Correction: Haegeman et al. Looking beyond Virus Detection in RNA Sequencing Data: Lessons Learned from a Community-Based Effort to Detect Cellular Plant Pathogens and Pests. Plants 2023, 12, 2139

In the original publication [...].

Looking beyond Virus Detection in RNA Sequencing Data: Lessons Learned from a Community-Based Effort to Detect Cellular Plant Pathogens and Pests

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.

A Primer on the Analysis of High-Throughput Sequencing Data for Detection of Plant Viruses

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.

Molecular Characterization of Divergent Closterovirus Isolates Infecting Ribes Species

Five isolates of a new member of the family Closteroviridae, tentatively named blackcurrant leafroll-associated virus 1 (BcLRaV-1), were identified in the currant. The 17-kb-long genome codes for 10 putative proteins. The replication-associated polyprotein has several functional domains, including papain-like proteases, methyltransferase, Zemlya, helicase, and RNA-dependent RNA polymerase. Additional open reading frames code for a small protein predicted to integrate into the host cell wall, a heat-shock protein 70 homolog, a heat-shock protein 90 homolog, two coat proteins, and three proteins of unknown functions. Phylogenetic analysis showed that BcLRaV-1 is related to members of the genus Closterovirus, whereas recombination analysis provided evidence of intraspecies recombination.

Agrobacteria Enhance Plant Defense Against Root-Knot Nematodes on Tomato

The increased incidence of the crown gall disease caused by Agrobacterium tumefaciens has long been associated with activities of root-knot nematodes (Meloidogyne spp.). Pot experiments on tomato were designed to assess plant vitality, nematode reproduction, and crown gall incidence in combined infection with Agrobacterium and Meloidogyne spp. on tomato roots. Results suggest that tomato plants infected with pathogenic A. tumefaciens 2 days before the nematodes show enhanced plant defense against M. ethiopica resulting in lower egg and gall counts on roots 45 and 90 days postinoculation (dpi); no significantly enhanced defense was observed when the plant was inoculated with bacteria and nematodes at the same time. Split-root experiments also showed that the observed interaction was systemic. Reverse-transcription quantitative polymerase chain reaction analysis that targeted several genes under plant hormonal control suggests that the suppression was mediated via systemic acquired resistance by the pathogenesis-related protein 1 and that M. ethiopica did not enhance the defense reaction of tomato against Agrobacterium spp. Nematodes completely inhibited tumor growth in a 45-day experiment if inoculated onto the roots before the pathogenic bacteria. We conclude that the observed antagonism in the tested pathosystem was the result of initially strong plant defense that was later suppressed by the invading pathogen and pest.