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.

Summary: In this study, we looked for genetic factors in the pepper ( Capsicum annuum ) germplasm that control the number of potato virus Y (PVY) particles entering the plant (i.e. effective population size at inoculation) and the PVY accumulation at the systemic level (i.e. census population size). Using genotyping‐by‐sequencing (GBS) in a core collection of 256 pepper accessions, we obtained 10 307 single nucleotide polymorphisms (SNPs) covering the whole genome. Genome‐wide association studies (GWAS) detected seven SNPs significantly associated with the virus population size at inoculation and/or systemic level on chromosomes 4, 6, 9 and 12. Two SNPs on chromosome 4 associated with both PVY population sizes map closely to the major resistance gene pvr2 encoding the eukaryotic initiation factor 4E. No obvious candidates for resistance were identified in the confidence intervals for the other chromosomes. SNPs detected on chromosomes 6 and 12 colocalized with resistance quantitative trait loci (QTLs) previously identified with a biparental population. These results show the efficiency of GBS and GWAS in C. annuum , indicate highly consistent results between GWAS and classical QTL mapping, and suggest that resistance QTLs identified with a biparental population are representative of a much larger collection of pepper accessions. Moreover, the resistance alleles at these different loci were more frequently combined than expected by chance in the core collection, indicating widespread pyramiding of resistance QTLs and widespread combination of resistance QTLs and major effect genes. Such pyramiding may increase resistance efficiency and/or durability.

High-throughput sequencing (HTS), more specifically RNA sequencing of plant tissues, has become an indispensable tool for plant virologists to detect and identify plant viruses. During the data analysis step, plant virologists typically compare the obtained sequences to reference virus databases. In this way, they are neglecting sequences without homologies to viruses, which usually represent the majority of sequencing reads. We hypothesized that traces of other pathogens might be detected in this unused sequence data. In the present study, our goal was to investigate whether total RNA-seq data, as generated for plant virus detection, is also suitable for the detection of other plant pathogens and pests. As proof of concept, we first analyzed RNA-seq datasets of plant materials with confirmed infections by cellular pathogens in order to check whether these non-viral pathogens could be easily detected in the data. Next, we set up a community effort to re-analyze existing Illumina RNA-seq datasets used for virus detection to check for the potential presence of non-viral pathogens or pests. In total, 101 datasets from 15 participants derived from 51 different plant species were re-analyzed, of which 37 were selected for subsequent in-depth analyses. In 29 of the 37 selected samples (78%), we found convincing traces of non-viral plant pathogens or pests. The organisms most frequently detected in this way were fungi (15/37 datasets), followed by insects (13/37) and mites (9/37). The presence of some of the detected pathogens was confirmed by independent (q)PCRs analyses. After communicating the results, 6 out of the 15 participants indicated that they were unaware of the possible presence of these pathogens in their sample(s). All participants indicated that they would broaden the scope of their bioinformatic analyses in future studies and thus check for the presence of non-viral pathogens. In conclusion, we show that it is possible to detect non-viral pathogens or pests from total RNA-seq datasets, in this case primarily fungi, insects, and mites. With this study, we hope to raise awareness among plant virologists that their data might be useful for fellow plant pathologists in other disciplines (mycology, entomology, bacteriology) as well.

Infection of plants by viruses is a complex process involving several steps: inoculation into plant cells, replication in inoculated cells and plant colonization. The success of the different steps depends, in part, on the viral effective population size (Ne ), defined as the number of individuals passing their genes to the next generation. During infection, the virus population will undergo bottlenecks, leading to drastic reductions in Ne and, potentially, to the loss of the fittest variants. Therefore, it is crucial to better understand how plants affect Ne . We aimed to (i) identify the plant genetic factors controlling Ne during inoculation, (ii) understand the mechanisms used by the plant to control Ne and (iii) compare these genetic factors with the genes controlling plant resistance to viruses. Ne was measured in a doubled-haploid population of Capsicum annuum. Plants were inoculated with either a Potato virus Y (PVY) construct expressing the green fluorescent protein or a necrotic variant of Cucumber mosaic virus (CMV). Ne was assessed by counting the number of primary infection foci on cotyledons for PVY or the number of necrotic local lesions on leaves for CMV. The number of foci and lesions was correlated (r=0.57) and showed a high heritability (h2 =0.93 for PVY and h2 =0.98 for CMV). The Ne of the two viruses was controlled by both common quantitative trait loci (QTLs) and virus-specific QTLs, indicating the contribution of general and specific mechanisms. The PVY-specific QTL colocalizes with a QTL that reduces PVY accumulation and the capacity to break down a major-effect resistance gene.

High-throughput sequencing technologies were used to identify plant viruses in cereal samples surveyed from 2012 to 2017. Fifteen genome sequences of a tenuivirus infecting wheat, oats, and spelt in Estonia, Norway, and Sweden were identified and characterized by their distances to other tenuivirus sequences. Like most tenuiviruses, the genome of this tenuivirus contains four genomic segments. The isolates found from different countries shared at least 92% nucleotide sequence identity at the genome level. The planthopper Javesella pellucida was identified as a vector of the virus. Laboratory transmission tests using this vector indicated that wheat, oats, barley, rye, and triticale, but none of the tested pasture grass species (Alopecurus pratensis, Dactylis glomerata, Festuca rubra, Lolium multiflorum, Phleum pratense, and Poa pratensis), are susceptible. Taking into account the vector and host range data, the tenuivirus we have found most probably represents European wheat striate mosaic virus first identified about 60 years ago. Interestingly, whereas we were not able to infect any of the tested cereal species mechanically, Nicotiana benthamiana was infected via mechanical inoculation in laboratory conditions, displaying symptoms of yellow spots and vein clearing evolving into necrosis, eventually leading to plant death. Surprisingly, one of the virus genome segments (RNA2) encoding both a putative host systemic movement enhancer protein and a putative vector transmission factor was not detected in N. benthamiana after several passages even though systemic infection was observed, raising fundamental questions about the role of this segment in the systemic spread in several hosts.