The purpose of the project is to develop high-throughput sequencing methods to rapidly detect and identify members of the ralstonia solanacearum species complex (rssc) directly from plant samples

The purpose of this $145,987 Cooperative Agreement award by the U.S. Department of Agriculture's Animal and Plant Health Inspection Service (APHIS) under CFDA Program 10.025 - Plant and Animal Disease, Pest Control, and Animal Care is to develop high-throughput sequencing methods to rapidly detect and identify members of the Ralstonia solanacearum species complex directly from plant samples. The project aims to expand the range of plant hosts for which the sequencing method has been validated, develop user-friendly bioinformatic tools to allow interpretation of results by non-experts, and generate validation data to enable broader adoption of this technology by the plant disease diagnostic community. The award was made to The Ohio State University and is expected to be completed by June 30, 2025.

With the advance of the genomics ERA, there has been a shift from the phenotypic classification of this pathogen, based on race and biovar typing towards a genotypic circumscription. Precise identification of ralstonia species and strains has important regulatory implications as there is a great degree of heterogeneity in host range and pathogenic potential both within and between species, where certain sub-specific groups that are already considered endemic to the southeast united states may be poorly distinguishable from exotic strains that have the potential to cause new disease outbreaks. In the first year of this project, a new bioinformatics and metagenomic sequencing assays were developed for the culture-independent identification of the rssc directly from geranium cuttings. In the second year, the list of plant hosts on which the library preparation and sequencing method has been validated will be expanded to include tomato and potato. Additionally, continued development of the bioinformatic pipeline for ralstonia identification will take place with the goal of making IT more user-friendly for diagnosticians and results easier to interpret. Finally, the reproducibility of the sequencing and bioinformatics approaches will be evaluated with a ring test at two different plant disease diagnostic clinics and aphis PPQ. The expected deliverables of this project include expanding the range of hosts for which this sequencing method has been validated, development of user-friendly bioinformatic tools that will allow for interpretation of results by non-experts, and the generation of validation data needed to allow for the broader adoption of this technology by the larger plant disease diagnostic community.