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Poster: Epidemiology: Analytical & Theoretical Plant Pathology


Predicting Robust Candidates for a Boxwood Blight Immunoassay: An Automated Computational Workflow with Broad Applications for Phytopathology
D. VELTRI (1), D. Luster (2), M. McMahon (2), J. Crouch (1) (1) USDA-ARS, U.S.A.; (2) USDA-ARS, U.S.A.

The boxwood blight fungal pathogens Calonectria pseudonaviculata and C. henricotiae have emerged as a serious threat to natural and cultivated plant populations around the world. These pathogens can persist as latent infections in asymptomatic plant tissue even after fungicide treatment. Therefore, a fast and field-deployable diagnostic test based on immunological detection would provide a useful tool to help minimize spread of the pathogen. Although several nucleic acid-based approaches are available to detect boxwood blight pathogens, none are field-deployable solutions. We are developing an integrated computational pipeline using recent genomic and RNA-Seq data to identify quality candidate sequences for use in designing a diagnostic immunoassay. The pipeline first identifies proteins likely to be secreted using SECRETOOL. To robustly test the putative secreted proteins for solubility, we developed a new classifier with improved recognition performance over other methods using a publicly-available dataset of secreted proteins. The candidate proteins are further narrowed down through a comparative genomics analysis that identifies proteins with predicted antigenicity that are specific to the target pathogens yet share low similarity to outgroup species. This pipeline stands to help speed up immunoassay development and lower costs by providing a starting set of candidate proteins with a higher likelihood of success in further wet laboratory experiments.