Diagnostic markers for rapid and accurate detection of the phytopathogenic Xanthomonas genus

Xanthomonas is a genus of gram-negative γ-proteobacteria that infects over 400 different plant hosts, including important crops like rice, wheat, citrus, tomato, cabbage, banana and bean, posing both an economical threat and a danger to food security. The highly diverse genus comprises of 33 validly named species, which are further subdivided into pathovars, which are defined by host and tissue specificities. Some Xanthomonas species pathovars are further subdivided into different races based on which host near-isogenic lines of cultivars they infect. Xanthomonas is mainly transmitted via infected seeds, with seed trade being one route that disseminates the pathogen over large geographical distances. While some treatments are available, they are not effective in all species and resistance to standard treatments is becoming more prevalent. Therefore, detection and accurate identification of infected seeds and plant material is crucial to stop the spread and manage plant diseases. Standard identifcation methods are often laborious, costly and slow, e.g. requiring isolation on semi-selective media or serological tests. While molecular diagnostics (e.g. conventional or real-time PCR) are available for some species, finding diagnostic markers has been challenging for pathovars and races and reliable molecular diagnostics for e.g. Xanthomonas campestris pv. campestris (Xcc) races were previously unavailable. To facilitate reliable and reproducible finding of diagnostic targets and to make picking primers for those targets as user-friendly as possible, we chose a phylogeny-driven, clade-based approach and created an easy-to-use pipeline. A phylogeny-driven, clade-based approach allows for the accurate determination of diagnostic targets and off-targets, providing an unbiased basis for the rest of the pipeline which automatically finds unique genomic regions for a target species, pathovar or race with moderate user intervention, automatically picks primers for these regions that are amenable to both conventional PCR and quantitative PCR, and finally tests them for specificity and sensitivity. In many cases, the unique genomic regions found can also be used to generate LAMP (loop-mediated isothermal amplification) primers.