A new approach for detecting fungal and oomycete plant pathogens in next generation sequencing metagenome data utilising electronic probes

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.

A Step Towards Validation of High-Throughput Sequencing for the Identification of Plant Pathogenic Oomycetes

The advancement in high-throughput sequencing (HTS) technology allows the detection of pathogens without the need for isolation or template amplification. Plant regulatory agencies worldwide are adopting HTS as a prescreening tool for plant pathogens in imported plant germplasm. The technique is a multipronged process and, often, the bioinformatic analysis complicates detection. Previously, we developed E-probe diagnostic nucleic acid analysis (EDNA), a bioinformatic tool that detects pathogens in HTS data. EDNA uses custom databases of signature nucleic acid sequences (e-probes) to reduce computational effort and subjectivity when determining pathogen presence in a sample. E-probes of Pythium ultimum and Phytophthora ramorum were previously validated only using simulated HTS data. However, HTS samples generated from infected hosts or pure culture may vary in pathogen concentration, sequencing bias, and data quality, suggesting that each pathosystem requires further validation. Here, we used metagenomic and genomic HTS data generated from infected hosts and pure culture, respectively, to further validate and curate e-probes of Pythium ultimum and Phytophthora ramorum. E-probe length was found to be a determinant of diagnostic sensitivity and specificity; 80-nucleotide e-probes increased the diagnostic specificity to 100%. Curating e-probes to increase specificity affected diagnostic sensitivity only for 80-nucleotide Pythium ultimum e-probes. Comparing e-probes with alternative databases and bioinformatic tools in their speed and ability to find Pythium ultimum and Phytophthora ramorum demonstrated that, although pathogen sequence reads were detected by other methods, they were less specific and slower when compared with e-probes.

Potential Use of Microbial Community Genomes in Various Dimensions of Agriculture Productivity and Its Management: A Review

Agricultural productivity is highly influenced by its associated microbial community. With advancements in omics technology, metagenomics is known to play a vital role in microbial world studies by unlocking the uncultured microbial populations present in the environment. Metagenomics is a diagnostic tool to target unique signature loci of plant and animal pathogens as well as beneficial microorganisms from samples. Here, we reviewed various aspects of metagenomics from experimental methods to techniques used for sequencing, as well as diversified computational resources, including databases and software tools. Exhaustive focus and study are conducted on the application of metagenomics in agriculture, deciphering various areas, including pathogen and plant disease identification, disease resistance breeding, plant pest control, weed management, abiotic stress management, post-harvest management, discoveries in agriculture, source of novel molecules/compounds, biosurfactants and natural product, identification of biosynthetic molecules, use in genetically modified crops, and antibiotic-resistant genes. Metagenomics-wide association studies study in agriculture on crop productivity rates, intercropping analysis, and agronomic field is analyzed. This article is the first of its comprehensive study and prospects from an agriculture perspective, focusing on a wider range of applications of metagenomics and its association studies.

An In Silico Detection of a Citrus Viroid from Raw High-Throughput Sequencing Data

E-probe Diagnostic for Nucleic Acid Analysis (EDNA) is a user-friendly bioinformatic tool that has been adapted for the detection and identification of citrus exocortis viroid (CEVd). Here, we describe the procedures for RNA extraction from citrus tissues, library and sequencing preparation, and the utilization of EDNA Mi-Finder online platform on raw high-throughput sequencing (HTS) data.

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.

Microbe Finder (MiFi®): Implementation of an Interactive Pathogen Detection Tool in Metagenomic Sequence Data

Agricultural high throughput diagnostics need to be fast, accurate and have multiplexing capacity. Metagenomic sequencing is being widely evaluated for plant and animal diagnostics. Bioinformatic analysis of metagenomic sequence data has been a bottleneck for diagnostic analysis due to the size of the data files. Most available tools for analyzing high-throughput sequencing (HTS) data require that the user have computer coding skills and access to high-performance computing. To overcome constraints to most sequencing-based diagnostic pipelines today, we have developed Microbe Finder (MiFi^®). MiFi^® is a web application for quick detection and identification of known pathogen species/strains in raw, unassembled HTS metagenomic data. HTS-based diagnostic tools developed through MiFi^® must pass rigorous validation, which is outlined in this manuscript. MiFi^® allows researchers to collaborate in the development and validation of HTS-based diagnostic assays using MiProbe™, a platform used for developing pathogen-specific e-probes. Validated e-probes are made available to diagnosticians through MiDetect™. Here we describe the e-probe development, curation and validation process of MiFi^® using grapevine pathogens as a model system. MiFi^® can be used with any pathosystem and HTS platform after e-probes have been validated.

Recent Advances in Molecular Diagnostics of Fungal Plant Pathogens: A Mini Review

Phytopathogenic fungal species can cause enormous losses in quantity and quality of crop yields and this is a major economic issue in the global agricultural sector. Precise and rapid detection and identification of plant infecting fungi are essential to facilitate effective management of disease. DNA-based methods have become popular methods for accurate plant disease diagnostics. Recent developments in standard and variant polymerase chain reaction (PCR) assays including nested, multiplex, quantitative, bio and magnetic-capture hybridization PCR techniques, post and isothermal amplification methods, DNA and RNA based probe development, and next-generation sequencing provide novel tools in molecular diagnostics in fungal detection and differentiation fields. These molecular based detection techniques are effective in detecting symptomatic and asymptomatic diseases of both culturable and unculturable fungal pathogens in sole and co-infections. Even though the molecular diagnostic approaches have expanded substantially in the recent past, there is a long way to go in the development and application of molecular diagnostics in plant diseases. Molecular techniques used in plant disease diagnostics need to be more reliable, faster, and easier than conventional methods. Now the challenges are with scientists to develop practical techniques to be used for molecular diagnostics of plant diseases. Recent advancement in the improvement and application of molecular methods for diagnosing the widespread and emerging plant pathogenic fungi are discussed in this review.

Promising Perspectives for Detection, Identification, and Quantification of Plant Pathogenic Fungi and Oomycetes through Targeting Mitochondrial DNA

Fungi and oomycetes encompass many pathogens affecting crops worldwide. Their effective control requires screening pathogens across the local and international trade networks along with the monitoring of pathogen inocula in the field. Fundamentals to all of these concerns are their efficient detection, identification, and quantification. The use of molecular markers showed the best promise in the field of plant pathogen diagnostics. However, despite the unquestionable benefits of DNA-based methods, two significant limitations are associated with their use. The first limitation concerns the insufficient level of sensitivity due to the very low and uneven distribution of pathogens in plant material. The second limitation pertains to the inability of widely used diagnostic assays to detect cryptic species. Targeting mtDNA appears to provide a solution to these challenges. Its high copy number in microbial cells makes mtDNA an attractive target for developing highly sensitive assays. In addition, previous studies on different pathogen taxa indicated that mitogenome sequence variation could improve cryptic species delimitation accuracy. This review sheds light on the potential application of mtDNA for pathogen diagnostics. This paper covers a brief description of qPCR and DNA barcoding as two major strategies enabling the diagnostics of plant pathogenic fungi and oomycetes. Both strategies are discussed along with the potential use of mtDNA, including their strengths and weaknesses.

Role of genomics in promoting the utilization of plant genetic resources in genebanks

Global efforts have seen the world's plant genetic resources (PGRs) conserved in about 1625 germ plasm repositories. Utility of these resources is important in increasing the resilience and productivity of agricultural production systems. However, despite their importance, utility of these resources has been poor. This article reviews the real and potential application of the current advances in genomic technologies in improving the utilization of these resources. The actual and potential application of these genomic approaches in plant identification, phylogenetic analysis, analysing the genetic value of germ plasm, facilitating germ plasm selection in genebanks as well as instilling confidence in international germ plasm exchange system is discussed. We note that if genebanks are to benefit from this genomic revolution, there is need for fundamental changes in the way genebanks are managed, perceived, organized and funded. Increased collaboration between genebank managers and the user community is also recommended.

Cluster oligonucleotide signatures for rapid identification by sequencing

BACKGROUND

Oligonucleotide signatures (signatures) have been widely used for studying microbial diversity and function in wet-lab settings, but using them for accurate in silico identification of organisms from high-throughput sequencing (HTS) data is only a proof of concept. Existing signature design programs for sequence signatures (signatures matching exactly one sequence) or clade signatures (signatures matching every sequence in a phylogenetic clade) are not able to identify all possible polymorphic sites for sequences with high similarity and perform poorly when handling large genome sequencing datasets.

RESULTS

We introduce cluster signatures: subsequences that match perfectly and exclusively any group of sequences in a data set. Cluster signatures provide complete recall for primer/probe design and increased discrimination between sequences beyond that of clade signatures. Using cluster signatures for in silico identification of HTS targets achieves good precision/recall and running time performance. This method has been implemented into an open source tool, the Automated Oligonucleotide Design Pipeline (adop), included in supplementary material and available at: https://bitbucket.org/wenchen_aafc/aodp_v2.0_release .

CONCLUSIONS

Cluster signatures provide a rapid and universal analysis tool to identify all possible short diagnostic DNA markers and variants from any DNA sequencing dataset. They are particularly useful in discriminating genetic material from closely related organisms and in detecting deleterious mutations in highly or perfectly conserved genomic sites.

Inferring the presence of aflatoxin-producing Aspergillus flavus strains using RNA sequencing and electronic probes as a transcriptomic screening tool

E-probe Diagnostic for Nucleic acid Analysis (EDNA) is a bioinformatic tool originally developed to detect plant pathogens in metagenomic databases. However, enhancements made to EDNA increased its capacity to conduct hypothesis directed detection of specific gene targets present in transcriptomic databases. To target specific pathogenicity factors used by the pathogen to infect its host or other targets of interest, e-probes need to be developed for transcripts related to that function. In this study, EDNA transcriptomics (EDNAtran) was developed to detect the expression of genes related to aflatoxin production at the transcriptomic level. E-probes were designed from genes up-regulated during A. flavus aflatoxin production. EDNAtran detected gene transcripts related to aflatoxin production in a transcriptomic database from corn, where aflatoxin was produced. The results were significantly different from e-probes being used in the transcriptomic database where aflatoxin was not produced (atoxigenic AF36 strain and toxigenic AF70 in Potato Dextrose Broth).

Nanopore Sequencing as a Surveillance Tool for Plant Pathogens in Plant and Insect Tissues

Plant pathogens are constantly emerging and spreading into new areas and there are often limited postdiagnosis treatment options for infection, making surveillance key to their control. Here we present results from a study testing the efficacy of a portable nanopore-based massively parallel sequencing (MPS) technology for use in the detection of diverse plant pathogens in selected samples. The Oxford MinION device was coupled with whole transcriptome amplification (WTA) to sequence the metatranscriptome of plant and insect tissues infected with either Candidatus Liberibacter asiaticus or plum pox virus. Results showed that this methodology is useful for detecting unsuspected viral and bacterial pathogens in plant and insect tissues. The percentage of generated reads assigned to plum pox virus was 95% from infected tissue and 3% from the viruliferous insect, Myzus persicae. Diaphorina citri sequencing led to 22% of the reads mapping as Ca. L. asiaticus. Plum pox virus and Ca. L. asiaticus were detected in both tissue and insect samples near the beginning of each sequencing run, demonstrating the capability of this methodology to obtain results rapidly. This approach also proved the capability of this system to determine the major components of the insect vector's microbiome and the specific strain of small-genome, high-titer pathogens.

Ignored fungal community in activated sludge wastewater treatment plants: diversity and altitudinal characteristics

Fungi are important contributors to the various functions of activated sludge wastewater treatment plants (WWTPs); however, the diversity and geographic characteristics of fungal populations have remained vastly unexplored. Here, quantitative polymerase chain reaction and 454 pyrosequencing were combined to investigate the abundance and diversity of the activated sludge fungal communities from 18 full-scale municipal WWTPs in China. Phylogenetic taxonomy revealed that the members of the fungal communities were assigned to 7 phyla and 195 genera. Ascomycota and Basidiomycota were the most abundant phyla, dominated by Pluteus, Wickerhamiella, and Penicillium. Twenty-three fungal genera, accounting for 50.1 % of the total reads, were shared by 18 WWTPs and constituted a core fungal community. The fungal communities presented similar community diversity but different community structures across the WWTPs. Significant distance decay relationships were observed for the dissimilarity in fungal community structure and altitudinal distance between WWTPs. Additionally, the community evenness increased from 0.25 to 0.7 as the altitude increased. Dissolved oxygen and the C/N ratio were determined to be the most dominant contributors to the variation in fungal community structure via redundancy analysis. The observed data demonstrated the diverse occurrence of fungal species and gave a marked view of fungal community characteristics based on the previously unexplored fungal communities in activated sludge WWTPs.