Development and Evaluation of an MLSA-ONT Barcoding Workflow for the Diagnosis of ‘Candidatus Phytoplasma'

Rapid and accurate diagnosis of ‘Candidatus Phytoplasma’ is critical for disease management but remains challenging due to the pathogen's unculturable nature, low titers, uneven distribution and genetic complexity. While current official guidelines provide a highly adaptable taxonomic foundation for species assignment, translating these broad recommendations into standardized routine diagnostics remains difficult. This study implements the established species-assignment guidelines by developing a structured, proof-of-concept diagnostic framework utilizing a Multi-Locus Sequence Analysis (MLSA) workflow coupled with Oxford Nanopore Technologies (ONT) sequencing. The developed workflow targets four barcodes — 16S, secA, rplV and tufB — and replaces fixed demarcation criteria with a best-match principle for non-16S barcodes. Furthermore, the study offers guidance for barcode selection and challenges the use of 16S rRNA sequencing as a sole screening assay. Moreover, a read-based quantification system incorporating control thresholds was implemented to provide an objective and reliable assessment of the results. The workflow was validated using spiked dilution series and naturally infected samples, demonstrating that the ONT workflow achieves sensitivity and accuracy comparable to, or exceeding, that of traditional Sanger sequencing. Although PCR multiplexing was found to reduce sensitivity, limiting the immediate throughput advantages over Sanger sequencing, the proposed framework offers significant value through flexible species assignment and stringent contamination controls. Consequently, this method serves as a robust, high-resolution tool for resolving complex phytoplasma infections.