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

Introduction. With expanding demand for diagnostics, newer methodologies are needed for faster, user-friendly and multiplexed pathogen detection. Metagenome-based diagnostics offer potential solutions to address these needs as sequencing technologies have become affordable. However, the diagnostic utility of sequencing technologies is currently limited since analysis of the large amounts of data generated, are either computationally expensive or carry lower sensitivity and specificity for pathogen detection.Hypothesis/Gap Statement. There is a need for novel, user friendly, and computationally inexpensive platforms for metagenome sequence analysis for diagnostic applications.Methods. In this study, we report the use of MiFi® (Microbe Finder), a computationally inexpensive algorithm with a user-friendly online interface, for accurate, rapid and multiplexed pathogen detection from metagenome sequence data. Detection is accomplished based on identification of signature genomic sequence segments of the target pathogen in metagenome sequence data. In this study we used bovine respiratory disease (BRD) complex as a model.Results and Conclusions. Using MiFi®, multiple target bacteria and a DNA virus were successfully detected in a multiplex format from metagenome sequences acquired from bovine lung tissue. Overall, 51 clinical samples were assessed and MiFi® showed 100 % analytical specificity and varying levels of analytical sensitivity (62.5 %-100 %) when compared with other traditional pathogen detection techniques, such as PCR. Consistent detection of bacteria was possible from lung samples artificially spiked with 109-104 c.f.u. of Mannheimia haemolytica.

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.

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.

Genome analysis of Spiroplasma citri strains from different host plants and its leafhopper vectors.

BACKGROUND: Spiroplasma citri comprises a bacterial complex that cause diseases in citrus, horseradish, carrot, sesame, and also infects a wide array of ornamental and weed species. S. citri is transmitted in a persistent propagative manner by the beet leafhopper, Neoaliturus tenellus in North America and Circulifer haematoceps in the Mediterranean region. Leafhopper transmission and the pathogen's wide host range serve as drivers of genetic diversity. This diversity was examined in silico by comparing the genome sequences of seven S. citri strains from the United States (BR12, CC-2, C5, C189, LB 319, BLH-13, and BLH-MB) collected from different hosts and times with other publicly available spiroplasmas. RESULTS: Phylogenetic analysis using 16S rRNA sequences from 39 spiroplasmas obtained from NCBI database showed that S. citri strains, along with S. kunkelii and S. phoeniceum, two other plant pathogenic spiroplasmas, formed a monophyletic group. To refine genetic relationships among S. citri strains, phylogenetic analyses with 863 core orthologous sequences were performed. Strains that clustered together were: CC-2 and C5; C189 and R8-A2; BR12, BLH-MB, BLH-13 and LB 319. Strain GII3-3X remained in a separate branch. Sequence rearrangements were observed among S. citri strains, predominantly in the center of the chromosome. One to nine plasmids were identified in the seven S. citri strains analyzed in this study. Plasmids were most abundant in strains isolated from the beet leafhopper, followed by strains from carrot, Chinese cabbage, horseradish, and citrus, respectively. All these S. citri strains contained one plasmid with high similarity to plasmid pSci6 from S. citri strain GII3-3X which is known to confer insect transmissibility. Additionally, 17 to 25 prophage-like elements were identified in these genomes, which may promote rearrangements and contribute to repetitive regions. CONCLUSIONS: The genome of seven S. citri strains were found to contain a single circularized chromosome, ranging from 1.58 Mbp to 1.74 Mbp and 1597-2232 protein-coding genes. These strains possessed a plasmid similar to pSci6 from the GII3-3X strain associated with leafhopper transmission. Prophage sequences found in the S. citri genomes may contribute to the extension of its host range. These findings increase our understanding of S. citri genetic diversity.

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.

Whole-Genome Sequences of Ralstonia solanacearum Strains P816, P822, and P824, Emerging Pathogens of Blueberry in Florida.

Ralstonia solanacearum is the causal agent of bacterial wilt in numerous species of plants. Here, we report the whole-genome sequence of three phylogenetically diverse R. solanacearum strains, P816, P822, and P824, reported for the first time as causal agents of an emerging blueberry disease in Florida.

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).

Population Structure of Pythium irregulare, P. ultimum, and P. sylvaticum in Forest Nursery Soils of Oregon and Washington.

Pythium species are important soilborne pathogens occurring in the forest nursery industry of the Pacific Northwest. However, little is known about their genetic diversity or population structure and it is suspected that isolates are moved among forest nurseries on seedling stock and shared field equipment. In order to address these concerns, a total of 115 isolates of three Pythium species (P. irregulare, P. sylvaticum, and P. ultimum) were examined at three forest nurseries using simple sequence repeat (SSR) and amplified fragment length polymorphism (AFLP) markers. Analyses revealed distinct patterns of intraspecific variation for the three species. P. sylvaticum exhibited the most diversity, followed by P. irregulare, while substantial clonality was found in P. ultimum. For both P. irregulare and P. sylvaticum, but not P. ultimum, there was evidence for significant variation among nurseries. However, all three species also exhibited at least two distinct lineages not associated with the nursery of origin. Finally, evidence was found that certain lineages and clonal genotypes, including fungicide-resistant isolates, are shared among nurseries, indicating that pathogen movement has occurred.

E-probe Diagnostic Nucleic acid Analysis (EDNA): a theoretical approach for handling of next generation sequencing data for diagnostics.

Plant biosecurity requires rapid identification of pathogenic organisms. While there are many pathogen-specific diagnostic assays, the ability to test for large numbers of pathogens simultaneously is lacking. Next generation sequencing (NGS) allows one to detect all organisms within a given sample, but has computational limitations during assembly and similarity searching of sequence data which extend the time needed to make a diagnostic decision. To minimize the amount of bioinformatic processing time needed, unique pathogen-specific sequences (termed e-probes) were designed to be used in searches of unassembled, non-quality checked, sequence data. E-probes have been designed and tested for several selected phytopathogens, including an RNA virus, a DNA virus, bacteria, fungi, and an oomycete, illustrating the ability to detect several diverse plant pathogens. E-probes of 80 or more nucleotides in length provided satisfactory levels of precision (75%). The number of e-probes designed for each pathogen varied with the genome size of the pathogen. To give confidence to diagnostic calls, a statistical method of determining the presence of a given pathogen was developed, in which target e-probe signals (detection signal) are compared to signals generated by a decoy set of e-probes (background signal). The E-probe Diagnostic Nucleic acid Analysis (EDNA) process provides the framework for a new sequence-based detection system that eliminates the need for assembly of NGS data.