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

<h4>Introduction</h4><i>Synchytrium endobioticum</i>, 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.<h4>Methods</h4>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.<h4>Results and discussion</h4>Microbiome compositional structure differed significantly across sieving fractions, host genotypes, and compartments (wart vs. tare soil). Wart microbiomes were enriched in <i>Pseudomonas trivialis</i> and <i>Bacillus atrophaeus</i>, taxa potentially involved in pathogen-specific suppression. Tare soils harbored transitional microbiomes shaped by host proximity, enriched with <i>Bacillus</i> 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 <i>Bacillus, Pseudomonas</i>, and <i>Paenibacillus</i>. 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 <i>S. endobioticum</i> 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 <i>in vitro</i>/<i>in planta</i> validation for BCA development.