Decoding microbiome responses to quarantine potato wart disease: first insights into suppression and biocontrol by full-length 16S rRNA gene profiling and functional prediction

Introduction: Synchytrium endobioticum, the biotrophic pathogen causing potato wart, poses persistent challenges due to its long-term soil survival and quarantine status. Biological control agents (BCAs) offer a promising avenue for sustainable management, yet the ecological context of wart-associated microbiomes remains unexplored. Methods: We present the first comprehensive microbiome characterization of the potato wart disease system using full-length 16S rRNA gene Nanopore sequencing across bioassay-grown warts, field-collected wart tissues, diseased tare soils, and long-term descheduled (wart-free) soils. Whole genome amplification (WGA) enabled profiling of low-biomass samples, albeit with compositional shifts towards dominant taxa. Results and discussion: Microbiome compositional structure differed significantly across sieving fractions, host genotypes, and compartments (wart vs. tare soil). Wart microbiomes were enriched in Pseudomonas trivialis and Bacillus atrophaeus, taxa potentially involved in pathogen-specific suppression. Tare soils harbored transitional microbiomes shaped by host proximity, enriched with Bacillus species that may offer both generalist and targeted BCA activity. Descheduled soils under long-term nonhost crop rotations harbored broad-spectrum BCAs contributing to environmental sensing and nutrient requisition. Functional prediction suggested enrichment of xenobiotic degradation and chitin metabolism pathways in diseased soils, primarily associated with Bacillus, Pseudomonas, and Paenibacillus. Network analysis indicated fragile yet densely connected communities in diseased soils versus modular and stable structures in descheduled systems. Altogether, this study represents a first critical step toward developing biocontrol strategies for S. endobioticum by revealing a gradient of biocontrol reservoirs associated with disease pressure and management history. The use of functional prediction and correlation network tools provides essential starting points for hypothesis-driven research into disease suppression and biocontrol in a system with no prior microbiome data, and these findings warrant targeted isolation and in vitro/in planta validation for BCA development.