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

Abstract

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 pre-screening 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 (Trow) and Phytophthora ramorum (Werres, De Cock & Man in’t Veld) 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 Py. ultimum and Ph. ramorum. E-probe length was found to be a determinant of diagnostic sensitivity and specificity; 80-nucleotides e-probes increased the diagnostic specificity to 100%. Curating e-probes to increase specificity affected diagnostic sensitivity only for 80-nucleotides Py. ultimum e-probes. Comparing e-probes with alternative databases and bioinformatic tools in their speed and ability to find Py. ultimum and Ph. ramorum demonstrated that while pathogen sequence reads were detected by other methods, they were less specific and slower when compared with e-probes.

Next-Generation Sequencing for the Detection of Microbial Agents in Avian Clinical Samples

“…The earlier the recognition of the associations between NGS results and clinical manifestations associated with pathology or transmission, the higher the chances of stopping a disease or a chain of transmission that may lead to irreparable damage. However, similar issues have been raised in other mammalian, human, and environmental studies [2,105,117,119,[126][127][128][129][130]. The combination of multiple reports in large regional or temporal databases for further epidemiological analysis is likely to have predictive power that could be used in poultry disease prevention and in operation management decisions.…”

Development and evaluation of Microbe Finder (MiFi)®: a novel in silico diagnostic platform for pathogen detection from metagenomic data

“…In this study, we developed a multiplexed metagenome-based disease diagnostic protocol using the Microbe Finder (MiFi ® ) platform. The MiFi ® platform has previously been used for metagenome-based detection of viral and fungal pathogens in plants [8][9][10][11][12]. The advantages of metagenome-based pathogen detection have been recognized by various researchers in recent years [13][14][15][16][17].…”

PhytoPipe: a phytosanitary pipeline for plant pathogen detection and diagnosis using RNA-seq data

“…More recently, high-throughput sequencing (HTS), also known as next-generation sequencing (NGS) or deep sequencing has been used by diagnosticians and researchers for detecting and identifying plant pathogens, which has resulted in a steady increase in the identification of plant pathogens affecting various crops [2,[7][8][9][10][11][12][13]. Since it does not require a priori phytosanitary status knowledge of the sample, HTS offers certain advantages when compared to targeted-diagnostic techniques such as ELISA or PCR [10,11,[14][15][16][17][18]. The use of HTS is now becoming a gold standard across continents after the International Plant Protection Convention (IPPC) recommended it as a diagnostic tool for phytosanitary purposes in 2019 [19].…”