no gold standard method. Therefore, the main aim of this work was to determine the sensitivity, specificity, and likelihood ratios of three commonly used methods, including one serological test (double-antibody sandwich enzyme-linked immunosorbent assay [DAS-ELISA]) and two nucleic acid-based techniques (spot and conventional real-time reverse transcription-polymerase chain reaction [RT-PCR]). Latent class models using a Bayesian approach have been applied to determine diagnostic test parameters and to facilitate decision-making regarding diagnostic test selection. Statistical analysis has been based on the results of a total of 281 samples, which were collected during the dormant period from three different populations. The best-fit model out of the 49 implemented models revealed that DAS-ELISA was the most specific method (value = 0.99) and provided the highest degree of confidence in positive results. Conversely, conventional real-time RT-PCR was the most sensitive method (value = 0.98) and produced the highest degree of confidence in negative results. Furthermore, the estimation of likelihood ratios showed that in populations with low GLRaV-3 prevalence the most appropriate method could be DAS-ELISA, while conventional real-time RT-PCR could be the most appropriate method in medium or high prevalence populations. Combining both techniques significantly increases detection accuracy. The flexibility and power of Bayesian latent class models open new possibilities for the evaluation of diagnostic tests for plant viruses.