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

Development of a CRISPR/SHERLOCK-Based Method for Rapid and Sensitive Detection of Selected Pospiviroids

Pospiviroids infect a wide range of plant species, and many pospiviroids can be transmitted to potato and tomato. Pospiviroids continue to be a major production constraint as well as of quarantine concern for the movement of germplasm, and are regulated in several countries/regions. The USDA APHIS issued a federal order requiring all imported tomato and pepper seeds be certified free of six pospiviroids of quarantine significance. The six pospiviroids of quarantine interest include CLVd, PCFVd, PSTVd, TASVd, TCDVd, TPMVd. Currently, those six viroids are detected by real-time RT-PCR. CRISPR/Cas-based genome editing has been increasingly used for virus detection in the past five years. We used a rapid Cas13-based Specific High-sensitivity Enzymatic Reporter unLOCKing (SHERLOCK) platform for pospiviroid detection, determined the limits of detection and specificity of CRISPR-Cas13a assays. This platform combines recombinase polymerase amplification (RPA) with CRISPR and CRISPR-associated (CRISPR-Cas) RNA-guided endoribonuclease that is rapid and does not require expensive equipment, and can be adapted for on-site detection.

Application of Biotechniques for In Vitro Virus and Viroid Elimination in Pome Fruit Crops

Sustainable production of pome fruit crops is dependent upon having virus-free planting materials. The production and distribution of plants derived from virus- and viroid-negative sources is necessary not only to control pome fruit viral diseases but also for sustainable breeding activities, as well as the safe movement of plant materials across borders. With variable success rates, different in vitro-based techniques, including shoot tip culture, micrografting, thermotherapy, chemotherapy, and shoot tip cryotherapy, have been employed to eliminate viruses from pome fruits. Higher pathogen eradication efficiencies have been achieved by combining two or more of these techniques. An accurate diagnosis that confirms complete viral elimination is crucial for developing effective management strategies. In recent years, considerable efforts have resulted in new reliable and efficient virus detection methods. This comprehensive review documents the development and recent advances in biotechnological methods that produce healthy pome fruit plants. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Viruses of Apple Are Seedborne but Likely Not Vertically Transmitted

Many viruses occur in apple (Malus domestica (Borkh.)), but no information is available on their seed transmissibility. Here, we report that six viruses infecting apple trees, namely, apple chlorotic leaf spot virus (ACLSV), apple green crinkle-associated virus (AGCaV), apple rubbery wood virus 2 (ARWV2), apple stem grooving virus (ASGV), apple stem pitting virus (ASPV), and citrus concave gum-associated virus (CCGaV) occur in seeds extracted from apple fruits produced by infected maternal trees. Reverse transcription polymerase chain reaction (RT-PCR) and quantitative RT-PCR (RT-qPCR) assays revealed the presence of these six viruses in untreated apple seeds with incidence rates ranging from 20% to 96%. Furthermore, ASPV was detected by RT-PCR in the flesh and peel of fruits produced by infected maternal trees, as well as from seeds extracted from apple fruits sold for fresh consumption. Finally, a large-scale seedling grow-out experiment failed to detect ACLSV, ASGV, or ASPV in over 1000 progeny derived from sodium hypochlorite surface sterilized seeds extracted from fruits produced by infected maternal trees, suggesting no detectable transmission via embryonic tissue. This is the first report on the seedborne nature of apple-infecting viruses.

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 .