MinION Nanopore-based detection of Clavibacter nebraskensis, the corn Goss's wilt pathogen, and bacteriomic profiling of necrotic lesions of naturally-infected leaf samples

Nanopore Sequencing with GraphMap for Comprehensive Pathogen Detection in Potato Field Soil

Early detection of causal pathogens is important to prevent crop loss from diseases. However, some diseases, such as soilborne diseases, are difficult to diagnose due to the absence of visible or characteristic symptoms. In the present study, the use of the Oxford Nanopore MinION sequencer as a molecular diagnostic tool was assessed due to its long-read sequencing capabilities and portability. Nucleotide samples (DNA or RNA) from potato field soils were sequenced and analyzed using a locally curated pathogen database, followed by identification via sequence mapping. We performed computational speed tests of three commonly used mapping/annotation tools (BLAST, BWA-BLAST, and BWA-GraphMap) and found BWA-GraphMap to be the fastest tool for local searching against our curated pathogen database. The data collected demonstrate the high potential of Nanopore sequencing as a minimally biased diagnostic tool for comprehensive pathogen detection in soil from potato fields. Our GraphMap-based MinION sequencing method could be useful as a predictive approach for disease management by identifying pathogens present in field soil prior to planting. Although this method still needs further experimentation with a larger sample size for practical use, the data analysis pipeline presented can be applied to other cropping systems and diagnostics for detecting multiple pathogens.

Clavibacter nebraskensis causing Goss's wilt of maize: Five decades of detaining the enemy in the New World

Goss's bacterial wilt and leaf blight of maize (Zea mays) caused by the gram-positive coryneform bacterium Clavibacter nebraskensis is an economically important disease in North America. C. nebraskensis is included within the high-risk list of quarantine pathogens by several plant protection organizations (EPPO code: CORBMI), hence it is under strict quarantine control around the world. The causal agent was reported for the first time on maize in Nebraska (USA) in 1969. After an outbreak during the 1970s, prevalence of the disease decreased in the 1980s to the early 2000s, before the disease resurged causing a serious threat to maize production in North America. The re-emergence of Goss's wilt in the corn belt of the United States led to several novel achievements in understanding the pathogen biology and disease control. In this review, we provide an updated overview of the pathogen taxonomy, biology, and epidemiology as well as management strategies of Goss's wilt disease. First, a taxonomic history of the pathogen is provided followed by symptomology and host range, genetic diversity, and pathogenicity mechanisms of the bacterium. Then, utility of high-throughput molecular approaches in the precise detection and identification of the pathogen and the management strategies of the disease are explained. Finally, we highlight the role of integrated pest management strategies to combat the risk of Goss's wilt in the 21st century maize industry.

DISEASE SYMPTOMS

Large (2-15 cm) tan to grey elongated oval lesions with wavy, irregular water-soaked margins on the leaves. The lesions often start at the leaf tip or are associated with wounding caused by hail or wind damage. Small (1 mm in diameter), dark, discontinuous water-soaked spots, known as "freckles", can be observed in the periphery of lesions. When backlit, the freckles appear translucent. Early infection (prior to growth stage V6) may become systemic and cause seedlings to wilt, wither, and die. Coalescence of lesions results in leaf blighting.

HOST RANGE

Maize (Zea mays) is the only economic host of the pathogen. A number of Poaceae species are reported to act as secondary hosts for C. nebraskensis.

TAXONOMIC STATUS OF THE PATHOGEN

Class: Actinobacteria; Order: Micrococcales; Family: Microbacteriaceae; Genus: Clavibacter; Species: Clavibacter nebraskensis.

SYNONYMS

Corynebacterium nebraskense (Schuster, 1970) Vidaver & Mandel 1974; Corynebacterium michiganense pv. nebraskense (Vidaver & Mandel 1974) Dye & Kemp 1977; Corynebacterium michiganense subsp. nebraskense (Vidaver & Mandel 1974) Carlson & Vidaver 1982; Clavibacter michiganense subsp. nebraskense (Vidaver & Mandel 1974) Davis et al. 1984; Clavibacter michiganensis subsp. nebraskensis (Vidaver & Mandel 1974) Davis et al. 1984.

TYPE MATERIALS

ATCC 27794^T ; CFBP 2405^T ; ICMP 3298^T ; LMG 3700^T ; NCPPB 2581^T .

MICROBIOLOGICAL PROPERTIES

Cells are gram-positive, orange-pigmented, pleomorphic club- or rod-shaped, nonspore-forming, nonmotile, and without flagella, approximately 0.5 × 1-2.0 μm.

DISTRIBUTION

The pathogen is restricted to Canada and the United States.

PHYTOSANITARY CATEGORIZATION

EPPO code CORBNE.

Advancing the automation of plant nucleic acid extraction for rapid diagnosis of plant diseases in space

Human space exploration missions will continue the development of sustainable plant cultivation in what are obviously novel habitat settings. Effective pathology mitigation strategies are needed to cope with plant disease outbreaks in any space-based plant growth system. However, few technologies currently exist for space-based diagnosis of plant pathogens. Therefore, we developed a method of extracting plant nucleic acid that will facilitate the rapid diagnosis of plant diseases for future spaceflight applications. The microHomogenizer^™ from Claremont BioSolutions, originally designed for bacterial and animal tissue samples, was evaluated for plant-microbial nucleic acid extractions. The microHomogenizer^™ is an appealing device in that it provides automation and containment capabilities that would be required in spaceflight applications. Three different plant pathosystems were used to assess the versatility of the extraction process. Tomato, lettuce, and pepper plants were respectively inoculated with a fungal plant pathogen, an oomycete pathogen, and a plant viral pathogen. The microHomogenizer^™, along with the developed protocols, proved to be an effective mechanism for producing DNA from all three pathosystems, in that PCR and sequencing of the resulting samples demonstrated clear DNA-based diagnoses. Thus, this investigation advances the efforts to automate nucleic acid extraction for future plant disease diagnosis in space.

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.

Metagenomic sequencing for detection and identification of the boxwood blight pathogen Calonectria pseudonaviculata

Pathogen detection and identification are key elements in outbreak control of human, animal, and plant diseases. Since many fungal plant pathogens cause similar symptoms, are difficult to distinguish morphologically, and grow slowly in culture, culture-independent, sequence-based diagnostic methods are desirable. Whole genome metagenomic sequencing has emerged as a promising technique because it can potentially detect any pathogen without culturing and without the need for pathogen-specific probes. However, efficient DNA extraction protocols, computational tools, and sequence databases are required. Here we applied metagenomic sequencing with the Oxford Nanopore Technologies MinION to the detection of the fungus Calonectria pseudonaviculata, the causal agent of boxwood (Buxus spp.) blight disease. Two DNA extraction protocols, several DNA purification kits, and various computational tools were tested. All DNA extraction methods and purification kits provided sufficient quantity and quality of DNA. Several bioinformatics tools for taxonomic identification were found suitable to assign sequencing reads to the pathogen with an extremely low false positive rate. Over 9% of total reads were identified as C. pseudonaviculata in a severely diseased sample and identification at strain-level resolution was approached as the number of sequencing reads was increased. We discuss how metagenomic sequencing could be implemented in routine plant disease diagnostics.

Diagnosing an Opportunistic Fungal Pathogen on Spaceflight-Grown Plants Using the MinION Sequencing Platform

Sustainable agriculture in microgravity is integral to future long-term human space exploration. To ensure the efficient and sustainable cultivation of plants in space, a contingency plan to monitor plant health and mitigate plant diseases is necessary. Yet, neither methods nor tools currently exist to evaluate the plant microbial interactions or to diagnose potential plant diseases in space-based bioregenerative life support systems. In this study, we show how the MinION sequencing platform can be used to diagnose the opportunistic pathogen Fusarium oxysporum sensu lato, a fungal infection on Zinnia hybrida (zinnia) plants that were grown on the International Space Station (ISS) in 2015-2016. Genomic DNA from the infected plant material (root and leaf tissues) retrieved from the ISS were extracted and sequenced. In addition, pure cultures of Burkholderia contaminans, F. oxysporum sensu lato, and Fusarium sporotrichioides were used as controls to test the specificity of the bioinformatics pipeline developed. The results show that the MinION platform can be used to accurately differentiate between fusaria species and strengthens the case for using the platform as a rapid plant disease diagnostic tool in space.

TASPERT: Target-Specific Reverse Transcript Pools to Improve HTS Plant Virus Diagnostics

High-throughput sequencing (HTS) is becoming the new norm of diagnostics in plant quarantine settings. HTS can be used to detect, in theory, all pathogens present in any given sample. The technique’s success depends on various factors, including methods for sample management/preparation and suitable bioinformatic analysis. The Limit of Detection (LoD) of HTS for plant diagnostic tests can be higher than that of PCR, increasing the risk of false negatives in the case of low titer of the target pathogen. Several solutions have been suggested, particularly for RNA viruses, including rRNA depletion of the host, dsRNA, and siRNA extractions, which increase the relative pathogen titer in a metagenomic sample. However, these solutions are costly and time-consuming. Here we present a faster and cost-effective alternative method with lower HTS-LoD similar to or lower than PCR. The technique is called TArget-SPecific Reverse Transcript (TASPERT) pool. It relies on pathogen-specific reverse primers, targeting all RNA viruses of interest, pooled and used in double-stranded cDNA synthesis. These reverse primers enrich the sample for only pathogens of interest. Evidence on how TASPERT is significantly superior to oligodT, random 6-mer, and 20-mer in generating metagenomic libraries containing the pathogen of interest is presented in this proof of concept.