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.

1 Introduction

Potato wart is a soil-borne disease characterized by the formation of cauliflower-like galls, primarily on stolons and tubers of potato (Solanum tuberosum L.) (Hampson, 1993). It is caused by Synchytrium endobioticum (Schilberszky) Percival, an obligate biotrophic chytrid fungus, capable of causing up to 100% yield losses and rendering affected tubers unmarketable (van de Vossenberg et al., 2022b). Owing to its severe economic impact and persistence in soil, S. endobioticum is classified as a high-risk quarantine pathogen in many potato-producing countries, including Canada and the Netherlands, where it was first identified in the early 20^th century (Hampson, 1993; Baayen et al., 2006; Government of Canada, 2012; van de Vossenberg et al., 2022b).

Managing S. endobioticum infection remains a formidable challenge due to the pathogen’s ability to produce highly resilient resting spores (winter spores) within hypertrophic wart tissues. Upon tissue decay or mechanical disruption, these spores are released into the surrounding soil, where they can remain viable for several decades (van de Vossenberg et al., 2022b). Once conditions become conducive (typically cool, moist soils), the resting spores germinate to produce motile zoospores capable of initiating new infections (Curtis and Blackman, 1921; EPPO Bulletin, 2017b; van de Vossenberg et al., 2022b). S. endobioticum can be disseminated to new locations via infested soil, infected seed tuber material, or contaminated agricultural equipment (van de Vossenberg et al., 2022b), exacerbating the risk of spread and reinforcing the need for vigilant surveillance and containment strategies.

Potato wart disease is primarily managed by strict phytosanitary regulations and the cultivation of resistant potato cultivars (van de Vossenberg et al., 2022b). Long-term descheduling of infested fields demands extensive timelines, sometimes exceeding 20 years (EPPO Bulletin, 2017b). Additional control measures, such as soil fumigation (Monro et al., 1970), chitin-based amendments (Hampson and Coombes, 1995), and fungicide applications (Çakir and Demirci, 2023) have also been investigated. However, chemical treatments pose risks to public and environmental health (Hampson, 1988; Zhang et al., 2024) and have demonstrated limited and inconsistent efficacy against S. endobioticum largely due to the extreme durability of its resting spores and the short treatment duration or low number of application cycles in existing studies (Obidiegwu et al., 2014). As a result, these chemical interventions are neither widely recommended nor approved for practical field management of potato wart. Management is further complicated by the presence of over 40 recognized S. endobioticum pathotypes globally, with breeding efforts for broad-spectrum resistance facing limited success (Obidiegwu et al., 2014; van de Vossenberg et al., 2022b). For instance, a 2020 outbreak in Stadskanaal, Netherlands, involving the emergence of pathotype 38 (Nevşehir) affected cultivars previously resistant to the (formerly) common pathotypes 1(D1), 2(G1), 6(O1), and 18(T1) (van de Vossenberg et al., 2024).

Given the challenges of managing potato wart, there is growing interest in sustainable alternatives to complement existing control measures. Biological control agents (BCAs) are beneficial microbes or their metabolites that suppress plant pathogens, offering an environmentally friendly strategy by leveraging natural interactions such as competition, antibiosis, and parasitism (Pandit et al., 2022; Chen et al., 2023). Bacterial strains of the genera Pseudomonas, Bacillus, Pantoea, Flavobacterium, and Actinobacteria, and fungal strains of the genera Trichoderma, Gliocladium, and Clonostachys, have demonstrated biocontrol and plant-growth promoting (PGP) potentials against a wide range of plant diseases (Lahlali et al., 2022). However, to date, no validated BCAs exist for potato wart and they have yet to achieve practical success in field descheduling programs, although Thiobacillus thiooxydans was previously tested in a field trial with promising outcome, but was not validated in subsequent repetitions (Obidiegwu et al., 2014).

A promising way for preventive disease control lies in harnessing soil suppressiveness, which is the natural capacity of microbial communities to inhibit pathogen establishment or proliferation (De Corato, 2020; Jayaraman et al., 2021). One key mechanism is soil fungistasis, whereby indigenous microbes prevent fungal spore germination through resource competition or antagonistic activities (Supronienė et al., 2023). Suppressive soils have been documented against some soilborne pathogens, such as Fusarium oxysporum (El-kazzaz et al., 2022), Phytophthora spp (Ozgonen and Erkilic, 2007), and Rhizoctonia solani (Barnett et al., 2006). Moreover, pathogen stress can enrich for beneficial endophytes with plant-growth-promoting and biocontrol traits, while plants themselves shape microbiomes through rhizodeposition and legacy effects (Yang et al., 2020; Hannula et al., 2021). These observations align with the keystone pathogen hypothesis, which posits that even low-abundance pathogens can exert disproportionate effects on host-microbiome interactions and ecological structure (Hajishengallis et al., 2012; Banerjee et al., 2018). Although S. endobioticum spores may be present in low quantities in infested soils, their ability to cause disease suggests biologically meaningful interactions that could leave detectable microbiome signatures. However, whether such microbiome-mediated suppressiveness exists or can be engineered against potato wart remains an open question.

Despite increasing interest in microbiome-based disease suppression, research into natural soil fungistasis against potato wart remains limited by several constraints. Quarantine regulations severely restrict access to infected field sites and samples, making large-scale or longitudinal studies difficult. Furthermore, genetic material from S. endobioticum is often scarce, limiting the feasibility of in vitro and in vivo experimentation. Uneven sampling across sample types (such as soil, wart tissue, and bioassay-derived materials), further complicates efforts to unravel complex pathogen-microbiome-plant interactions. Overcoming these barriers is critical for advancing microbiome-informed biocontrol strategies and developing more sustainable approaches to managing potato wart.

This study employed Oxford Nanopore full-length 16S rRNA gene metabarcoding to profile bacterial communities associated with potato wart disease using environmental DNA (eDNA) samples from the Netherlands. Leveraging the improved taxonomic resolution offered by Nanopore long-read sequencing over short-read platforms (Alteio et al., 2021; Zhang et al., 2021; Satam et al., 2023), we investigated how S. endobioticum infection alters microbiome composition, potentially selecting for bacterial taxa with biocontrol capabilities. To better identify potential BCAs, functional predictions based on 16S rRNA gene, while inherently inferential, offer valuable ecological insights when direct measurements are not feasible, especially in the context of early-stage studies on quarantine pathogens such as S. endobioticum. PICRUSt2 (Douglas et al., 2020), a widely adopted tool in microbial ecology, leverages phylogenetic placement and genome-informed inference to predict functional profiles of microbial communities based on sequenced reference genomes of phylogenetically close taxa. While we recognize the limitations of such predictive tools, their ability to generate functional hypotheses in our current exploratory study is crucial for systems where culturing is constrained by regulatory or logistical barriers (Douglas et al., 2020; Wang et al., 2022). Indeed, recent studies have adopted similar staged approaches using PICRUSt2 to predict microbial functions across diverse ecological systems (Duan et al., 2024; Lee et al., 2025; Tang et al., 2025). For example, Li et al. (2024) revealed contrasts in microbial metabolic activity across soil pore scales, while Vigil et al. (2024) characterized bacterial communities associated with Peruvian macroalgae, both explicitly acknowledging the predictive nature of PICRUSt2 and the need for experimental validation. In agreement with these studies, we emphasize that our use of PICRUSt2 in this study serves solely to generate testable hypotheses and guide subsequent targeted isolation and functional assays.

We hypothesized that wart disease pressure and management history shape distinct microbiome signatures across field soils, wart tissues, and bioassay systems, revealing core microbial players, fungistasis signals, and legacy effects. To our knowledge, this is the first comprehensive study to characterize microbiome structures and predicted functions associated with potato wart disease using a metagenomics-informed approach. Our findings provide ecological and functional insights into natural suppression and persistence, offering a framework to guide microbiome-based biocontrol strategies for managing S. endobioticum.

2 Materials and methods

2.1 Overall experimental design and research questions

To investigate how S. endobioticum infection and disease management influence the potato-associated microbiome, we designed a study incorporating diverse sample types representing different levels of disease pressure, biosecurity constraints, and ecological contexts. We analyzed bacterial communities in four major sample types: naturally infested soils, freshly collected field wart tissues, bioassay-derived wart tissues, and healthy (descheduled) soils. This diversity allowed us to examine microbial variation both within and between environments under different disease statuses.

To enrich for pathogen-associated signals and capture spatial microhabitats, samples were size-fractionated and stratified by origin (field vs. bioassay), helping to distinguish environmental effects from those driven by pathogen or host genotype. The inclusion of wart tissues from Spieckermann bioassays (EPPO Bulletin, 2017b; van de Vossenberg et al., 2019a) was deliberate. These standardized pathogenicity tests, routinely used in quarantine diagnostics, provide a controlled system for reproducible wart formation. They allowed us to investigate wart-associated microbiomes under standardized conditions while also meeting biosecurity requirements that limit direct access to infested field sites.

To overcome low biomass and DNA yield from most of these samples, we employed whole genome amplification (WGA) using multiple displacement amplification, which has been shown to preserve representative taxonomic profiles in low-input samples (Hawkins et al., 2002; Sabina and Leamon, 2015; Ordóñez and Redrejo-Rodríguez, 2023; Ospino et al., 2024; Agyabeng-Dadzie et al., 2025).

The overarching research questions guiding this study are: (i) Can microbiome dynamics across field soils, wart tissues, and bioassay systems reveal signatures of fungistasis, core microbial players, or legacy effects; and (ii) can this knowledge uncover microbial traits or taxa with potential for biocontrol and natural suppression of potato wart disease? To address these objectives within the context of diverse sample types and WGA-enabled low-input profiling, we developed five focused research questions (Table 1) that collectively examine microbial shifts, functional potential, biocontrol relevance, and disease suppression mechanisms.

Table 1

Table 1. The five research questions and samples used in this study.

2.2 Sample requisition from Netherlands

A total of 61 DNA samples were provided by Netherlands’ National Plant Protection Organization (NPPO), encompassing three distinct sample groups (Supplementary Table S1). The first group (n = 16) was collected from the Mussel and Onstwedde fields during the 2020 potato wart outbreak in the municipality of Stadskanaal. These fields were infected with S. endobioticum pathotype 38 (Nevşehir), and the samples consisted of field wart tissues (n = 10) and associated diseased tare soil (n = 6). Field wart tissues were homogenized using a Grindomix system, followed by sieving through a 75 µm mesh, which retained resting spores of S. endobioticum, plant material and debris. Diseased tare soil samples were also processed using a 75 µm sieve. The second group comprised wart tissues (n = 12) obtained from a Spieckermann bioassay, in which potato cultivars Deodara, Saphir, and Talent were inoculated with pathotype 38 (Nevşehir) isolates from either the Mussel or Onstwedde field. Details of the Spieckermann bioassay protocol are provided in Supplementary Figure S1. For this group, two size fractions (25 µm and 75 µm) were collected to evaluate microbiome difference based on particle size. The 25 µm fraction retained higher quantity of S. endobioticum spores than the 75 µm fraction, confirmed by S. endobioticum-specific qPCR assays (Weller et al., 2000; Smith et al., 2014; EPPO Bulletin, 2017b). The third group consisted of healthy (descheduled) soil samples (n = 34) sieved to create a 25 µm fraction as part of a descheduling survey as per EPPO guidelines (EPPO Bulletin, 2017a). Sample processing followed the European and Mediterranean Plant Protection Organization (EPPO) standard protocols for descheduling surveys (EPPO Bulletin, 2017b; van de Vossenberg et al., 2022a), as illustrated in Supplementary Figure S2. The 25 µm sieving fraction was used for descheduled soils because it generally retains a higher concentration of S. endobioticum resting spores compared to the 75 µm fraction, which was used for potato wart-infested soils. Differences in sieving fraction sizes and unknown field operations under descheduling containment limited the direct comparison of potato wart-infested soils (75 µm fraction) and descheduled soils (25 µm fraction) in the context of microbiomes taxonomic and functional profiling under disease pressures. However, our focus was to evaluate each compartment’s potential as a reservoir of plant growth-promoting bacteria (PGPB) and microbial traits relevant to wart suppression. This conceptual shift allows us to consider the role of both pathogen-driven selection and soil legacy effects in shaping microbiomes with biocontrol potential, especially under restricted access to infested fields.

Netherlands soil samples were confirmed to be infested or free of S. endobioticum spores by visual examination and validated by Quantitative PCR (qPCR) assays targeting the pathogen. The qPCR assay (Smith et al., 2014; EPPO Bulletin, 2017b) targets a 143 bp region of the 18S small subunit ribosomal RNA gene (SSU rDNA) using a S. endobioticum-specific primer set, Se18S_RTF1 (forward, 5′-CTC TGG TTG AGC TCC ATT TAC-3′) and Se18S_RTR2 (reverse, 5′-CCT ATT CTA TTA TTC CAT GCT GTA-3′), with TaqMan probe Se18S_TM1 (5′- 6-FAM -TAT CCT GGT TCC CCA CAG GCA CTC- BHQ1- 3′) (qPCR reagents listed in Supplementary Table S2; qPCR results summarized in Supplementary Table S3). Amplification reactions were performed in a real-time PCR thermal cycler (CFX Opus 96, Bio-Rad, CA, USA) under the following conditions: an initial denaturation at 90 °C for 10 minutes, followed by 40 cycles of denaturation at 95 °C for 15 seconds and annealing/extension at 60 °C for 1 minute.

2.3 DNA extraction and whole genome amplification

The eDNA samples from The Netherlands as well as Newfoundland & Labrador were extracted using QIAGEN’s DNeasy PowerSoil Pro Kit (QIAGEN, Germany), following the manufacturer’s protocol. Due to low DNA inputs, whole genome amplification (WGA) was used to obtain sufficient input for Nanopore sequencing.

Two amplification kits were tested on a standardized mock community (ZymoBIOMICS D6305, Zymo Research, USA), including the GenomiPhi V3 Ready-To-Go DNA Amplification Kit (Cytiva, USA) and the REPLI-g Single Cell Kit (QIAGEN, Germany) according to the manufacturer’s instructions.

GenomiPhi V3 was selected for further testing on Canadian DNA samples from potato wart-infested fields (n = 7) at the Avondale substation of Agriculture and Agri-Food Canada’s St. John’s Research and Development Center in Newfoundland and Labrador, Canada. The Canadian soil samples were collected from a depth of 0-15 cm using a composite sampling strategy, where three 500 g soil cores were mixed per site. From each composite, two 200 mg replicates were subjected to DNA extraction without prior sieving or drying and subsequently pooled. Although the Avondale site is known to harbor pathotypes 2(G1), 6(O1), and 8(F1), the presence of S. endobioticum spores in these specific soil samples was not confirmed.

All WGA-amplified DNA samples were purified the Short Read Eliminator XS Kit (PacBio, USA) to remove DNA fragments less than 5 kb. DNA concentration, quality, and fragment size distribution were assessed on the Agilent TapeStation system (Agilent Technologies, USA), according to the manufacturer’s instructions.

Additionally, a no-template (water) negative control was included throughout WGA, purification, library preparation, and Nanopore sequencing (See Section 2.4) to verify the absence of contamination during all processing steps.

2.4 Library preparation and nanopore sequencing

Library preparation for full-length 16S rRNA gene metabarcoding targeted the V1-V9 hypervariable regions (~1.5 kb) using a two-step PCR approach. In the first PCR, amplification was performed using primer pair 27F (5′-TTT CTG TTG GTG CTG ATA TTG CAG AGT TTG ATC MTG GCT CAG-3′) and 1492R (5′-ACT TGC CTG TCG CTC TAT CTT CCG GTT ACC TTG TTA CGA CTT-3′) (dos Santos et al., 2019). The PCR program included initial denaturation at 95 °C for 3 minutes, followed by 40 cycles of 95 °C for 30 seconds, 55 °C for 30 seconds, and 72 °C for 90 seconds, with a final extension at 72 °C for 5 minutes. Amplicon concentration was measured using the Qubit 1× HS dsDNA assay (Thermo Fisher Scientific, USA) on a Qubit 2.0 fluorometer, and product size (~1.5 kb) was confirmed by electrophoresis on a 1% agarose gel at 70 V for 30 minutes.

The second PCR step introduced sample-specific barcodes using the PCR Barcoding Expansion 1–96 Kit (EXP-PBC096; Oxford Nanopore Technologies, UK). Thermal cycling conditions were: initial denaturation at 95 °C for 3 minutes; 15 cycles of 95 °C for 15 seconds, 62 °C for 15 seconds, and 65 °C for 90 seconds; and a final extension at 65 °C for 5 minutes. Barcoded amplicons (~1.6 kb) were verified by gel electrophoresis, and DNA concentrations were re-measured using the Qubit assay (ThermoFisher Scientific, USA).

Barcoded libraries were purified using AMPure XP beads (Beckman Coulter, USA) and visualized on a 1% agarose gel to confirm size and integrity. Samples were pooled in equimolar concentrations based on Qubit quantification. The pooled library was further assessed using the Agilent TapeStation D5000 assay (Agilent Technologies, USA). End repair and clean-up were performed with the NEBNext Ultra II End Repair Kit (New England Biolabs, USA), followed by AMPure XP purification. Adapter ligation was conducted using the Oxford Nanopore SQK-LSK110 and SQK-LSK114 ligation kits, following the manufacturer’s instructions. Final libraries were loaded at 20 fmol and 50 fmol onto MinION flow cells R9.4.1 and R10.4.1, respectively. Sequencing was performed on the MinION platform (Oxford Nanopore Technologies, UK) for 72 hours.

2.5 Bioinformatic processing of sequencing data

Raw sequencing data were generated in POD5 format, the native output of the Oxford Nanopore Technologies (ONT) MinION platform, which retains signal-level information for downstream processing. Basecalling was performed using DORADO (v0.5.1+a7fb3e3) (Samarakoon et al., 2023) which uses neural network algorithms to decode raw electrical signals into nucleotide sequences, identifying both canonical and modified bases while applying error correction to address the inherent noise of nanopore data. The high-fidelity “sup” (super accuracy) model was used for basecalling: dna_r9.4.1_e8_sup@v3.6 for R9.4.1 flow cells and dna_r10.4.1_e8.2_400bps_sup@v5.0.0 for R10.4.1 flow cells. A single BAM file containing all reads was produced. Demultiplexing into sample-level FASTQ files was also performed using DORADO, with adapter trimming disabled at this stage. Read quality was assessed with FastQC 0.12.1 (Andrews, 2010), and results were summarized across all samples using MultiQC 1.25.2 (Ewels et al., 2016). Per-base Phred scores (Ewing and Green, 1998) indicated suboptimal quality, necessitating additional trimming and filtering.

Subsequent processing followed the Apogee pipeline (Supplementary Figure S3), a modified version of the Spaghetti workflow optimized for full-length 16S rRNA gene nanopore reads (Latorre-Pérez et al., 2021). In brief, adapter trimming was performed with Porechop (Bonenfant et al., 2023), followed by length filtering using NanoFilt 2.8.0 (De Coster et al., 2018) to retain reads between 1,200 and 1,800 bp. Read metrics were summarized with NanoStat 1.6.0 (De Coster et al., 2018). Chimeric reads were identified via all-vs-all alignments using Minimap2 2.28 (Li, 2018) with nanopore-specific settings (-x ava-ont, -f1000 to ensure a minimum mapped fragment length of 1000 base pairs for computational efficiency and accuracy, and a maximum seed distance of 500). The resulting PAF files were processed with yacrd 1.0.0 (Marijon et al., 2020), applying a minimum coverage threshold of 0.4. High-quality reads were then aligned to the SILVA reference database (version 138) using Minimap2 2.28 (Li, 2018). Alignments shorter than 500 bp were excluded using a custom Python script to ensure reliable taxonomic assignment. An amplicon sequence variant (ASV) table was constructed, listing read counts per taxon across samples. ASVs representing less than 1% of total reads were filtered using the filterPAF script in Apogee. A companion taxonomy table mapped ASV identifiers to taxonomic ranks from kingdom to species.

Functional profiling was performed using PICRUSt2 2.6.2 (Douglas et al., 2020), which infers the metabolic potential of microbial communities from 16S rRNA gene data. Compared to PICRUSt1 (Langille et al., 2013), the updated PICRUSt2 pipeline incorporates representative sequences of de novo taxonomic clusters and improves prediction accuracy through Hidden State Prediction and expanded genome databases (Douglas et al., 2020). Predicted functions were annotated using KEGG orthologs and enzyme commission (EC) numbers (Kanehisa and Goto, 2000), enabling insight into putative microbial pathways for future experimental validation.

2.6 Statistical analysis

All statistical analyses were conducted in R (ver. 4.3.2). Alpha diversity indices were calculated using the diversity function from the vegan 2.6.8 package (Oksanen et al., 2025), and true diversity matrices based on Simpson and Shannon indices were calculated as described by Jost (Jost, 2006). Data normality was assessed using the Shapiro-Wilk test. Due to non-normal distributions, generalized linear mixed models (GLMMs) were implemented via the glmmTMB 1.1.10 package (Brooks et al., 2017) which leverages the Template Model Builder (TMB) framework. In the models, factors such as potato variety, field location, and sieving fraction were specified as fixed or random effects depending on the analytical objective. Bonferroni correction was applied to adjust p-values and control for Type I errors.

Beta diversity was assessed using the vegan’s vegdist function with the Robust Aitchison method to generate dissimilarity matrices, which were visualized via principal component analysis (PCA) using the prcomp function. Treatment effects on community composition were tested using PERMANOVA via the adonis2 function, and pairwise comparisons were performed using pairwise.adonis.

To identify core microbiome members of different communities, we applied the abundance-occupancy framework described by Shade and Stopnisek (2019). In this approach, each treatment was considered a distinct ecological unit, and its corresponding replicates were treated as within-treatment samples. Occupancy for each ASV was defined as the proportion of replicates within a treatment in which the ASV was detected (range: 0-1), reflecting its consistency across replicates. Abundance was calculated as the mean relative abundance of each ASV within the treatment. ASVs with an occupancy of 1.0 (i.e., present in all replicates of a treatment) were considered consistently detected and prioritized for further analysis. ASVs were then ranked by both occupancy and abundance to identify those that were both prevalent and abundant, serving as potential core community members. The contribution of these core taxa to overall community structure was evaluated using Bray-Curtis similarity. To assess potential plant-beneficial functions, ASVs were screened against an in-house list of known PGPB compiled from published literature (Chao et al., 2016).

Differential abundance analysis of genera and species was performed using the ANCOM-BC2 package, which accounts for sample- and taxon-specific biases (Lin and Peddada, 2024). Significance was deemed at p = 0.05. Heatmaps were created using pheatmap (Kolde, 2025) to visualize the taxa differences across samples. Venn diagrams were created using ggvenn (Yan, 2025) to show the unique and shared ASVs across treatments.

Fast expectation-maximization microbial source tracking (FEAST) analysis was performed using the FEAST 0.1.0 package (Shenhav et al., 2019) to estimate source contributions to sink samples based on an ASV count matrix data and metadata classifying sources and sinks. The output was a normalized matrix estimating proportional contributions. The analysis provided insight on what proportion of the 25 µm and 75 µm sieving fraction communities are represented by microbial sources associated with diseased tare soil versus unknown sources.

Network analysis was conducted using the Molecular Ecological Network Analysis (MENA) pipeline (http://ieg2.ou.edu/MENA) (Deng et al., 2012) to compare the microbial network between diseased wart and soil samples, and healthy soils. CLR-transformed ASV table and Pearson correlation were used to construct similarity matrices. Random Matrix Theory (RMT) thresholds were applied, and networks were visualized with Gephi 0.10.1 (Bastian et al., 2009). Potential PGPB species identified as the network nodes were evaluated using heatmap visualization and ANCOMBC2 2.6.0 (Lin and Peddada, 2024). Species that were significantly abundant in diseased communities were selected. Spearman correlation was conducted to assess the relationship between these species and S. endobioticum abundance quantified by qPCR.

Functional predictions from PICRUSt2 were analyzed using DESeq2 1.44.0 (Love et al., 2014), and significantly enriched KEGG orthologs were passed to clusterProfiler 4.12.6 (Yu et al., 2012) for gene set enrichment using the gseKEGG function. Human-related pathways were removed, and enriched functions were visualized using cnetplot in clusterProfiler 4.12.6 (Yu et al., 2012) and pathview 1.44.0 (Luo and Brouwer, 2013).

3 Results and discussion

3.1 Summary of sequencing reads

Nanopore sequencing generated approximately 24 million reads of the full length 16S rRNA gene region, yielding a total of 20 Gb of data across all samples. The average N50 was 1.6kb, and after error correction using the Apogee pipeline, high-quality reads had an average length of 1.5 kb. Median Phred quality scores reached 38, corresponding to a raw basecalling accuracy of 99.98%. In total, 3,995,215 reads and 69,333 amplicon sequence variants (ASVs) were recovered. Per sample, this corresponded to an average of 53,269 ± 22,391 reads and 4,837 ± 3,324 ASVs. Across all samples, 54 bacterial phyla, 140 classes, 356 orders, 636 families, 1,990 genera, and 4,651 species were identified (Supplementary Table S4). The sequencing files corresponding to the negative control (water) contained no detectable sequences, confirming that no contamination occurred during library preparation (Supplementary Figure S4).

3.2 Whole genome amplification preserved dominant taxa but introduced bias in rare taxon recovery

We first evaluated WGA-associated bias using a commercial mock bacterial community and eDNA from potato wart-infected soil collected in Newfoundland and Labrador. Canadian samples were used due to limited DNA availability from the Netherlands.

All members of the mock community (ZymoBIOMICS D6305) were recovered from Cytiva-kit-amplified samples, including the least abundant species, Pseudomonas aeruginosa (theoretical abundance 0.4%), although it was detected at a reduced relative abundance (0.1%). In contrast, P. aeruginosa was not detected in samples amplified with the REPLI-g kit (Figure 1a, Supplementary Table S5). The relative abundances of other community members also deviated from the expected composition in both amplification treatments. Based on its more complete recovery of mock community taxa, the Cytiva kit was selected for all subsequent eDNA amplification in this study.

Figure 1

Figure 1. Effect of whole genome amplification on microbial recovery from mock and environmental samples. (a) Relative abundance profiles of eight bacterial species in the ZymoBIOMICS D6305 mock community under different treatments: Non-WGA, WGA using the Cytiva GenomiPhi kit, and WGA using the REPLI-g kit, compared against the theoretical composition. The Cytiva kit recovered all expected taxa, including low-abundance Pseudomonas aeruginosa, whereas REPLI-g failed to detect this species. (b) Alpha diversity metrics (Shannon true diversity, Simpson true diversity, and Chao1 richness index) for potato wart-infected soil samples, showing significantly reduced diversity in WGA-treated samples (p < 0.05). (c) Principal component analysis (PCA) of CLR-transformed ASV tables reveals distinct community composition between Non-WGA and WGA treatments (p < 0.05, PERMANOVA). (d) Venn diagrams comparing ASV overlap between Non-WGA and WGA samples before (left) and after (right) filtering out low-abundance ASVs (<10 reads). Without filtering, only 30.6% of ASVs were shared, accounting for 54% and 94% of total relative abundance in Non-WGA and WGA samples, respectively. After filtering, 74.6% of ASVs were shared, representing 65% and 97% of total relative abundance, indicating WGA preserved dominant taxa but distorted overall diversity and rare taxon representation.

To further evaluate WGA-induced bias, paired non-WGA and Cytiva-WGA samples from potato wart-infected soils (n = 7 each) were compared. Non-WGA samples exhibited significantly higher alpha diversity (Simpson- and Shannon-based true diversity, Chao1 index; p < 0.05; Figure 1b), and PCA on CLR-transformed ASV tables revealed distinct community compositions between WGA and non-WGA treatments (p < 0.05, PERMANOVA; Figure 1c). By filtering low-abundance ASVs (<10 reads), 74.6% of ASVs were shared between WGA and non-WGA treatments, accounting for over 94% of total relative abundance in WGA samples, suggesting dominant taxa were reliably recovered (Figure 1d).

Although WGA preserved overall community structure, it introduced detectable bias affecting diversity estimates and low-abundance taxa detection. Unique ASVs in WGA samples contributed just 3-6% of total abundance but may reflect inflation of rare taxa or artifacts, despite low-abundance. Both Cytiva and REPLI-g kits employ multiple displacement amplification, which reduces bias compared to PCR-based methods but remains susceptible to representation errors due to stochastic primer binding and template accessibility, particularly in low-input samples (Raghunathan et al., 2005; Abulencia et al., 2006; Marine et al., 2014; Babayan et al., 2016; Gawad et al., 2016; Biezuner et al., 2021).

In addition to WGA-related distortions, potential biases introduced by Oxford Nanopore Technologies (ONT) sequencing must also be considered. ONT platforms are known to exhibit GC and/or AT content-dependent sequencing efficiency, variable ligation efficiency during library preparation, and basecalling errors, particularly in homopolymer regions (Chen et al., 2025). While recent advances in basecalling algorithms (e.g., Dorado super-accuracy models) have significantly improved read accuracy (Kuśmirek, 2023), error propagation in long reads can still affect taxonomic resolution and diversity estimates if not adequately corrected. These platform-specific limitations, combined with WGA biases, underscore the need for cautious interpretation of low-abundance taxa and alpha diversity metrics in WGA-treated ONT datasets. Where possible, validation using mock communities and read abundance filtering can mitigate some of these effects.

Overall, WGA enabled recovery of dominant bacterial taxa from low-input wart DNA, facilitating community profiling under quarantine constraints. However, it introduced compositional bias that affected rare taxa and alpha diversity estimates. These results emphasize the trade-off between data yield and taxonomic accuracy and the need to validate WGA-treated datasets when interpreting diversity or functional patterns.

3.3 Microbial partitioning between Spieckermann wart tissue fractions reveals physical structuring and taxonomic enrichment

To assess how microbes partition between host-associated and externally attached niches, we analyzed WGA-treated eDNA from Spieckermann bioassay-induced wart lesions to compare microbial communities across 25 µm and 75 µm fractions.

Samples were derived from two field sites in The Netherlands (Mussel and Onstwedde) and three susceptible cultivars (Deodara, Saphir, and Talent), with both fractions processed in parallel. The 75 µm fraction showed significantly higher Shannon-based true diversity (p < 0.05; Figure 2a), likely reflecting the inclusion of rhizoplane- or soil-associated taxa. In contrast, the finer 25 µm fraction harbored fewer taxa but displayed higher inter-sample compositional heterogeneity. No significant differences in Simpson diversity or Chao1 richness were observed, suggesting that variation stemmed primarily from rare taxa rather than shifts in dominant members.

Figure 2

Figure 2. Microbial community differences between 25 and 75 µm sieving fractions of Spieckermann wart tissue. (a) Alpha diversity comparisons (Shannon-based true diversity, Simpson-based true diversity, and Chao1 richness index) reveal significantly higher Shannon diversity in the 75 µm fraction (p < 0.05), with no significant differences for Simpson or Chao1 metrics. (b) Principal component analysis (PCA) of CLR-transformed ASV data shows that sieve size did not significantly influence community structure (PERMANOVA p > 0.05), while cultivar had a significant effect (p = 0.012), with Deodara samples clustering separately from Saphir and Talent. (c, d) Relative abundance of the top 20 genera in the core microbiomes of the 25 µm (c) and 75 µm (d) fractions across samples, showing greater compositional consistency in the finer fraction and higher variability in the coarser fraction. (e, f) Microbial source tracking using FEAST estimates the proportion of each sample’s community originating from (e) diseased tare soil (source) and (f) field-derived warts (both in 75 µm fraction). The 75 µm fraction showed strong soil association, while the 25 µm fraction had a higher proportion of field wart sources, consistent with a more host-associated microbiome.

PCA and PERMANOVA revealed no significant compositional differences between sieve sizes or field sites (p > 0.05; Figure 2b). However, microbial composition differed significantly by cultivar (PERMANOVA p = 0.012), with Deodara samples forming a distinct cluster (pairwise PERMANOVA p < 0.05). This highlights the influence of host genotype such as cultivar-specific susceptibility to S. endobioticum pathotypes (van de Vossenberg et al., 2022b) and pedigree differences (van Berloo et al., 2007) in shaping wart-associated microbiota, even under standardized conditions. In this context, variation in microbial composition may also contribute to differences in the success rate of the Spieckermann bioassay, which has been used for over a century, yet this microbiome aspect has been largely overlooked (van de Vossenberg et al., 2019a). These findings reinforce our overarching hypothesis that S. endobioticum reshapes microbial communities via both pathogen stress and host genetic filtering, in line with broader trends in potato-microbiome-pathogen interactions (Qing et al., 2024; Yang et al., 2024).

Core microbiome analysis identified 237 and 294 ASVs in the 25 µm and 75 µm fractions, respectively, accounting for ~15-16% of total abundance. The 75 µm fraction exhibited greater compositional consistency across cultivars and sites, suggesting environmental buffering and dominance of generalized taxa. In contrast, the 25 µm fraction was more variable, potentially enriched in microbes tightly associated with resting spores or wart internal tissues. Despite these differences, both fractions shared 12 genera of potential biocontrol interest, including Pseudomonas, Bacillus, Klebsiella, Enterobacter, Citrobacter, and Staphylococcus (Figures 2c, d). While not differentially abundant between fractions (ANCOM-BC2, p ≥ 0.05), these genera include known plant-growth-promoting bacteria (PGPB) and biocontrol agents, though care must be taken in inferring function from genus-level resolution.

FEAST-based microbial source tracking revealed that the 75 µm fraction closely resembled diseased tare soils, with >70% of community origins attributed to soil in five of six samples. In contrast, three of six 25 µm samples had >75% of their predicted origin classified as “unknown,” indicating limited soil contribution and stronger host association. Notably, both fractions showed substantial attribution to field wart microbiomes, with the 25 µm fraction slightly more enriched (50-75%) than the 75 µm fraction (30-75%) (Figures 2e, f). These results suggest that finer fractions capture more host-filtered or internalized communities, while coarser fractions retain more transient, environmental taxa.

In summary, the 25 µm and 75 µm fractions represent distinct yet overlapping niches: the finer fraction captures host-associated and endophytic taxa relevant to wart-specific interactions, while the coarser fraction reflects more diverse, soil-derived reservoirs of potential PGPB and suppressive microbes.

3.4 Bioassay-derived wart microbiomes recapitulate diversity but differ in composition from field samples

To test whether Spieckermann bioassays replicate field wart microbiomes, we compared alpha and beta diversity between bioassay- and field-derived 75 µm samples to evaluate the bioassay’s suitability as a controlled model system for microbiome study.

Alpha diversity metrics (Shannon, Simpson, and Chao1) showed no significant differences between field and bioassay samples (p > 0.05; Figure 3a), indicating that overall microbial richness and evenness were comparable across systems. However, PCA revealed significant differences in microbial community composition (p < 0.05; PERMANOVA; Figure 3b). We observed that host genotype had a significant impact on bioassay-derived wart microbiome (Figure 3b); the absence of overlapping host genotypes between field and lab conditions, therefore, limits our ability to resolve whether the observed differences are driven by environmental context (field vs. lab), host genotype, or their interaction. Nevertheless, Figures 2e, f showed that the 75 µm fraction of bioassay-derived wart microbiomes showed strong similarity to diseased tare soils and also retained substantial resemblance to field-grown warts, with 30-75% of its composition attributed the microbiomes of field samples. This dual attribution supports the notion that the 75 µm fraction includes both loosely associated soil taxa and residual host-associated communities.

Figure 3

Figure 3. Alpha and beta diversity of field- and bioassay-derived wart microbiomes in 75 µm fractions. (a) Alpha diversity indices (Shannon-based true diversity, Simpson-based true diversity, and Chao1 richness) show no significant differences between field (FW) and Spieckermann bioassay-derived (SW) wart samples (p > 0.05), indicating similar levels of richness and evenness across systems. (b) Principal component analysis (PCA) of CLR-transformed ASV data reveals significant differences in community composition between FW and SW samples (p < 0.05, PERMANOVA). FW, field-derived; SW, bioassay-derived.

In summary, the bioassay reflects wart-associated diversity but differs compositionally from field samples, likely due to host genotype and environment. Despite these differences, it remains a useful controlled model, and genotype-matched studies are needed to better separate host and environmental effects. The consistent variety effect across systems reinforces host genotype as a key microbiome driver.

3.5 Compartment-aware prioritization of biocontrol candidates: contrasting microbiomes in wart tissues and diseased soils

To assess their potential as biocontrol reservoirs against S. endobioticum, we compared microbiomes of field wart tissues and diseased tare soils (both 75 µm fractions). Wart fractions were enriched with pathogen spores, whereas infected soils contained only trace amounts.

Alpha diversity indices differed insignificantly between wart and tare soil samples (p > 0.05); however, wart samples exhibited greater variability, likely reflecting their heterogeneous origins ranging from fresh to decaying warts collected across different field sites (Supplementary Figure S5a). Beta diversity analysis revealed significant differences in community composition (p < 0.05, PERMANOVA; Supplementary Figure S5b), supporting the existence of compartment-specific microbial assemblages.

Wart tissues hosted more variable and host-associated taxa, including Klebsiella, Enterobacter, Citrobacter, Pantoea, and Serratia, many with known endophytic traits or stress-associated functions such as siderophore production (ko01053), biosynthesis of exopolysaccharides (ko00543), biofilm formation (ko02025), and aromatic compound degradation (ko01220) (Purkayastha et al., 2018; Hennessy et al., 2020; Kharat et al., 2022; Akber et al., 2023; Figure 4c). Klebsiella oxytoca, for example, is known to induce systemic resistance via antioxidant signaling (Elsharkawy et al., 2022a), while Enterobacter spp. had previously recovered in high abundance from potato scabby tubers (Macharoen et al., 2024). These traits are consistent with enhanced microbial persistence, detoxification, and host colonization capacity under stress conditions (Maindad et al., 2014; Khalil et al., 2019).

Figure 4

Figure 4. Taxonomic and functional differences between bacterial communities in field wart tissues and diseased tare soils. (a) Differential abundance of bacterial genera between wart and diseased tare soil samples identified using ANCOM-BC2. Bars indicate log-fold change in centered log-ratio (CLR) transformed abundance, with positive values enriched in wart tissues and negative values enriched in tare soils. (b) Heatmap of CLR-transformed relative abundances of differentially abundant species from both compartments that possess annotated chitin-degradation pathways, including chitinases, chitin deacetylases, and lytic monooxygenases (Supplementary Table S6). (c) Gene set enrichment analysis (GSEA) of KEGG orthologs shows distinct functional enrichments across compartments. Circle size represents -log10(p) values, and direction of enrichment is based on log-fold change: positive values (in blue) indicate enrichment in field wart tissues, while negative values (in red) indicate enrichment in diseased tare soils. Selected pathways are grouped by ecological relevance (e.g., soil-, tuber-proximal, or endophyte-associated functions) based on prior literature.

In contrast, diseased tare soils exhibited more consistent microbial profiles (Supplementary Figure S6) enriched in Bacillus, such as B. acidiceler, B. drentensis, and B. firmus (ANCOM-BC2 analysis, p < 0.05, Figures 4a, b), with known functions in chitin degradation, antimicrobial compound synthesis, and plant growth promotion (Ding et al., 2005; Mahmood et al., 2016; Yousuf et al., 2017; Saxena et al., 2020; Alshammari et al., 2024). For example, the enrichment of nonribosomal peptide biosynthesis pathway (ko01054) (Figure 4c), a hallmark of bioactive compound production, has been found associated with suppressive soils (Roongsawang et al., 2011; Wang et al., 2018; Rabbee et al., 2019; Tracanna et al., 2021). These taxa and functionalities are broadly active against soilborne pathogens and are easier to culture (Zhao et al., 2018; Saxena et al., 2020; Tracanna et al., 2021), but their suppressive potential may be more generalized (Brown et al., 2013; Chen et al., 2013; Alijani et al., 2019; Buchholz et al., 2019; Gao et al., 2020) rather than specifically effective against S. endobioticum.

Notably, PICRUSt2 predicted the presence of chitin degradation pathways in both compartments, highlighting the potential for isolating bacterial taxa capable of targeting the chitin-rich cell walls of S. endobioticum (Hayes et al., 2017; van de Vossenberg et al., 2019b). Warts were enriched in chitin deacetylase (ko01452), putative chitinase (ko03791), and bifunctional chitinase/lysozyme (ko13381), while tare soils were enriched in lytic chitin monooxygenase (ko21713) (Supplementary Table S6), suggesting divergent enzymatic strategies for chitin breakdown.

In summary, wart tissues contained variable, host-associated taxa with stress-adaptive traits, while diseased soils hosted consistent, culturable taxa with broad antagonistic potential. Both showed potential chitinolytic activity, indicating complementary niches for targeting S. endobioticum.

3.6 Soil microbiome legacy and PGPB reservoirs in wart-infested and descheduled fields with implications for long-term wart suppression

We examined diseased and descheduled soils to determine whether long-term microbial legacy and recovery reveal traits linked to wart suppression (fifth research question). Descheduled soils, once infested with S. endobioticum but rendered disease-free after prolonged non-host rotations (EPPO Bulletin, 2017a), provide a unique lens on legacy effects without active infection. In potato wart-infested soils, although spores persist at low levels, their residual pathogenicity implies ongoing host-microbe interactions. Drawing on concepts of keystone pathogen effects (Hajishengallis et al., 2012), microbiome restructuring, and ecological memory, we hypothesized that even minimal pathogen pressure can drive lasting shifts in community structure and function. Our analysis focused on bacterial guilds with potential S. endobioticum-specific or broad-spectrum antagonism.

Corroborating our findings on compartment-specific microbial assemblages from the 25 µm and 75 µm wart fractions, diseased soils processed using the coarser 75 µm sieve exhibited significantly higher alpha diversity than descheduled (healthy) soils processed using the finer 25 µm sieve (Figure 5a). This result must be interpreted with caution due to the non-equivalent sieving fractions. The 75 µm fraction likely retains more root debris and spore-laden particles which can harbor diverse, pathogen-associated microbiota (Hemkemeyer et al., 2018; Dittrich et al., 2024). Most studies have reported higher microbial diversity in healthy soils versus when samples were processed in parallel (Jia et al., 2022; Yang et al., 2024). Descheduled soils, however, displayed higher compositional heterogeneity across samples (Figure 5b), with lower representation in wart-associated microbiomes based on FEAST source-tracking (Supplementary Figure S7). Such observations are, at least partially, attributed to the impacts of various long-term non-host rotation regimes and other field operational practices, which have not yet been disclosed to us due to confidentiality under quarantine regulations. This also highlights another implication of host influence on soil microbiomes (Mészárošová et al., 2024).

Figure 5

Figure 5. Microbial diversity and network structure differ markedly between healthy (HS) and diseased (DS) soil communities. (a) Alpha diversity indices show significantly higher richness and evenness in DS compared to HS samples (p < 0.05), potentially reflecting microbial proliferation under pathogen pressure. (b) Beta diversity based on PCA of CLR-transformed ASV profiles reveals distinct clustering between HS and DS communities (p < 0.05, PERMANOVA), indicating compositional divergence. (c) Co-occurrence networks highlight structural contrasts: the DS network forms dense, highly clustered modules with short path lengths, but shows lower connectedness and modularity, suggesting locally reinforced but ecologically narrow interactions. In contrast, the HS network displays higher modularity, greater connectedness, and more dispersed interactions, reflecting a resilient and functionally compartmentalized microbial ecosystem.

Co-occurrence network analysis revealed stark contrasts in microbial community structure between diseased and descheduled soils (Supplementary Table S7), albeit with caution due to differing sieving fractions (75 µm vs. 25 µm) and field operations. The healthy soil network exhibited greater modularity (0.777), connectedness (0.648), geodesic efficiency (0.988), along with higher betweenness centralization (CB = 0.242), and higher proportion of competitive (negative) correlations which are features of mature, functionally partitioned ecosystems supporting regulatory stability (Coyte et al., 2015). In contrast, the diseased soil network showed dense clustering (avgCC = 0.462), shorter path lengths (GD = 2.459), and near-complete dominance of positive correlations (99.96%), yet had lower connectedness (0.372) and modularity (0.449). These features suggest insular microbial modules with weak regulatory feedback and limited cross-module integration which are conditions that may foster a disrupted, less resilient soil ecosystem. Such contrasting ecological network representations of course is largely influenced by soil physicochemical properties and organic matter, local climate and agronomic practices, and timing of sampling of crop lifecycle, etc (Banerjee et al., 2018; Mandakovic et al., 2018; Pechlivanis et al., 2024; Wei et al., 2024), besides different levels of disease pressures.

From a BCA reservoir perspective, Bacillus was the only genus consistently present in both networks, comprising 24.6-37.8% of all nodes (Figure 5c). Several species, such as B. atrophaeus, B. mojavensis, and B. tequilensis, were enriched in diseased soils and wart tissues (Figure 6a, b) and are known for antifungal activity, ISR induction (Cao et al., 2025), enhanced resource use efficiency (Sdiri Ghidawi et al., 2025), biofilm formation, pathogen suppression (Liu et al., 2020; Xu et al., 2021; Bi et al., 2025), and had shown ability to reduce disease severity by up to 60% across diverse cropping systems (Miljaković et al., 2020; Saxena et al., 2020; Mahapatra et al., 2022; Serrão et al., 2024). Moreover, some Bacillus spp. showed positive correlations with S. endobioticum abundance in field wart samples and diseased tare soils (Figure 6c). The presence of Bacillus spp. in both diseased and descheduled soils reinforces its status as a ubiquitous, beneficial soil taxon (Saxena et al., 2020), suggesting that biocontrol activity may already be occurring in situ and could represent a microbial legacy shaped by prior disease pressure.

Figure 6

Figure 6. Species-level microbial patterns in diseased versus healthy soils, highlighting potential biocontrol taxa. (a) Heatmap of centered log-ratio (CLR) transformed relative abundances of key bacterial species identified from co-occurrence network analysis, stratified by sample type. (b) Relative abundance profiles of significantly enriched Bacillus and Pseudomonas plant growth-promoting bacteria (PGPB) in diseased versus healthy soils. (c) Correlation analysis between the abundance of these potential PGPB species and S. endobioticum levels (represented as ΔCq values from qPCR). Trend lines indicate species-specific association patterns, with several Bacillus spp. showing positive correlations and Pseudomonas trivialis exhibiting a negative relationship.

By contrast, Pseudomonas trivialis was specifically enriched in wart tissues (Figure 6b), suggesting it may be responding to localized stress or plant-derived signals within infected tissue. Despite its known plant growth-promoting and biocontrol properties (Abd El-Aziz and Bashandy, 2019), P. trivialis exhibited a negative correlation with S. endobioticum abundance (Figure 6c). This may be due to the inability of P. trivialis to form endospores unlike Bacillus spp (Beskrovnaya et al., 2021), which may limit its persistence under fluctuating or disease-altered soil conditions. These observations potentially reflect reduced compatibility of P. trivialis with pathogen-dominated microenvironments. However, previous studies suggest that Pseudomonas spp. can be recruited or stabilized by Bacillus-mediated interactions (Sun et al., 2022; Zhang et al., 2023), raising the possibility that co-inoculation strategies may enhance its role in biocontrol or resilience under wart pressure.

Functional prediction showed that nine genes involved in chitin metabolism (padj < 0.05) were found in both diseased and healthy communities, with some contributing taxa being B. drentensis, B. mojavensis, B. tequilensis (Supplementary Tables S8 and Supplementary Figure S8), corroborating with taxonomic recovery. While studies on B. drentensis remains limited, both B. mojavensis and B. tequilinesis have been reported for their ability to produce chitinase or chitosanase in addition to other bioactive compounds (Mounia et al., 2014; Liaqat et al., 2018; Baard et al., 2023; Afzal et al., 2025). The enriched chitin metabolism pathways in disease systems were likely driven by host and soil properties in additional to their response to S. endobioticum, particularly in wart tissues (Garbeva et al., 2011; Supronienė et al., 2023). Pathview analysis revealed that the chitin deacetylase-coding gene K01452 (EC 3.5.1.41) was more abundant in diseased systems than in the descheduled soils (Figures 7a, b). This enzyme hydrolyzes N-acetamido groups in N-acetyl-D-glucosamine residues of chitin, a key step in chitin degradation (Araki and Ito, 1974). The network (Figure 7c) demonstrated the association between the KEGG orthologues involved in chitin metabolism and bacterial ASVs, which predominantly belonged to genera Bacillus, Pseudomonas, and Paenibacillus. Strains of these genera have been studied for their ability to produce chitinase and act as biocontrol agents against phytopathogens (Govindasamy et al., 2010; Elbouazaoui et al., 2022; Dobrzyński and Naziębło, 2024).

Figure 7

Figure 7. Functional prediction of chitin metabolic genes and pathways associated with the diseased and healthy systems. (a) Heatmap displaying differentially abundant genes involved in chitin metabolism across all sample types (p < 0.05). (b) The KEGG orthologues and corresponding protein with enzyme commission (EC) numbers are shown associated with chitin metabolism in the amino sugar and nucleotide sugar metabolism pathway (ko00520). Red and green background of each enzyme represents increased and decreased copy numbers in diseased systems in relation to descheduled soils, respectively. (c) Association network showing the links between KEGG orthologues involved in chitin metabolism and contributing bacterial ASVs coloured by genus.

Together, our results show contrasting taxonomic and functional profiles between diseased and descheduled (healthy) systems. While some differences may reflect sieve size, field practices, or crop histories, diseased compartments (warts and infested soils) were enriched for xenobiotic-degradation pathways and stress-adapted taxa, consistent with microbial responses to pathogen pressure. Descheduled soils exhibited more stabilized network structure and traits linked to generalized suppressiveness. Notably, functions inferred via PICRUSt2 are hypothesis-generating rather than confirmatory; they prioritize targets for isolation and testing. These complementary signatures point to opportunities to isolate BCAs that are either potato-wart-targeted or broadly antagonistic.

4 Conclusion and future perspectives

This study presents the first microbiome characterization of the potato wart disease pathosystem (Synchytrium endobioticum-Solanum tuberosum), a strictly regulated and previously unexplored quarantine system with limited global research access. By integrating wart tissues, tare soils, and descheduled soils, we identified microbiome patterns and potential microbial reservoirs across a gradient of disease pressure, providing essential starting points for hypothesis-driven research into disease suppression and biocontrol. The integration of Whole Genome Amplification (WGA), long-read sequencing, and co-occurrence network and functional prediction analyses establishes an ecological and methodological framework that can guide similar studies under quarantine or biosafety constraints. Collectively, this work advances both the fundamental understanding and applied research capacity for a previously inaccessible pathosystem.

Our central hypothesis, that S. endobioticum infection reshapes the potato-associated microbiome, potentially favoring taxa with biocontrol potential, was partially supported. Consistent enrichment of putative PGPBs such as Bacillus and Pseudomonas spp. suggests possible suppressive roles, though their ecological functions remain unresolved. Network fragmentation in diseased soils and higher modularity in descheduled soils indicate pathogen-driven restructuring, yet causality cannot be inferred. The use of WGA, while necessary for low-input samples, introduced recognized biases that we explicitly acknowledge. Functional inferences drawn from predictive metagenomics such as the use of PICRUSt2 or FAPROTAX (Louca et al., 2016; Douglas et al., 2020) and network topology (Lupatini et al., 2014; Cardona et al., 2016; MatChado et al., 2021) offer valuable leads but require experimental validation, especially given the methodological caveats (e.g., non-uniform sieving fractions and variable field histories) that limit taxonomic comparability. Given the logistical constraints of working with a quarantine pathogen, predictive approaches remain indispensable for hypothesis generation. Our findings identify candidate taxa for targeted isolation and functional testing, forming a foundation for subsequent in vitro, in planta, and field-level biocontrol validation. As such, we view this study as a necessary first step in a broader biovigilance and biocontrol discovery pipeline for potato wart disease, which lays a foundational framework for developing microbiome-informed strategies to manage potato wart disease.

We propose a conceptual model illustrating a gradient of disease pressure and microbiome composition as reservoirs of biocontrol agents (BCAs) (Figure 8). In high-pressure wart compartments, taxa such as Pseudomonas trivialis and Bacillus atrophaeus may contribute to pathogen-specific suppression via endophytic exclusion. Transitional niches, including geocaulosphere (pre-harvest) and tare (post-harvest) soils, harbor plant-interactive taxa (e.g., Bacillus tequilensis and B. mojavensis) linked to host-mediated microbial recruitment (Roquigny et al., 2018; Shi et al., 2025). These systems are also enriched in xenobiotic and aromatic compound degradation pathways. In contrast, long-term descheduled soils, typically under non-host rotations, harbor broad-spectrum BCAs such as B. drentensis, contributing to general soil suppressiveness through enhanced sensing, nutrient competition, and resilience. Together, these gradients illustrate the ecological plasticity of BCA reservoirs shaped disease history and management practices.

Figure 8

Figure 8. Conceptual model of potato wart gradient and associated microbiomes as biocontrol agent reservoirs. The potato wart compartment (left) represents high disease pressure, enabling intimate interaction between Synchytrium endobioticum and its associated microbiota. This niche is enriched in specialized taxa such as Pseudomonas trivialis and Bacillus atrophaeus, which may contribute to pathogen-specific suppression via mechanisms like endophytic exclusion. The tuber-proximal soils harbor transitional microbial structuring and functional signatures, enriched in plant-interactive taxa such as Bacillus tequilensis and B. mojavensis that may offer both generalist and targeted BCA activity, indicative of microbial recruitment influenced by the host-pathogen interface. These diseased systems are also enriched in xenobiotic and aromatic compound degradation pathways. At the opposite end of the gradient, descheduled soils (right), are typically managed under long-term non-host crop rotations (10–20 years) and harbor a legacy microbiome dominated by broad-spectrum BCAs such as Bacillus drentensis. These wart-free systems support soil-borne resilience and general disease suppression through enhanced environmental sensing, nutrient requisition, and microbial competition. Together, this gradient highlights the functional diversity and ecological plasticity of BCA reservoirs shaped by potato wart disease history and field management practices.

While this study focused on bacterial communities, a complete understanding of potato wart suppression requires integrating fungi, oomycetes, archaea, and protists. Cross-kingdom interactions influence both disease progression and suppression, for instance, antagonism between Pseudomonas fluorescens and Rhizoctonia solani via iron competition (Elsharkawy et al., 2022b), or synergistic bacterial-fungal effects on plant immunity and nutrient uptake (Ansari et al., 2024). Fungal endophytes such as Cladosporium and Trichoderma spp. have also been associated with variable pathogen resistance in potatoes (Niyokwizigirwa, 2022), Future multi-kingdom metabarcoding or shotgun metagenomics will be essential to disentangle these cross-domain interactions and develop a holistic view of the phytomicrobiome influencing pathogen persistence and host resilience.

We are continuing to investigate microbiome dynamics under disease-conducive and suppressive conditions, as well as under targeted interventions that modify soil microbiota. These studies will employ statistically robust experimental designs and standardized sampling across field types, coupled with mechanistic assays and in vitro/in planta validation of candidate PGPB. We will continue addressing technical caveats such as WGA bias to preserve original microbial signals. Only through this integrative, hypothesis-driven approach can we transition from ecological inference to actionable biocontrol strategies for sustainable potato wart management.

Data availability statement

Raw sequencing data of the full length 16S rRNA gene are available under BioProject PRJNA1300411, Biosample accession SAMN50379165 - SAMN50379243.

Author contributions

IA: Writing – original draft, Writing – review & editing, Investigation, Methodology, Software, Visualization. YS: Methodology, Software, Writing – original draft, Writing – review & editing. BV: Resources, Data curation, Methodology, Writing – review & editing. TL: Resources, Data curation, Methodology, Writing – review & editing. LY: Methodology, Writing – review & editing. XL: Writing – review & editing, Resources. JC: Software, Writing – review & editing. LJ: Resources, Writing – review & editing. HN: Resources, Writing – review & editing. WC: Conceptualization, Resources, Data curation, Methodology, Software, Writing - review & editing, Supervision, Project administration.

Funding

The author(s) declare financial support was received for the research and/or publication of this article. This study was funded by the following grants: APMS for Potato Wart (J-002905); Fungal and Bacterial Biosystematics (J-002272); Ontario Graduate Scholarship (OGS).

Acknowledgments

We thank Dr. Sarah Hambleton and Julie Carey, as well as the Molecular Technology Laboratory (MTL) at the Ottawa Research & Development Centre, Agriculture and Agri-Food Canada (AAFC), for sharing laboratory resources, providing training, and assisting with Nanopore library preparation and sequencing. We thank Ms. Shelly Bancud for optimizing the Nanopore sequencing protocol, and to colleagues in the co-authors’ teams for conducting field sampling and wet-lab work. We acknowledge the support of the Canadian federal government’s Science Data Centre of Excellence team, the Science Data Storage Solution (SDSS), and the General-Purpose Science Cluster (GPSC) for providing the computational infrastructure for this study.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that no Generative AI was used in the creation of this manuscript.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2025.1707759/full#supplementary-material

References

Abd El-Aziz, F. E.-Z. A. and Bashandy, S. R. (2019). Dose-dependent effects of Pseudomonas trivialis rhizobacteria and synergistic growth stimulation effect with earthworms on the common radish. Rhizosphere 10, 100156. doi: 10.1016/j.rhisph.2019.100156

Crossref Full Text | Google Scholar

Abulencia, C. B., Wyborski, D. L., Garcia, J. A., Podar, M., Chen, W., Chang, S. H., et al. (2006). Environmental whole-genome amplification to access microbial populations in contaminated sediments. Appl. Environ. Microbiol. 72, 3291–3301. doi: 10.1128/AEM.72.5.3291-3301.2006

PubMed Abstract | Crossref Full Text | Google Scholar

Afzal, M., Tan, X., Fang, M. L., Zeng, D., Huang, M., Chen, X., et al. (2025). Chitosan-silver nanoparticles and Bacillus tequilensis modulate antioxidant pathways and microbiome dynamics for sheath blight resistance in rice. Plant Stress 17, 100917. doi: 10.1016/j.stress.2025.100917

Crossref Full Text | Google Scholar

Agyabeng-Dadzie, F., Beaudry, M. S., Deyanov, A., Slanis, H., Duong, M. Q., Turner, R., et al. (2025). Evaluating the benefits and limits of multiple displacement amplification with whole-genome oxford nanopore sequencing. Mol. Ecol. Resour. 25, e14094. doi: 10.1111/1755-0998.14094

PubMed Abstract | Crossref Full Text | Google Scholar

Akber, M. A., Mubeen, M., Sohail, M. A., Khan, S. W., Solanki, M. K., Khalid, R., et al. (2023). Global distribution, traditional and modern detection, diagnostic, and management approaches of Rhizoctonia solani associated with legume crops. Front. Microbiol. 13. doi: 10.3389/fmicb.2022.1091288

PubMed Abstract | Crossref Full Text | Google Scholar

Alijani, Z., Amini, J., Ashengroph, M., and Bahramnejad, B. (2019). Antifungal activity of volatile compounds produced by Staphylococcus sciuri strain MarR44 and its potential for the biocontrol of Colletotrichum nymphaeae, causal agent strawberry anthracnose. Int. J. Food Microbiol. 307, 108276. doi: 10.1016/j.ijfoodmicro.2019.108276

PubMed Abstract | Crossref Full Text | Google Scholar

Alshammari, W. B., Alshammery, K., Lotfi, S., Altamimi, H., Alshammari, A., Al-Harbi, N. A., et al. (2024). Improvement of morphophysiological and anatomical attributes of plants under abiotic stress conditions using plant growth-promoting bacteria and safety treatments. PeerJ 12, e17286. doi: 10.7717/peerj.17286

PubMed Abstract | Crossref Full Text | Google Scholar

Alteio, L. V., Séneca, J., Canarini, A., Angel, R., Jansa, J., Guseva, K., et al. (2021). A critical perspective on interpreting amplicon sequencing data in soil ecological research. Soil Biol. Biochem. 160, 108357. doi: 10.1016/j.soilbio.2021.108357

Crossref Full Text | Google Scholar

Andrews, S. (2010). FastQC: A Quality Control Tool for High Throughput Sequence Data. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (Accessed June 13, 2025).

Google Scholar

Ansari, M. M., Bisht, N., Singh, T., and Chauhan, P. S. (2024). Symphony of survival: Insights into cross-talk mechanisms in plants, bacteria, and fungi for strengthening plant immune responses. Microbiol. Res. 285, 127762. doi: 10.1016/j.micres.2024.127762

PubMed Abstract | Crossref Full Text | Google Scholar

Araki, Y. and Ito, E. (1974). A pathway of chitosan formation in Mucor rouxiiMucor rouxii: Enzymatic deacetylation of chitin. Biochem. Biophys. Res. Commun. 56, 669–675. doi: 10.1016/0006-291X(74)90657-3

PubMed Abstract | Crossref Full Text | Google Scholar

Baard, V., Bakare, O. O., Daniel, A. I., Nkomo, M., Gokul, A., Keyster, M., et al. (2023). Biocontrol Potential of Bacillus subtilis and Bacillus tequilensis against Four Fusarium Species. Pathogens 12, 254. doi: 10.3390/pathogens12020254

PubMed Abstract | Crossref Full Text | Google Scholar

Baayen, R. P., Cochius, G., Hendriks, H., Meffert, J. P., Bakker, J., Bekker, M., et al. (2006). History of potato wart disease in Europe – a proposal for harmonisation in defining pathotypes. Eur. J. Plant Pathol. 116, 21–31. doi: 10.1007/s10658-006-9039-y

Crossref Full Text | Google Scholar

Babayan, A., Alawi, M., Gormley, M., Müller, V., Wikman, H., McMullin, R. P., et al. (2016). Comparative study of whole genome amplification and next generation sequencing performance of single cancer cells. Oncotarget 8, 56066–56080. doi: 10.18632/oncotarget.10701

PubMed Abstract | Crossref Full Text | Google Scholar

Banerjee, S., Schlaeppi, K., and van der Heijden, M. G. A. (2018). Keystone taxa as drivers of microbiome structure and functioning. Nat. Rev. Microbiol. 16, 567–576. doi: 10.1038/s41579-018-0024-1

PubMed Abstract | Crossref Full Text | Google Scholar

Barnett, S. J., Roget, D. K., and Ryder, M. H. (2006). Suppression of Rhizoctonia solani AG-8 induced disease on wheat by the interaction between Pantoea, Exiguobacterium, and Microbacteria. Soil Res. 44, 331–342. doi: 10.1071/SR05113

Crossref Full Text | Google Scholar

Bastian, M., Heymann, S., and Jacomy, M. (2009). Gephi: An Open Source Software for Exploring and Manipulating Networks. Proceedings of the International AAAI Conference on Web and Social Media3, 361–362. doi: 10.1609/icwsm.v3i1.13937

Crossref Full Text | Google Scholar

Beskrovnaya, P., Sexton, D. L., Golmohammadzadeh, M., Hashimi, A., and Tocheva, E. I. (2021). Structural, metabolic and evolutionary comparison of bacterial endospore and exospore formation. Front. Microbiol. 12. doi: 10.3389/fmicb.2021.630573

PubMed Abstract | Crossref Full Text | Google Scholar

Bi, Q., Lu, F., Wu, J., Liu, X., Han, X., and Zhao, J. (2025). The control effect and induced disease resistance mechanism of Bacillus tequilensis on wheat powdery mildew. Biol. Control 201, 105698. doi: 10.1016/j.biocontrol.2025.105698

Crossref Full Text | Google Scholar

Biezuner, T., Raz, O., Amir, S., Milo, L., Adar, R., Fried, Y., et al. (2021). Comparison of seven single cell whole genome amplification commercial kits using targeted sequencing. Sci. Rep. 11, 17171. doi: 10.1038/s41598-021-96045-9

PubMed Abstract | Crossref Full Text | Google Scholar

Bonenfant, Q., Noé, L., and Touzet, H. (2023). Porechop_ABI: discovering unknown adapters in Oxford Nanopore Technology sequencing reads for downstream trimming. Bioinf. Adv. 3, vbac085. doi: 10.1093/bioadv/vbac085

PubMed Abstract | Crossref Full Text | Google Scholar

Brooks, M., Kristensen, K., Benthem, K. ,. J., van Magnusson, A., C., W., Nielsen, A., et al. (2017). glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378. doi: 10.32614/RJ-2017-066

Crossref Full Text | Google Scholar

Brown, S., Santa Maria, J. P., and Walker, S. (2013). Wall teichoic acids of gram-positive bacteria. Annu. Rev. Microbiol. 67, 10.1146/annurev-micro-092412–155620. doi: 10.1146/annurev-micro-092412-155620

PubMed Abstract | Crossref Full Text | Google Scholar

Buchholz, F., Antonielli, L., Kostić, T., Sessitsch, A., and Mitter, B. (2019). The bacterial community in potato is recruited from soil and partly inherited across generations. PloS One 14, e0223691. doi: 10.1371/journal.pone.0223691

PubMed Abstract | Crossref Full Text | Google Scholar

Çakir, E. and Demirci, F. (2023). Control of potato wart disease (Synchytrium endobioticum) by fungicide soil treatments. Interdepartmental Thematic Sci. Collection Phytosanitary Saf. 301–313, 301–313. doi: 10.36495/PHSS.2023.69.301-313

Crossref Full Text | Google Scholar

Cao, J., Ma, Y., Fu, J., Wang, Z., Zhao, Y., Zhong, N., et al. (2025). Bacillus atrophaeus DX-9 biocontrol against potato common scab involves significant changes in the soil microbiome and metabolome. aBIOTECH 6, 33–49. doi: 10.1007/s42994-025-00199-3

PubMed Abstract | Crossref Full Text | Google Scholar

Cardona, C., Weisenhorn, P., Henry, C., and Gilbert, J. A. (2016). Network-based metabolic analysis and microbial community modeling. Curr. Opin. Microbiol. 31, 124–131. doi: 10.1016/j.mib.2016.03.008

PubMed Abstract | Crossref Full Text | Google Scholar

Chao, Y., Liu, W., Chen, Y., Chen, W., Zhao, L., Ding, Q., et al. (2016). Structure, variation, and co-occurrence of soil microbial communities in abandoned sites of a rare earth elements mine. Environ. Sci. Technol. 50, 11481–11490. doi: 10.1021/acs.est.6b02284

PubMed Abstract | Crossref Full Text | Google Scholar

Chen, W., Modi, D., and Picot, A. (2023). Soil and phytomicrobiome for plant disease suppression and management under climate change: A review. Plants 12, 2736. doi: 10.3390/plants12142736

PubMed Abstract | Crossref Full Text | Google Scholar

Chen, Z., Ong, C. T., Nguyen, L. T., Lamb, H. J., González-Recio, O., Gutiérrez-Rivas, M., et al. (2025). Biases from Oxford Nanopore library preparation kits and their effects on microbiome and genome analysis. BMC Genomics 26, 504. doi: 10.1186/s12864-025-11649-z

PubMed Abstract | Crossref Full Text | Google Scholar

Chen, Y., Yan, F., Chai, Y., Liu, H., Kolter, R., Losick, R., et al. (2013). Biocontrol of tomato wilt disease by acillus subtilis isolates from natural environments depends on conserved genes mediating biofilm formation. Environ. Microbiol. 15, 848–864. doi: 10.1111/j.1462-2920.2012.02860.x

PubMed Abstract | Crossref Full Text | Google Scholar

Coyte, K. Z., Schluter, J., and Foster, K. R. (2015). The ecology of the microbiome: Networks, competition, and stability. Science 350, 663–666. doi: 10.1126/science.aad2602

PubMed Abstract | Crossref Full Text | Google Scholar

Curtis, K. M. and Blackman, V. H. (1921). IX.— The life-history and cytology of synchytrium endobioticum (schilb.), perc., the cause of wart disease in potato. Philos. Trans. R. Soc. London Ser. B Containing Papers Biol. Character 210, 409–478. doi: 10.1098/rstb.1921.0009

Crossref Full Text | Google Scholar

De Corato, U. (2020). Soil microbiota manipulation and its role in suppressing soil-borne plant pathogens in organic farming systems under the light of microbiome-assisted strategies. Chem. Biol. Technol. Agric. 7, 17. doi: 10.1186/s40538-020-00183-7

Crossref Full Text | Google Scholar

De Coster, W., D’Hert, S., Schultz, D. T., Cruts, M., and Van Broeckhoven, C. (2018). NanoPack: visualizing and processing long-read sequencing data. Bioinformatics 34, 2666–2669. doi: 10.1093/bioinformatics/bty149

PubMed Abstract | Crossref Full Text | Google Scholar

Deng, Y., Jiang, Y.-H., Yang, Y., He, Z., Luo, F., and Zhou, J. (2012). Molecular ecological network analyses. BMC Bioinf. 13, 113. doi: 10.1186/1471-2105-13-113

PubMed Abstract | Crossref Full Text | Google Scholar

Ding, Y., Wang, J., Liu, Y., and Chen, S. (2005). Isolation and identification of nitrogen-fixing bacilli from plant rhizospheres in Beijing region. J. Appl. Microbiol. 99, 1271–1281. doi: 10.1111/j.1365-2672.2005.02738.x

PubMed Abstract | Crossref Full Text | Google Scholar

Dittrich, F., Klaes, B., Brandt, L., Groschopf, N., and Thiele-Bruhn, S. (2024). The stonesphere in agricultural soils: A microhabitat associated with rock fragments bridging rock and soil. Eur. J. Soil Sci. 75, e70025. doi: 10.1111/ejss.70025

Crossref Full Text | Google Scholar

Dobrzyński, J. and Naziębło, A. (2024). Paenibacillus as a biocontrol agent for fungal phytopathogens: is P. polymyxa the only one worth attention? Microb. Ecol. 87, 1–14. doi: 10.1007/s00248-024-02450-8

PubMed Abstract | Crossref Full Text | Google Scholar

dos Santos, H. R. M., Argolo, C. S., Argôlo-Filho, R. C., and Loguercio, L. L. (2019). A 16S rDNA PCR-based theoretical to actual delta approach on culturable mock communities revealed severe losses of diversity information. BMC Microbiol. 19, 74. doi: 10.1186/s12866-019-1446-2

PubMed Abstract | Crossref Full Text | Google Scholar

Douglas, G. M., Maffei, V. J., Zaneveld, J. R., Yurgel, S. N., Brown, J. R., Taylor, C. M., et al. (2020). PICRUSt2 for prediction of metagenome functions. Nat. Biotechnol. 38, 685–688. doi: 10.1038/s41587-020-0548-6

PubMed Abstract | Crossref Full Text | Google Scholar

Duan, T., Li, Z., Han, X., Hong, Q., and Yang, Y. (2024). Changes functional prediction of ear canal flora in chronic bacterial otitis externa. Front. Cell. Infect. Microbiol. 14. doi: 10.3389/fcimb.2024.1434754

PubMed Abstract | Crossref Full Text | Google Scholar

Elbouazaoui, A., Sijilmassi, B., Maafa, I., Allal, D., and Ahmed, S. (2022). Biocontrol activity of Bacillus, Paenibacillus and Pseudomonas against Fusarium wilt of chickpea in Morocco. Acta Agric. Scand. Sect. B — Soil Plant Sci. 72, 847–859. doi: 10.1080/09064710.2022.2100819

Crossref Full Text | Google Scholar

El-kazzaz, M. K., Ghoneim, K. E., Agha, M. K. M., Helmy, A., Behiry, S. I., Abdelkhalek, A., et al. (2022). Suppression of Pepper Root Rot and Wilt Diseases Caused by Rhizoctonia solani and Fusarium oxysporum. Life 12, 587. doi: 10.3390/life12040587

PubMed Abstract | Crossref Full Text | Google Scholar

Elsharkawy, M. M., Alotibi, F. O., Al-Askar, A. A., Adnan, M., Kamran, M., Abdelkhalek, A., et al. (2022a). Systemic Resistance Induction of Potato and Tobacco Plants against Potato Virus Y by Klebsiella oxytoca. Life 12, 1521. doi: 10.3390/life12101521

PubMed Abstract | Crossref Full Text | Google Scholar

Elsharkawy, M. M., Sakran, R. M., Ahmad, A. A., Behiry, S. I., Abdelkhalek, A., Hassan, M. M., et al. (2022b). Induction of systemic resistance against sheath blight in rice by different pseudomonas isolates. Life 12, 349. doi: 10.3390/life12030349

PubMed Abstract | Crossref Full Text | Google Scholar

EPPO Bulletin (2017a). PM 3/59 (3) Synchytrium endobioticum: descheduling of previously infested plots. EPPO Bull. 47, 366–368. doi: 10.1111/epp.12409

Crossref Full Text | Google Scholar

EPPO Bulletin (2017b). PM 7/28 (2) synchytrium endobioticum. EPPO Bull. 47, 420–440. doi: 10.1111/epp.12441

Crossref Full Text | Google Scholar

Ewels, P., Magnusson, M., Lundin, S., and Käller, M. (2016). MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32, 3047–3048. doi: 10.1093/bioinformatics/btw354

PubMed Abstract | Crossref Full Text | Google Scholar

Ewing, B. and Green, P. (1998). Base-calling of automated sequencer traces using phred. II. Error probabilities. Genome Res. 8, 186–194. doi: 10.1101/gr.8.3.186

PubMed Abstract | Crossref Full Text | Google Scholar

Gao, Y., Zeng, X. D., Ren, B., Zeng, J. R., Xu, T., Yang, Y. Z., et al. (2020). Antagonistic activity against rice blast disease and elicitation of host-defence response capability of an endophytic Streptomyces albidoflavus OsiLf-2. Plant Pathol. 69, 259–271. doi: 10.1111/ppa.13118

Crossref Full Text | Google Scholar

Garbeva, P., Hol, W. H. G., Termorshuizen, A. J., Kowalchuk, G. A., and de Boer, W. (2011). Fungistasis and general soil biostasis – A new synthesis. Soil Biol. Biochem. 43, 469–477. doi: 10.1016/j.soilbio.2010.11.020

Crossref Full Text | Google Scholar

Gawad, C., Koh, W., and Quake, S. R. (2016). Single-cell genome sequencing: current state of the science. Nat. Rev. Genet. 17, 175–188. doi: 10.1038/nrg.2015.16

PubMed Abstract | Crossref Full Text | Google Scholar

Government of Canada, C. F. I. (2012).About potato wart. Available online at: http://inspection.Canada.ca/en/plant-health/invasive-species/plant-diseases/potato-wart/fact-sheet (Accessed April 25, 2025).

Google Scholar

Govindasamy, V., Senthilkumar, M., Magheshwaran, V., Kumar, U., Bose, P., Sharma, V., et al. (2010). “Bacillus and paenibacillus spp.: potential PGPR for sustainable agriculture,” in Plant Growth and Health Promoting Bacteria (Springer, Berlin, Heidelberg), 333–364. doi: 10.1007/978-3-642-13612-2_15

Crossref Full Text | Google Scholar

Hajishengallis, G., Darveau, R. P., and Curtis, M. A. (2012). The keystone-pathogen hypothesis. Nat. Rev. Microbiol. 10, 717–725. doi: 10.1038/nrmicro2873

PubMed Abstract | Crossref Full Text | Google Scholar

Hampson, M. C. (1988). Control of potato wart disease through the application of chemical soil treatments: a historical review of early studies, (1909-1928)1. EPPO Bull. 18, 153–161. doi: 10.1111/j.1365-2338.1988.tb00360.x

Crossref Full Text | Google Scholar

Hampson, M. (1993). History, biology, and control of potato wart disease in Canada. Can. J. Plant Pathol. 15, 223–244. doi: 10.1080/07060669309501918

Crossref Full Text | Google Scholar

Hampson, M. C. and Coombes, J. W. (1995). Reduction of potato wart disease with crushed crabshell: suppression or eradication? Can. J. Plant Pathol. 17, 69–74. doi: 10.1080/07060669509500721

Crossref Full Text | Google Scholar

Hannula, S. E., Heinen, R., Huberty, M., Steinauer, K., De Long, J. R., Jongen, R., et al. (2021). Persistence of plant-mediated microbial soil legacy effects in soil and inside roots. Nat. Commun. 12, 5686. doi: 10.1038/s41467-021-25971-z

PubMed Abstract | Crossref Full Text | Google Scholar

Hawkins, T. L., Detter, J. C., and Richardson, P. M. (2002). Whole genome amplification — applications and advances. Curr. Opin. Biotechnol. 13, 65–67. doi: 10.1016/S0958-1669(02)00286-0

PubMed Abstract | Crossref Full Text | Google Scholar

Hayes, C. A., Dalia, T. N., and Dalia, A. B. (2017). Systematic genetic dissection of chitin degradation and uptake in Vibrio cholerae. Environ. Microbiol. 19, 4154–4163. doi: 10.1111/1462-2920.13866

PubMed Abstract | Crossref Full Text | Google Scholar

Hemkemeyer, M., Dohrmann, A. B., Christensen, B. T., and Tebbe, C. C. (2018). Bacterial preferences for specific soil particle size fractions revealed by community analyses. Front. Microbiol. 9. doi: 10.3389/fmicb.2018.00149

PubMed Abstract | Crossref Full Text | Google Scholar

Hennessy, R. C., Dichmann, S. I., Martens, H. J., Zervas, A., and Stougaard, P. (2020). Serratia inhibens sp. nov., a new antifungal species isolated from potato (Solanum tuberosum). Int. J. Syst. Evol. Microbiol. 70, 4204–4211. doi: 10.1099/ijsem.0.004270

PubMed Abstract | Crossref Full Text | Google Scholar

Jayaraman, S., Naorem, A. K., Lal, R., Dalal, R. C., Sinha, N. K., Patra, A. K., et al. (2021). Disease-suppressive soils—Beyond food production: a critical review. J. Soil Sci. Plant Nutr. 21, 1437–1465. doi: 10.1007/s42729-021-00451-x

PubMed Abstract | Crossref Full Text | Google Scholar

Jia, M., Sun, X., Chen, M., Liu, S., Zhou, J., and Peng, X. (2022). Deciphering the microbial diversity associated with healthy and wilted Paeonia suffruticosa rhizosphere soil. Front. Microbiol. 13. doi: 10.3389/fmicb.2022.967601

PubMed Abstract | Crossref Full Text | Google Scholar

Jost, L. (2006). Entropy and diversity. Oikos 113, 363–375. doi: 10.1111/j.2006.0030-1299.14714.x

Crossref Full Text | Google Scholar

Kanehisa, M. and Goto, S. (2000). KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30. doi: 10.1093/nar/28.1.27

PubMed Abstract | Crossref Full Text | Google Scholar

Khalil, M., Lerat, S., Beaudoin, N., and Beaulieu, C. (2019). The Plant Pathogenic Bacterium Streptomyces scabies Degrades the Aromatic Components of Potato Periderm via the β-Ketoadipate Pathway. Front. Microbiol. 10. doi: 10.3389/fmicb.2019.02795

PubMed Abstract | Crossref Full Text | Google Scholar

Kharat, B. A., Said, M. S., and Dastager, S. G. (2022). Antifungal compound from marine Serratia marcescens BKACT and its potential activity against Fusarium sp. Int. Microbiol. 25, 851–862. doi: 10.1007/s10123-022-00268-3

PubMed Abstract | Crossref Full Text | Google Scholar

Kolde, R. (2025). pheatmap: Pretty Heatmaps. https://github.com/raivokolde/pheatmap.

Google Scholar

Kuśmirek, W. (2023). Estimated nucleotide reconstruction quality symbols of basecalling tools for oxford nanopore sequencing. Sensors 23, 6787. doi: 10.3390/s23156787

PubMed Abstract | Crossref Full Text | Google Scholar

Lahlali, R., Ezrari, S., Radouane, N., Kenfaoui, J., Esmaeel, Q., El Hamss, H., et al. (2022). Biological control of plant pathogens: A global perspective. Microorganisms 10, 596. doi: 10.3390/microorganisms10030596

PubMed Abstract | Crossref Full Text | Google Scholar

Langille, M. G. I., Zaneveld, J., Caporaso, J. G., McDonald, D., Knights, D., Reyes, J. A., et al. (2013). Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat. Biotechnol. 31, 814–821. doi: 10.1038/nbt.2676

PubMed Abstract | Crossref Full Text | Google Scholar

Latorre-Pérez, A., Gimeno-Valero, H., Tanner, K., Pascual, J., Vilanova, C., and Porcar, M. (2021). A round trip to the desert: in situ nanopore sequencing informs targeted bioprospecting. Front. Microbiol. 12. doi: 10.3389/fmicb.2021.768240

PubMed Abstract | Crossref Full Text | Google Scholar

Lee, M.-J., Lee, S.-H., Min, H., Nam, T.-W., Hong, S. G., Oh, B., et al. (2025). Longitudinal gut microbiota dynamics in Antarctic research mission crews. Front. Microbiol. 16. doi: 10.3389/fmicb.2025.1593617

PubMed Abstract | Crossref Full Text | Google Scholar

Li, H. (2018). Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100. doi: 10.1093/bioinformatics/bty191

PubMed Abstract | Crossref Full Text | Google Scholar

Li, Z., Kravchenko, A. N., Cupples, A., Guber, A. K., Kuzyakov, Y., Philip Robertson, G., et al. (2024). Composition and metabolism of microbial communities in soil pores. Nat. Commun. 15, 3578. doi: 10.1038/s41467-024-47755-x

PubMed Abstract | Crossref Full Text | Google Scholar

Liaqat, F., Sözer Bahadır, P., Elibol, M., and Eltem, R. (2018). Optimization of chitosanase production by Bacillus mojavensis EGE-B-5.2i. J. Basic Microbiol. 58, 836–847. doi: 10.1002/jobm.201800132

PubMed Abstract | Crossref Full Text | Google Scholar

Lin, H. and Peddada, S. D. (2024). Multigroup analysis of compositions of microbiomes with covariate adjustments and repeated measures. Nat. Methods 21, 83–91. doi: 10.1038/s41592-023-02092-7

PubMed Abstract | Crossref Full Text | Google Scholar

Liu, M., Gong, Y., Sun, H., Zhang, J., Zhang, L., Sun, J., et al. (2020). Characterization of a Novel Chitinase from Sweet Potato and Its Fungicidal Effect against Ceratocystis fimbriata. J. Agric. Food Chem. 68, 7591–7600. doi: 10.1021/acs.jafc.0c01813

PubMed Abstract | Crossref Full Text | Google Scholar

Louca, S., Parfrey, L. W., and Doebeli, M. (2016). Decoupling function and taxonomy in the global ocean microbiome. Science 353, 1272–1277. doi: 10.1126/science.aaf4507

PubMed Abstract | Crossref Full Text | Google Scholar

Love, M. I., Huber, W., and Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550. doi: 10.1186/s13059-014-0550-8

PubMed Abstract | Crossref Full Text | Google Scholar

Luo, W. and Brouwer, C. (2013). Pathview: an R/Bioconductor package for pathway-based data integration and visualization. Bioinformatics 29, 1830–1831. doi: 10.1093/bioinformatics/btt285

PubMed Abstract | Crossref Full Text | Google Scholar

Lupatini, M., Suleiman, A. K. A., Jacques, R. J. S., Antoniolli, Z. I., de Siqueira Ferreira, A., Kuramae, E. E., et al. (2014). Network topology reveals high connectance levels and few key microbial genera within soils. Front. Environ. Sci. 2. doi: 10.3389/fenvs.2014.00010

Crossref Full Text | Google Scholar

Macharoen, P., Mhuantong, W., Wannawong, T., Leesutthiphonchai, W., Tanasupawat, S., Suwannarach, N., et al. (2024). Bacterial diversity, community structure and function in association of potato scabby tubers during storage in northern Thailand. Folia Microbiol. 69, 941–952. doi: 10.1007/s12223-024-01140-9

PubMed Abstract | Crossref Full Text | Google Scholar

Mahapatra, S., Yadav, R., and Ramakrishna, W. (2022). Bacillus subtilis impact on plant growth, soil health and environment: Dr. Jekyll and Mr. Hyde. J. Appl. Microbiol. 132, 3543–3562. doi: 10.1111/jam.15480

PubMed Abstract | Crossref Full Text | Google Scholar

Mahmood, S., Daur, I., Al-Solaimani, S. G., Ahmad, S., Madkour, M. H., Yasir, M., et al. (2016). Plant growth promoting rhizobacteria and silicon synergistically enhance salinity tolerance of mung bean. Front. Plant Sci. 7. doi: 10.3389/fpls.2016.00876

PubMed Abstract | Crossref Full Text | Google Scholar

Maindad, D. V., Kasture, V. M., Chaudhari, H., Dhavale, D. D., Chopade, B. A., and Sachdev, D. P. (2014). Characterization and fungal inhibition activity of siderophore from wheat rhizosphere associated acinetobacter calcoaceticus strain HIRFA32. Indian J. Microbiol. 54, 315–322. doi: 10.1007/s12088-014-0446-z

PubMed Abstract | Crossref Full Text | Google Scholar

Mandakovic, D., Rojas, C., Maldonado, J., Latorre, M., Travisany, D., Delage, E., et al. (2018). Structure and co-occurrence patterns in microbial communities under acute environmental stress reveal ecological factors fostering resilience. Sci. Rep. 8, 5875. doi: 10.1038/s41598-018-23931-0

PubMed Abstract | Crossref Full Text | Google Scholar

Marijon, P., Chikhi, R., and Varré, J.-S. (2020). yacrd and fpa: upstream tools for long-read genome assembly | Bioinformatics | Oxford Academic. Available online at: https://academic.oup.com/bioinformatics/article/36/12/3894/5823296 (Accessed October 14, 2024).

Google Scholar

Marine, R., McCarren, C., Vorrasane, V., Nasko, D., Crowgey, E., Polson, S. W., et al. (2014). Caught in the middle with multiple displacement amplification: the myth of pooling for avoiding multiple displacement amplification bias in a metagenome. Microbiome 2, 3. doi: 10.1186/2049-2618-2-3

PubMed Abstract | Crossref Full Text | Google Scholar

MatChado, M. S., Lauber, M., Reitmeier, S., Kacprowski, T., Baumbach, J., Haller, D., et al. (2021). Network analysis methods for studying microbial communities: A mini review. Comput. Struct. Biotechnol. J. 19, 2687–2698. doi: 10.1016/j.csbj.2021.05.001

PubMed Abstract | Crossref Full Text | Google Scholar

Mészárošová, L., Kuťáková, E., Kohout, P., Münzbergová, Z., and Baldrian, P. (2024). Plant effects on microbiome composition are constrained by environmental conditions in a successional grassland. Environ. Microb. 19, 8. doi: 10.1186/s40793-024-00550-z

PubMed Abstract | Crossref Full Text | Google Scholar

Miljaković, D., Marinković, J., and Balešević-Tubić, S. (2020). The significance of bacillus spp. in disease suppression and growth promotion of field and vegetable crops. Microorganisms 8, 1037. doi: 10.3390/microorganisms8071037

PubMed Abstract | Crossref Full Text | Google Scholar

Monro, H. A. U., Buckland, C. T., and Olsen, O. A. (1970). Methyl bromide and ethylene oxide fumigation of synchytrium endobioticum. Can. J. Plant Sci. 50, 649–658. doi: 10.4141/cjps70-123

Crossref Full Text | Google Scholar

Mounia, Y.-A., Noreddine, K. C., Laid, D., Insaf, B., Mounira, K. A., Hlne, C., et al. (2014). Antifungal activity and bioactive compounds produced by Bacillus mojavensis and Bacillus subtilis. Afr. J. Microbiol. Res. 8, 476–484. doi: 10.5897/ajmr2013.6327

Crossref Full Text | Google Scholar

Niyokwizigirwa, I. (2022). Exploratory study on the potential of potato-associated fungi as biological control agents aganist major fungal pathogens (Rwanda: University of Rwanda). Available online at: https://www.researchgate.net/publication/362230953_EXPLORATORY_STUDY_ON_THE_POTENTIAL_OF_POTATO-ASSOCIATED_FUNGI_AS_BIOLOGICAL_CONTROL_AGENTS_AGAINST_MAJOR_FUNGAL_PATHOGENS (Accessed August 1, 2025).

Google Scholar

Obidiegwu, J. E., Flath, K., and Gebhardt, C. (2014). Managing potato wart: a review of present research status and future perspective. Theor. Appl. Genet. 127, 763–780. doi: 10.1007/s00122-014-2268-0

PubMed Abstract | Crossref Full Text | Google Scholar

Oksanen, J., Simpson, G. L., Blanchet, F. G., Kindt, R., Legendre, P., Minchin, P. R., et al. (2025). vegan: Community Ecology Package https://CRAN.R-project.org/package=vegan.

Google Scholar

Ordóñez, C. D. and Redrejo-Rodríguez, M. (2023). DNA polymerases for whole genome amplification: considerations and future directions. Int. J. Mol. Sci. 24, 9331. doi: 10.3390/ijms24119331

PubMed Abstract | Crossref Full Text | Google Scholar

Ospino, M. C., Engel, K., Ruiz-Navas, S., Binns, W. J., Doxey, A. C., and Neufeld, J. D. (2024). Evaluation of multiple displacement amplification for metagenomic analysis of low biomass samples. ISME Commun. 4, ycae024. doi: 10.1093/ismeco/ycae024

PubMed Abstract | Crossref Full Text | Google Scholar

Ozgonen, H. and Erkilic, A. (2007). Growth enhancement and Phytophthora blight (Phytophthora capsici Leonian) control by arbuscular mycorrhizal fungal inoculation in pepper. Crop Prot. 26, 1682–1688. doi: 10.1016/j.cropro.2007.02.010

Crossref Full Text | Google Scholar

Pandit, M. A., Kumar, J., Gulati, S., Bhandari, N., Mehta, P., Katyal, R., et al. (2022). Major biological control strategies for plant pathogens. Pathogens 11, 273. doi: 10.3390/pathogens11020273

PubMed Abstract | Crossref Full Text | Google Scholar

Pechlivanis, N., Karakatsoulis, G., Kyritsis, K., Tsagiopoulou, M., Sgardelis, S., Kappas, I., et al. (2024). Microbial co-occurrence network demonstrates spatial and climatic trends for global soil diversity. Sci. Data 11, 672. doi: 10.1038/s41597-024-03528-1

PubMed Abstract | Crossref Full Text | Google Scholar

Purkayastha, G. D., Mangar, P., Saha, A., and Saha, D. (2018). Evaluation of the biocontrol efficacy of a Serratia marcescens strain indigenous to tea rhizosphere for the management of root rot disease in tea. PloS One 13, e0191761. doi: 10.1371/journal.pone.0191761

PubMed Abstract | Crossref Full Text | Google Scholar

Qing, Z., Jida, Y., Chengxiu, F., Yanli, Y., Xia, L., and Sihe, D. (2024). Ralstonia solanacearum infection drives the assembly and functional adaptation of potato rhizosphere microbial communities. Plant Pathol. J. 40, 498–511. doi: 10.5423/PPJ.OA.06.2024.0086

PubMed Abstract | Crossref Full Text | Google Scholar

Rabbee, M. F., Ali, M. S., Choi, J., Hwang, B. S., Jeong, S. C., and Baek, K. (2019). Bacillus velezensis: A Valuable Member of Bioactive Molecules within Plant Microbiomes. Molecules 24, 1046. doi: 10.3390/molecules24061046

PubMed Abstract | Crossref Full Text | Google Scholar

Raghunathan, A., Ferguson, H. R., Bornarth, C. J., Song, W., Driscoll, M., and Lasken, R. S. (2005). Genomic DNA amplification from a single bacterium. Appl. Environ. Microbiol. 71, 3342–3347. doi: 10.1128/AEM.71.6.3342-3347.2005

PubMed Abstract | Crossref Full Text | Google Scholar

Roongsawang, N., Washio, K., and Morikawa, M. (2011). Diversity of nonribosomal peptide synthetases involved in the biosynthesis of lipopeptide biosurfactants. Int. J. Mol. Sci. 12, 141–172. doi: 10.3390/ijms12010141

PubMed Abstract | Crossref Full Text | Google Scholar

Roquigny, R., Novinscak, A., Léger, G., Marcoux, N., Joly, D. L., and Filion, M. (2018). Deciphering the rhizosphere and geocaulosphere microbiomes of potato following inoculation with the biocontrol agent pseudomonas fluorescens strain LBUM223. Phytobiomes J. 2, 92–99. doi: 10.1094/PBIOMES-03-18-0013-R

Crossref Full Text | Google Scholar

Sabina, J. and Leamon, J. H. (2015). “Bias in whole genome amplification: causes and considerations,” in Whole Genome Amplification: Methods and Protocols. Ed. Kroneis, T. (Springer, New York, NY), 15–41. doi: 10.1007/978-1-4939-2990-0_2

PubMed Abstract | Crossref Full Text | Google Scholar

Samarakoon, H., Ferguson, J. M., Gamaarachchi, H., and Deveson, I. W. (2023). Accelerated nanopore basecalling with SLOW5 data format. Bioinformatics 39, btad352. doi: 10.1093/bioinformatics/btad352

PubMed Abstract | Crossref Full Text | Google Scholar

Satam, H., Joshi, K., Mangrolia, U., Waghoo, S., Zaidi, G., Rawool, S., et al. (2023). Next-generation sequencing technology: current trends and advancements. Biology 12, 997. doi: 10.3390/biology12070997

PubMed Abstract | Crossref Full Text | Google Scholar

Saxena, A. K., Kumar, M., Chakdar, H., Anuroopa, N., and Bagyaraj, D. J. (2020). Bacillus species in soil as a natural resource for plant health and nutrition. J. Appl. Microbiol. 128, 1583–1594. doi: 10.1111/jam.14506

PubMed Abstract | Crossref Full Text | Google Scholar

Sdiri Ghidawi, J., Ghazala, I., Haddar, A., Bouazizi, O., Gargouri-Bouzid, R., and Nouri-Ellouz, O. (2025). Effect of the Plant Growth-Promoting Bacteria Strain Bacillus Mojavensis I4 on Potato Growth, Physiology, Tuber Yield, and Quality Under Salt Stress Conditions. Potato Research 68 (2), 2205–2227. doi: 10.1007/s11540-024-09829-7

Crossref Full Text | Google Scholar

Serrão, C. P., Ortega, J. C. G., Rodrigues, P. C., and de Souza, C. R. B. (2024). Bacillus species as tools for biocontrol of plant diseases: A meta-analysis of twenty-two years of research 2000–2021. World J. Microbiol. Biotechnol. 40, 110. doi: 10.1007/s11274-024-03935-x

PubMed Abstract | Crossref Full Text | Google Scholar

Shade, A. and Stopnisek, N. (2019). Abundance-occupancy distributions to prioritize plant core microbiome membership. Curr. Opin. Microbiol. 49, 50–58. doi: 10.1016/j.mib.2019.09.008

PubMed Abstract | Crossref Full Text | Google Scholar

Shenhav, L., Thompson, M., Joseph, T. A., Briscoe, L., Furman, O., Bogumil, D., et al. (2019). FEAST: fast expectation-maximization for microbial source tracking. Nat. Methods 16, 627–632. doi: 10.1038/s41592-019-0431-x

PubMed Abstract | Crossref Full Text | Google Scholar

Shi, W., Lv, M., Wang, R., Saleem, M., Wang, L., Li, M., et al. (2025). Geocaulosphere soil bacterial community drives potato common scab outcomes beyond pathogen abundance. NPJ Biofilms Microb. 11, 134. doi: 10.1038/s41522-025-00774-y

PubMed Abstract | Crossref Full Text | Google Scholar

Smith, D. S., Rocheleau, H., Chapados, J. T., Abbott, C., Ribero, S., Redhead, S. A., et al. (2014). Phylogeny of the genus synchytrium and the development of taqMan PCR assay for sensitive detection of synchytrium endobioticum in soil. Phytopathology® 104, 422–432. doi: 10.1094/phyto-05-13-0144-r

PubMed Abstract | Crossref Full Text | Google Scholar

Sun, X., Xu, Z., Xie, J., Hesselberg-Thomsen, V., Tan, T., Zheng, D., et al. (2022). Bacillus velezensis stimulates resident rhizosphere Pseudomonas stutzeri for plant health through metabolic interactions. ISME J. 16, 774–787. doi: 10.1038/s41396-021-01125-3

PubMed Abstract | Crossref Full Text | Google Scholar

Supronienė, S., Kadžienė, G., Shamshitov, A., Veršulienė, A., Šneideris, D., Ivanauskas, A., et al. (2023). Soil Fungistasis against Fusarium Graminearum under Different tillage Systems. Plants 12, 966. doi: 10.3390/plants12040966

PubMed Abstract | Crossref Full Text | Google Scholar

Tang, Y., She, Y., Chen, D., Zhou, Y., Xie, D., and Liu, Z. (2025). 16S rRNA sequencing-based evaluation of the protective effects of key gut microbiota on inhaled allergen-induced allergic rhinitis. Front. Microbiol. 15. doi: 10.3389/fmicb.2024.1497262

PubMed Abstract | Crossref Full Text | Google Scholar

Tracanna, V., Ossowicki, A., Petrus, M. L. C., Overduin, S., Terlouw, B. R., Lund, G., et al. (2021). Dissecting disease-suppressive rhizosphere microbiomes by functional amplicon sequencing and 10× metagenomics. mSystems 6, e01116. doi: 10.1128/mSystems.01116-20

PubMed Abstract | Crossref Full Text | Google Scholar

van Berloo, R., Hutten, R. C. B., van Eck, H. J., and Visser, R. G. F. (2007). An online potato pedigree database resource. Potato Res. 50, 45–57. doi: 10.1007/s11540-007-9028-3

Crossref Full Text | Google Scholar

van de Vossenberg, B. T. L. H., te Braak, N., Warbroek, T., Meffert, J. P., Rosendahl-Peters, K. H. M., van Gent-Pelzer, M. P. E., et al. (2022a). Comparison of real-time PCR tests for the detection of Synchytrium endobioticum resting spores in soil and their potential application in descheduling of previously infested plots. EPPO Bull. 52, 154–163. doi: 10.1111/epp.12813

Crossref Full Text | Google Scholar

van de Vossenberg, B. T. L. H., van Gent, M., Meffert, J. P., Nguyen, H. D. T., Smith, D., van Kempen, T., et al. (2024). Molecular characterization and comparisons of potato wart (Synchytrium endobioticum) in historic collections to recent findings in Canada and the Netherlands. Journal of Plant Pathology. 106 (2), 363–375. doi: 10.1007/s42161-022-01299-5

Crossref Full Text | Google Scholar

van de Vossenberg, B. T. L. H., van Gent-Pelzer, M. P. E., Boerma, M., van der Gouw, L. P., van der Lee, T., and Vossen, J. H. (2019a). An alternative bioassay for synchytrium endobioticum demonstrates the expression of potato wart resistance in aboveground plant parts. Phytopathology® 109, 1043–1052. doi: 10.1094/PHYTO-01-19-0024-R

PubMed Abstract | Crossref Full Text | Google Scholar

van de Vossenberg, B., Warris, S., Nguyen, H. D. T., van Gent-Pelzer, M. P. E., Joly, D. L., van de Geest, H. C., et al. (2019b). Comparative genomics of chytrid fungi reveal insights into the obligate biotrophic and pathogenic lifestyle of Synchytrium endobioticum. Sci. Rep. 9, 8672. doi: 10.1038/s41598-019-45128-9

PubMed Abstract | Crossref Full Text | Google Scholar

van de Vossenberg, Prodhomme, C., Vossen, J. H., and van der Lee, T. A. J. (2022b). Synchytrium endobioticum, the potato wart disease pathogen. Mol. Plant Pathol. 23, 461–474. doi: 10.1111/mpp.13183

PubMed Abstract | Crossref Full Text | Google Scholar

Vigil, B. E., Ascue, F., Ayala, R. Y., Murúa, P., Calderon, M. S., and Bustamante, D. E. (2024). Functional prediction based on 16S rRNA metagenome data from bacterial microbiota associated with macroalgae from the Peruvian coast. Sci. Rep. 14, 18577. doi: 10.1038/s41598-024-69538-6

PubMed Abstract | Crossref Full Text | Google Scholar

Wang, S., Wang, Y., Liu, H., Li, X., Zhao, J., Dong, Z., et al. (2022). Using PICRUSt2 to explore the functional potential of bacterial community in alfalfa silage harvested at different growth stages. Chem. Biol. Technol. Agric. 9, 98. doi: 10.1186/s40538-022-00372-6

Crossref Full Text | Google Scholar

Wang, X. Q., Zhao, D. L., Shen, L. L., Jing, C. L., and Zhang, C. S. (2018). “Application and mechanisms of bacillus subtilis in biological control of plant disease,” in Role of Rhizospheric Microbes in Soil: Volume 1: Stress Management and Agricultural Sustainability. Ed. Meena, V. S. (Springer, Singapore), 225–250. doi: 10.1007/978-981-10-8402-7_9

Crossref Full Text | Google Scholar

Wei, C., Liang, J., Wang, R., Chi, L., Wang, W., Tan, J., et al. (2024). Response of bacterial community metabolites to bacterial wilt caused by Ralstonia solanacearum: a multi-omics analysis. Front. Plant Sci. 14. doi: 10.3389/fpls.2023.1339478

PubMed Abstract | Crossref Full Text | Google Scholar

Weller, S. A., Elphinstone, J. G., Smith, N. C., Boonham, N., and Stead, D. E. (2000). Detection of ralstonia solanacearumStrains with a quantitative, multiplex, real-time, fluorogenic PCR (TaqMan) assay. Appl. Environ. Microbiol. 66, 2853–2858. doi: 10.1128/AEM.66.7.2853-2858.2000

PubMed Abstract | Crossref Full Text | Google Scholar

Xu, M., Guo, J., Li, T., Zhang, C., Peng, X., Xing, K., et al. (2021). Antibiotic Effects of Volatiles Produced by Bacillus tequilensis XK29 against the Black Spot Disease Caused by Ceratocystis fimbriata in Postharvest Sweet Potato. J. Agric. Food Chem. 69, 13045–13054. doi: 10.1021/acs.jafc.1c04585

PubMed Abstract | Crossref Full Text | Google Scholar

Yan, L. (2025). ggvenn: Draw Venn Diagram by 'ggplot2'. https://github.com/yanlinlin82/ggvenn.

Google Scholar

Yang, Y., Hu, J., Wei, X., Huang, K., Li, C., and Yang, G. (2024). Deciphering core microbiota in rhizosphere soil and roots of healthy and Rhizoctonia solani-infected potato plants from various locations. Front. Microbiol. 15. doi: 10.3389/fmicb.2024.1386417

PubMed Abstract | Crossref Full Text | Google Scholar

Yang, F., Zhang, J., Zhang, H., Ji, G., Zeng, L., Li, Y., et al. (2020). Bacterial blight induced shifts in endophytic microbiome of rice leaves and the enrichment of specific bacterial strains with pathogen antagonism. Front. Plant Sci. 11. Available online at: https://www.frontiersin.org/articles/10.3389/fpls.2020.00963 (November 21, 2023).

PubMed Abstract | Google Scholar

Yousuf, J., Thajudeen, J., Rahiman, M., Krishnankutty, S., P. Alikunj, A., and XXXA. Abdulla, M. H. (2017). Nitrogen fixing potential of various heterotrophic Bacillus strains from a tropical estuary and adjacent coastal regions. J. Basic Microbiol. 57, 922–932. doi: 10.1002/jobm.201700072

PubMed Abstract | Crossref Full Text | Google Scholar

Yu, G., Wang, L.-G., Han, Y., and He, Q.-Y. (2012). clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: A J. Integr. Biol. 16, 284–287. doi: 10.1089/omi.2011.0118

PubMed Abstract | Crossref Full Text | Google Scholar

Zhang, L., Chen, F., Zeng, Z., Xu, M., Sun, F., Yang, L., et al. (2021). Advances in metagenomics and its application in environmental microorganisms. Front. Microbiol. 12. Available online at: https://www.frontiersin.org/articles/10.3389/fmicb.2021.766364 (January 12, 2024).

Google Scholar

Zhang, N., Wang, Z., Shao, J., Xu, Z., Liu, Y., Xun, W., et al. (2023). Biocontrol mechanisms of Bacillus: Improving the efficiency of green agriculture. Microb. Biotechnol. 16, 2250–2263. doi: 10.1111/1751-7915.14348

PubMed Abstract | Crossref Full Text | Google Scholar

Zhang, L., Zuo, Q., Cai, H., Li, S., Shen, Z., and Song, T. (2024). Fungicides reduce soil microbial diversity, network stability and complexity in wheat fields with different disease resistance. Appl. Soil Ecol. 201, 105513. doi: 10.1016/j.apsoil.2024.105513

Crossref Full Text | Google Scholar

Zhao, M., Yuan, J., Zhang, R., Dong, M., Deng, X., Zhu, C., et al. (2018). Microflora that harbor the NRPS gene are responsible for Fusarium wilt disease-suppressive soil. Appl. Soil Ecol. 132, 83–90. doi: 10.1016/j.apsoil.2018.08.022

Crossref Full Text | Google Scholar