An Advanced One-Step RT-LAMP for Rapid Detection of Little cherry virus 2 Combined with High-Throughput Sequence-Based Phylogenomics Reveal Divergent Flowering Cherry Isolates.

Little cherry virus 2 (LChV-2, genus Ampelovirus) is considered to be the main causal agent of the economically damaging little cherry disease, which can only be controlled by removal of infected trees. The widespread viral disease of sweet cherry (Prunus avium L.) is affecting the survival of long-standing orchards in North America and Europe, hence the dire need for an early and accurate diagnosis to establish a sound disease control strategy. The endemic presence of LChV-2 is mainly confirmed using laborious time-consuming reverse-transcription (RT-PCR). A rapid reverse transcription loop-mediated isothermal amplification (RT-LAMP) assay targeting a conserved region of the coat protein was developed and compared with conventional RT-PCR for the specific detection of LChV-2. This affordable assay, combined with a simple RNA extraction, deploys desirable characteristics such as higher ability for faster (<15 min), more analytically sensitive (100-fold), and robust broad-range diagnosis of LChV-2 isolates from sweet cherry, ornamental flowering cherry displaying heterogenous viral etiology and, for the first time, newly identified potential insect vectors. Moreover, use of Sanger and total RNA high-throughput sequencing as complementary metaviromics approaches confirmed the LChV-2 RT-LAMP detection of divergent LChV-2 isolates in new hosts and the relationship of their whole-genome was exhaustively inferred using maximum-likelihood phylogenomics. This entails unprecedented critical understanding of a novel evolutionary clade further expanding LChV-2 viral diversity. In conclusion, this highly effective diagnostic platform facilitates strategical support for early in-field testing to reliably prevent dissemination of new LChV-2 outbreaks from propagative plant stocks or newly postulated insect vectors. Validated results and major advantages are herein thoroughly discussed, in light of the knowledge required to increase the potential accuracy of future diagnostics and the essential epidemiological considerations to proactively safeguard cherries and Prunus horticultural crop systems from little cherry disease.

Biological and Genetic Characterization of Physostegia Chlorotic Mottle Virus in Europe Based on Host Range, Location, and Time.

Application of high throughput sequencing (HTS) technologies enabled the first identification of Physostegia chlorotic mottle virus (PhCMoV) in 2018 in Austria. Subsequently, PhCMoV was detected in Germany and Serbia on tomatoes showing severe fruit mottling and ripening anomalies. We report here how prepublication data-sharing resulted in an international collaboration across eight laboratories in five countries, enabling an in-depth characterization of PhCMoV. The independent studies converged toward its recent identification in eight additional European countries and confirmed its presence in samples collected 20 years ago (2002). The natural plant host range was expanded from two to nine species across seven families, and we confirmed the association of PhCMoV presence with severe fruit symptoms on economically important crops such as tomato, eggplant, and cucumber. Mechanical inoculations of selected isolates in the greenhouse established the causality of the symptoms on a new indexing host range. In addition, phylogenetic analysis showed a low genomic variation across the 29 near-complete genome sequences available. Furthermore, a strong selection pressure within a specific ecosystem was suggested by nearly identical sequences recovered from different host plants through time. Overall, this study describes the European distribution of PhCMoV on multiple plant hosts, including economically important crops on which the virus can cause severe fruit symptoms. This work demonstrates how to efficiently improve knowledge on an emergent pathogen by sharing HTS data and provides a solid knowledge foundation for further studies on plant rhabdoviruses.[Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

First Report of Apricot vein clearing-associated virus (AVCaV) infecting Prunus domestica revealed by High-Throughput Sequencing in Morocco.

Plum (Prunus domestica L., Rosaceae) trees, like many stone fruit trees, are known to be infected by numerous plant viruses, predominantly as consequence of their clonal mode of propagation and perennial cultivation (Jelkmann and Eastwell, 2011). Apricot vein clearing-associated virus (AVCaV) is a member of the genus Prunevirus in the family Betaflexiviridae. AVCaV was first reported in Italy infecting apricot (P. armeniaca L.) associated with foliar vein clearing symptoms (Elbeaino et al. 2014). It has also been detected in various Prunus species, like plum, Japanese plum (P. salicina L.), sour cherry (P. cerasus L.), and Japanese apricot (P. mume L.), apricot and peach (P. persica L.) sourced from Asian and European countries (Marais et al. 2015), as well as in the ornamental Myrobolan plum (P. cerasifera L.) in Australia (Kinoti et al. 2017). In 2018, during the vegetative season, a survey was carried out in two different apricot and plum orchards in the southern region of Agdez (Agadir, Morocco) where stone fruit trees are grown. Five branches with leaves were sampled from three apricot and three plum trees of unknown cultivars, all asymptomatic. Total RNA was extracted from 100 mg plant tissue (leaves and cambial scrapping) using RNeasy Plant Mini Kit (QIAGEN, Hilden, Germany) and separate samples (one per species) were used for library preparation (NEBNext Ultra RNA library kit; New England BioLabs, MA, USA), and sequencing (Illumina NextSeq v2, totRNA sequencing) at Admera Health (New Jersey, USA). All generated reads (6,756,881) from the plum sample were quality filtered and submitted to the VirusDetect pipeline (Zheng et al., 2017). The plum cDNA library, a total of 20 viral contigs (68-1928 bp) mapped to several AVCaV accessions in GenBank. A reference mapping (CLC Genomics Workbench 12, Qiagen, Denmark) was conducted against all four available AVCaV full genomes (KM507062-63, KY132099 and HG008921), revealing 100% coverage of the full sequence (8358 nt) with 97-98 % nucleotide (nt) identities (BLASTn). Analysis of the derived sequences allowed to identify the location of the four predicted ORFs i.e. (ORF1: 6066 nt/2,021 aa), (ORF2: 1383 nt/460 aa), (ORF3: 666 nt/221 aa) and (ORF4: 420 nt/139 aa), previously described for the AVCaV genome (Elbeaino et al. 2014). The amino acid sequences of the encoded proteins of AVCaV isolate from Morocco also shared 97-98% identities with the corresponding sequences of complete genome AVCaV isolates in GenBank. To confirm the detection of AVCaV in the three plum samples, specific RT-PCR primers (VC37657s: 5'-CCATAGCCACCCTTTTTCAA-3' / VC28239a: 5'-GTCGTCAAGGGTCCAGTGAT-3') (Elbeaino et al. 2014) were used and the expected 330 bp fragment from the replicase gene was amplified in all three samples and subsequently sequenced (MT980794-96). Sanger sequences were 100% identical to corresponding HTS derived sequence. This is the first report of AVCaV infecting plum in Africa. The incidence of AVCaV in Moroccan Prunus species is unknown. Plum trees from the surveyed orchards were also confirmed to be co-infected with little cherry virus 1 (LChV-1) using HTS. Further investigation is required to determine the impact of AVCaV on these asymptomatic plum trees and other stone fruits species.

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 [...].

Detection of single nucleotide polymorphisms in virus genomes assembled from high-throughput sequencing data: large-scale performance testing of sequence analysis strategies.

Recent developments in high-throughput sequencing (HTS) technologies and bioinformatics have drastically changed research in virology, especially for virus discovery. Indeed, proper monitoring of the viral population requires information on the different isolates circulating in the studied area. For this purpose, HTS has greatly facilitated the sequencing of new genomes of detected viruses and their comparison. However, bioinformatics analyses allowing reconstruction of genome sequences and detection of single nucleotide polymorphisms (SNPs) can potentially create bias and has not been widely addressed so far. Therefore, more knowledge is required on the limitations of predicting SNPs based on HTS-generated sequence samples. To address this issue, we compared the ability of 14 plant virology laboratories, each employing a different bioinformatics pipeline, to detect 21 variants of pepino mosaic virus (PepMV) in three samples through large-scale performance testing (PT) using three artificially designed datasets. To evaluate the impact of bioinformatics analyses, they were divided into three key steps: reads pre-processing, virus-isolate identification, and variant calling. Each step was evaluated independently through an original, PT design including discussion and validation between participants at each step. Overall, this work underlines key parameters influencing SNPs detection and proposes recommendations for reliable variant calling for plant viruses. The identification of the closest reference, mapping parameters and manual validation of the detection were recognized as the most impactful analysis steps for the success of the SNPs detections. Strategies to improve the prediction of SNPs are also discussed.

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.

Interlaboratory Comparison Study on Ribodepleted Total RNA High-Throughput Sequencing for Plant Virus Diagnostics and Bioinformatic Competence.

High-throughput sequencing (HTS) technologies and bioinformatic analyses are of growing interest to be used as a routine diagnostic tool in the field of plant viruses. The reliability of HTS workflows from sample preparation to data analysis and results interpretation for plant virus detection and identification must be evaluated (verified and validated) to approve this tool for diagnostics. Many different extraction methods, library preparation protocols, and sequence and bioinformatic pipelines are available for virus sequence detection. To assess the performance of plant virology diagnostic laboratories in using the HTS of ribosomal RNA depleted total RNA (ribodepleted totRNA) as a diagnostic tool, we carried out an interlaboratory comparison study in which eight participants were required to use the same samples, (RNA) extraction kit, ribosomal RNA depletion kit, and commercial sequencing provider, but also their own bioinformatics pipeline, for analysis. The accuracy of virus detection ranged from 65% to 100%. The false-positive detection rate was very low and was related to the misinterpretation of results as well as to possible cross-contaminations in the lab or sequencing provider. The bioinformatic pipeline used by each laboratory influenced the correct detection of the viruses of this study. The main difficulty was the detection of a novel virus as its sequence was not available in a publicly accessible database at the time. The raw data were reanalysed using Virtool to assess its ability for virus detection. All virus sequences were detected using Virtool in the different pools. This study revealed that the ribodepletion target enrichment for sample preparation is a reliable approach for the detection of plant viruses with different genomes. A significant level of virology expertise is needed to correctly interpret the results. It is also important to improve and complete the reference data.

New sensitive and fast detection of Little cherry virus 1 using loop-mediated isothermal amplification (RT-LAMP).

Little cherry virus 1 (LChV-1) belongs to the genus Velarivirus, family Closteroviridae, is an economically important pathogen affecting mainly cherry around the world emphasizing the impetus for its efficient and accurate on-site detection. This study describes the development of a reliable diagnostic protocol of LChV-1 based on a one-step reverse-transcription loop-mediated isothermal amplification (RT-LAMP). The protocol detects LChV-1 isolates in less than 10 min by fluorescence monitoring using a mobile detection device and is most optimal when performed at 67 °C. Sharp melting curves and unique melting temperatures (Tm) were obtained for the positive samples. Both the RT-LAMP and classical RT-PCR methods are capable of specifically detecting LChV-1 in infected leaf tissues. In addition, the RT-LAMP has remarkable advantages in comparison to RT-PCR. It is at least hundred fold more sensitive, significantly faster (allowing on-field leaf-to-result diagnostic) and efficient at minimal cost. In conclusion, this innovative RT-LAMP approach can contribute to the implementation of sustainable integrated management strategies for detection of LChV-1 in commercial orchards or for horticultural research stations. It is also suitable for decision support in phytosanitary epidemiological programs.

High-Throughput Sequencing Assists Studies in Genomic Variability and Epidemiology of Little Cherry Virus 1 and 2 infecting Prunus spp. in Belgium.

Little cherry disease, caused by little cherry virus 1 (LChV-1) and little cherry virus 2 (LChV-2), which are both members of the family Closteroviridae, severely affects sweet (Prunus avium L.) and sour cherry (P. cerasus L.) orchards lifelong production worldwide. An intensive survey was conducted across different geographic regions of Belgium to study the disease presence on these perennial woody plants and related species. Symptomatic as well as non-symptomatic Prunus spp. trees tested positive via RT-PCR for LChV-1 and -2 in single or mixed infections, with a slightly higher incidence for LChV-1. Both viruses were widespread and highly prevalent in nearly all Prunus production areas as well as in private gardens and urban lane trees. The genetic diversity of Belgian LChV-1 and -2 isolates was assessed by Sanger sequencing of partial genomic regions. A total RNA High-Throughput Sequencing (HTS) approach confirmed the presence of both viruses, and revealed the occurrence of other Prunus-associated viruses, namely cherry virus A (CVA), prune dwarf virus (PDV) and prunus virus F (PrVF). The phylogenetic inference from full-length genomes revealed well-defined evolutionary phylogroups with high genetic variability and diversity for LChV-1 and LChV-2 Belgian isolates, yet with little or no correlation with planting area or cultivated varieties. The global diversity and the prevalence in horticultural areas of LChV-1 and -2 variants, in association with other recently described fruit tree viruses, are of particular concern. Future epidemiological implications as well as new investigation avenues are exhaustively discussed.

The Use of High-Throughput Sequencing for the Study and Diagnosis of Plant Viruses and Viroids in Pollen.

This protocol details the wet lab preparation, extraction of fruit pollen samples, and analysis of the sequencing data following Illumina NextSeq small and total RNA sequencing. The protocol was developed for virus and viroid detection using NGS sequencing and was based on the results of a comparison between different extraction methods followed by yield, RNA purity, and integrity assessment. Moreover, the advantage of an additional ribosomal (r)RNA depletion step to the total RNA extraction protocol was evaluated. The smallRNA procedure is the preferred method of choice. If the total RNA protocol is chosen, the use of the mirVana kit followed by an rRNA depletion step is the best option. The library preparation and sequencing steps were outsourced. As a final step in the data analysis, the VirusDetect software was used to detect the viruses and viroids in the pollen samples.