Side-by-Side Comparison of Post-Entry Quarantine and High Throughput Sequencing Methods for Virus and Viroid Diagnosis

Diagnosis and Characterization of Plant Viruses Using HTS to Support Virus Management and Tomato Breeding

Viral diseases pose a significant threat to tomato crops (Solanum lycopersicum L.), one of the world's most economically important vegetable crops. The limited genetic diversity of cultivated tomatoes contributes to their high susceptibility to viral infections. To address this challenge, tomato breeding programs must harness the genetic resources found in native populations and wild relatives. Breeding efforts may aim to develop broad-spectrum resistance against the virome. To identify the viruses naturally infecting 19 advanced lines, derived from native tomatoes, high-throughput sequencing (HTS) of small RNAs and confirmation with PCR and RT-PCR were used. Single and mixed infections with tomato mosaic virus (ToMV), tomato golden mosaic virus (ToGMoV), and pepper huasteco yellow vein virus (PHYVV) were detected. The complete consensus genomes of three variants of Mexican ToMV isolates were reconstructed, potentially forming a new ToMV clade with a distinct 3' UTR. The absence of reported mutations associated with resistance-breaking to ToMV suggests that the Tm-1, Tm-2, and Tm-22 genes could theoretically be used to confer resistance. However, the high mutation rates and a 63 nucleotide insertion in the 3' UTR, as well as amino acid mutations in the ORFs encoding 126 KDa, 183 KDa, and MP of Mexican ToMV isolates, suggest that it is necessary to evaluate the capacity of these variants to overcome Tm-1, Tm-2, and Tm-22 resistance genes. This evaluation, along with the characterization of advanced lines using molecular markers linked to these resistant genes, will be addressed in future studies as part of the breeding strategy. This study emphasizes the importance of using HTS for accurate identification and characterization of plant viruses that naturally infect tomato germplasm based on the consensus genome sequences. This study provides crucial insights to select appropriate disease management strategies and resistance genes and guide breeding efforts toward the development of virus-resistant tomato varieties.

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 .

Cont-ID: detection of sample cross-contamination in viral metagenomic data

BACKGROUND

High-throughput sequencing (HTS) technologies completed by the bioinformatic analysis of the generated data are becoming an important detection technique for virus diagnostics. They have the potential to replace or complement the current PCR-based methods thanks to their improved inclusivity and analytical sensitivity, as well as their overall good repeatability and reproducibility. Cross-contamination is a well-known phenomenon in molecular diagnostics and corresponds to the exchange of genetic material between samples. Cross-contamination management was a key drawback during the development of PCR-based detection and is now adequately monitored in routine diagnostics. HTS technologies are facing similar difficulties due to their very high analytical sensitivity. As a single viral read could be detected in millions of sequencing reads, it is mandatory to fix a detection threshold that will be informed by estimated cross-contamination. Cross-contamination monitoring should therefore be a priority when detecting viruses by HTS technologies.

RESULTS

We present Cont-ID, a bioinformatic tool designed to check for cross-contamination by analysing the relative abundance of virus sequencing reads identified in sequence metagenomic datasets and their duplication between samples. It can be applied when the samples in a sequencing batch have been processed in parallel in the laboratory and with at least one specific external control called Alien control. Using 273 real datasets, including 68 virus species from different hosts (fruit tree, plant, human) and several library preparation protocols (Ribodepleted total RNA, small RNA and double-stranded RNA), we demonstrated that Cont-ID classifies with high accuracy (91%) viral species detection into (true) infection or (cross) contamination. This classification raises confidence in the detection and facilitates the downstream interpretation and confirmation of the results by prioritising the virus detections that should be confirmed.

CONCLUSIONS

Cross-contamination between samples when detecting viruses using HTS (Illumina technology) can be monitored and highlighted by Cont-ID (provided an alien control is present). Cont-ID is based on a flexible methodology relying on the output of bioinformatics analyses of the sequencing reads and considering the contamination pattern specific to each batch of samples. The Cont-ID method is adaptable so that each laboratory can optimise it before its validation and routine use.

First detection and complete genome sequence of a new potexvirus naturally infecting Adenium obesum

Here, we report the detection and complete genome sequence of a novel potexvirus, tentatively named "Adenium obesum virus X" (AobVX), isolated from Adenium obesum, that was sent for virus screening at Australian Government post-entry quarantine (PEQ) facilities after being imported into Australia from China. The AobVX genome is 6781 nucleotides in length excluding the poly(A) tail and is predicted to encode conserved potexvirus proteins and sequence motifs across five open reading frames. The RNA-dependent RNA polymerase of this virus shares the highest amino acid sequence similarity with that of nerine potexvirus 1 (58.7% identity) and nerine virus X (58.58% identity). This is the first report of a positive-sense single-stranded RNA virus in A. obesum related to members of the genus Potexvirus in the family Alphaflexiviridae.

Surveillance for Avocado Sunblotch Viroid Utilizing the European Honey Bee (Apis mellifera)

Avocado is one of the world's fastest growing tropical fruit industries, and the pathogen avocado sunblotch viroid (ASBVd) is a major threat to both production and access to international export markets. ASBVd is seed transmissible, with infection possible via either the male (pollen) or female gametes. Surveillance for ASBVd across commercial orchards is a major logistical task, particularly when aiming to meet the stringent standards of evidence required for a declaration of pest freedom. As with many fruit crops, insect pollination is important for high avocado yields, and honey bee (Apis mellifera) hives are typically moved into orchards for paid pollination services. Exploiting the foraging behavior of honey bees can provide a complementary strategy to traditional surveillance methods. High-throughput sequencing (HTS) of bee samples for plant viruses shows promise, but this surveillance method has not yet been tested for viroids or in a targeted plant biosecurity context. Here, we tested samples of bees and pollen collected from pollination hives in two ASBVd orchard locations, one in Australia, where only four trees in a block were known to be infected, and a second in South Africa, where the estimated incidence of infection was 10%. Using real-time RT-PCR and HTS (total RNA-seq and small RNA-seq), we demonstrated that ASBVd can be confidently detected in bees and pollen samples from hives within 100 m of infected trees. The potential for using this approach in ASBVd surveillance for improved orchard management and supporting market access is discussed.

New Virus Diagnostic Approaches to Ensuring the Ongoing Plant Biosecurity of Aotearoa New Zealand

To protect New Zealand's unique ecosystems and primary industries, imported plant materials must be constantly monitored at the border for high-threat pathogens. Techniques adopted for this purpose must be robust, accurate, rapid, and sufficiently agile to respond to new and emerging threats. Polymerase chain reaction (PCR), especially real-time PCR, remains an essential diagnostic tool but it is now being complemented by high-throughput sequencing using both Oxford Nanopore and Illumina technologies, allowing unbiased screening of whole populations. The demand for and value of Point-of-Use (PoU) technologies, which allow for in situ screening, are also increasing. Isothermal PoU molecular diagnostics based on recombinase polymerase amplification (RPA) and loop-mediated amplification (LAMP) do not require expensive equipment and can reach PCR-comparable levels of sensitivity. Recent advances in PoU technologies offer opportunities for increased specificity, accuracy, and sensitivities which makes them suitable for wider utilization by frontline or border staff. National and international activities and initiatives are adopted to improve both the plant virus biosecurity infrastructure and the integration, development, and harmonization of new virus diagnostic technologies.

Virion-Associated Nucleic Acid-Based Metagenomics: A Decade of Advances in Molecular Characterization of Plant Viruses

Over the last decade, viral metagenomic studies have resulted in the discovery of thousands of previously unknown viruses. These studies are likely to play a pivotal role in obtaining an accurate and robust understanding of how viruses affect the stability and productivity of ecosystems. Among the metagenomics-based approaches that have been developed since the beginning of the 21st century, shotgun metagenomics applied specifically to virion-associated nucleic acids (VANA) has been used to disentangle the diversity of the viral world. We summarize herein the results of 24 VANA-based studies, focusing on plant and insect samples conducted over the last decade (2010 to 2020). Collectively, viruses from 85 different families were reliably detected in these studies, including capsidless RNA viruses that replicate in fungi, oomycetes, and plants. Finally, strengths and weaknesses of the VANA approach are summarized and perspectives of applications in detection, epidemiological surveillance, environmental monitoring, and ecology of plant viruses are provided. [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.

Viroscope: Plant viral diagnosis from high-throughput sequencing data using biologically-informed genome assembly coverage

High-throughput sequencing (HTS) methods are transforming our capacity to detect pathogens and perform disease diagnosis. Although sequencing advances have enabled accessible and point-of-care HTS, data analysis pipelines have yet to provide robust tools for precise and certain diagnosis, particularly in cases of low sequencing coverage. Lack of standardized metrics and harmonized detection thresholds confound the problem further, impeding the adoption and implementation of these solutions in real-world applications. In this work, we tackle these issues and propose biologically-informed viral genome assembly coverage as a method to improve diagnostic certainty. We use the identification of viral replicases, an essential function of viral life cycles, to define genome coverage thresholds in which biological functions can be described. We validate the analysis pipeline, Viroscope, using field samples, synthetic and published datasets, and demonstrate that it provides sensitive and specific viral detection. Furthermore, we developed Viroscope.io a web-service to provide on-demand HTS data viral diagnosis to facilitate adoption and implementation by phytosanitary agencies to enable precise viral diagnosis.

Development and Validation of a Bioinformatic Workflow for the Rapid Detection of Viruses in Biosecurity

The field of biosecurity has greatly benefited from the widespread adoption of high-throughput sequencing technologies, for its ability to deeply query plant and animal samples for pathogens for which no tests exist. However, the bioinformatics analysis tools designed for rapid analysis of these sequencing datasets are not developed with this application in mind, limiting the ability of diagnosticians to standardise their workflows using published tool kits. We sought to assess previously published bioinformatic tools for their ability to identify plant- and animal-infecting viruses while distinguishing from the host genetic material. We discovered that many of the current generation of virus-detection pipelines are not adequate for this task, being outperformed by more generic classification tools. We created synthetic MinION and HiSeq libraries simulating plant and animal infections of economically important viruses and assessed a series of tools for their suitability for rapid and accurate detection of infection, and further tested the top performing tools against the VIROMOCK Challenge dataset to ensure that our findings were reproducible when compared with international standards. Our work demonstrated that several methods provide sensitive and specific detection of agriculturally important viruses in a timely manner and provides a key piece of ground truthing for method development in this space.

Implementation of GA-VirReport, a Web-Based Bioinformatics Toolkit for Post-Entry Quarantine Screening of Virus and Viroids in Plants

High-throughput sequencing (HTS) of host plant small RNA (sRNA) is a popular approach for plant virus and viroid detection. The major bottlenecks for implementing this approach in routine virus screening of plants in quarantine include lack of computational resources and/or expertise in command-line environments and limited availability of curated plant virus and viroid databases. We developed: (1) virus and viroid report web-based bioinformatics workflows on Galaxy Australia called GA-VirReport and GA-VirReport-Stats for detecting viruses and viroids from host plant sRNA extracts and (2) a curated higher plant virus and viroid database (PVirDB). We implemented sRNA sequencing with unique dual indexing on a set of plants with known viruses. Sequencing data were analyzed using GA-VirReport and PVirDB to validate these resources. We detected all known viruses in this pilot study with no cross-sample contamination. We then conducted a large-scale diagnosis of 105 imported plants processed at the post-entry quarantine facility (PEQ), Australia. We detected various pathogens in 14 imported plants and discovered that de novo assembly using 21–22 nt sRNA fraction and the megablast algorithm yielded better sensitivity and specificity. This study reports the successful, large-scale implementation of HTS and a user-friendly bioinformatics workflow for virus and viroid screening of imported plants at the PEQ.