PhytoPipe: a phytosanitary pipeline for plant pathogen detection and diagnosis using RNA-seq data.

BACKGROUND: Detection of exotic plant pathogens and preventing their entry and establishment are critical for the protection of agricultural systems while securing the global trading of agricultural commodities. High-throughput sequencing (HTS) has been applied successfully for plant pathogen discovery, leading to its current application in routine pathogen detection. However, the analysis of massive amounts of HTS data has become one of the major challenges for the use of HTS more broadly as a rapid diagnostics tool. Several bioinformatics pipelines have been developed to handle HTS data with a focus on plant virus and viroid detection. However, there is a need for an integrative tool that can simultaneously detect a wider range of other plant pathogens in HTS data, such as bacteria (including phytoplasmas), fungi, and oomycetes, and this tool should also be capable of generating a comprehensive report on the phytosanitary status of the diagnosed specimen. RESULTS: We have developed an open-source bioinformatics pipeline called PhytoPipe (Phytosanitary Pipeline) to provide the plant pathology diagnostician community with a user-friendly tool that integrates analysis and visualization of HTS RNA-seq data. PhytoPipe includes quality control of reads, read classification, assembly-based annotation, and reference-based mapping. The final product of the analysis is a comprehensive report for easy interpretation of not only viruses and viroids but also bacteria (including phytoplasma), fungi, and oomycetes. PhytoPipe is implemented in Snakemake workflow with Python 3 and bash scripts in a Linux environment. The source code for PhytoPipe is freely available and distributed under a BSD-3 license. CONCLUSIONS: PhytoPipe provides an integrative bioinformatics pipeline that can be used for the analysis of HTS RNA-seq data. PhytoPipe is easily installed on a Linux or Mac system and can be conveniently used with a Docker image, which includes all dependent packages and software related to analyses. It is publicly available on GitHub at https://github.com/healthyPlant/PhytoPipe and on Docker Hub at https://hub.docker.com/r/healthyplant/phytopipe .

Molecular characterization of horse nettle virus A, a new member of subgroup B of the genus Nepovirus.

A new positive-strand RNA virus was discovered in a horse nettle plant, using high-throughput sequencing (HTS), and its complete genome, consisting of RNA1 and RNA2, which are 7522 and 4710 nucleotides in length, respectively, was characterized. Each genome segment contains a single open reading frame flanked by 5' and 3' untranslated regions (UTRs), followed by a poly(A) tail at the 3' end. The encoded proteins have the highest amino acid sequence identity (55% and 45%) to the polyprotein encoded by RNA1 of tomato black ring virus (TBRV) and RNA2 of potato virus B (PVB), respectively. Its genome organization and phylogenetic relationship to other nepoviruses suggested that this virus is a novel member of subgroup B, and recombination analysis revealed its evolutionary history within the subgroup. These results suggest the new virus, provisionally named "horse nettle virus A", represents a new species within the genus Nepovirus.

Complete genome sequence of zoysia mosaic virus, a novel member of the genus Poacevirus.

Here, we report the detection and characterization of the genome of a novel poacevirus isolated from Zoysia matrella (Merrill) imported into the United States from Japan. The novel virus, tentatively named "zoysia mosaic virus" (ZoMV), is a single-stranded RNA virus with a genome of 9,728 nucleotides (nt) in length, encoding a large putative polyprotein of 3,119 amino acids (aa). The ZoMV genome is closely related to the triticum mosaic virus (TriMV; FJ263671) genome, with 57.18% nt and 51.74% aa sequence identity in the polyprotein region. Moreover, phylogenetic analysis showed that ZoMV is closely related to all other members of the genus Poacevirus. A survey of imported grasses showed that ZoMV was detected only in zoysiagrass. This is the first report of the complete genome sequence of a novel viral pathogen of zoysiagrass of the genus Poacevirus, for which we propose the binomial species name "Poacevirus zoisiae".

First report of three bunya-like viruses, apple luteovirus 1, and apple hammerhead viroid in apples from Hakkari, Türkiye.

Türkiye is a major apple fruit producer in the crossroads of Europe and the Middle East. Several reports have described the presence of multiple viruses affecting apple production in Türkiye, including apple stem grooving virus (ASGV), apple stem pitting virus (ASPV), apple chlorotic leafspot virus (ACLSV), and apple mosaic virus (ApMV) (Kurçman 1977; Fidan 1994; Çaglayan et al. 2003). However, there are no reports of the presence of the recently discovered bunya-like viruses citrus concave gum-associated virus (CCGaV), and apple rubbery wood viruses 1 and 2 (ARWV1 and 2), as well as apple luteovirus 1 (ALV-1), and apple hammerhead viroid (AHVd) in Türkiye, all of which have been previously reported in other apple-producing countries (Wright et al. 2018; Liu et al. 2018; Zhang et al. 2014). Leaves from one Gala, two Granny Smith, and one Golden Delicious apple trees showing mild symptoms of curling, chlorosis, and yellowing were collected from four different orchards in the province of Hakkari, southeast Türkiye during June 2022 and sent to USDA APHIS Plant Germplasm Quarantine Program (under permit) for virus and viroid HTS-based diagnostics. Total RNA was isolated using the RNeasy Plant Mini Kit (Qiagen) following the manufacturer's guidelines to prepare RNAseq libraries using the TruSeq Stranded Total RNA Library Plant Kit (Illumina, Inc) as described in Malapi-Wight et al. (2021). Libraries were sequenced on the NextSeq500 sequencer (PE 2x75), and approximately 45 million reads were obtained per each sample on average. Bioinformatic analysis was performed as described in Costa et al. (2022) using Phytopipe, where unclassified pathogen-derived reads were de novo assembled and contigs were compared to the NCBI viral nucleotide and protein databases by BlastN and BlastX respectively using a 10-4 e-value cutoff. Nearly complete genome contigs were obtained for ACLSV (OR640150) and ASPV (OR640151) in all four samples and for ASGV (OR640152) in 3 of the 4 samples. The average BlastN identity to sequences in GenBank was 92.3% for ACLSV, ranging from 89-94 %. BlastN identity for ASPV was 86%, ranging from 81-92 % while the ASGV average BlastN identity was 98.2%. Nearly complete genomes with average genome coverage of 92.4% and 95.6% for RNA1 and RNA2 of CCGaV (OR640153 and OR640154), were found in two of the four samples with BlastN identity of 94.7% and 94.8% to GenBank sequences. Additionally, nearly complete genome of the large (L), medium (M), and small (S) segments for ARWV1 were found in two samples with average genome coverage of 99.9%, 99.4%, and 100% respectively and BlastN identity of 98.8%, 95.2%, and 98.4% (OR640155, OR640156, OR640157). ARWV2 contigs were also found in 1 sample where M and S segments had a coverage of 99.8% and BlastN identity of 95.4% (OR640158 and OR640159). The nearly complete genome of ALV-1 was also found in two of four samples with genome coverage of 94.1% and an average BlastN identity of 93.4% (OR640160). AHVd was found in one of the Granny Smith trees with 19,260 mapped reads to the reference GenBank MH049335.1 and identity of 98.3% (OR640149). The HTS findings of CCGaV, ARWV1, ARWV2, and ALV-1, from Türkiye were later confirmed by Sanger sequencing using custom-designed primers targeting the coat protein, the RNA-dependent RNA polymerase, or ~390bp for the AHVd genome (Supplementary Table 1). To further learn about the incidence of these agents, we tested 12 other apple samples from six different neighboring orchards and found them at 18.8% rate for CCGaV, 12.5% for both ARWV1 and ARWV2, 25% for ALV-1, and 37.5% for AHVd respectively. To our knowledge, this is the first report of the apple viruses CCGaV, ARWV1, ARWV2, and ALV-1, and the AHVd viroid in Türkiye. Further studies of the impact of these agents on orchard's health are necessary, including their prevalence in high apple production regions of Türkiye.

Genomic characterization of silvergrass cryptic virus 1, a novel partitivirus infecting Miscanthus sinensis.

In the present study we report the identification of a novel partitivirus recovered from Miscanthus sinensis, for which the provisional name "silvergrass cryptic virus 1" (SgCV-1) is proposed. High-throughput sequencing (HTS) and rapid amplification of cDNA ends (RACE) allowed the assembly of the complete sequence of each double-stranded RNA genome segment of this novel virus. The largest dsRNA segment, dsRNA1 (1699 bp), was predicted to encode a viral RNA-dependent RNA polymerase protein (RdRp) with 478 aa, and dsRNA2 (1490 bp) and dsRNA3 (1508 bp) were predicted to encode putative capsid proteins (CPs) with 347 and 348 aa, respectively. SgCV-1 has the highest amino acid sequence identity (≤ 70.80% in RdPp and ≤ 34.5% in CPs) to members of the genus Deltapartitivirus, family Partitiviridae, especially to unclassified viruses related to members of this genus. Its genome segment and protein lengths are also within the range of those of deltapartitiviruses. Moreover, phylogenetic analysis based on RdRp amino acid sequences also showed clustering of this novel virus with the related unclassified deltapartitiviruses. An RT-PCR survey of 94 imported M. sinensis samples held in quarantine identified seven additional samples carrying SgCV-1. This new virus fulfils all ICTV criteria to be considered a new member of the genus Deltapartitivirus.

Identification and characterization of a novel virus associated with an eriophyid mite in extracts of fruit trees leaves.

Eriophyid mites are commonly found on the leaf surface of different plant species. In the present study, a novel virus associated with an eriophyid mite species was detected using high-throughput sequencing (HTS) of total RNA from fruit tree leaves, primarily growing under greenhouse conditions. The complete genome sequence was characterized using rapid amplification of cDNA ends followed by Sanger sequencing, revealing a genome of 8885 nucleotides in length. The single positive-stranded RNA genome was predicted to encode typical conserved domains of members of the genus Iflavirus in the family Iflaviridae. Phylogenetic analysis showed this virus to be closely related to the unclassified iflavirus tomato matilda associated virus (TMaV), with a maximum amino acid sequence identity of 59% in the RNA-dependent RNA polymerase domain. This low identity value justifies the recognition of the novel virus as a potential novel iflavirus. In addition to a lack of graft-transmissibility evidence, RT-PCR and HTS detection of this virus in the putative host plants were not consistent through different years and growing seasons, raising the possibility that rather than a plant virus, this was a virus infecting an organism associated with fruit tree leaves. Identification of Tetra pinnatifidae HTS-derived contigs in all fruit tree samples carrying the novel virus suggested this mite as the most likely host of the new virus (p-value < 1e-11), which is tentatively named "eriophyid mite-associated virus" (EMaV). This study highlights the importance of a careful biological study before assigning a new virus to a particular plant host when using metagenomics data.

High-throughput detection of a large set of viruses and viroids of pome and stone fruit trees by multiplex PCR-based amplicon sequencing.

A comprehensive diagnostic method of known plant viruses and viroids is necessary to provide an accurate phytosanitary status of fruit trees. However, most widely used detection methods have a small limit on either the number of targeted viruses/viroids or the number of samples to be evaluated at a time, hampering the ability to rapidly scale up the test capacity. Here we report that by combining the power of high multiplexing PCR (499 primer pairs) of small amplicons (120-135bp), targeting 27 viruses and 7 viroids of fruit trees, followed by a single high-throughput sequencing (HTS) run, we accurately diagnosed the viruses and viroids on as many as 123 pome and stone fruit tree samples. We compared the accuracy, sensitivity, and reproducibility of this approach and contrast it with other detection methods including HTS of total RNA (RNA-Seq) and individual RT-qPCR for every fruit tree virus or viroid under the study. We argue that this robust and high-throughput cost-effective diagnostic tool will enhance the viral/viroid knowledge of fruit trees while increasing the capacity for large scale diagnostics. This approach can also be adopted for the detection of multiple viruses and viroids in other crops.

HTS-Based Diagnostics of Sugarcane Viruses: Seasonal Variation and Its Implications for Accurate Detection.

Rapid global germplasm trade has increased concern about the spread of plant pathogens and pests across borders that could become established, affecting agriculture and environment systems. Viral pathogens are of particular concern due to their difficulty to control once established. A comprehensive diagnostic platform that accurately detects both known and unknown virus species, as well as unreported variants, is playing a pivotal role across plant germplasm quarantine programs. Here we propose the addition of high-throughput sequencing (HTS) from total RNA to the routine quarantine diagnostic workflow of sugarcane viruses. We evaluated the impact of sequencing depth needed for the HTS-based identification of seven regulated sugarcane RNA/DNA viruses across two different growing seasons (spring and fall). Our HTS analysis revealed that viral normalized read counts (RPKM) was up to 23-times higher in spring than in the fall season for six out of the seven viruses. Random read subsampling analyses suggested that the minimum number of reads required for reliable detection of RNA viruses was 0.5 million, with a viral genome coverage of at least 92%. Using an HTS-based total RNA metagenomics approach, we identified all targeted viruses independent of the time of the year, highlighting that higher sequencing depth is needed for the identification of DNA viruses.