No abstract

Early stage infections caused by fungal/oomycete spores may not be detected until signs or symptoms develop. Serological and molecular techniques are currently used for detecting these pathogens. Next-generation sequencing (NGS) has potential as a diagnostic tool, due to the capacity to target multiple unique signature loci of pathogens in an infected plant metagenome. NGS has significant potential for diagnosis of important eukaryotic plant pathogens. However, the assembly and analysis of huge amounts of sequence is laborious, time consuming, and not necessary for diagnostic purposes. Previous work demonstrated that a bioinformatic tool termed Electronic probe Diagnostic Nucleic acid Analysis (EDNA) had potential for greatly simplifying detecting fungal and oomycete plant pathogens in simulated metagenomes. The initial study demonstrated limitations for detection accuracy related to the analysis of matches between queries and metagenome reads. This study is a modification of EDNA demonstrating a better accuracy for detecting fungal and oomycete plant pathogens.

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).

Phymatotrichopsis omnivora, the causal pathogen of cotton root rot, is a devastating ascomycete that affects numerous important dicotyledonous plants grown in the southwestern United States and northern Mexico. P. omnivora is notoriously difficult to isolate from infected plants; therefore methods for accurate and sensitive detection directly from symptomatic and asymptomatic plant samples are needed for disease diagnostics and pathogen identification. Primers were designed for P. omnivora based on consensus sequences of the nuclear ribosomal internal transcribed spacer (ITS) region of geographically representative isolates. Primers were compared against published P. omnivora sequences and validated against DNA from P. omnivora isolates and infected plant samples. The primer combinations amplified products from a range of P. omnivora isolates representative of known ITS haplotypes using standard end-point polymerase chain reaction (PCR) methodology. The assays detected P. omnivora from infected root samples of cotton (Gossypium hirsutum) and alfalfa (Medicago sativa). Healthy plants and other relevant root pathogens did not produce PCR products with the P. omnivora–specific primers. Primer pair PO2F/PO2R was the most sensitive in end-point PCR assays and is recommended for use for pathogen identification from mycelial tissue and infected plant materials when quantitative PCR (qPCR) is not available. Primer pair PO3F/PO2R was highly sensitive (1 fg) when used in SYBR Green qPCR assays and is recommended for screening of plant materials potentially infected by P. omnivora or samples with suboptimal DNA quality. The described PCR-based detection methods will be useful for rapid and sensitive screening of infected plants in diagnostic laboratories, plant health inspections, and plant breeding programs.

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.