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 so far to their sensitivity and reliability for diagnostic purposes. We therefore 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 ten datasets of 21-24 nt small (s)RNA 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 to detect viral agents when they are novel and/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 pipelines results. Overall, this work underlines key parameters and proposes recommendations for reliable sRNA-based detection of known and unknown viruses.

High throughput sequencing informed diagnostics is revolutionising plant pathology. The application of this technology is most advanced in plant virology, where it is already becoming a front-line diagnostic tool and it is envisaged that for other types of pathogen and pests this will be the case in the near future. However, there are implications to deploying this technology due to a number of technical and scientific challenges. Firstly, interpretation of data and the assessment of plant health risk against a limited baseline of existing knowledge of the presence of pathogens in a given geographic region. Secondly, evidence of causality and the separation of pathogenic from commensal organisms in the sequence data, thirdly, the tension between the generation of a rapid sequence result with the necessary but laborious epidemiological characterisation in support of plant health risk assessment. Finally, the validation and accreditation of methods based on this rapidly evolving technology. These in turn present challenges for plant health policy and regulation. This review discusses the development of this technology, its application in plant health diagnostics, and explores the implications of applying this technology in the plant health setting.

In March of 2014, carrot plants (Daucus carota L. var. Mascot) exhibiting symptoms of yellowing, purpling, and curling of leaves, proliferation of shoots, formation of hairy secondary roots, general stunting, and plant decline were observed in commercial fields in the Gharb region of Morocco. The symptoms resembled those caused by phytoplasmas, Spiroplasma citri, or ‘Candidatus Liberibacter solanacearum’ infection (1,2,3). About 30% of the plants in each field were symptomatic and plants were infested with unidentified psyllid nymphs; some psyllids are known vectors of ‘Ca. L. solanacearum.’ A total of 10 symptomatic and 2 asymptomatic plants were collected from three fields. Total DNA was extracted from petiole and root tissues of each of the carrots, using the CTAB buffer extraction method (3). The DNA samples were tested for phytoplasmas and spiroplasmas by PCR (3) but neither pathogen was detected in the samples. The DNA extracts were tested for ‘Ca. L. solanacearum’ by PCR using specific primer pairs OA2/OI2c, Lso adkF/R, and CL514F/R, to amplify a partial fragment of the 16S rDNA, the adenylate kinase gene, and rpIJ/rpIL50S rDNA ribosomal protein genes, respectively (1,2,5). DNA samples from all 10 symptomatic carrots yielded specific bands; 1,168 bp for the 16S rDNA fragment, 770 bp for the adk fragment, and 669 bp for rpIJ/rpIL, indicating the presence of ‘Ca. L. solanacearum.’ No ‘Ca. L. solanacearum’ was detected in asymptomatic plants. DNA amplicons of three plant samples (one plant/field) for each primer pair were directly sequenced (Macrogen Inc., Amsterdam). Sequencing results identified two distinct products for the OA2/OI2c primer pair (GenBank Accession Nos. KJ740159 and KJ740160), and BLAST analysis of the 16S rDNA amplicons showed 99 and 100% identity to ‘Ca. L. solanacearum’ (KF737346 and HQ454302, respectively). Two different sequences of the adk amplicon were obtained (KJ740162 and KJ740163), both of which were 98% identical to ‘Ca. L. solanacearum’ (CP002371). Sequencing results also identified two distinct products for the CL514F/R primer pair (KJ754506 and KJ754507), and BLAST analysis of the 50S rDNA ribosomal protein showed 99 and 100% identity to ‘Ca. L. solanacearum’ (KF357912 and HQ454321, respectively). The differences in our 16S and 50S rDNA sequences identified the presence of both ‘Ca. L. solanacearum’ haplotypes D and E (4). To our knowledge, this is the first report of the occurrence of ‘Ca. L. solanacearum’ in Morocco and Africa, suggesting a wider distribution of the bacterium in carrot crops in the Mediterranean region, including North Africa. ‘Ca. L. solanacearum’ has caused economic damages to carrot and celery crops in the Canary Islands and mainland Spain, France, Sweden, Norway, and Finland (3). This bacterium has also caused millions of dollars in losses to potato and several other solanaceous crops in the United States, Mexico, Central America, and New Zealand (1,2,5). Given the economic impact of ‘Ca. L. solanacearum’ on numerous important crops worldwide, it is imperative that preventive measures be taken to limit its spread. References: (1) L. W. Liefting et al. Plant Dis. 93:208, 2009. (2) J. E. Munyaneza et al. Plant Dis. 93:552, 2009. (3) J. E. Munyaneza et al. J. Plant Pathol. 93:697, 2011. (4) W. R. Nelson et al. Eur. J. Plant Pathol. 135:633, 2013. (5) A. Ravindran et al. Plant Dis. 95:1542, 2011.

Rhamnolipids, extracellular metabolites of Pseudomonas aeruginosa with surfactant properties, proved to be very effective in controlling the spread of brown root rot disease caused by Phytophthora cryptogea in the hydroponic forcing system of witloof chicory ( Cichorium intybus var. foliosum ). The biosurfactant was applied as the product PRO1, a formulation of 25% rhamnolipids in oil. Both an in vitro screening and in vivo experiments in a mini-hydroponic system demonstrated the ability of PRO1 to control brown root rot. A 25 µ g mL − 1 rhamnolipids nutrient solution was enough to obtain good control of an artificial infection with a zoospore suspension of P. cryptogea . The biosurfactant PRO1 performed well in a semicommercial system under growers' conditions. A treatment of 25 µ g mL − 1 rhamnolipids (100 µ g mL − 1 PRO1) reduced the disease incidence significantly in two independent experiments. However, PRO1 was not effective when a mycelial suspension was used as inoculum. Rhamnolipids have good potential to limit the spread of P. cryptogea in the hydroponic forcing system of witloof chicory, and can be used as a preventive measure against brown root rot.

High-throughput sequencing (HTS) technologies have become indispensable tools assisting plant virus diagnostics and research thanks to their ability to detect any plant virus in a sample without prior knowledge. As HTS technologies are heavily relying on bioinformatics analysis of the huge amount of generated sequences, it is of utmost importance that researchers can rely on efficient and reliable bioinformatic tools and can understand the principles, advantages, and disadvantages of the tools used. Here, we present a critical overview of the steps involved in HTS as employed for plant virus detection and virome characterization. We start from sample preparation and nucleic acid extraction as appropriate to the chosen HTS strategy, which is followed by basic data analysis requirements, an extensive overview of the in-depth data processing options, and taxonomic classification of viral sequences detected. By presenting the bioinformatic tools and a detailed overview of the consecutive steps that can be used to implement a well-structured HTS data analysis in an easy and accessible way, this paper is targeted at both beginners and expert scientists engaging in HTS plant virome projects.

High-throughput sequencing (HTS) is a powerful tool that enables the simultaneous detection and potential identification of any organisms present in a sample. The growing interest in the application of HTS technologies for routine diagnostics in plant health laboratories is triggering the development of guidelines on how to prepare laboratories for performing HTS testing. This paper describes general and technical recommendations to guide laboratories through the complex process of

High-throughput sequencing (HTS) technologies have the potential to become one of the most significant advances in molecular diagnostics. Their use by researchers to detect and characterize plant pathogens and pests has been growing steadily for more than a decade and they are now envisioned as a routine diagnostic test to be deployed by plant pest diagnostics laboratories. Nevertheless, HTS technologies and downstream bioinformatics analysis of the generated datasets represent a complex process including many steps whose reliability must be ensured. The aim of the present guidelines is to provide recommendations for researchers and diagnosticians aiming to reliably use HTS technologies to detect plant pathogens and pests. These guidelines are generic and do not depend on the sequencing technology or platform. They cover all the adoption processes of HTS technologies from test selection to test validation as well as their routine implementation. A special emphasis is given to key elements to be considered: undertaking a risk analysis, designing sample panels for validation, using proper controls, evaluating performance criteria, confirming and interpreting results. These guidelines cover any HTS test used for the detection and identification of any plant pest (viroid, virus, bacteria, phytoplasma, fungi and fungus-like protists, nematodes, arthropods, plants) from any type of matrix. Overall, their adoption by diagnosticians and researchers should greatly improve the reliability of pathogens and pest diagnostics and foster the use of HTS technologies in plant health.

The widespread use of High-Throughput Sequencing (HTS) for detection of plant viruses and sequencing of plant virus genomes has led to the generation of large amounts of data and of bioinformatics challenges to process them. Many bioinformatics pipelines for virus detection are available, making the choice of a suitable one difficult. A robust benchmarking is needed for the unbiased comparison of the pipelines, but there is currently a lack of reference datasets that could be used for this purpose. We present 7 semi-artificial datasets composed of real RNA-seq datasets from virus-infected plants spiked with artificial virus reads. Each dataset addresses challenges that could prevent virus detection. We also present 3 real datasets showing a challenging virus composition as well as 8 completely artificial datasets to test haplotype reconstruction software. With these datasets that address several diagnostic challenges, we hope to encourage virologists, diagnosticians and bioinformaticians to evaluate and benchmark their pipeline(s).