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The study of an emerging yellows disease of pepper crops (pepper yellows disease [PYD]) in Greece led to the identification of a polerovirus closely related to Pepper vein yellows virus (PeVYV). Recovery of its full genome sequence by next-generation sequencing of small interfering RNAs allowed its characterization as a new poleroviruses, which was provisionally named Pepper yellows virus (PeYV). Transmission experiments revealed its association with the disease. Sequence similarity and phylogenetic analysis highlighted the common ancestry of the three poleroviruses (PeVYV, PeYV, and Pepper yellow leaf curl virus [PYLCV]) currently reported to be associated with PYD, even though significant genetic differences were identified among them, especially in the C-terminal region of P5 and the 3′ noncoding region. Most of the differences observed can be attributed to a modular type of evolution, which produces mosaic-like variants giving rise to these different poleroviruses Overall, similar to other polerovirus-related diseases, PYD is caused by at least three species (PeVYV, PeYV, and PYLCV) belonging to this group of closely related pepper-infecting viruses. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Recent developments in high-throughput sequencing (HTS), also called next-generation sequencing (NGS), technologies and bioinformatics have drastically changed research on viral pathogens and spurred growing interest in the field of virus diagnostics. However, the reliability of HTS-based virus detection protocols must be evaluated before adopting them for diagnostics. Many different bioinformatics algorithms aimed at detecting viruses in HTS data have been reported but little attention has been paid thus far to their sensitivity and reliability for diagnostic purposes. Therefore, we compared the ability of 21 plant virology laboratories, each employing a different bioinformatics pipeline, to detect 12 plant viruses through a double-blind large-scale performance test using 10 datasets of 21- to 24-nucleotide small RNA (sRNA) sequences from three different infected plants. The sensitivity of virus detection ranged between 35 and 100% among participants, with a marked negative effect when sequence depth decreased. The false-positive detection rate was very low and mainly related to the identification of host genome-integrated viral sequences or misinterpretation of the results. Reproducibility was high (91.6%). This work revealed the key influence of bioinformatics strategies for the sensitive detection of viruses in HTS sRNA datasets and, more specifically (i) the difficulty in detecting viral agents when they are novel or their sRNA abundance is low, (ii) the influence of key parameters at both assembly and annotation steps, (iii) the importance of completeness of reference sequence databases, and (iv) the significant level of scientific expertise needed when interpreting pipeline results. Overall, this work underlines key parameters and proposes recommendations for reliable sRNA-based detection of known and unknown viruses. [ABSTRACT FROM AUTHOR]

The dynamics of virus interference between two isolates of Plum pox virus (PPV) belonging to the main PPV types, D and M, were analyzed in Japanese plum (Prunus salicina) by challenge inoculations. To assess the consequences of a PPV-M infection on plum already infected with PPV-D, and vice versa (predominance of one of the strains, recombination, synergism, symptoms aggravation, and so on), 30 Japanese plum trees were graft inoculated with PPV-D or PPV-M isolates in quarantine conditions. One year postinoculation, in the event that the inoculated isolates were detected in the whole plant, a second challenge inoculation (PPV-M or PPV-D, respectively) was performed by grafting. The presence of PPV-D, PPV-M, or both was monitored for 7 years by double-antibody sandwich indirect enzyme-linked immunosorbent assay using specific monoclonal antibodies. Reverse transcription-polymerase chain reaction (RT-PCR) with D- and M-specific primers confirmed the serological typing. Real-time RT-PCR assays were performed using D- and M-specific fluorescent 3′ minor groove binder-DNA probes, which were able to detect and quantify PPV populations in the inoculated plants with greater precision. The presence of PPV-D in Japanese plum did not cross-protect the trees against PPV-M infection. In PPV-D-infected plants, the PPV-M strain used as challenge inoculum behaved differently depending on the plum cultivar assayed. In cv. Black Diamond, PPV-M invaded the plant progressively, displacing the previous PPV-D population; whereas, in cv. Sun Gold, both PPV isolates coexisted in the plant. In contrast, the PPV-D isolate used was unable to infect plants of both cultivars in which a PPV-M population already was established. After 7 years, no synergism was observed and no recombination event between PPV-D and PPV-M genomes was detected. [ABSTRACT FROM AUTHOR]

ABSTRACT A multiplex nested reverse transcription-polymerase chain reaction (RT-PCR) in a single closed tube was developed for the simultaneous detection of four RNA viruses: Cucumber mosaic virus, Cherry leaf roll virus, Strawberry latent ringspot virus, and Arabis mosaic virus, and the bacterium Pseudomonas savastanoi pv. savastanoi. The method enabled, for the first time, the sensitive and simultaneous detection of RNA and DNA targets from plant viruses and a bacterium, saving time, decreasing risks of contamination, and reducing costs compared with conventional monospecific nested amplifications. The method was successfully coupled with colorimetric detection of amplicons using specific oligoprobes to simplify routine detection. Two hundred forty-five olive trees from 15 different cultivars were analyzed by multiplex RT-nested PCR coupled with colorimetric detection. Multiplex nested RT-PCR for viral detection increased the identification of positive trees by 8.1%. An uneven distribution of the viruses was observed in the infected trees. The bacterium was detected in 28.7% of the analyzed trees by the developed multiplex nested method and by a nested PCR previously developed. This powerful methodology could be applied to other models for the detection of several pathogens in a single assay.