Many scientists, if not all, feel that their particular plant virus should appear in any list of the most important plant viruses. However, to our knowledge, no such list exists. The aim of this review was to survey all plant virologists with an association with Molecular Plant Pathology and ask them to nominate which plant viruses they would place in a 'Top 10' based on scientific/economic importance. The survey generated more than 250 votes from the international community, and allowed the generation of a Top 10 plant virus list for Molecular Plant Pathology. The Top 10 list includes, in rank order, (1) Tobacco mosaic virus, (2) Tomato spotted wilt virus, (3) Tomato yellow leaf curl virus, (4) Cucumber mosaic virus, (5) Potato virus Y, (6) Cauliflower mosaic virus, (7) African cassava mosaic virus, (8) Plum pox virus, (9) Brome mosaic virus and (10) Potato virus X, with honourable mentions for viruses just missing out on the Top 10, including Citrus tristeza virus, Barley yellow dwarf virus, Potato leafroll virus and Tomato bushy stunt virus. This review article presents a short review on each virus of the Top 10 list and its importance, with the intent of initiating discussion and debate amongst the plant virology community, as well as laying down a benchmark, as it will be interesting to see in future years how perceptions change and which viruses enter and leave the Top 10.

This Guidance describes a two-phase approach for a fit-for-purpose method for the assessment of plant pest risk in the territory of the EU. Phase one consists of pest categorisation to determine whether the pest has the characteristics of a quarantine pest or those of a regulated non-quarantine pest for the area of the EU. Phase two consists of pest risk assessment, which may be requested by the risk managers following the pest categorisation results. This Guidance provides a template for pest categorisation and describes in detail the use of modelling and expert knowledge elicitation to conduct a pest risk assessment. The Guidance provides support and a framework for assessors to provide quantitative estimates, together with associated uncertainties, regarding the entry, establishment, spread and impact of plant pests in the EU. The Guidance allows the effectiveness of risk reducing options (RROs) to be quantitatively assessed as an integral part of the assessment framework. A list of RROs is provided. A two-tiered approach is proposed for the use of expert knowledge elicitation and modelling. Depending on data and resources available and the needs of risk managers, pest entry, establishment, spread and impact steps may be assessed directly, using weight of evidence and quantitative expert judgement (first tier), or they may be elaborated in substeps using quantitative models (second tier). An example of an application of the first tier approach is provided. Guidance is provided on how to derive models of appropriate complexity to conduct a second tier assessment. Each assessment is operationalised using Monte Carlo simulations that can compare scenarios for relevant factors, e.g. with or without RROs. This document provides guidance on how to compare scenarios to draw conclusions on the magnitude of pest risks and the effectiveness of RROs and on how to communicate assessment results.

The concept of pathogenesis has evolved considerably over recent years, and the scenario “a microbe + virulence factors = disease” is probably far from reality in a number of cases. Actual pathogens have extremely broad biological diversity and are found in all major groups of microorganisms (viruses, bacteria, fungi, protozoa…). Their pathogenicity results from strong and often highly specific interactions they have with either their microbial environment, hosts and/or arthropod vectors. In this review, we explore the contribution of metagenomic approaches toward understanding pathogens within the context of microbial communities. With this broader view, we discussed the concept of “pathobiome” and the research questions that this raises.

The eIF4E and eIF(iso)4E cDNAs from several genotypes of lettuce (Lactuca sativa) that are susceptible, tolerant, or resistant to infection by Lettuce mosaic virus (LMV; genus Potyvirus) were cloned and sequenced. Although Ls-eIF(iso)4E was monomorphic in sequence, three types of Ls-eIF4E differed by point sequence variations, and a short in-frame deletion in one of them. The amino acid variations specific to Ls-eIF4E(1) and Ls-eIF4E(2) were predicted to be located near the cap recognition pocket in a homology-based tridimensional protein model. In 19 lettuce genotypes, including two near-isogenic pairs, there was a strict correlation between these three allelic types and the presence or absence of the recessive LMV resistance genes mo1(1) and mo1(2). Ls-eIF4E(1) and mo1(1) cosegregated in the progeny of two separate crosses between susceptible genotypes and an mo1(1) genotype. Finally, transient ectopic expression of Ls-eIF4E restored systemic accumulation of a green fluorescent protein-tagged LMV in LMV-resistant mo1(2) plants and a recombinant LMV expressing Ls-eIF4E degrees from its genome, but not Ls-eIF4E(1) or Ls-eIF(iso)4E, accumulated and produced symptoms in mo1(1) or mo1(2) genotypes. Therefore, sequence correlation, tight genetic linkage, and functional complementation strongly suggest that eIF4E plays a role in the LMV cycle in lettuce and that mo1(1) and mo1(2) are alleles coding for forms of eIF4E unable or less effective to fulfill this role. More generally, the isoforms of eIF4E appear to be host factors involved in the cycle of potyviruses in plants, probably through a general mechanism yet to be clarified.

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A few natural sources of resistance to PPV have been found so far in Prunus species, which are being used in classical breeding programmes. Different genetic engineering approaches are being used to generate resistance to PPV, and a transgenic plum, 'HoneySweet', transformed with the viral CP gene, has demonstrated high resistance to PPV in field tests in several countries and has obtained regulatory approval in the USA.

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.

Recent advances in high-throughput sequencing technologies and bioinformatics have generated huge new opportunities for discovering and diagnosing plant viruses and viroids. Plant virology has undoubtedly benefited from these new methodologies, but at the same time, faces now substantial bottlenecks, namely the biological characterization of the newly discovered viruses and the analysis of their impact at the biosecurity, commercial, regulatory, and scientific levels. This paper proposes a scaled and progressive scientific framework for efficient biological characterization and risk assessment when a previously known or a new plant virus is detected by next generation sequencing (NGS) technologies. Four case studies are also presented to illustrate the need for such a framework, and to discuss the scenarios.