Responses of root microbiome and metabolome are linked to crop disease severity

Responses of root microbiome and metabolome are linked to crop disease severity | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Responses of root microbiome and metabolome are linked to crop disease severity Brajesh Singh, Ayomide Fadiji This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7161758/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Plant microorganisms are an essential component of the host and perform critical functions in plant development and health. Emerging evidence shows that plants use their root exudates to recruit beneficial microbes that protect them against abiotic and biotic stresses, including diseases. However, the metabolic responses of plant under pathogen infection remain underexplored. In this study, using a manipulative experiment, we employed amplicon sequencing and untargeted metabolomics to investigate the response of rhizosphere microbial communities and metabolites of root exudates to potato-wilt disease caused by Ralstonia solanacearum (RS) across two developmental stages (vegetative and tuber bulking). Our results revealed that β-diversity showed distinct shifts in bacterial and fungal communities between healthy and diseased plants. Higher relative abundance of bacterial taxa from genera, Bradyrhizobium, Cadidatus, Paenibacillus and the fungal genus Terramyces were observed in the rhizosphere of healthy plants. Similarly, Burkholderia spp and the fungal Apiotrichum spp dominated the rhizosphere of diseased plants across the developmental stages. Further compared to healthy plants, microbial functional potentials and metabolomic profiles of root exudates linked to pathogen resistance were significantly enhanced in diseased plants. Particularly, metabolites from alkaloids, triterpenoids and polyketides were enriched in disease plants and exhibited associations with microbial groups known to influence host immunity, nutrient acquisition, and stress adaptation. We observed that variations in disease index were associated with the identified enriched metabolites. Our integrative analysis provides evidence for multifaceted signalling, sensing between plants, pathogens and beneficial microbiota that may shape plant health status and microbiome assembly under pathogen pressure. These insights not only advance our understanding of crop pathophysiology but also lay the foundation for developing targeted biological strategies or metabolic markers for early disease detection and sustainable crop protection. Agroecology Biotic stress metabolites plant resilience pathogen Ralstonia solanacearum Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1.0 Introduction Disease impacts all crop and non-crop plants and has a significant negative impact on food security and environmental sustainability (Singh et al., 2023a , b ). Current global modelling suggests that pathogen pressure will further intensify as global climate changes (Singh et al., 2023a , Delgado-Baquerizo et al., 2020 , Singh et al., 2025b ). Chemical solutions are usually not effective in controlling soil-borne diseases, and they also have negative impacts on soil and human health (Panth et al., 2020 ). Therefore, developing biological solutions is considered a priority, but are constrained by knowledge gaps on interactions between plant- microbiome and pathogens (Mitter et al., 2019 ). However, recent studies has reported production of multiple primary and secondary metabolites as signal molecules by plants in root exudates to attract beneficial microbiota to mitigate both abiotic and biotic stresses (Wen et al., 2023 , Singh et al., 2025a ). Beneficial microbiota that can sense those metabolites and are able to colonise the plant, respond to those signal to mitigate stresses (Robert et al., 2025 , Yang et al., 2025 ). Therefore, plants actively shape rhizosphere microbial communities by secreting signalling compounds in root exudates, a process that is strongly governed by health status, plant genotype and soil conditions (Li et al., 2021 , Gu et al., 2023 , Yan et al., 2024 ). For example, current evidence suggests that plants under pathogen attack secrete metabolites to attract beneficial microbiota to mitigate disease impact, but the identity of metabolites that are produced and the microbes which respond to these metabolites are not fully known (Yan et al., 2024 ). Further, beneficial microbiota that respond to metabolites potentially contribute to plant defence through a variety of mechanisms, including triggering systemic resistance, synthesizing phytohormones, outcompeting harmful organisms for nutrients and habitat niches, and enhancing the overall resilience of plants to stress (Chang et al., 2022 , Zhong et al., 2022 , Yan et al., 2024 ). However, empirical evidence remains limited. Potato ( Solanum tuberosum L.) is one of the world's most significant crops, ranking fourth behind wheat, corn, and rice (Birch et al., 2012 , Aksoy et al., 2021 ; Devaux et al., 2021 ). Also, potatoes are vulnerable to many soil-borne pathogens including Ralstonia solanacearum ( RS), which causes bacterial wilt disease (Ababa, 2024 , Tahat and Sijam, 2010 , Gutarra et al., 2017 ). Bacterial wilt is a major potato disease which causes significant yield loss and, in some cases, complete crop failure. Once set in a field condition, managing bacterial wilt is difficult because the pathogen can survive in soils for many years even in the absence of host, and can be transmitted by infected seed tubers, soils and irrigation water. Previous reports suggested that in response to RS infection and other biotic threats, the rhizosphere—the narrow region of soil influenced by root activity undergoes dynamic changes regulated by the host plant (Yaqoob et al., 2020 , Ren et al., 2021 ). Given that the rhizosphere zone is a highly specialized environment which is known to be shaped by root exudation and the activities of soil microbiota, and their interactions ultimately determine the plant health and productivity under stress conditions (Berendsen et al., 2012 , Li et al., 2020 ). Theoretically, the metabolic composition of root exudates and the structure of rhizosphere microbial communities are largely governed by factors such as plant health, developmental stage and plant genotype (Xing et al., 2024 , Yan et al., 2024 ), but empirical evidence remain limited (Kaur et al., 2022 , Qiu et al., 2022 ). It is proposed that even when grown in the same soil, different plants under pathogen infection may release distinct exudate profiles depending on plant developmental stages, which in turn may attract beneficial microbiota to mitigate disease pressures (Haichar et al., 2008 , Yan et al., 2024 ). However, the metabolic and microbial response to RS infection in potato remains largely unknown. This is a critical knowledge gap that constraints our ability to advance fundamental science on plant-microbial interactions and to develop effective biological solutions for disease management (Fadiji et al., 2023 ). Here, in this study, we used untargeted metabolomics and microbiome sequencing to examine the impacts of potato bacterial wilt disease caused by RS on rhizosphere microbial communities and root exudates in both healthy and diseased potato plants across two developmental stages (vegetative and tuber bulking). We hypothesize that the composition, structure, and diversity of both the rhizosphere microbiome and root exudate profiles are influenced by the health status of the host plant. We propose that disease stress alters the recruitment patterns of beneficial microbial taxa via the production of distinct plant metabolites. 2.0 Methodology 2.1 Soil collection, pathogen and potato seeds, pathogenicity test A controlled glasshouse experiment was carried out at the Hawkesbury Institute for the Environment, Western Sydney University (WSU), New South Wales (NSW), Australia, from July to October 2024. The soil used in the study was sourced from the nearby experimental plot within the Institute’s teaching and research farm. Ralstonia solanacearum strain RS2 were obtained from the gene bank of the Department of Primary Industries, Australia. Before use in the experiment, the isolates were stored at − 80°C on sucrose peptone agar (SPA) as recommended to maintain viability. Certified seed tubers of the potato cultivar ‘Russet Burbank’ were sourced from a commercial supplier (Happy Valley Seeds) based in Sydney, New South Wales, Australia. Five days prior to inoculation, plants were subjected to restricted watering to simulate mild water stress. In the dose-response assay, 30-day-old, unwounded potato plants were inoculated using a soil-drench method adapted from (Tans-Kersten et al., 2001 ). A 50 mL suspension of Ralstonia solanacearum , prepared at the desired concentration, was applied directly to the soil surrounding the base of each plant, followed by 200 mL of water to facilitate movement of the inoculum into the rhizosphere. Given the pot surface area of 415 cm², this corresponded to a total irrigation depth of 6 mm, of which 1.2 mm was from the inoculum. For the potato tested, 12 plants were used—6 plants received the bacterial suspension, while the remaining 6 were mock-treated with 50 mL of sterile Ringer’s solution and served as negative controls. As anticipated, plants treated with the 10⁸ CFU/mL inoculum exhibited full infection (Figure S1). Pathogen infection was subsequently confirmed using a modified detection protocol of Eisfeld et al. ( 2022 ). 2.4 Greenhouse experiment and sample collection For the experiment, approximately 100 pre-sprouted mini-tubers were prepared by exposing them to light at 15°C for two weeks. Once sprouting occurred, the tubers were planted individually into 5 L pots filled with 4 kg of clay loam soil. The soil composition included approximately 27% clay, 33% sand, and 32% silt. Prior to potting, the air-dried soil was loosely sieved using a 1 x 1 cm mesh to remove coarse debris. In each pot, a hole roughly 5 cm deep was made, into which a single mini-tuber was placed with the sprouts facing upward and then lightly covered with soil. Pots were placed on saucers and watered from above. The glasshouse was maintained at a constant temperature of 23°C and relative humidity of 70%. After plant emergence, a 16-hour photoperiod was maintained, supplemented with high-pressure sodium lamps (150 W/m²) when natural light was insufficient. Daily top watering was continued until five days before inoculation, at which point irrigation was restricted to induce mild water stress. Subsequently, the potato tested, 24 plants were used for each developmental stage (vegetative and tuber bulking stages). Among which, 12 plants received the bacterial suspensions as earlier described, while the remaining 12 plants were treated with sterile Ringer’s solution. All pots with the plant samples were placed in the same glasshouse till the experiment was terminated. Samples were grouped based on plant health and developmental stage: ED: Diseased (RS-infected) potato plants at early (vegetative) stage; EH: Healthy Potato plant at early stage; PD: Diseased (RS-infected) potato plants at tuber bulking (Post-emergence) stage; and PH: Healthy potato plant at tuber bulking (Post-emergence). Post-inoculation, plants were watered through the saucers to prevent cross-contamination via splash or aerosol movement. For plants showing bacterial wilt symptoms, watering volumes were carefully adjusted based on physical plant water requirements to avoid waterlogging. Plants were monitored weekly for disease symptom development throughout the duration of the experiment. Potato plants were carefully removed intact from the pots at two key developmental stages: the vegetative stage (35 days after planting) and the tuber bulking stage (65 days after planting). Rhizosphere soil samples were collected using the "soil adhering to fine roots after shaking" method as described by Huo et al. ( 2024 ). Briefly, fine roots were gently brushed with sterile brushes to collect soil particles closely adhering to the root surface, which were then defined as rhizosphere soil. Each sample was divided into two portions—one was air-dried for physicochemical analysis, and the other was stored at − 80°C for molecular and biochemical analyses. To ensure consistent and reliable extraction, we divided the roots from each group into two sets of six plants. This provided biological duplicates across treatment groups and helped to obtain enough material for water-based extraction, given the gentle nature of ultrapure water as a solvent. The plants were transferred to 250 mL conical flasks containing ultrapure water, ensuring that the roots were fully submerged. After a 24-hour incubation (16 hours under light and 8 hours in darkness), the root exudate solution was collected and centrifuged at 1200 rpm for 15 minutes at 4°C (Xing et al., 2024 ). The resulting supernatant was filtered through a 0.22 µm membrane filter, and the filtrate was subsequently freeze-dried. The dried root exudate samples were stored at − 80°C for further analysis. Sample collections were grouped based on early vegetative healthy (EH), early vegetative diseased (ED), post-emergence at tuber bulking healthy (PH), and diseased (PD). Disease index was calculated using the modified method of Siddique et al. ( 2020 ) (Table S1). Root exudate samples were analyzed at the Mass Spectrometry Facility (MSF) of Western Sydney University. 2.5 DNA extraction and sequencing Soil genomic DNA (gDNA) was extracted from ∼0.25 g rhizosphere soil samples using the PowerSoil PRO DNA Isolation Kit (Qiagen, Hilden, Germany) following the manufacturer's instructions. The quantity and quality of extracted gDNA were verified using NanoDrop (Thermo Scientific, Wilmington, DE) and electrophoresis (1% agarose gel, including a 1 kb plus ladder). PCR amplification was performed for each soil DNA extract in triplicate and combined into a single composite sample. Amplicons targeting the V5–V7 region of the 16S rRNA gene (799F–1193R) (Bodenhausen et al., 2013 ) and the ITS2 region (FITS7-ITS4R, (1) were obtained by PCR to characterize bacterial and fungal communities, respectively, according to Qiu et al. ( 2020 ). Amplicon sequencing was performed at the Ramaciotti Centre for Genomics at the University of New South Wales (Sydney, Australia), using the Illumina NextSeQ (PE 300 bp) platform. Paired-end (PE) reads obtained from previous steps were assembled by USEARCH (version 10) (Segata et al., 2012 ), and followed by chimera removal using UCHIME (version 8.1) (Edgar et al., 2011 ). Representative sequences were annotated against the Silva database for 16S rRNA reads (Quast et al., 2012 ) and the UNITE database for ITS2 reads (Kõljalg et al., 2005 ). Chloroplast/mitochondrial sequences were excluded from the downstream analysis. The high-quality reads generated by the above steps were used for subsequent analysis. 2.6 Non-targeted metabolomic analysis Untargeted metabolite profiling of the root exudates was conducted using liquid chromatography coupled with tandem mass spectrometry (LC–MS/MS), carried out at Biomarker Technologies Co., Ltd. Briefly, 50 mg of freeze-dried exudate sample was extracted with 1 mL of a cold solvent mixture consisting of methanol, acetonitrile, and water in a 2:2:1 ratio. The mixture was vortexed for 30 seconds, after which steel grinding beads were added, and samples were homogenized using a high-frequency grinder at 45 Hz for 10 minutes. This was followed by 10 minutes of ultrasonication in an ice-water bath, as adapted from Xing et al. ( 2024 ). The mixture was then allowed to stand at 20°C for 1 hour to precipitate proteins, followed by centrifugation at 12,000 rpm for 15 minutes at 4°C. A volume of 500 µL of the supernatant was transferred to a clean EP tube and evaporated to dryness using a vacuum concentrator. The residue was then reconstituted in 160 µL of extraction solvent (acetonitrile:water, 1:1 v/v), vortexed for 30 seconds, ultrasonicated again for 10 minutes in an ice-water bath, and centrifuged at 12,000 rpm for another 15 minutes at 4°C. Finally, 120 µL of the resulting supernatant was subjected to LC–MS/MS analysis using an ACQUITY UPLC I-Class FTN system coupled to a Waters Synapt G2-Si HDMS mass spectrometer (Waters, Wilmslow, UK) equipped with a Unispray ionization source (see Methods S1 for full details). To ensure comprehensive coverage of chemically diverse metabolites, both positive and negative electrospray ionization (ESI) modes were performed. Mass spectrometry data were processed using Progenesis QI software, version 3.0 (Nonlinear Dynamics, Waters Corporation, UK). Automated workflows within the software were used for peak alignment, peak picking, and deconvolution to extract peak intensity values for downstream statistical analysis. Putative identification and annotation of metabolomic features were conducted via Progenesis QI’s integrated access to the ChemSpider web-based structure database, incorporating several public repositories including ChEBI, Phenol-Explorer, PlantCyc, KEGG, and the Golm Metabolome Database. Identification parameters were set with a precursor ion mass tolerance of 15 ppm and a fragment ion tolerance of 50 ppm. To ensure comprehensive coverage of chemically diverse metabolites, untargeted metabolomics was performed in both positive and negative electrospray ionization (ESI) modes Each candidate identification was evaluated using a confidence scoring system that integrated mass accuracy, isotope pattern similarity, and fragmentation match quality. Metabolites with the highest overall confidence score or the best fragmentation match were selected as putative identifications. Additionally, chemical classification of metabolites was performed based on their core structural characteristics. 2.7 Data processing and analysis All statistical analyses were conducted using R software. To account for differences in sequencing depth among samples and reduce potential bias, all bacterial community samples were rarefied to 2,000 reads per sample, and fungal community samples were rarefied to 1,000 reads per sample, corresponding to the minimum read count observed and retained across samples in each dataset. Visualization of microbial patterns, including bar plots, heatmaps, and Venn diagrams, was performed using R version 3.2.3, employing the “pheatmap” and “VennDiagram” packages. Microbial β-diversity was assessed using Bray–Curtis dissimilarity, followed by PERMANOVA to test for overall differences in community composition. When PERMANOVA results were significant, post hoc pairwise comparisons were conducted with Bonferroni correction to identify group-specific differences. Additionally, analysis of similarity (ANOSIM) was used to evaluate community dissimilarities between different sampling groups. Post hoc significance tests used the Bonferroni correction. Linear discriminant analysis (LDA) of effect size (LEfSe analysis) was applied on the OTU level to identify the differentially abundant bacterial and fungal taxa (at phylum to genus levels) that significantly change between samples from the diseased and healthy plant sites. Wilcoxon rank-sum test for pairwise comparison (false discovery rate (FDR) adjusted p 4) were used to analyze the statistical significance and strength, respectively. To infer microbial ecological functions, the FAPROTAX database was used for predicting putative functions of bacterial taxa (Jewell et al., 2016 , Liu et al., 2021a ), while fungal functional guilds were annotated using the FUNGuild database (Nguyen et al., 2016 ). The functional profiles were computed from OTUs defined as consistently prevalent and relatively abundant taxa within each group and standardized using z-score transformation to emphasize compartment-specific enrichment patterns. Functional profiles were derived from operational taxonomic units (OTUs) that were both consistently prevalent and relatively abundant within each group. These profiles were standardized using z-score transformation to highlight compartment-specific patterns of functional enrichment. Multivariate statistical analyses of metabolite peak intensities (positive and negative ion modes) were conducted using MetaboAnalyst 6.0 (Pang et al., 2024 ). To ensure data quality and focus on biologically relevant features, metabolomic peaks with low variability across samples were filtered out. Specifically, features with log₁₀ fold change (log₁₀FC) values below 2 between treatments were excluded, along with those exhibiting low intensity (peak intensity < 50 across all samples). A clustered heatmap was generated to visualize the relative abundance of dominant metabolites across treatment conditions. Subsequent analyses were performed on data from four biological replicates per treatment. To explore discriminative patterns between healthy and diseased plants, Partial Least Squares Discriminant Analysis (PLS-DA) was applied to all expressed metabolites, with a 95% confidence region defining group separation. A fold-change analysis (threshold FC ≥ 2.0) was used to identify significantly altered metabolites between healthy and diseased potato roots (Weinberger et al., 2025 ). Metabolomic features with consistent differences (defined as > 75% reproducibility) were considered significantly up- or downregulated. To investigate potential interactions between microbial taxa, functional potentials, and metabolite profiles, Spearman’s rank correlation was calculated between microbial genera, functional potentials and enriched identified metabolites. The results were visualised using the pheatmap package in R. Hierarchical clustering was applied to both rows (metabolites) and columns (genera and microbial functions), using Euclidean distance and complete linkage to identify similar correlation patterns. 3.0 Results 3.1 Response of rhizosphere microbiomes to plant health and developmental stage in potato plants Bray-Curtis dissimilarity indices showed that both bacterial and fungal communities exhibited significant compositional shifts across plant health and developmental stages (Fig. 1 ). Interestingly, at the early vegetative stage, no significant difference (> 0.05) in bacterial community was observed between diseased plants (ED, green box) and healthy plants (EH, orange box). However, during the tuber bulking stage, a highly significant difference was observed for microbial communities between diseased plants (PD, purple box) and healthy plants (PH, pink box) (p < 0.0001) (Fig. 1 A). For fungi community, Bray–Curtis dissimilarity showed structural variation among healthy and RS-infected potato plants across growth stages (Fig. 1 B). A significant difference in community dissimilarity was observed in early healthy plants (EH, orange box) compared to early diseased plants (ED, green box) (p < 0.001), suggesting distinct fungal community structuring in response to RS infection at the vegetative stage. At the tuber bulking stage, a modest but statistically significant difference was also detected between healthy plants (PH, pink box) and diseased plants (PD, purple box) (p < 0.05), showing that fungal communities in PH were more dissimilar compared to PD. Alpha diversity metrics revealed insights into the richness and evenness of microbial communities associated with RS-infected and healthy potato plants at different developmental stages (Figure S2). For the bacteria community, no significant difference (p > 0.05) was observed in richness estimates (Chao1 and Observed OTUs) between EH and ED plants at the vegetative stage. However, a significant difference was detected in the Shannon diversity index, with EH exhibiting higher diversity than ED (p < 0.05), indicating greater bacterial community evenness and complexity in healthy plants (Figure S2A). Simpson diversity, however, showed no significant variation across groups. At the tuber bulking stage, comparisons between PH and PD plants revealed no statistically significant differences across all four alpha diversity indices. Both richness (Chao1, Observed) and diversity (Shannon, Simpson) metrics remained relatively stable between PH and PD. For the fungi community, no significant differences were observed between groups for any of the alpha diversity indices (richness and diversity) assessed across the stages and treatments (Figure S2B). 3.2 Microbial community structure and composition between diseased and healthy potato The Vein diagram (Figs. 2 A and 2 B) showed the shared bacteria and fungi OTUs between the samples, respectively, which increased in relative abundance in the diseased as compared to the healthy plants. The dominant bacterial phyla were primarily composed of Proteobacteria (40.6–54.4%), Firmicutes (12.9-22.19%), Actinobacteriota (19.0-26.47%), Bacteroidota (2.46–11.05%), Acidobacteriota (7.39–14.9%), Planctomycetota (2.46–11.05%), and Verrucomicrobiota (2.54–4.45%). At the vegetative stage, Firmicutes were uniquely dominant in ED, while no phylum was uniquely dominant in EH (Figure S3A). On the other hand, Actinobacteriota, Acidobacteriota and Verrucomicrobiota were uniquely dominant in PH, while Proteobacteria were uniquely dominant in PD. Furthermore, the shared dominant bacterial genera were primarily composed of Burkholderia (4.74–17.83%), Bacillus (4.72–7.13%), Comamonas (0.42–5.02%), Chryseobacterium (0.58–5.49%) and Flavobacterium (0.27–3.17%), Acidothermus (3.13–5.38%), Massilia (0.78–2.52%) and Bradyrhizobium (2.03–3.64%). Notably, at the vegetative stage, Comamonas dominated the ED group, while Bacillus dominated the EH samples. Similarly, Burkholderia dominated the PD group, and Bradyrhizobium was dominant in PH (Fig. 2 C). On the other hand, the dominant fungal phyla were primarily composed of Ascomycota (56.03-72.0%), Basidiomycota (16.74–28.68%), Mucoromycota (8.83–18.24%), Mortierellomycota (8.35–14.22%), and Chytridiomycota (2.09–4.70%). At the vegetative stage, Mucoromycota dominated EH, while no phylum was found dominant in ED. Also, Ascomycota and Mortierellomycota were uniquely dominant in PH, while Basidiomycota and Chytridiomycota were found dominant in PD (Figure S3B). Similarly, the shared dominant fungal genera were primarily composed of Fusarium (6.77–12.08%), Apiotrichum (2.21–8.53%), Fusarium (6.77–6.41%) Saitozyma (3.13–6.41%), Neocosmospora (4.13–5.74%), Absidia (2.18–5.19%), and Talaromyces (1.52–3.14%). Notably, Trichoderma and Umbelopsis were uniquely dominant in EH, while Apiotrichum was dominant in ED. For tuber bulking stage, Mortierella was uniquely dominant in PH, Saitozyma was dominant in PD (Fig. 2 D). 3.3 Distinct taxonomic biomarkers associated with rhizosphere bacterial and fungal communities in diseased and healthy potato plants LEfSe identified distinct taxonomic biomarkers associated with bacterial communities in each treatment group. PH had the most extensive set of relatively abundant taxa, which was strongly associated with the genera Bradyrhizobium, and Cadidatus. PD was characterized by an enrichment of several Proteobacteria and Burkholderiales, which were strongly associated with the genus Burkholderia. At the vegetative stage, ED had the most extensive set of relatively abundant taxa, which was characterized by an enrichment of several Bacteroidota and Enterobacterales, although no significant genus marker was detected (Fig. 3 A, S4 A). EH showed minimal biomarker enrichment, with only a single significant taxon, strongly associated with the Genus Paenibacillus (Figure S4A). Similarly, fungal biomarker taxa were identified across the groups, where PH revealed enrichment in a broader range of enriched taxa, which was strongly associated with the genus Terramyces. PD was primarily associated with Tremellomycetes and Xylopini, although no significant genus markers were detected (Fig. 3 B, S4 B). At the vegetative stage, ED (green) showed a relatively abundant taxa, which was strongly associated with the genera Apiotrichum, while EH showed enrichment of genus Hygrocybe. 3.4 Functional shift of bacterial and fungal communities between diseased and healthy potato plants A distinct shift was also observed in the microbial functional potentials (obtained from FAPROTAX and FungalGuild databases) across plant health and developmental stages, with more functional potentials dominance in the diseased plants. The heatmap (Figure S5A) revealed clear clustering of bacterial functions across groups, reflecting distinct metabolic and ecological roles at different stages and health conditions using FAPROTAX. RS-infected treatments (ED and PD) showed strong enrichment (red) in functional groups associated with nitrogen and energy metabolism, including nitrite respiration, nitrate reduction, denitrification, fermentation, and chemoheterotrophy. Additionally, ED and PD exhibited elevated levels of functional groups of xenobiotic degradation pathways (e.g., hydrocarbon degradation, aromatic compound degradation). In contrast, PH and EH (healthy samples) were characterised by reduced relative abundance of functional groups (blue shades), particularly in categories related to pathogenesis (human pathogens, intracellular parasites), showing a more stable and less metabolically active microbial environment. Fungal functions predicted using FUNGuild (Figure S5B) also displayed distinct patterns across conditions, with a clear separation between infected and healthy stages. ED and EH together showed high relative abundance (red) of various saprotrophic and symbiotic groups, including wood saprotroph, ectomycorrhizal, lichenized, and endomycorrhizal. The PD treatment was the most distinct, exhibiting enhanced relative abundance of plant- and animal-associated parasitic functions such as plant pathogen, plant parasite, fungal parasite, animal endosymbiont, and clavicipitaceous endophyte. PH formed a separate cluster, marked by higher relative abundances of functional groups such as lichen parasite, leaf saprotroph, endophyte, and soil saprotroph. Furthermore, no significant differences were observed for functional alpha-diversity, which is consistent with what we observed for bacterial amplicon alpha-diversity results (Figure S6). 3.5 Abundance and differential root exudate metabolites between diseased and healthy potato plants The differential abundance heatmap revealed that several metabolic compounds, mostly unidentified, were more abundant in diseased than healthy potato plants (Figure S7). Furthermore, Partial Least Squares Discriminant Analysis (PLS-DA) further confirmed clear separation between the two groups, with distinct clustering and minimal overlap, indicating reliable class discrimination, Diseased and Healthy. Variation explained by the first and second components was 18.4% and 15.5%, respectively, for class separation in the positive node (Figure S8A). While variation explained by the first and second components was 20.1% and 12.9%, respectively, for the class separation in the negative node (Figure S8B). To screen for significantly different metabolites in the Diseased and Healthy potato plants, we used p < 0.05 and PLS-DA VIP ≥ 2.0 as the evaluation criteria. A total of 749 different metabolites were identified. In comparison with healthy plants, at the positive ion mode, 233 different metabolites were identified, of which 150 were relatively enriched in the diseased Potato plant (Figure S9A). Meanwhile, at the negative ion mode, 516 different metabolites were identified, of which 312 were enriched in diseased potato plant (Figure S9B). The most significant features contributing to the separations are highlighted in Figure S10. The above 749 differential metabolites were also annotated according to their chemical pathways (Table S2). The notable relative enriched and depleted metabolites in diseased plants were the Alkaloid derivative (hyponine D and Karakoline) Triterpenoid Derivative (Rotundifolioside I, Globostellatic Acid B), Polyketide derivative(Inflexin, Tylosin, Michaolide E, and Rotiorinol A), Indole derivative (Tryprostatin A, Paxilline and Ancistrotanzanine B), Acetylsalicylic acid (Martefragegin A), Diterpenoid derivative( Kansuiphorin B and Nicotianoside I), Monoterpenoid derivative (lactinolide and Lactiflorin), Indole derivative (Abacopterin A), Amino acid derivative (Valine), Siderophore (ferrirubin), Polyamine derivative (Caldopentamine(4+)) and phosphate derivative (5-(methylsulfanyl)-2,3-dioxopentyl phosphate) (Table S2 and S3). 3.6 Linking metabolic signatures to plant pathological outcomes Correlation analysis between metabolite profiles and disease index (DI) revealed a distinct pattern of metabolic response across plant health states, with metabolites showing a significant association with DI. Notably, metabolites which positively correlated with the Disease Index were represented on the right side of Fig. 4 A. Paxilline, Ferrirubin, Hyponine D, Caldopentamine (4+) strongly positively correlated, especially associated with ED and PD stages. On the other hand, Tyrosin, Abacopterin A, Lucensimycin E, Tryprostatin A negatively correlated with PH and EH. Furthermore, Partial Least Squares Discriminant Analysis (PLS-DA) was conducted to evaluate whether the disease index (DI) influenced the enriched metabolite profiles across different plant groups. The resulting score plot (Fig. 4 B) revealed distinct groupings, particularly for the EH and ED treatments, which showed tight and well-separated clusters. The PH group also formed a distinguishable cluster, although with some internal spread, while the PD group partially overlapped with PH and EH but still retained a recognizable boundary. Similarly, Partial Least Squares (PLS) Regression analysis was also employed to further investigate the relationship between metabolite composition and DI (Figure S11). A positive linear relationship was observed between DI and PLS Component 1 (metabolite scores). The regression model yielded an equation of y = 0.02x − 0.90 with an R² of 0.27, showing that approximately 27% of the variance in the metabolite score could be explained by DI. 3.7 Linking metabolomic signatures to microbial composition and functional potential Spearman correlation revealed that enriched root exudate metabolic profiles in the rhizosphere are linked to dominant microbial community structure. Among the strongest positive correlations, Abacopterin A exhibited a significant association with taxa from B radyrhizobium and Candidatus. Lactiflorin, Karakoline and Caldopentamine 4 + exhibited a positive correlation with Burkholderia spp While Acrophiarin, alongside Tylosin and Ferrirubin were positively correlated with taxa from Comamonas and Bacillus , respectively. In contrast, metabolites such as Valine and Abacopterin A displayed strong negative interactions with Comamonas, Chryseobacterium and Azospirilliums spp. Interestingly, several species from Chryseobacterium and Azospirillum , often associated with plant stress responses, were positively correlated with a cluster of metabolites, including tylosin, jaconine, and Acalyphin, respectively (Fig. 5 A). Furthermore, for the fungal community, Trichoderma, Talaromyces , and Mortierella spp showed positive associations with metabolites such as Abacopterin A, Dihydrocapsaicin, Acrophiarin, and Lucensimycin E. Also, Hygrocybe and Apiotrichum spp displayed strong negative correlations with metabolites such as 5-(methylsulfanyl)-2,3-dioxopentyl phosphate, Hyponine D, Inflexin, Ferrirubin and Scutebarbatine G. Neocosmospora and Umbelopsis displayed moderate to high correlation with Acrophiarin and Kansuiphorin B (Fig. 5 B). Furthermore, our results also showed a distinct link between enriched metabolites and microbial functional potentials associated with plant diseases, plant health, disease or health signal molecules, or the utilisation of microbes as predicted by FUNGuild and FAPROTAX. We performed a targeted Spearman correlation analysis using a curated subset of FAPROTAX-derived bacterial functions (Fig. 6 A). The analysis revealed distinct metabolite-function relationships. Notably, Acalychin displayed a strong positive correlation (p < 0.01) with plant_pathogen function, while Abacopterin A, Globostellatic Acid B showed a positive correlation with intracellular_parasites functions. Similarly, we performed a targeted Spearman correlation analysis using a curated subset of FUNGuild-derived fungal functions (Fig. 6 B). Our result showed that certain metabolites like Karakoline, Kansuiphorin B, and Acrophiarin were positively correlated with Plant.Pathogen and Pathotroph guilds. Also, Inflexin positively correlated with Plant.Parasite. On the other hand, Mycorrhizal and Endophytes were positively associated with metabolites including Rotiorinol A, Globostellatic Acid B, and Michaolide E. 4.0 Discussion Our results suggest a distinct shift in β diversity measure of the rhizosphere microbial communities to disease caused by RS, highlighting the impact of health status on both bacterial and fungal assemblages. Our findings are supported by previous studies conducted on crops such as cotton, tomato, and pepper (Batista et al., 2024 , Yan et al., 2024 ). Interestingly, no statistically significant differences (p > 0.05) were detected in the bacterial communities between RS-infected (ED) and healthy (EH) plants at the vegetative stage. This suggests that, during early plant development, bacterial communities remain relatively consistent regardless of infection status (Xiong et al., 2021 ). This pattern aligns with earlier observations indicating that early-stage microbial assembly is often driven more by host genetics, soil properties and environmental conditions than by host physiology (Zhalnina et al., 2018 ). However, in contrast to the bacterial community, significant differences (p < 0.05) were detected in the fungal communities between ED and EH. This may reflect rapid pathogen-induced suppression or recruitment of specific fungal taxa that reduce overall community variability (Gao et al., 2021 ). Notably, fungal community structure did not significantly differ between RS-infected plants at the vegetative (ED) and tuber bulking (PD) stages. This is in contrast with previous reports that emphasized the combined effects of plant development and disease on fungal assemblages (Gao et al., 2021 , Tao et al., 2025 ). However, the lack of statistical significance may mask more nuanced shifts within the community. Broader analysis suggests that fungal populations do undergo notable restructuring between developmental stages, potentially driven by host developmental signals and infection-related stress (Agler et al., 2016 , Gao et al., 2020 ). Our analyses further illustrate the strong influence of plant health status on microbial community composition across developmental stages, with a notably biomarker-rich profile observed in healthy plants, especially during the tuber bulking phase. This finding aligns with previous reports in crops such as pepper and tomato (Yan et al., 2024 ). Specifically, our data showed that Paenibacillus spp. had a high relative abundance in healthy plants at the vegetative stage (EH), while Bradyrhizobium spp and Candidatus taxa, as well as the fungus Terramyces spp were significantly enhanced at the tuber bulking stage (PH) (Fig. 4 A, B). These patterns support the idea that the recruitment of beneficial microbes is shaped by a synchronization between plant secretory activity and microbial metabolic needs, which may vary across developmental stages (Trivedi et al., 2020 , Xiong et al., 2021 ). Emerging evidence suggests that microbial communities with higher diversity tend to confer greater resistance to disease, likely due to intensified competition for resources and ecological niches (Yan et al., 2024 ). Plants are believed to engage in a strategy often described as “crying for help,” whereby they selectively recruit beneficial microbes from the surrounding soil to assist in combating pathogen invasion (Liu et al., 2021b, Wen et al., 2023 ). Several studies have highlighted the critical roles of these beneficial microbes in promoting plant growth and enhancing resistance to pathogens (Yan et al., 2024 , Wen et al., 2020 ). For instance, Paenibacillus spp and Stenotrophomonas rhizophila were reported to induce systemic resistance in host plants by priming defense-related gene expression, thereby enhancing immunity without causing stress-related damage (Lal et al., 2016 ). Similarly, Bradyrhizobium spp. have been reported to suppress soil-borne pathogens through the activation of defense signaling pathways, such as salicylic acid-mediated responses and the production of antimicrobial compounds (Meena et al., 2018 ). In contrast, Burkholderia spp. and the fungal Apiotrichum spp. were predominantly increased in relative abundance in the rhizosphere of diseased plants. The Burkholderia spp are commonly found in diseased plant rhizospheres due to thier production of beneficial metabolites and this include antimicrobial compounds that can inhibit the growth of pathogens, chitinases that break down fungal cell walls, and siderophores that scavenge iron, an essential nutrient for microbial growth (Elshafie and Camele, 2021 ). These features make it a promising candidate for biocontrol in pathogen-challenged soils (Magalhães et al., 2017 ). Similarly, the occurrence of Apiotrichum spp in diseased plant rhizospheres may reflect its opportunistic nature and its capacity to utilize stress-induced root exudates as a carbon source (James et al., 2016 ). Our findings suggest that microbial community composition is more strongly influenced by plant health status than by developmental stage, reinforcing previous observations made in tomato systems (Adedayo et al., 2022 ). This highlights the critical role of plant-pathogen interactions in shaping rhizosphere microbiomes, particularly considering the significant shifts in microbial recruitment dynamics under disease stress observed in this study. Furthermore, we observed distinct bacterial functional patterns based on FAPROTAX and FUNGUILD in ED and PD that reflect the functional microbiome’s response to RS infection, potentially pointing to microbial functions involved in defense, competition, or pathogen facilitation. We observed an enhancement in relative abundance of some potential functional microbiota in the diseased plants as compared to the healthy plants. RS-Infected treatment exhibited a marked increase in relative abundance of potential functional groups linked to nitrogen and energy metabolism, such as denitrification, nitrate reduction, nitrite respiration, and chemoheterotrophs, suggesting enhanced microbial respiration and nutrient turnover under diseased conditions (Louca et al., 2016 ). These functions may reflect microbial adaptation to hypoxic conditions or increased organic matter fluxes in the rhizosphere driven by disease-induced root damage (Luo et al., 2022 ). Moreover, elevated representation of potential xenobiotic degradation pathways (e.g., aromatic compound and hydrocarbon degradation) in ED and PD indicates that the diseased rhizosphere favoured stress-tolerant microbial consortia with detoxification capabilities, potentially part of a microbial response to infection-induced chemical shifts or oxidative stress (Guo et al., 2021 ). Conversely, the healthy plants (EH and PH) exhibited overall lower relative abundances of functional microbiota, particularly those associated with pathogenicity and parasitism, including pathogens and intracellular parasites. This suggests a more stable microbial environment, likely reflecting effective host regulation and ecological balance (Tkacz et al., 2020 ). The fungal functional patterns followed a slightly different path, where both EH and ED (early vegetative stages) showed similar relative abundance in saprotrophic and symbiotic guilds, such as wood Saprotroph, mycorrhizal, and Lichenized fungi, indicating a relatively conserved fungal baseline during early development, regardless of infection status (Carteron et al., 2021 , Yang et al., 2022 ). This shared relative abundance likely supports early-stage root colonization, organic matter degradation, and nutrient mobilization, consistent with early mycobiome establishment (Guo et al., 2024 ). In contrast, PD was functionally distinct, with increasing relative abundance of pathogenic and parasitic traits (Brown, 2023 ). This shift implies that fungi may actively contribute to disease progression or exploit weakened host defenses at advanced growth stages (García-Guzmán and Heil, 2014 ). Meanwhile, PH showed increased abundance of leaf saprotrophs, endophytes, and soil saprotrophs. These guilds are often linked to host/ecosystem function stability, nutrient turnover, and beneficial host associations, reinforcing the idea that mature, healthy plants selectively recruit functional fungi that enhance resilience (Albornoz et al., 2022 , Fadiji et al., 2023 ). Our results suggest that bacterial and fungal communities not only shift taxonomically but potentially also functionally in response to disease stress. Infected plants harbor microbiomes enriched in functions associated with stress response, while healthy plants tend to support microbial communities with more balanced and mutualistic ecological roles. Our Metabolomics result showed dominance of specific enriched root exudate metabolites in the diseased plants (Wen et al., 2020 ). We also observed distinct associations between enriched metabolites and disease index scores across different potato health stages, providing novel correlative evidence for a role of metabolites in disease outcomes (Wen et al., 2023 ). Notably, some metabolites exhibited positive correlations with the disease index, indicating progressive metabolic shifts associated with disease development and thus suggesting a potential role in the manifestation or signalling of disease stress as well as predicting plant health (Salam et al., 2023 ). Notably, several classes of root exudate metabolites, including alkaloids, polyketides, terpenoids, indole derivatives, and amino acid-related compounds, were differentially expressed, supporting that root exudation is a dominant and dynamic response mechanism in plants undergoing disease stress (Xing et al., 2024 , Yan et al., 2024 ). Although the primary goal of our study was to examine how plant health status influences root exudate profiles, it is important to acknowledge that a key limitation of this metabolomic analysis is that it cannot conclusively determine that all detected metabolites originate exclusively from root exudation. The identified metabolites likely represent a mixture of root-derived compounds and root microbial metabolic products. However, among the key enriched metabolites in the diseased plants were alkaloid derivatives such as hyponine D and karakoline, compounds known for their antimicrobial and allelopathic properties (Friedman, 2006 ). Alkaloids are commonly secreted by plant as a part of chemical defense, suggesting a targeted strategy to suppress pathogen proliferation or interfere with microbial signaling in the rhizosphere (Erb and Kliebenstein, 2020 ). Similarly, we observed the upregulation of triterpenoid derivatives like rotundifolioside I and globostellatic acid B which are associated with membrane disruption in microbes and may contribute to pathogen resistance by fortifying the rhizosphere against RS invasion (Akbar et al., 2024 ). The enhancement of various polyketide derivatives (inflexin, tylosin, michaolide E, rotiorinol A) further emphasizes the plant attempts to modulate microbial dynamics. Polyketides are structurally diverse secondary metabolites with potent antibacterial and antifungal properties, often involved in microbial community structuring (Bills and Gloer, 2016 ). Their accumulation under RS stress plant indicates an active chemical modulation of the microbiome, potentially recruiting beneficial or excluding antagonistic microbes (Berendsen et al., 2012 ). Additionally, the enrichment of indole derivatives such as tryprostatin A, paxilline, abacopterin A, and ancistrotanzanine B supports the importance of tryptophan-derived signaling compounds in shaping rhizosphere interactions. Indole compounds are well-known mediators of plant–microbe communication, and their increased presence may signal microbiome restructuring efforts or attempts to activate systemic resistance pathways (Mhlongo et al., 2018 ). Also, the detection of acetylsalicylic acid derivatives (e.g., martefragegin A) points toward the involvement of salicylic acid (SA)-mediated defense responses, a well-known plant immunity response against biotrophic pathogens (Roychowdhury et al., 2024 ). Meanwhile, diterpenoid and monoterpenoid derivatives (kansuiphorin B, nicotianoside I, lactinolide, lactiflorin) are consistent with compounds involved in stress signaling, defensive volatiles, and antioxidant activities, suggesting a complex chemical ecology underlying disease adaptation and resilience (Kutty and Mishra, 2023 ). Particularly intersting is the detection of ferrirubin, a siderophore with strong iron-chelating activity, and caldopentamine (4+), a polyamine derivative was downregulated. This agrees with an earlier study on root metabolites in potato (Xing et al., 2024 ). Although these molecules are known for nutrient competition and redox modulation in the rhizosphere, they may also suppress RS by limiting iron availability or stabilizing oxidative stress responses (Aznar and Dellagi, 2015 ). Furthermore, we observed significant microbial-metabolite interactions, supporting the hypothesis that specific microbial taxa likely play targeted roles in shaping the chemical landscape of the plant environment. The observed positive correlations between species for beneficial genera (e.g., Bradyrhizobium, Burkholderia ) and secondary metabolites such as Dihydrocapsaicin, iso-precytochalasin, and Lucensimycin E suggest microbial enhancement of host defense chemistry (Hacquard and Martin, 2024 ). This aligns with prior reports where plant-associated Bradyrhizobium spp was linked with systemic resistance induction and metabolite upregulation (Huang et al., 2022 , Greetatorn et al., 2025 ). Conversely, negative associations between species from opportunistic genera ( Chryseobacterium , and Comamonas ) and enriched metabolites may indicate competitive or degradative roles, potentially affecting the bioavailability of signaling compounds in the rhizosphere; such depletion patterns could impair host chemical defenses or alter plant-microbe feedback loops (Zhalnina et al., 2018 ). Importantly, the positive correlations of Candidatus taxa with some metabolites suggest that these uncultured or lesser-studied taxa may hold some functional importance in metabolite mediation, deserving further functional validation through metatranscriptomic or metabolomic approaches. On the other hand, species fungal genera such as Trichoderma and Mortierella are well-documented for promoting plant growth and defense through both hormonal modulation and secondary metabolite induction (Harman et al., 2004 , Ozimek and Hanaka, 2020 ). Their positive correlations with antimicrobial compounds like Lucensimycin E and Acrophiarin support the hypothesis that beneficial fungi may trigger or tolerate plant defensive metabolism as part of their beneficial colonization strategies (Yan et al., 2019 ). In contrast, negative associations observed for Hygrocybe and Apiotrichum spp may indicate metabolite-sensitive taxa, potentially suppressed in chemically active niches, or functioning as neutral endophytes/pathobionts that avoid metabolite-rich zones (Li et al., 2022 , Morales-Vargas et al., 2024 ). These interactions support the notion that metabolite-mediated signaling and sensing processes shape microbial community response, with implications for developing biocontrol and bioinoculant strategies in agriculture (Zhalnina et al., 2018 , Gupta et al., 2020 ). It also highlights the dynamic metabolic crosstalk occurring at the root-microbe interface, where microbial metabolism may actively shape or be shaped by the plant biochemical landscape (Haldar and Sengupta, 2015 ). To gain deeper insight into the ecological significance of changes in root exudate profiles, we examined how enriched metabolites are linked to microbial functional potentials relevant to plant health and disease. Using the FAPROTAX database to predict bacterial functions, we found that Acalyphin showed a strong positive correlation with the plant_pathogen function, implying that it may contribute to disease development by either supporting pathogen growth or acting as a chemical signal for microbial recruitment (Zhang et al., 2021 ). Similarly, Abacopterin A and Globostellatic Acid B were positively linked to the intracellular_parasites function, pointing to their potential role in facilitating or signaling intracellular bacterial colonization, in line with previous observations from pathobiome studies (Mannaa and Seo, 2021 ). In the case of fungi, functional predictions via FUNGuild revealed that metabolites such as Karakoline, Kansuiphorin B, and Acrophiarin were associated with the pathotroph and plant.pathogen guilds. This suggests that these compounds may be involved in fungal virulence or are produced as part of the plant stress response to pathogenic fungi (Stępień and Lalak-Kańczugowska, 2021 ). Inflexin also showed a positive relationship with the plant.parasite guild, suggesting its potential role in disease-related metabolic activity. On the other hand, some metabolites were more aligned with mutualistic interactions. For instance, Rotiorinol A, Acrophiarin and Michaolide E were positively correlated with mycorrhizal and endophyte guilds, suggesting they may facilitate the establishment or maintenance of beneficial fungi that enhance nutrient uptake or help suppress pathogens (Fadiji and Babalola, 2020 , Omomowo et al., 2018 ). Our study, therefore, reveals that the rhizosphere metabolite landscape plays a critical role in structuring the microbial functional composition during disease progression. Such metabolite-function associations offer potential for developing biochemical markers to track disease states and identify functional microbiome shifts from symbiosis to pathogenesis. However, we only provide correlative evidence in this study, and future manipulative experiments with individual metabolites are needed to assign exact functions of these metabolites in disease and microbiome assembly. Conclusions Taken together, our findings demonstrate that microbial community composition responds to both plant health status and developmental stage, while functional potentials and root exudate metabolomes respond more to disease stress. Our integrative analysis reveals that root exudate metabolic profiles and microbial functional potentials are likely intertwined in shaping plant health status under disease stress. Through parallel metabolomic and microbial community assessments, we identify specific root exudate metabolite signatures that strongly correlate with disease index variability in potato, highlighting potential key biochemical determinants of resistance and susceptibility. Beyond acting as passive indicators, enriched root exudate metabolites exhibited targeted associations with microbial groups known to influence host immunity, nutrient acquisition, and stress adaptation. Our results support the hypothesis that disease stress alters the recruitment patterns of beneficial microbial taxa and microbial functional traits. These findings advance our fundamental understanding of signaling and sensing, where plant-derived compounds and microbial responses may co-define plant health status (disease outcomes). These insights provide a new platform for designing metabolite-informed microbial interventions, advancing both sustainable crop management and early disease detection strategies in agricultural systems. Declarations Acknowledgment The authors would like to acknowledge the Mass Spectrometry Facility (MSF) of Western Sydney University for access to its instrumentation and staff. AEF is grateful to Western Sydney University for the Early Career Award. Author Contributions AEF and BKS designed the study, collected all data, conducted the analyses and wrote the manuscript in close consultation with BKS. Both authors approved the final version of the manuscript Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could influence the work reported in this paper. Funding. Potato disease works is funded by CRC Future Food Systems, Horticulture Innovation Australia to BKS and Western Sydney Early Career Gran to AF. Data availability statement The datasets presented in this study has been deposited in National Centre for Biotechnology Information SRA with data identification number: PRJNA1287307 (www.ncbi.nlm.nih.gov/bioproject/PRJNA1287307) and PRJNA1287285 (www.ncbi.nlm.nih.gov/bioproject/PRJNA1287285). References Ababa G (2024) Pathogenic diversity, ecology, epidemiology, and management practices of the potato bacterial wilt (Ralstonia solanacearum) disease. Cogent Food Agric 10:2407953 Adedayo AA, Fadiji AE, Babalola OO (2022) The effects of plant health status on the community structure and metabolic pathways of rhizosphere microbial communities associated with Solanum lycopersicum . Horticulturae 8:404 Agler MT, Ruhe J, Kroll S, Morhenn C, Kim S-T, Weigel D, Kemen EM (2016) Microbial hub taxa link host and abiotic factors to plant microbiome variation. PLoS Biol 14:e1002352 Akbar R, Sun J, Bo Y, Khattak WA, Khan AA, Jin C, Zeb U, Ullah N, Abbas A, Liu W (2024) Understanding the influence of secondary metabolites in plant invasion strategies: A comprehensive review. Plants 13:3162 Aksoy E, Demirel U, Bakhsh A, Zia MAB, Naeem M, Saeed F, Çalışkan S, Çalışkan ME (2021) Recent advances in potato ( Solanum tuberosum L.) breeding. In Al khyari J.M, Jain S.M and Johnson D.V (Eds) Advances in plant breeding strategies: Vegetable Crops: Volume 8: Bulbs, Roots and Tubers. Springer Cham, pp 409 – 408. https://doi.org/10.1007/978-3-030-66965-2 Albornoz FE, Prober SM, Ryan MH, Standish RJ (2022) Ecological interactions among microbial functional guilds in the plant-soil system and implications for ecosystem function. Plant Soil 476:301–313 Aznar A, Dellagi A (2015) New insights into the role of siderophores as triggers of plant immunity: what can we learn from animals? J Exp Bot 66:3001–3010 Batista BD, Wang J, Liu H, Kaur S, Macdonald CA, Qiu Z, Trivedi P, Delgado-Baquerizo M, Xiong C, Liang J (2024) Biotic and abiotic responses to soilborne pathogens and environmental predictors of soil health. Soil Biology and Biochemistry 189, 109246 Berendsen RL, Pieterse CM, Bakker PA (2012) The rhizosphere microbiome and plant health. Trends Plant Sci 17:478–486 Bills GF, Gloer JB (2016) Biologically active secondary metabolites from the fungi. Microbiol Spectr 4. 10.1128/microbiolspec. funk-0009-2016 Birch PR, Bryan G, Fenton B, Gilroy EM, Hein I, Jones JT, Prashar A, Taylor MA, Torrance L, Toth IK (2012) Crops that feed the world 8: potato: are the trends of increased global production sustainable? Food Secur 4:477–508 Bodenhausen N, Horton MW, Bergelson J (2013) Bacterial communities associated with the leaves and the roots of Arabidopsis thaliana . PLoS ONE 8:e56329 Brown AJ (2023) Fungal resilience and host–pathogen interactions: Future perspectives and opportunities. Parasite Immunol 45:e12946 Carteron A, Beigas M, Joly S, Turner BL, Laliberté E (2021) Temperate forests dominated by arbuscular or ectomycorrhizal fungi are characterized by strong shifts from saprotrophic to mycorrhizal fungi with increasing soil depth. Microb Ecol 82:377–390 Chang X, Wei D, Zeng Y, Zhao X, Hu Y, Wu X, Song C, Gong G, Chen H, Yang C (2022) Maize-soybean relay strip intercropping reshapes the rhizosphere bacterial community and recruits beneficial bacteria to suppress Fusarium root rot of soybean. Front Microbiol 13:1009689 Delgado-Baquerizo M, Guerra CA, Cano-Díaz C, Egidi E, Wang J-T, Eisenhauer N, Singh BK, Maestre FT (2020) The proportion of soil-borne pathogens increases with warming at the global scale. Nat Clim Change 10:550–554 Devaux A, Goffart J-P, Kromann P, Andrade-Piedra J, Polar V, Hareau G (2021) The potato of the future: opportunities and challenges in sustainable agri-food systems. Potato Res 64:681–720 Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R (2011) UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27:2194–2200 Eisfeld C, Schijven JF, Kastelein P, van Breukelen BM, Medema G, Velstra J, Teunis PF, van der Wolf JM (2022) Dose-response relationship of Ralstonia solanacearum and potato in greenhouse and in vitro experiments. Front Plant Sci 13:1074192 Elshafie HS, Camele I (2021) An overview of metabolic activity, beneficial and pathogenic aspects of Burkholderia spp. Metabolites 11:321 Erb M, Kliebenstein DJ (2020) Plant secondary metabolites as defenses, regulators, and primary metabolites: the blurred functional trichotomy. Plant Physiol 184:39–52 Fadiji AE, Babalola OO (2020) Elucidating mechanisms of endophytes used in plant protection and other bioactivities with multifunctional prospects. Front Bioeng Biotechnol 8:467 Fadiji AE, Yadav AN, Santoyo G, Babalola OO (2023) Understanding the plant-microbe interactions in environments exposed to abiotic stresses: An overview. Microbiol Res 271:127368 Friedman M (2006) Potato glycoalkaloids and metabolites: roles in the plant and in the diet. J Agric Food Chem 54:8655–8681 Gao C, Montoya L, Xu L, Madera M, Hollingsworth J, Purdom E, Singan V, Vogel J, Hutmacher RB, Dahlberg JA (2020) Fungal community assembly in drought-stressed sorghum shows stochasticity, selection, and universal ecological dynamics. Nat Commun 11:1–14 Gao M, Xiong C, Gao C, Tsui CK, Wang M-M, Zhou X, Zhang A-M, Cai L (2021) Disease-induced changes in plant microbiome assembly and functional adaptation. Microbiome 9:1–18 García-Guzmán G, Heil M (2014) Life histories of hosts and pathogens predict patterns in tropical fungal plant diseases. New Phytol 201:1106–1120 Greetatorn T, Boonchuen P, Piromyou P, Songwattana P, Wongdee J, Teamtisong K, Boonkerd N, Sato S, Teaumroong N, Tittabutr P (2025) Differential responses of Bradyrhizobium sp. SUTN9-2 to plant extracts and implications for endophytic interactions within different host plants. Sci Rep 15:3154 Gu Y, Liang W, Li Z, Liu S, Liang S, Lei P, Wang R, Gao N, Li S, Xu Z (2023) The biocontrol agent Bacillus velezensis T-5 changes the soil bacterial community composition by affecting the tomato root exudate profile. Plant Soil 490:669–680 Guo J, Ning H, Li Y, Xu Q, Shen Q, Ling N, Guo S (2024) Assemblages of rhizospheric and root endospheric mycobiota and their ecological associations with functional traits of rice. MBio 15:e02733–e02723 Guo Y, Luo H, Wang L, Xu M, Wan Y, Chou M, Shi P, Wei G (2021) Multifunctionality and microbial communities in agricultural soils regulate the dynamics of a soil-borne pathogen. Plant Soil 461:309–322 Gupta R, Bar MJTHPI, Applications (2020) Plant immunity, priming, and systemic resistance as mechanisms for Trichoderma spp. biocontrol. 81–110 Gutarra L, Herrera J, Fernandez E, Kreuze J, Lindqvist-Kreuze H (2017) Diversity, pathogenicity, and current occurrence of bacterial wilt bacterium Ralstonia solanacearum in Peru. Front Plant Sci 8:1221 Hacquard S, Martin FM (2024) The chemical language of plant–microbe–microbe associations: An introduction to a virtual issue. New Phytologist, Haichar FeZ, Marol C, Berge O, Rangel-Castro JI, Prosser JI, Balesdent J, Heulin T, Achouak W (2008) Plant host habitat and root exudates shape soil bacterial community structure. ISME J 2:1221–1230 Haldar S, Sengupta S (2015) Plant-microbe cross-talk in the rhizosphere: insight and biotechnological potential. Open Microbiol J 9:1–7 Harman GE, Howell CR, Viterbo A, Chet I, Lorito M (2004) Trichoderma species—opportunistic, avirulent plant symbionts. Nat Rev Microbiol 2:43–56 Huang X, Zeng Z, Chen Z, Tong X, Jiang J, He C, Xiang T (2022) Deciphering the potential of a plant growth promoting endophyte Rhizobium sp. WYJ-E13, and functional annotation of the genes involved in the metabolic pathway. Front Microbiol 13:1035167 Huo C, Lu J, Yin L, Wang P, Cheng WJP, Soil (2024) Coupled of carbon and nitrogen mineralization in rhizosphere soils along a temperate forest altitudinal gradient. Plant Soil 500:197–211 James SA, Bond CJ, Stanley R, Ravella SR, Péter G, Dlauchy D, Roberts IN (2016) Apiotrichum terrigenum sp. nov., a soil-associated yeast found in both the UK and mainland Europe. Int J Syst Evol MicroBiol 66:5046–5050 Jewell KS, Castronovo S, Wick A, Falås P, Joss A, Ternes TA (2016) New insights into the transformation of trimethoprim during biological wastewater treatment. Water Res 88:550–557 Kaur S, Egidi E, Qiu Z, Macdonald CA, Verma JP, Trivedi P, Wang J, Liu H, Singh BK (2022) Synthetic community improves crop performance and alters rhizosphere microbial communities. J Sustainable Agric Environ Ecol 1:118–131 Kõljalg U, Larsson KH, Abarenkov K, Nilsson RH, Alexander IJ, Eberhardt U, Erland S, Høiland K, Kjøller R, Larsson E (2005) UNITE: a database providing web-based methods for the molecular identification of ectomycorrhizal fungi. New Phytol 166:1063–1068 Kutty NN, Mishra M (2023) Dynamic distress calls: volatile info chemicals induce and regulate defense responses during herbivory. Front Plant Sci 14:1135000 Lal S, Chiarini L, Tabacchioni S (2016) New Insights in plant-associated Paenibacillus species: biocontrol and plant growth-promoting activity. In: Islam, M., Rahman, M., Pandey, P., Jha, C., Aeron, A. (eds) Bacilli and Agrobiotechnology. Springer, Cham.pp 237–239 https://doi.org/10.1007/978-3-319-44409-3_11 Li F, Jin Z, Wang Z, Liao Y, Yu L, Li X (2022) Host plant selection imprints structure and assembly of fungal community along the soil-root continuum. Msystems 7:e00361–e00322 Li J, Fan M, Shangguan Z (2020) Research advances in the main ecological functions of root exudates. Chin Bull Bot 55:788 Li J, Wang C, Liang W, Liu S (2021) Rhizosphere microbiome: The emerging barrier in plant-pathogen interactions. Front Microbiol 12:772420 Liu C, Cui Y, Li X, Yao M (2021a) microeco: an R package for data mining in microbial community ecology. FEMS Microbiol Ecol 97:fiaa255 Liu H, Li J, Carvalhais LC, Percy CD, Verma P, Schenk J, P. M., Singh BK 2021b. Evidence for the plant recruitment of beneficial microbes to suppress soil-borne pathogens. New Phytol 229, 2873–2885 Louca S, Parfrey LW, Doebeli M (2016) Decoupling function and taxonomy in the global ocean microbiome. Science 353:1272–1277 Luo L, Zhang J, Ye C, Li S, Duan S, Wang Z, Huang H, Liu Y, Deng W, Mei X (2022) Foliar pathogen infection manipulates soil health through root exudate-modified rhizosphere microbiome. Microbiol Spectr 10:e02418–e02422 Magalhães VC, de Oliveira Barbosa L, Andrade JP, Soares ACF, de Souza JT, Marbach PAS (2017) Burkholderia isolates from a sand dune leaf litter display biocontrol activity against the bole rot disease of Agave sisalana . Biol Control 112:41–48 Mannaa M, Seo Y-S (2021) Plants under the attack of allies: moving towards the plant pathobiome paradigm. Plants 10:125 Meena RS, Vijayakumar V, Yadav GS, Mitran T (2018) Response and interaction of Bradyrhizobium japonicum and arbuscular mycorrhizal fungi in the soybean rhizosphere. Plant Growth Regul 84:207–223 Mhlongo MI, Piater LA, Madala NE, Labuschagne N, Dubery IA (2018) The chemistry of plant–microbe interactions in the rhizosphere and the potential for metabolomics to reveal signaling related to defense priming and induced systemic resistance. Front Plant Sci 9:112 Mitter B, Brader G, Pfaffenbichler N, Sessitsch A (2019) Next generation microbiome applications for crop production—limitations and the need of knowledge-based solutions. Curr Opin Microbiol 49:59–65 Morales-Vargas AT, López-Ramírez V, Álvarez-Mejía C, Vázquez-Martínez JJM (2024) Endophytic fungi for crops adaptation to abiotic stresses. 12, 1357 Nguyen NH, Song Z, Bates ST, Branco S, Tedersoo L, Menke J, Schilling JS, Kennedy PG (2016) FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol 20:241–248 Omomowo IO, Fadiji AE, Omomowo OI (2018) Assessment of bio-efficacy of Glomus versiforme and Trichoderma harzianum in inhibiting powdery mildew disease and enhancing the growth of cowpea. Annals Agricultural Sci 63:9–17 Ozimek E, Hanaka A (2020) Mortierella species as the plant growth-promoting fungi present in the agricultural soils. Agriculture 11:7 Pang Z, Lu Y, Zhou G, Hui F, Xu L, Viau C, Spigelman AF, MacDonald PE, Wishart DS, Li S (2024) MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation. Nucleic Acids Res 52:W398–W406 Panth M, Hassler SC, Baysal-Gurel F (2020) Methods for management of soilborne diseases in crop production. Agriculture 10:16 Qiu Z, Verma JP, Liu H, Wang J, Batista BD, Kaur S, de Araujo Pereira AP, Macdonald CA, Trivedi P, Weaver T (2022) Response of the plant core microbiome to Fusarium oxysporum infection and identification of the pathobiome. Environ Microbiol 24:4652–4669 Qiu Z, Wang J, Delgado-Baquerizo M, Trivedi P, Egidi E, Chen Y-M, Zhang H, Singh BK (2020) Plant microbiomes: do different preservation approaches and primer sets alter our capacity to assess microbial diversity and community composition? Front Plant Sci 11:993 Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glöckner FO (2012) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41:D590–D596 Ren G, Wang G, Ma Y (2021) Research progresses on relationship between plant root exudates and soil-borne diseases. Soil 53:229–235 Robert CAM, Himmighofen P, McLaughlin S, Cofer TM, Khan SA, Siffert A, Sasse J (2025) Environmental and Biological Drivers of Root Exudation. Annu Rev Plant Biol 76:317–339 Roychowdhury R, Mishra S, Anand G, Dalal D, Gupta R, Kumar A, Gupta R (2024) Decoding the molecular mechanism underlying salicylic acid (SA)-mediated plant immunity: an integrated overview from its biosynthesis to the mode of action. Physiol Plant 176:e14399 Salam U, Ullah S, Tang Z-H, Elateeq AA, Khan Y, Khan J, Khan A, Ali S (2023) Plant metabolomics: an overview of the role of primary and secondary metabolites against different environmental stress factors. Life 13:706 Segata N, Waldron L, Ballarini A, Narasimhan V, Jousson O, Huttenhower C (2012) Metagenomic microbial community profiling using unique clade-specific marker genes. Nat Methods 9:811 Siddique MAB, Fateh FS, Saleem H (2020) Black scurf of potato disease prevalence in the markets of federal capital territory, Pakistan. Pakistan J Agricultural Res 33:440 Singh A, Delgad-Baquerizo M, Egidi E, Guirado E, Leach JE, Liu H, Trivedi P (2023a) 100253. Climate change impacts on plant pathogens, food secutiry and paths forward. Nat Rev Microbiol 21:640–656 Singh BK, Hu H-W, Macdonald CA, Xiong C (2025a) Microbiome-facilitated plant nutrient acquisition. Cell Host Microbe 33:869–881 Singh BK, Jiang G, Wei Z, Sáez-Sandino T, Gao M, Liu H, Xiong C (2025b) Plant pathogens, microbiomes, and soil health. Trends Microbiol. https://doi.org/10.1016/j.tim.2025.03.013 Singh BK, Yan Z-Z, Whittaker M, Vargas R, Abdelfattah A (2023b) Soil microbiomes must be explicitly included in One Health policy. Nat Microbiol 1–6. https://doi.org/10.1038/s41564-023-01386-y Stępień Ł, Lalak-Kańczugowska J (2021) Signaling pathways involved in virulence and stress response of plant-pathogenic Fusarium species. Fungal Biology Reviews 35:27–39 Tahat MM, Sijam K (2010) Ralstoina solanacearum: The bacterial wilt causal agent. Asian J Plant Sci 9:385 Tans-Kersten J, Huang H, Allen C (2001) Ralstonia solanacearum needs motility for invasive virulence on tomato. J Bacteriol 183:3597–3605 Tao J, Jin J, Lu P, Yu S, Gu M, Wang J, Zhang J, Cao P (2025) Bacterial wilt disease alters the structure and function of fungal communities around plant roots. BMC Plant Biolog 25:39 Tkacz A, Bestion E, Bo Z, Hortala M, Poole PS (2020) Influence of plant fraction, soil, and plant species on microbiota: a multikingdom comparison. MBio 11:e02785–e02719 Trivedi P, Leach JE, Tringe SG, Sa T, Singh BK (2020) Plant–microbiome interactions: from community assembly to plant health. Nat Rev Microbiol 18:607–621 Weinberger NV, Cibils-Stewart X, Brien C, Jewell N, Berger B, Cavagnaro TR, Salomon MJ, Mikhael M, Plett JM, Plett KL (2025) Plant phenotyping and root‐associated metabolomics reveal insights into pathogen protection by diverse arbuscular mycorrhizal fungi. Plants, People, Planet 2025, 1–15 Wen T, Xie P, Liu H, Liu T, Zhao M, Yang S, Niu G, Hale L, Singh BK, Kowalchuk GA (2023) Tapping the rhizosphere metabolites for the prebiotic control of soil-borne bacterial wilt disease. Nat Commun 14:4497 Wen T, Zhao M, Liu T, Huang Q, Yuan J, Shen Q (2020) High abundance of Ralstonia solanacearum changed tomato rhizosphere microbiome and metabolome. BMC Plant Biol 20:1–11 Xing Y, Zhang P, Zhang W, Yu C, Luo Z (2024) Continuous cropping of potato changed the metabolic pathway of root exudates to drive rhizosphere microflora. Front Microbiol 14:1318586 Xiong C, Singh BK, He J-Z, Han Y-L, Li P-P, Wan L-H, Meng G-Z, Liu S-Y, Wang J-T, Wu C-F (2021) Plant developmental stage drives the differentiation in ecological role of the maize microbiome. Microbiome 9:1–15 Yan L, Zhu J, Zhao X, Shi J, Jiang C, Shao D (2019) Beneficial effects of endophytic fungi colonization on plants. Appl Microbiol Biotechnol 103:3327–3340 Yan M, Wu M, Liu M, Li G, Liu K, Qiu C, Bao Y, Li Z (2024) Comparative analysis on root exudate and rhizosphere soil bacterial assembly between tomatoes and peppers infected by Ralstonia. Chem Biol Technol Agric 11:36 Yang C-X, Chen S-J, Hong X-Y, Wang L-Z, Wu H-M, Tang Y-Y, Gao Y-Y, Hao G-F (2025) Plant exudates driven microbiome recruitment and assembly facilitates plant health management. FEMS Microbiol Rev, fuaf008 Yang N, Zhang Y, Li J, Li X, Ruan H, Bhople P, Keiblinger K, Mao L, Liu D (2022) Interaction among soil nutrients, plant diversity and hypogeal fungal trophic guild modifies root-associated fungal diversity in coniferous forests of Chinese Southern Himalayas. Plant Soil 481:395–408 Yaqoob S, Bhatti HN, Sultana B, Shahid M (2020) Prognosticating the potential of Sorghum bicolor root exudates in response to abiotic stress. Pakistan J Agricultural Sci 57:1661–1668 Zhalnina K, Louie KB, Hao Z, Mansoori N, da Rocha UN, Shi S, Cho H, Karaoz U, Loqué D, Bowen BP (2018) Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly. Nat Microbiol 3:470–480 Zhang J, Liu Y-X, Guo X, Qin Y, Garrido-Oter R, Schulze-Lefert P, Bai Y (2021) High-throughput cultivation and identification of bacteria from the plant root microbiota. Nat Protoc 16:988–1012 Zhong Y, Xun W, Wang X, Tian S, Zhang Y, Li D, Zhou Y, Qin Y, Zhang B, Zhao G (2022) Root-secreted bitter triterpene modulates the rhizosphere microbiota to improve plant fitness. Nat Plants 8:887–896 Additional Declarations The authors declare no competing interests. Supplementary Files Supplementarymaterials.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7161758","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":487696924,"identity":"f99d6e66-b764-473b-8109-339a43cd9fd7","order_by":0,"name":"Brajesh Singh","email":"data:image/png;base64,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","orcid":"","institution":"Western Sydney University","correspondingAuthor":true,"prefix":"","firstName":"Brajesh","middleName":"","lastName":"Singh","suffix":""},{"id":487696925,"identity":"becbaf3d-e846-4dc4-a0a3-f12d2ab3f12d","order_by":1,"name":"Ayomide Fadiji","email":"","orcid":"","institution":"Western Sydney University","correspondingAuthor":false,"prefix":"","firstName":"Ayomide","middleName":"","lastName":"Fadiji","suffix":""}],"badges":[],"createdAt":"2025-07-19 04:25:15","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7161758/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7161758/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87247926,"identity":"8d6d1ae1-9970-4d0c-a929-97ecbf0a1f76","added_by":"auto","created_at":"2025-07-22 03:33:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":169037,"visible":true,"origin":"","legend":"\u003cp\u003eThe boxplots represent beta-dispersion analyses (based on Bray-Curtis dissimilarity) between of bacterial (A) and fungal (B) communities in diseased and healthy rhizosphere of potato plants. ED: Diseased (RS-infected) potato plants at vegetative stage (Green boxes); EH: Healthy Potato plant at vegetative stage (Orange boxes); PD: Diseased (RS-infected) potato plants at tuber bulking (Purple boxes); PH: Healthy potato plant at tuber bulking (Pink boxes). *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001, ns=not significant.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7161758/v1/170fd46f9cc02c1ba057d6a9.png"},{"id":87248583,"identity":"36e9ffaa-6971-42c7-b27e-6631c7a79554","added_by":"auto","created_at":"2025-07-22 03:41:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":535001,"visible":true,"origin":"","legend":"\u003cp\u003e(A)The Vein diagram showing the shared bacterial OTUs; (B)The Vein diagram showing the shared fungal OTUs between the samples. Rhizosphere microbial compositions at the genus level (C) bacteria and (D) fungi across diseased and healthy potato plants under different developmental stages. ED: Diseased (RS-infected) potato plants at vegetative stage; EH: Healthy Potato plant at vegetative stage; PD: Diseased (RS-infected) potato plants at tuber bulking stage; PH: Healthy potato plant at tuber bulking stage).\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7161758/v1/9f9219787c3e4bd15db3dfcf.png"},{"id":87247928,"identity":"fb4e3a71-fa5e-4e41-b700-53cc723f8b78","added_by":"auto","created_at":"2025-07-22 03:33:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":459489,"visible":true,"origin":"","legend":"\u003cp\u003eLinear Discriminant Analysis Effect Size (LEfSe) of differentially abundant bacterial and fungal taxa across diseased and healthy potato plants under different developmental stages. This figure displays the results of LEfSe, identifying taxa significantly enriched (LDA score ≥ 2.0, p \u0026lt; 0.05) in bacterial (A) and fungal (B) communities across four sample groups. Different prefixes indicate different levels (p, phylum; c, class; o, order; f, family; g, genus). ED: Diseased (RS-infected) potato plants at vegetative stage (Green boxes); EH: Healthy Potato plant at vegetative stage (Orange boxes); PD: Diseased (RS-infected) potato plants at tuber bulking (Purple boxes); PH: Healthy potato plant at tuber bulking (Pink boxes).\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7161758/v1/a86355cbd0309b61b9ab0430.png"},{"id":87247930,"identity":"fed1dc2a-3bf8-4d72-a2cc-28c2cff24162","added_by":"auto","created_at":"2025-07-22 03:33:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":548290,"visible":true,"origin":"","legend":"\u003cp\u003eIntegrated metabolomic insights reveal disease index-associated metabolic signatures in potato. (A) Spearman correlation between enriched metabolites and disease index, highlighting stage- specific associations. Positive correlations (right side) suggest potential markers of disease susceptibility, whereas negative correlations (left side) may reflect protective responses. (B) PLS-DA plot illustrating group-specific metabolic signatures across disease stages. Components 1 and 2 account for 8.7% and 6.5% of the total variance, respectively, contributing to class separation. ED (RS-infected early vegetative stage), EH (healthy early vegetative stage), PD (RS-infected post-emergence tuber bulking stage), and PH (healthy post-emergence tuber bulking stage)\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7161758/v1/1ae92ad88a3a1b00f19877ef.png"},{"id":87247931,"identity":"dd62f207-2038-4124-85cd-8a1a2518eb16","added_by":"auto","created_at":"2025-07-22 03:33:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":689155,"visible":true,"origin":"","legend":"\u003cp\u003eSpearman correlation heatmap between dominant (A) bacterial genera, (B) Fungal genera, and root exudate metabolites detected in the plant rhizosphere. Correlation coefficients were computed using Spearman’s method, and significance is indicated by asterisks (* 0.01 \u0026lt; p \u0026lt; 0.05, ** 0.001 \u0026lt; p \u0026lt; 0.01, *** p \u0026lt; 0.001). Hierarchical clustering was applied to both differential genera and metabolite profiles. Positive correlations are shaded red; negative correlations are blue.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7161758/v1/99b03f84d4f6bcf10c5949b8.png"},{"id":87248585,"identity":"4b834eec-5a5d-4d85-bc47-35f0caa1af2b","added_by":"auto","created_at":"2025-07-22 03:41:06","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":838491,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation heatmap reveals association enriched rhizosphere metabolites and ecological functions of bacterial and fungal communities. Spearman correlations between differentially abundant rhizosphere metabolites, functional potentials and ecological guilds predicted using FAPROTAX and FUNGuild annotation. Only guilds relevant to plant health, disease, or symbiotic interactions are shown. Red indicates positive correlation, blue indicates negative correlation, and significant correlations are marked (*p \u0026lt; 0.05, **p \u0026lt; 0.01).\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7161758/v1/b1af06007537b6d0fa03949b.png"},{"id":87468334,"identity":"aec72761-48cb-4526-800d-33ed19d6b866","added_by":"auto","created_at":"2025-07-24 08:14:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4302225,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7161758/v1/056a6ed1-578d-44a0-9d70-6533a29ac25d.pdf"},{"id":87248593,"identity":"2981cf0f-e57f-4ece-b153-1366982b39d8","added_by":"auto","created_at":"2025-07-22 03:41:06","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5816052,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-7161758/v1/fe5f434c83e19e4507e4f59a.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eResponses of root microbiome and metabolome are linked to crop disease severity\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1.0 Introduction","content":"\u003cp\u003eDisease impacts all crop and non-crop plants and has a significant negative impact on food security and environmental sustainability (Singh et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003eb\u003c/span\u003e). Current global modelling suggests that pathogen pressure will further intensify as global climate changes (Singh et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e, Delgado-Baquerizo et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Singh et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e). Chemical solutions are usually not effective in controlling soil-borne diseases, and they also have negative impacts on soil and human health (Panth et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, developing biological solutions is considered a priority, but are constrained by knowledge gaps on interactions between plant- microbiome and pathogens (Mitter et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, recent studies has reported production of multiple primary and secondary metabolites as signal molecules by plants in root exudates to attract beneficial microbiota to mitigate both abiotic and biotic stresses (Wen et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Singh et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e). Beneficial microbiota that can sense those metabolites and are able to colonise the plant, respond to those signal to mitigate stresses (Robert et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2025\u003c/span\u003e, Yang et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Therefore, plants actively shape rhizosphere microbial communities by secreting signalling compounds in root exudates, a process that is strongly governed by health status, plant genotype and soil conditions (Li et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Gu et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Yan et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For example, current evidence suggests that plants under pathogen attack secrete metabolites to attract beneficial microbiota to mitigate disease impact, but the identity of metabolites that are produced and the microbes which respond to these metabolites are not fully known (Yan et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Further, beneficial microbiota that respond to metabolites potentially contribute to plant defence through a variety of mechanisms, including triggering systemic resistance, synthesizing phytohormones, outcompeting harmful organisms for nutrients and habitat niches, and enhancing the overall resilience of plants to stress (Chang et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Zhong et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Yan et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, empirical evidence remains limited.\u003c/p\u003e\u003cp\u003ePotato (\u003cem\u003eSolanum tuberosum\u003c/em\u003e L.) is one of the world's most significant crops, ranking fourth behind wheat, corn, and rice (Birch et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Aksoy et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Devaux et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Also, potatoes are vulnerable to many soil-borne pathogens including \u003cem\u003eRalstonia solanacearum (\u003c/em\u003eRS), which causes bacterial wilt disease (Ababa, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Tahat and Sijam, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Gutarra et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Bacterial wilt is a major potato disease which causes significant yield loss and, in some cases, complete crop failure. Once set in a field condition, managing bacterial wilt is difficult because the pathogen can survive in soils for many years even in the absence of host, and can be transmitted by infected seed tubers, soils and irrigation water. Previous reports suggested that in response to RS infection and other biotic threats, the rhizosphere\u0026mdash;the narrow region of soil influenced by root activity undergoes dynamic changes regulated by the host plant (Yaqoob et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Ren et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Given that the rhizosphere zone is a highly specialized environment which is known to be shaped by root exudation and the activities of soil microbiota, and their interactions ultimately determine the plant health and productivity under stress conditions (Berendsen et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Li et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Theoretically, the metabolic composition of root exudates and the structure of rhizosphere microbial communities are largely governed by factors such as plant health, developmental stage and plant genotype (Xing et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Yan et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), but empirical evidence remain limited (Kaur et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Qiu et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It is proposed that even when grown in the same soil, different plants under pathogen infection may release distinct exudate profiles depending on plant developmental stages, which in turn may attract beneficial microbiota to mitigate disease pressures (Haichar et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Yan et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, the metabolic and microbial response to RS infection in potato remains largely unknown. This is a critical knowledge gap that constraints our ability to advance fundamental science on plant-microbial interactions and to develop effective biological solutions for disease management (Fadiji et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHere, in this study, we used untargeted metabolomics and microbiome sequencing to examine the impacts of potato bacterial wilt disease caused by RS on rhizosphere microbial communities and root exudates in both healthy and diseased potato plants across two developmental stages (vegetative and tuber bulking). We hypothesize that the composition, structure, and diversity of both the rhizosphere microbiome and root exudate profiles are influenced by the health status of the host plant. We propose that disease stress alters the recruitment patterns of beneficial microbial taxa via the production of distinct plant metabolites.\u003c/p\u003e"},{"header":"2.0 Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Soil collection, pathogen and potato seeds, pathogenicity test\u003c/h2\u003e\u003cp\u003eA controlled glasshouse experiment was carried out at the Hawkesbury Institute for the Environment, Western Sydney University (WSU), New South Wales (NSW), Australia, from July to October 2024. The soil used in the study was sourced from the nearby experimental plot within the Institute\u0026rsquo;s teaching and research farm. \u003cem\u003eRalstonia solanacearum\u003c/em\u003e strain RS2 were obtained from the gene bank of the Department of Primary Industries, Australia. Before use in the experiment, the isolates were stored at \u0026minus;\u0026thinsp;80\u0026deg;C on sucrose peptone agar (SPA) as recommended to maintain viability. Certified seed tubers of the potato cultivar \u0026lsquo;Russet Burbank\u0026rsquo; were sourced from a commercial supplier (Happy Valley Seeds) based in Sydney, New South Wales, Australia. Five days prior to inoculation, plants were subjected to restricted watering to simulate mild water stress. In the dose-response assay, 30-day-old, unwounded potato plants were inoculated using a soil-drench method adapted from (Tans-Kersten et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). A 50 mL suspension of \u003cem\u003eRalstonia solanacearum\u003c/em\u003e, prepared at the desired concentration, was applied directly to the soil surrounding the base of each plant, followed by 200 mL of water to facilitate movement of the inoculum into the rhizosphere. Given the pot surface area of 415 cm\u0026sup2;, this corresponded to a total irrigation depth of 6 mm, of which 1.2 mm was from the inoculum. For the potato tested, 12 plants were used\u0026mdash;6 plants received the bacterial suspension, while the remaining 6 were mock-treated with 50 mL of sterile Ringer\u0026rsquo;s solution and served as negative controls. As anticipated, plants treated with the 10⁸ CFU/mL inoculum exhibited full infection (Figure S1). Pathogen infection was subsequently confirmed using a modified detection protocol of Eisfeld et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Greenhouse experiment and sample collection\u003c/h2\u003e\u003cp\u003eFor the experiment, approximately 100 pre-sprouted mini-tubers were prepared by exposing them to light at 15\u0026deg;C for two weeks. Once sprouting occurred, the tubers were planted individually into 5 L pots filled with 4 kg of clay loam soil. The soil composition included approximately 27% clay, 33% sand, and 32% silt. Prior to potting, the air-dried soil was loosely sieved using a 1 x 1 cm mesh to remove coarse debris. In each pot, a hole roughly 5 cm deep was made, into which a single mini-tuber was placed with the sprouts facing upward and then lightly covered with soil. Pots were placed on saucers and watered from above. The glasshouse was maintained at a constant temperature of 23\u0026deg;C and relative humidity of 70%. After plant emergence, a 16-hour photoperiod was maintained, supplemented with high-pressure sodium lamps (150 W/m\u0026sup2;) when natural light was insufficient. Daily top watering was continued until five days before inoculation, at which point irrigation was restricted to induce mild water stress. Subsequently, the potato tested, 24 plants were used for each developmental stage (vegetative and tuber bulking stages). Among which, 12 plants received the bacterial suspensions as earlier described, while the remaining 12 plants were treated with sterile Ringer\u0026rsquo;s solution. All pots with the plant samples were placed in the same glasshouse till the experiment was terminated. Samples were grouped based on plant health and developmental stage: ED: Diseased (RS-infected) potato plants at early (vegetative) stage; EH: Healthy Potato plant at early stage; PD: Diseased (RS-infected) potato plants at tuber bulking (Post-emergence) stage; and PH: Healthy potato plant at tuber bulking (Post-emergence). Post-inoculation, plants were watered through the saucers to prevent cross-contamination via splash or aerosol movement. For plants showing bacterial wilt symptoms, watering volumes were carefully adjusted based on physical plant water requirements to avoid waterlogging. Plants were monitored weekly for disease symptom development throughout the duration of the experiment.\u003c/p\u003e\u003cp\u003ePotato plants were carefully removed intact from the pots at two key developmental stages: the vegetative stage (35 days after planting) and the tuber bulking stage (65 days after planting). Rhizosphere soil samples were collected using the \"soil adhering to fine roots after shaking\" method as described by Huo et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Briefly, fine roots were gently brushed with sterile brushes to collect soil particles closely adhering to the root surface, which were then defined as rhizosphere soil. Each sample was divided into two portions\u0026mdash;one was air-dried for physicochemical analysis, and the other was stored at \u0026minus;\u0026thinsp;80\u0026deg;C for molecular and biochemical analyses. To ensure consistent and reliable extraction, we divided the roots from each group into two sets of six plants. This provided biological duplicates across treatment groups and helped to obtain enough material for water-based extraction, given the gentle nature of ultrapure water as a solvent. The plants were transferred to 250 mL conical flasks containing ultrapure water, ensuring that the roots were fully submerged. After a 24-hour incubation (16 hours under light and 8 hours in darkness), the root exudate solution was collected and centrifuged at 1200 rpm for 15 minutes at 4\u0026deg;C (Xing et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The resulting supernatant was filtered through a 0.22 \u0026micro;m membrane filter, and the filtrate was subsequently freeze-dried. The dried root exudate samples were stored at \u0026minus;\u0026thinsp;80\u0026deg;C for further analysis. Sample collections were grouped based on early vegetative healthy (EH), early vegetative diseased (ED), post-emergence at tuber bulking healthy (PH), and diseased (PD). Disease index was calculated using the modified method of Siddique et al. (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) (Table S1). Root exudate samples were analyzed at the Mass Spectrometry Facility (MSF) of Western Sydney University.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.5 DNA extraction and sequencing\u003c/h2\u003e\u003cp\u003eSoil genomic DNA (gDNA) was extracted from \u0026sim;0.25 g rhizosphere soil samples using the PowerSoil PRO DNA Isolation Kit (Qiagen, Hilden, Germany) following the manufacturer's instructions. The quantity and quality of extracted gDNA were verified using NanoDrop (Thermo Scientific, Wilmington, DE) and electrophoresis (1% agarose gel, including a 1 kb plus ladder). PCR amplification was performed for each soil DNA extract in triplicate and combined into a single composite sample. Amplicons targeting the V5\u0026ndash;V7 region of the 16S rRNA gene (799F\u0026ndash;1193R) (Bodenhausen et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and the ITS2 region (FITS7-ITS4R, (1) were obtained by PCR to characterize bacterial and fungal communities, respectively, according to Qiu et al. (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Amplicon sequencing was performed at the Ramaciotti Centre for Genomics at the University of New South Wales (Sydney, Australia), using the Illumina NextSeQ (PE 300 bp) platform. Paired-end (PE) reads obtained from previous steps were assembled by USEARCH (version 10) (Segata et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and followed by chimera removal using UCHIME (version 8.1) (Edgar et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Representative sequences were annotated against the Silva database for 16S rRNA reads (Quast et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and the UNITE database for ITS2 reads (K\u0026otilde;ljalg et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Chloroplast/mitochondrial sequences were excluded from the downstream analysis. The high-quality reads generated by the above steps were used for subsequent analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Non-targeted metabolomic analysis\u003c/h2\u003e\u003cp\u003eUntargeted metabolite profiling of the root exudates was conducted using liquid chromatography coupled with tandem mass spectrometry (LC\u0026ndash;MS/MS), carried out at Biomarker Technologies Co., Ltd. Briefly, 50 mg of freeze-dried exudate sample was extracted with 1 mL of a cold solvent mixture consisting of methanol, acetonitrile, and water in a 2:2:1 ratio. The mixture was vortexed for 30 seconds, after which steel grinding beads were added, and samples were homogenized using a high-frequency grinder at 45 Hz for 10 minutes. This was followed by 10 minutes of ultrasonication in an ice-water bath, as adapted from Xing et al. (\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The mixture was then allowed to stand at 20\u0026deg;C for 1 hour to precipitate proteins, followed by centrifugation at 12,000 rpm for 15 minutes at 4\u0026deg;C. A volume of 500 \u0026micro;L of the supernatant was transferred to a clean EP tube and evaporated to dryness using a vacuum concentrator. The residue was then reconstituted in 160 \u0026micro;L of extraction solvent (acetonitrile:water, 1:1 v/v), vortexed for 30 seconds, ultrasonicated again for 10 minutes in an ice-water bath, and centrifuged at 12,000 rpm for another 15 minutes at 4\u0026deg;C. Finally, 120 \u0026micro;L of the resulting supernatant was subjected to LC\u0026ndash;MS/MS analysis using an ACQUITY UPLC I-Class FTN system coupled to a Waters Synapt G2-Si HDMS mass spectrometer (Waters, Wilmslow, UK) equipped with a Unispray ionization source (see Methods S1 for full details). To ensure comprehensive coverage of chemically diverse metabolites, both positive and negative electrospray ionization (ESI) modes were performed.\u003c/p\u003e\u003cp\u003eMass spectrometry data were processed using Progenesis QI software, version 3.0 (Nonlinear Dynamics, Waters Corporation, UK). Automated workflows within the software were used for peak alignment, peak picking, and deconvolution to extract peak intensity values for downstream statistical analysis. Putative identification and annotation of metabolomic features were conducted via Progenesis QI\u0026rsquo;s integrated access to the ChemSpider web-based structure database, incorporating several public repositories including ChEBI, Phenol-Explorer, PlantCyc, KEGG, and the Golm Metabolome Database. Identification parameters were set with a precursor ion mass tolerance of 15 ppm and a fragment ion tolerance of 50 ppm. To ensure comprehensive coverage of chemically diverse metabolites, untargeted metabolomics was performed in both positive and negative electrospray ionization (ESI) modes Each candidate identification was evaluated using a confidence scoring system that integrated mass accuracy, isotope pattern similarity, and fragmentation match quality. Metabolites with the highest overall confidence score or the best fragmentation match were selected as putative identifications. Additionally, chemical classification of metabolites was performed based on their core structural characteristics.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Data processing and analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were conducted using R software. To account for differences in sequencing depth among samples and reduce potential bias, all bacterial community samples were rarefied to 2,000 reads per sample, and fungal community samples were rarefied to 1,000 reads per sample, corresponding to the minimum read count observed and retained across samples in each dataset. Visualization of microbial patterns, including bar plots, heatmaps, and Venn diagrams, was performed using R version 3.2.3, employing the \u0026ldquo;pheatmap\u0026rdquo; and \u0026ldquo;VennDiagram\u0026rdquo; packages. Microbial β-diversity was assessed using Bray\u0026ndash;Curtis dissimilarity, followed by PERMANOVA to test for overall differences in community composition. When PERMANOVA results were significant, post hoc pairwise comparisons were conducted with Bonferroni correction to identify group-specific differences. Additionally, analysis of similarity (ANOSIM) was used to evaluate community dissimilarities between different sampling groups. Post hoc significance tests used the Bonferroni correction. Linear discriminant analysis (LDA) of effect size (LEfSe analysis) was applied on the OTU level to identify the differentially abundant bacterial and fungal taxa (at phylum to genus levels) that significantly change between samples from the diseased and healthy plant sites. Wilcoxon rank-sum test for pairwise comparison (false discovery rate (FDR) adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and the absolute LDA score (\u0026gt;\u0026thinsp;4) were used to analyze the statistical significance and strength, respectively.\u003c/p\u003e\u003cp\u003eTo infer microbial ecological functions, the FAPROTAX database was used for predicting putative functions of bacterial taxa (Jewell et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Liu et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e), while fungal functional guilds were annotated using the FUNGuild database (Nguyen et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The functional profiles were computed from OTUs defined as consistently prevalent and relatively abundant taxa within each group and standardized using z-score transformation to emphasize compartment-specific enrichment patterns. Functional profiles were derived from operational taxonomic units (OTUs) that were both consistently prevalent and relatively abundant within each group. These profiles were standardized using z-score transformation to highlight compartment-specific patterns of functional enrichment.\u003c/p\u003e\u003cp\u003eMultivariate statistical analyses of metabolite peak intensities (positive and negative ion modes) were conducted using MetaboAnalyst 6.0 (Pang et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). To ensure data quality and focus on biologically relevant features, metabolomic peaks with low variability across samples were filtered out. Specifically, features with log₁₀ fold change (log₁₀FC) values below 2 between treatments were excluded, along with those exhibiting low intensity (peak intensity\u0026thinsp;\u0026lt;\u0026thinsp;50 across all samples). A clustered heatmap was generated to visualize the relative abundance of dominant metabolites across treatment conditions. Subsequent analyses were performed on data from four biological replicates per treatment. To explore discriminative patterns between healthy and diseased plants, Partial Least Squares Discriminant Analysis (PLS-DA) was applied to all expressed metabolites, with a 95% confidence region defining group separation. A fold-change analysis (threshold FC\u0026thinsp;\u0026ge;\u0026thinsp;2.0) was used to identify significantly altered metabolites between healthy and diseased potato roots (Weinberger et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Metabolomic features with consistent differences (defined as \u0026gt;\u0026thinsp;75% reproducibility) were considered significantly up- or downregulated. To investigate potential interactions between microbial taxa, functional potentials, and metabolite profiles, Spearman\u0026rsquo;s rank correlation was calculated between microbial genera, functional potentials and enriched identified metabolites. The results were visualised using the pheatmap package in R. Hierarchical clustering was applied to both rows (metabolites) and columns (genera and microbial functions), using Euclidean distance and complete linkage to identify similar correlation patterns.\u003c/p\u003e\u003c/div\u003e"},{"header":"3.0 Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Response of rhizosphere microbiomes to plant health and developmental stage in potato plants\u003c/h2\u003e\u003cp\u003eBray-Curtis dissimilarity indices showed that both bacterial and fungal communities exhibited significant compositional shifts across plant health and developmental stages (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Interestingly, at the early vegetative stage, no significant difference (\u0026gt;\u0026thinsp;0.05) in bacterial community was observed between diseased plants (ED, green box) and healthy plants (EH, orange box). However, during the tuber bulking stage, a highly significant difference was observed for microbial communities between diseased plants (PD, purple box) and healthy plants (PH, pink box) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). For fungi community, Bray\u0026ndash;Curtis dissimilarity showed structural variation among healthy and RS-infected potato plants across growth stages (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). A significant difference in community dissimilarity was observed in early healthy plants (EH, orange box) compared to early diseased plants (ED, green box) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting distinct fungal community structuring in response to RS infection at the vegetative stage. At the tuber bulking stage, a modest but statistically significant difference was also detected between healthy plants (PH, pink box) and diseased plants (PD, purple box) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), showing that fungal communities in PH were more dissimilar compared to PD.\u003c/p\u003e\u003cp\u003eAlpha diversity metrics revealed insights into the richness and evenness of microbial communities associated with RS-infected and healthy potato plants at different developmental stages (Figure S2). For the bacteria community, no significant difference (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) was observed in richness estimates (Chao1 and Observed OTUs) between EH and ED plants at the vegetative stage. However, a significant difference was detected in the Shannon diversity index, with EH exhibiting higher diversity than ED (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating greater bacterial community evenness and complexity in healthy plants (Figure S2A). Simpson diversity, however, showed no significant variation across groups. At the tuber bulking stage, comparisons between PH and PD plants revealed no statistically significant differences across all four alpha diversity indices. Both richness (Chao1, Observed) and diversity (Shannon, Simpson) metrics remained relatively stable between PH and PD. For the fungi community, no significant differences were observed between groups for any of the alpha diversity indices (richness and diversity) assessed across the stages and treatments (Figure S2B).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Microbial community structure and composition between diseased and healthy potato\u003c/h2\u003e\u003cp\u003eThe Vein diagram (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) showed the shared bacteria and fungi OTUs between the samples, respectively, which increased in relative abundance in the diseased as compared to the healthy plants. The dominant bacterial phyla were primarily composed of Proteobacteria (40.6\u0026ndash;54.4%), Firmicutes (12.9-22.19%), Actinobacteriota (19.0-26.47%), Bacteroidota (2.46\u0026ndash;11.05%), Acidobacteriota (7.39\u0026ndash;14.9%), Planctomycetota (2.46\u0026ndash;11.05%), and Verrucomicrobiota (2.54\u0026ndash;4.45%). At the vegetative stage, Firmicutes were uniquely dominant in ED, while no phylum was uniquely dominant in EH (Figure S3A). On the other hand, Actinobacteriota, Acidobacteriota and Verrucomicrobiota were uniquely dominant in PH, while Proteobacteria were uniquely dominant in PD. Furthermore, the shared dominant bacterial genera were primarily composed of Burkholderia (4.74\u0026ndash;17.83%), Bacillus (4.72\u0026ndash;7.13%), Comamonas (0.42\u0026ndash;5.02%), Chryseobacterium (0.58\u0026ndash;5.49%) and Flavobacterium (0.27\u0026ndash;3.17%), Acidothermus (3.13\u0026ndash;5.38%), Massilia (0.78\u0026ndash;2.52%) and Bradyrhizobium (2.03\u0026ndash;3.64%). Notably, at the vegetative stage, Comamonas dominated the ED group, while Bacillus dominated the EH samples. Similarly, Burkholderia dominated the PD group, and Bradyrhizobium was dominant in PH (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003eOn the other hand, the dominant fungal phyla were primarily composed of Ascomycota (56.03-72.0%), Basidiomycota (16.74\u0026ndash;28.68%), Mucoromycota (8.83\u0026ndash;18.24%), Mortierellomycota (8.35\u0026ndash;14.22%), and Chytridiomycota (2.09\u0026ndash;4.70%). At the vegetative stage, Mucoromycota dominated EH, while no phylum was found dominant in ED. Also, Ascomycota and Mortierellomycota were uniquely dominant in PH, while Basidiomycota and Chytridiomycota were found dominant in PD (Figure S3B). Similarly, the shared dominant fungal genera were primarily composed of Fusarium (6.77\u0026ndash;12.08%), Apiotrichum (2.21\u0026ndash;8.53%), Fusarium (6.77\u0026ndash;6.41%) Saitozyma (3.13\u0026ndash;6.41%), Neocosmospora (4.13\u0026ndash;5.74%), Absidia (2.18\u0026ndash;5.19%), and Talaromyces (1.52\u0026ndash;3.14%). Notably, Trichoderma and Umbelopsis were uniquely dominant in EH, while Apiotrichum was dominant in ED. For tuber bulking stage, Mortierella was uniquely dominant in PH, Saitozyma was dominant in PD (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.3 Distinct taxonomic biomarkers associated with rhizosphere bacterial and fungal communities in diseased and healthy potato plants\u003c/b\u003e\u003c/p\u003e\u003cp\u003eLEfSe identified distinct taxonomic biomarkers associated with bacterial communities in each treatment group. PH had the most extensive set of relatively abundant taxa, which was strongly associated with the genera Bradyrhizobium, and Cadidatus. PD was characterized by an enrichment of several Proteobacteria and Burkholderiales, which were strongly associated with the genus Burkholderia. At the vegetative stage, ED had the most extensive set of relatively abundant taxa, which was characterized by an enrichment of several Bacteroidota and Enterobacterales, although no significant genus marker was detected (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003eS4\u003c/span\u003eA). EH showed minimal biomarker enrichment, with only a single significant taxon, strongly associated with the Genus Paenibacillus (Figure S4A).\u003c/p\u003e\u003cp\u003eSimilarly, fungal biomarker taxa were identified across the groups, where PH revealed enrichment in a broader range of enriched taxa, which was strongly associated with the genus Terramyces. PD was primarily associated with Tremellomycetes and Xylopini, although no significant genus markers were detected (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003eS4\u003c/span\u003eB). At the vegetative stage, ED (green) showed a relatively abundant taxa, which was strongly associated with the genera Apiotrichum, while EH showed enrichment of genus Hygrocybe.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Functional shift of bacterial and fungal communities between diseased and healthy potato plants\u003c/h2\u003e\u003cp\u003eA distinct shift was also observed in the microbial functional potentials (obtained from FAPROTAX and FungalGuild databases) across plant health and developmental stages, with more functional potentials dominance in the diseased plants. The heatmap (Figure S5A) revealed clear clustering of bacterial functions across groups, reflecting distinct metabolic and ecological roles at different stages and health conditions using FAPROTAX. RS-infected treatments (ED and PD) showed strong enrichment (red) in functional groups associated with nitrogen and energy metabolism, including nitrite respiration, nitrate reduction, denitrification, fermentation, and chemoheterotrophy. Additionally, ED and PD exhibited elevated levels of functional groups of xenobiotic degradation pathways (e.g., hydrocarbon degradation, aromatic compound degradation). In contrast, PH and EH (healthy samples) were characterised by reduced relative abundance of functional groups (blue shades), particularly in categories related to pathogenesis (human pathogens, intracellular parasites), showing a more stable and less metabolically active microbial environment.\u003c/p\u003e\u003cp\u003eFungal functions predicted using FUNGuild (Figure S5B) also displayed distinct patterns across conditions, with a clear separation between infected and healthy stages. ED and EH together showed high relative abundance (red) of various saprotrophic and symbiotic groups, including wood saprotroph, ectomycorrhizal, lichenized, and endomycorrhizal. The PD treatment was the most distinct, exhibiting enhanced relative abundance of plant- and animal-associated parasitic functions such as plant pathogen, plant parasite, fungal parasite, animal endosymbiont, and clavicipitaceous endophyte. PH formed a separate cluster, marked by higher relative abundances of functional groups such as lichen parasite, leaf saprotroph, endophyte, and soil saprotroph. Furthermore, no significant differences were observed for functional alpha-diversity, which is consistent with what we observed for bacterial amplicon alpha-diversity results (Figure S6).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Abundance and differential root exudate metabolites between diseased and healthy potato plants\u003c/h2\u003e\u003cp\u003eThe differential abundance heatmap revealed that several metabolic compounds, mostly unidentified, were more abundant in diseased than healthy potato plants (Figure S7). Furthermore, Partial Least Squares Discriminant Analysis (PLS-DA) further confirmed clear separation between the two groups, with distinct clustering and minimal overlap, indicating reliable class discrimination, Diseased and Healthy. Variation explained by the first and second components was 18.4% and 15.5%, respectively, for class separation in the positive node (Figure S8A). While variation explained by the first and second components was 20.1% and 12.9%, respectively, for the class separation in the negative node (Figure S8B). To screen for significantly different metabolites in the Diseased and Healthy potato plants, we used p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and PLS-DA VIP\u0026thinsp;\u0026ge;\u0026thinsp;2.0 as the evaluation criteria. A total of 749 different metabolites were identified. In comparison with healthy plants, at the positive ion mode, 233 different metabolites were identified, of which 150 were relatively enriched in the diseased Potato plant (Figure S9A). Meanwhile, at the negative ion mode, 516 different metabolites were identified, of which 312 were enriched in diseased potato plant (Figure S9B). The most significant features contributing to the separations are highlighted in Figure S10. The above 749 differential metabolites were also annotated according to their chemical pathways (Table S2). The notable relative enriched and depleted metabolites in diseased plants were the Alkaloid derivative (hyponine D and Karakoline) Triterpenoid Derivative (Rotundifolioside I, Globostellatic Acid B), Polyketide derivative(Inflexin, Tylosin, Michaolide E, and Rotiorinol A), Indole derivative (Tryprostatin A, Paxilline and Ancistrotanzanine B), Acetylsalicylic acid (Martefragegin A), Diterpenoid derivative( Kansuiphorin B and Nicotianoside I), Monoterpenoid derivative (lactinolide and Lactiflorin), Indole derivative (Abacopterin A), Amino acid derivative (Valine), Siderophore (ferrirubin), Polyamine derivative (Caldopentamine(4+)) and phosphate derivative (5-(methylsulfanyl)-2,3-dioxopentyl phosphate) (Table S2 and S3).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Linking metabolic signatures to plant pathological outcomes\u003c/h2\u003e\u003cp\u003eCorrelation analysis between metabolite profiles and disease index (DI) revealed a distinct pattern of metabolic response across plant health states, with metabolites showing a significant association with DI. Notably, metabolites which positively correlated with the Disease Index were represented on the right side of Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA. Paxilline, Ferrirubin, Hyponine D, Caldopentamine (4+) strongly positively correlated, especially associated with ED and PD stages. On the other hand, Tyrosin, Abacopterin A, Lucensimycin E, Tryprostatin A negatively correlated with PH and EH. Furthermore, Partial Least Squares Discriminant Analysis (PLS-DA) was conducted to evaluate whether the disease index (DI) influenced the enriched metabolite profiles across different plant groups. The resulting score plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB) revealed distinct groupings, particularly for the EH and ED treatments, which showed tight and well-separated clusters. The PH group also formed a distinguishable cluster, although with some internal spread, while the PD group partially overlapped with PH and EH but still retained a recognizable boundary. Similarly, Partial Least Squares (PLS) Regression analysis was also employed to further investigate the relationship between metabolite composition and DI (Figure S11). A positive linear relationship was observed between DI and PLS Component 1 (metabolite scores). The regression model yielded an equation of y\u0026thinsp;=\u0026thinsp;0.02x\u0026thinsp;\u0026minus;\u0026thinsp;0.90 with an R\u0026sup2; of 0.27, showing that approximately 27% of the variance in the metabolite score could be explained by DI.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.7 Linking metabolomic signatures to microbial composition and functional potential\u003c/h2\u003e\u003cp\u003eSpearman correlation revealed that enriched root exudate metabolic profiles in the rhizosphere are linked to dominant microbial community structure. Among the strongest positive correlations, Abacopterin A exhibited a significant association with taxa from B\u003cem\u003eradyrhizobium\u003c/em\u003e and Candidatus. Lactiflorin, Karakoline and Caldopentamine 4\u0026thinsp;+\u0026thinsp;exhibited a positive correlation with \u003cem\u003eBurkholderia\u003c/em\u003e spp While Acrophiarin, alongside Tylosin and Ferrirubin were positively correlated with taxa from \u003cem\u003eComamonas\u003c/em\u003e and \u003cem\u003eBacillus\u003c/em\u003e, respectively. In contrast, metabolites such as Valine and Abacopterin A displayed strong negative interactions with \u003cem\u003eComamonas, Chryseobacterium and Azospirilliums\u003c/em\u003e spp. Interestingly, several species from \u003cem\u003eChryseobacterium\u003c/em\u003e and \u003cem\u003eAzospirillum\u003c/em\u003e, often associated with plant stress responses, were positively correlated with a cluster of metabolites, including tylosin, jaconine, and Acalyphin, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Furthermore, for the fungal community, \u003cem\u003eTrichoderma, Talaromyces\u003c/em\u003e, and \u003cem\u003eMortierella\u003c/em\u003e spp showed positive associations with metabolites such as Abacopterin A, Dihydrocapsaicin, Acrophiarin, and Lucensimycin E. Also, \u003cem\u003eHygrocybe\u003c/em\u003e and \u003cem\u003eApiotrichum\u003c/em\u003e spp displayed strong negative correlations with metabolites such as 5-(methylsulfanyl)-2,3-dioxopentyl phosphate, Hyponine D, Inflexin, Ferrirubin and Scutebarbatine G. Neocosmospora and Umbelopsis displayed moderate to high correlation with Acrophiarin and Kansuiphorin B (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eFurthermore, our results also showed a distinct link between enriched metabolites and microbial functional potentials associated with plant diseases, plant health, disease or health signal molecules, or the utilisation of microbes as predicted by FUNGuild and FAPROTAX. We performed a targeted Spearman correlation analysis using a curated subset of FAPROTAX-derived bacterial functions (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The analysis revealed distinct metabolite-function relationships. Notably, Acalychin displayed a strong positive correlation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) with plant_pathogen function, while Abacopterin A, Globostellatic Acid B showed a positive correlation with intracellular_parasites functions. Similarly, we performed a targeted Spearman correlation analysis using a curated subset of FUNGuild-derived fungal functions (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Our result showed that certain metabolites like Karakoline, Kansuiphorin B, and Acrophiarin were positively correlated with Plant.Pathogen and Pathotroph guilds. Also, Inflexin positively correlated with Plant.Parasite. On the other hand, Mycorrhizal and Endophytes were positively associated with metabolites including Rotiorinol A, Globostellatic Acid B, and Michaolide E.\u003c/p\u003e\u003c/div\u003e"},{"header":"4.0 Discussion","content":"\u003cp\u003eOur results suggest a distinct shift in β diversity measure of the rhizosphere microbial communities to disease caused by RS, highlighting the impact of health status on both bacterial and fungal assemblages. Our findings are supported by previous studies conducted on crops such as cotton, tomato, and pepper (Batista et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Yan et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Interestingly, no statistically significant differences (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) were detected in the bacterial communities between RS-infected (ED) and healthy (EH) plants at the vegetative stage. This suggests that, during early plant development, bacterial communities remain relatively consistent regardless of infection status (Xiong et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This pattern aligns with earlier observations indicating that early-stage microbial assembly is often driven more by host genetics, soil properties and environmental conditions than by host physiology (Zhalnina et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, in contrast to the bacterial community, significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were detected in the fungal communities between ED and EH. This may reflect rapid pathogen-induced suppression or recruitment of specific fungal taxa that reduce overall community variability (Gao et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Notably, fungal community structure did not significantly differ between RS-infected plants at the vegetative (ED) and tuber bulking (PD) stages. This is in contrast with previous reports that emphasized the combined effects of plant development and disease on fungal assemblages (Gao et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Tao et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, the lack of statistical significance may mask more nuanced shifts within the community. Broader analysis suggests that fungal populations do undergo notable restructuring between developmental stages, potentially driven by host developmental signals and infection-related stress (Agler et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Gao et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOur analyses further illustrate the strong influence of plant health status on microbial community composition across developmental stages, with a notably biomarker-rich profile observed in healthy plants, especially during the tuber bulking phase. This finding aligns with previous reports in crops such as pepper and tomato (Yan et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Specifically, our data showed that \u003cem\u003ePaenibacillus\u003c/em\u003e spp. had a high relative abundance in healthy plants at the vegetative stage (EH), while \u003cem\u003eBradyrhizobium\u003c/em\u003e spp and Candidatus taxa, as well as the fungus \u003cem\u003eTerramyces\u003c/em\u003e spp were significantly enhanced at the tuber bulking stage (PH) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B). These patterns support the idea that the recruitment of beneficial microbes is shaped by a synchronization between plant secretory activity and microbial metabolic needs, which may vary across developmental stages (Trivedi et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Xiong et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Emerging evidence suggests that microbial communities with higher diversity tend to confer greater resistance to disease, likely due to intensified competition for resources and ecological niches (Yan et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Plants are believed to engage in a strategy often described as \u0026ldquo;crying for help,\u0026rdquo; whereby they selectively recruit beneficial microbes from the surrounding soil to assist in combating pathogen invasion (Liu et al., 2021b, Wen et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Several studies have highlighted the critical roles of these beneficial microbes in promoting plant growth and enhancing resistance to pathogens (Yan et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Wen et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For instance, \u003cem\u003ePaenibacillus\u003c/em\u003e spp and \u003cem\u003eStenotrophomonas rhizophila\u003c/em\u003e were reported to induce systemic resistance in host plants by priming defense-related gene expression, thereby enhancing immunity without causing stress-related damage (Lal et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Similarly, \u003cem\u003eBradyrhizobium\u003c/em\u003e spp. have been reported to suppress soil-borne pathogens through the activation of defense signaling pathways, such as salicylic acid-mediated responses and the production of antimicrobial compounds (Meena et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn contrast, \u003cem\u003eBurkholderia\u003c/em\u003e spp. and the fungal \u003cem\u003eApiotrichum\u003c/em\u003e spp. were predominantly increased in relative abundance in the rhizosphere of diseased plants. The Burkholderia spp are commonly found in diseased plant rhizospheres due to thier production of beneficial metabolites and this include antimicrobial compounds that can inhibit the growth of pathogens, chitinases that break down fungal cell walls, and siderophores that scavenge iron, an essential nutrient for microbial growth (Elshafie and Camele, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These features make it a promising candidate for biocontrol in pathogen-challenged soils (Magalh\u0026atilde;es et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Similarly, the occurrence of \u003cem\u003eApiotrichum\u003c/em\u003e spp in diseased plant rhizospheres may reflect its opportunistic nature and its capacity to utilize stress-induced root exudates as a carbon source (James et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Our findings suggest that microbial community composition is more strongly influenced by plant health status than by developmental stage, reinforcing previous observations made in tomato systems (Adedayo et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This highlights the critical role of plant-pathogen interactions in shaping rhizosphere microbiomes, particularly considering the significant shifts in microbial recruitment dynamics under disease stress observed in this study.\u003c/p\u003e\u003cp\u003eFurthermore, we observed distinct bacterial functional patterns based on FAPROTAX and FUNGUILD in ED and PD that reflect the functional microbiome\u0026rsquo;s response to RS infection, potentially pointing to microbial functions involved in defense, competition, or pathogen facilitation. We observed an enhancement in relative abundance of some potential functional microbiota in the diseased plants as compared to the healthy plants. RS-Infected treatment exhibited a marked increase in relative abundance of potential functional groups linked to nitrogen and energy metabolism, such as denitrification, nitrate reduction, nitrite respiration, and chemoheterotrophs, suggesting enhanced microbial respiration and nutrient turnover under diseased conditions (Louca et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These functions may reflect microbial adaptation to hypoxic conditions or increased organic matter fluxes in the rhizosphere driven by disease-induced root damage (Luo et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Moreover, elevated representation of potential xenobiotic degradation pathways (e.g., aromatic compound and hydrocarbon degradation) in ED and PD indicates that the diseased rhizosphere favoured stress-tolerant microbial consortia with detoxification capabilities, potentially part of a microbial response to infection-induced chemical shifts or oxidative stress (Guo et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Conversely, the healthy plants (EH and PH) exhibited overall lower relative abundances of functional microbiota, particularly those associated with pathogenicity and parasitism, including pathogens and intracellular parasites. This suggests a more stable microbial environment, likely reflecting effective host regulation and ecological balance (Tkacz et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe fungal functional patterns followed a slightly different path, where both EH and ED (early vegetative stages) showed similar relative abundance in saprotrophic and symbiotic guilds, such as wood Saprotroph, mycorrhizal, and Lichenized fungi, indicating a relatively conserved fungal baseline during early development, regardless of infection status (Carteron et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Yang et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This shared relative abundance likely supports early-stage root colonization, organic matter degradation, and nutrient mobilization, consistent with early mycobiome establishment (Guo et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In contrast, PD was functionally distinct, with increasing relative abundance of pathogenic and parasitic traits (Brown, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This shift implies that fungi may actively contribute to disease progression or exploit weakened host defenses at advanced growth stages (Garc\u0026iacute;a-Guzm\u0026aacute;n and Heil, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Meanwhile, PH showed increased abundance of leaf saprotrophs, endophytes, and soil saprotrophs. These guilds are often linked to host/ecosystem function stability, nutrient turnover, and beneficial host associations, reinforcing the idea that mature, healthy plants selectively recruit functional fungi that enhance resilience (Albornoz et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Fadiji et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Our results suggest that bacterial and fungal communities not only shift taxonomically but potentially also functionally in response to disease stress. Infected plants harbor microbiomes enriched in functions associated with stress response, while healthy plants tend to support microbial communities with more balanced and mutualistic ecological roles.\u003c/p\u003e\u003cp\u003eOur Metabolomics result showed dominance of specific enriched root exudate metabolites in the diseased plants (Wen et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). We also observed distinct associations between enriched metabolites and disease index scores across different potato health stages, providing novel correlative evidence for a role of metabolites in disease outcomes (Wen et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Notably, some metabolites exhibited positive correlations with the disease index, indicating progressive metabolic shifts associated with disease development and thus suggesting a potential role in the manifestation or signalling of disease stress as well as predicting plant health (Salam et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Notably, several classes of root exudate metabolites, including alkaloids, polyketides, terpenoids, indole derivatives, and amino acid-related compounds, were differentially expressed, supporting that root exudation is a dominant and dynamic response mechanism in plants undergoing disease stress (Xing et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Yan et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Although the primary goal of our study was to examine how plant health status influences root exudate profiles, it is important to acknowledge that a key limitation of this metabolomic analysis is that it cannot conclusively determine that all detected metabolites originate exclusively from root exudation. The identified metabolites likely represent a mixture of root-derived compounds and root microbial metabolic products. However, among the key enriched metabolites in the diseased plants were alkaloid derivatives such as hyponine D and karakoline, compounds known for their antimicrobial and allelopathic properties (Friedman, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Alkaloids are commonly secreted by plant as a part of chemical defense, suggesting a targeted strategy to suppress pathogen proliferation or interfere with microbial signaling in the rhizosphere (Erb and Kliebenstein, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Similarly, we observed the upregulation of triterpenoid derivatives like rotundifolioside I and globostellatic acid B which are associated with membrane disruption in microbes and may contribute to pathogen resistance by fortifying the rhizosphere against RS invasion (Akbar et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The enhancement of various polyketide derivatives (inflexin, tylosin, michaolide E, rotiorinol A) further emphasizes the plant attempts to modulate microbial dynamics. Polyketides are structurally diverse secondary metabolites with potent antibacterial and antifungal properties, often involved in microbial community structuring (Bills and Gloer, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Their accumulation under RS stress plant indicates an active chemical modulation of the microbiome, potentially recruiting beneficial or excluding antagonistic microbes (Berendsen et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAdditionally, the enrichment of indole derivatives such as tryprostatin A, paxilline, abacopterin A, and ancistrotanzanine B supports the importance of tryptophan-derived signaling compounds in shaping rhizosphere interactions. Indole compounds are well-known mediators of plant\u0026ndash;microbe communication, and their increased presence may signal microbiome restructuring efforts or attempts to activate systemic resistance pathways (Mhlongo et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Also, the detection of acetylsalicylic acid derivatives (e.g., martefragegin A) points toward the involvement of salicylic acid (SA)-mediated defense responses, a well-known plant immunity response against biotrophic pathogens (Roychowdhury et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Meanwhile, diterpenoid and monoterpenoid derivatives (kansuiphorin B, nicotianoside I, lactinolide, lactiflorin) are consistent with compounds involved in stress signaling, defensive volatiles, and antioxidant activities, suggesting a complex chemical ecology underlying disease adaptation and resilience (Kutty and Mishra, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Particularly intersting is the detection of ferrirubin, a siderophore with strong iron-chelating activity, and caldopentamine (4+), a polyamine derivative was downregulated. This agrees with an earlier study on root metabolites in potato (Xing et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Although these molecules are known for nutrient competition and redox modulation in the rhizosphere, they may also suppress RS by limiting iron availability or stabilizing oxidative stress responses (Aznar and Dellagi, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFurthermore, we observed significant microbial-metabolite interactions, supporting the hypothesis that specific microbial taxa likely play targeted roles in shaping the chemical landscape of the plant environment. The observed positive correlations between species for beneficial genera (e.g., \u003cem\u003eBradyrhizobium, Burkholderia\u003c/em\u003e) and secondary metabolites such as Dihydrocapsaicin, iso-precytochalasin, and Lucensimycin E suggest microbial enhancement of host defense chemistry (Hacquard and Martin, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This aligns with prior reports where plant-associated \u003cem\u003eBradyrhizobium\u003c/em\u003e spp was linked with systemic resistance induction and metabolite upregulation (Huang et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Greetatorn et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Conversely, negative associations between species from opportunistic genera (\u003cem\u003eChryseobacterium\u003c/em\u003e, and \u003cem\u003eComamonas\u003c/em\u003e) and enriched metabolites may indicate competitive or degradative roles, potentially affecting the bioavailability of signaling compounds in the rhizosphere; such depletion patterns could impair host chemical defenses or alter plant-microbe feedback loops (Zhalnina et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Importantly, the positive correlations of Candidatus taxa with some metabolites suggest that these uncultured or lesser-studied taxa may hold some functional importance in metabolite mediation, deserving further functional validation through metatranscriptomic or metabolomic approaches. On the other hand, species fungal genera such as \u003cem\u003eTrichoderma\u003c/em\u003e and \u003cem\u003eMortierella\u003c/em\u003e are well-documented for promoting plant growth and defense through both hormonal modulation and secondary metabolite induction (Harman et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2004\u003c/span\u003e, Ozimek and Hanaka, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Their positive correlations with antimicrobial compounds like Lucensimycin E and Acrophiarin support the hypothesis that beneficial fungi may trigger or tolerate plant defensive metabolism as part of their beneficial colonization strategies (Yan et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In contrast, negative associations observed for \u003cem\u003eHygrocybe\u003c/em\u003e and \u003cem\u003eApiotrichum\u003c/em\u003e spp may indicate metabolite-sensitive taxa, potentially suppressed in chemically active niches, or functioning as neutral endophytes/pathobionts that avoid metabolite-rich zones (Li et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Morales-Vargas et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These interactions support the notion that metabolite-mediated signaling and sensing processes shape microbial community response, with implications for developing biocontrol and bioinoculant strategies in agriculture (Zhalnina et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Gupta et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It also highlights the dynamic metabolic crosstalk occurring at the root-microbe interface, where microbial metabolism may actively shape or be shaped by the plant biochemical landscape (Haldar and Sengupta, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo gain deeper insight into the ecological significance of changes in root exudate profiles, we examined how enriched metabolites are linked to microbial functional potentials relevant to plant health and disease. Using the FAPROTAX database to predict bacterial functions, we found that Acalyphin showed a strong positive correlation with the plant_pathogen function, implying that it may contribute to disease development by either supporting pathogen growth or acting as a chemical signal for microbial recruitment (Zhang et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Similarly, Abacopterin A and Globostellatic Acid B were positively linked to the intracellular_parasites function, pointing to their potential role in facilitating or signaling intracellular bacterial colonization, in line with previous observations from pathobiome studies (Mannaa and Seo, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the case of fungi, functional predictions via FUNGuild revealed that metabolites such as Karakoline, Kansuiphorin B, and Acrophiarin were associated with the pathotroph and plant.pathogen guilds. This suggests that these compounds may be involved in fungal virulence or are produced as part of the plant stress response to pathogenic fungi (Stępień and Lalak-Kańczugowska, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Inflexin also showed a positive relationship with the plant.parasite guild, suggesting its potential role in disease-related metabolic activity. On the other hand, some metabolites were more aligned with mutualistic interactions. For instance, Rotiorinol A, Acrophiarin and Michaolide E were positively correlated with mycorrhizal and endophyte guilds, suggesting they may facilitate the establishment or maintenance of beneficial fungi that enhance nutrient uptake or help suppress pathogens (Fadiji and Babalola, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Omomowo et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Our study, therefore, reveals that the rhizosphere metabolite landscape plays a critical role in structuring the microbial functional composition during disease progression. Such metabolite-function associations offer potential for developing biochemical markers to track disease states and identify functional microbiome shifts from symbiosis to pathogenesis. However, we only provide correlative evidence in this study, and future manipulative experiments with individual metabolites are needed to assign exact functions of these metabolites in disease and microbiome assembly.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eTaken together, our findings demonstrate that microbial community composition responds to both plant health status and developmental stage, while functional potentials and root exudate metabolomes respond more to disease stress. Our integrative analysis reveals that root exudate metabolic profiles and microbial functional potentials are likely intertwined in shaping plant health status under disease stress. Through parallel metabolomic and microbial community assessments, we identify specific root exudate metabolite signatures that strongly correlate with disease index variability in potato, highlighting potential key biochemical determinants of resistance and susceptibility. Beyond acting as passive indicators, enriched root exudate metabolites exhibited targeted associations with microbial groups known to influence host immunity, nutrient acquisition, and stress adaptation. Our results support the hypothesis that disease stress alters the recruitment patterns of beneficial microbial taxa and microbial functional traits. These findings advance our fundamental understanding of signaling and sensing, where plant-derived compounds and microbial responses may co-define plant health status (disease outcomes). These insights provide a new platform for designing metabolite-informed microbial interventions, advancing both sustainable crop management and early disease detection strategies in agricultural systems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to acknowledge the Mass Spectrometry Facility (MSF) of Western Sydney University for access to its instrumentation and staff. AEF is grateful to Western Sydney University for the Early Career Award.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAEF and BKS designed the study, collected all data, conducted the analyses and wrote the manuscript in close consultation with BKS. Both authors approved the final version of the manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePotato disease works is funded by CRC Future Food Systems, Horticulture Innovation Australia to BKS and Western Sydney Early Career Gran to AF.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets presented in this study has been deposited in National Centre for Biotechnology Information SRA with data identification number: PRJNA1287307 (www.ncbi.nlm.nih.gov/bioproject/PRJNA1287307) and PRJNA1287285\u0026nbsp;(www.ncbi.nlm.nih.gov/bioproject/PRJNA1287285).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbaba G (2024) Pathogenic diversity, ecology, epidemiology, and management practices of the potato bacterial wilt (Ralstonia solanacearum) disease. Cogent Food Agric 10:2407953\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdedayo AA, Fadiji AE, Babalola OO (2022) The effects of plant health status on the community structure and metabolic pathways of rhizosphere microbial communities associated with \u003cem\u003eSolanum lycopersicum\u003c/em\u003e. Horticulturae 8:404\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAgler MT, Ruhe J, Kroll S, Morhenn C, Kim S-T, Weigel D, Kemen EM (2016) Microbial hub taxa link host and abiotic factors to plant microbiome variation. PLoS Biol 14:e1002352\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAkbar R, Sun J, Bo Y, Khattak WA, Khan AA, Jin C, Zeb U, Ullah N, Abbas A, Liu W (2024) Understanding the influence of secondary metabolites in plant invasion strategies: A comprehensive review. Plants 13:3162\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAksoy E, Demirel U, Bakhsh A, Zia MAB, Naeem M, Saeed F, \u0026Ccedil;alışkan S, \u0026Ccedil;alışkan ME (2021) Recent advances in potato (\u003cem\u003eSolanum tuberosum\u003c/em\u003e L.) breeding. In Al khyari J.M, Jain S.M and Johnson D.V (Eds) Advances in plant breeding strategies: Vegetable Crops: Volume 8: Bulbs, Roots and Tubers. Springer Cham, pp 409\u0026thinsp;\u0026ndash;\u0026thinsp;408. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-030-66965-2\u003c/span\u003e\u003cspan address=\"10.1007/978-3-030-66965-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlbornoz FE, Prober SM, Ryan MH, Standish RJ (2022) Ecological interactions among microbial functional guilds in the plant-soil system and implications for ecosystem function. Plant Soil 476:301\u0026ndash;313\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAznar A, Dellagi A (2015) New insights into the role of siderophores as triggers of plant immunity: what can we learn from animals? J Exp Bot 66:3001\u0026ndash;3010\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBatista BD, Wang J, Liu H, Kaur S, Macdonald CA, Qiu Z, Trivedi P, Delgado-Baquerizo M, Xiong C, Liang J (2024) Biotic and abiotic responses to soilborne pathogens and environmental predictors of soil health. \u003cem\u003eSoil Biology and Biochemistry\u003c/em\u003e189, 109246\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBerendsen RL, Pieterse CM, Bakker PA (2012) The rhizosphere microbiome and plant health. Trends Plant Sci 17:478\u0026ndash;486\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBills GF, Gloer JB (2016) Biologically active secondary metabolites from the fungi. Microbiol Spectr 4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1128/microbiolspec. funk-0009-2016\u003c/span\u003e\u003cspan address=\"10.1128/microbiolspec. funk-0009-2016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBirch PR, Bryan G, Fenton B, Gilroy EM, Hein I, Jones JT, Prashar A, Taylor MA, Torrance L, Toth IK (2012) Crops that feed the world 8: potato: are the trends of increased global production sustainable? Food Secur 4:477\u0026ndash;508\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBodenhausen N, Horton MW, Bergelson J (2013) Bacterial communities associated with the leaves and the roots of \u003cem\u003eArabidopsis thaliana\u003c/em\u003e. PLoS ONE 8:e56329\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrown AJ (2023) Fungal resilience and host\u0026ndash;pathogen interactions: Future perspectives and opportunities. Parasite Immunol 45:e12946\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCarteron A, Beigas M, Joly S, Turner BL, Lalibert\u0026eacute; E (2021) Temperate forests dominated by arbuscular or ectomycorrhizal fungi are characterized by strong shifts from saprotrophic to mycorrhizal fungi with increasing soil depth. Microb Ecol 82:377\u0026ndash;390\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChang X, Wei D, Zeng Y, Zhao X, Hu Y, Wu X, Song C, Gong G, Chen H, Yang C (2022) Maize-soybean relay strip intercropping reshapes the rhizosphere bacterial community and recruits beneficial bacteria to suppress Fusarium root rot of soybean. Front Microbiol 13:1009689\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDelgado-Baquerizo M, Guerra CA, Cano-D\u0026iacute;az C, Egidi E, Wang J-T, Eisenhauer N, Singh BK, Maestre FT (2020) The proportion of soil-borne pathogens increases with warming at the global scale. Nat Clim Change 10:550\u0026ndash;554\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDevaux A, Goffart J-P, Kromann P, Andrade-Piedra J, Polar V, Hareau G (2021) The potato of the future: opportunities and challenges in sustainable agri-food systems. Potato Res 64:681\u0026ndash;720\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEdgar RC, Haas BJ, Clemente JC, Quince C, Knight R (2011) UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27:2194\u0026ndash;2200\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEisfeld C, Schijven JF, Kastelein P, van Breukelen BM, Medema G, Velstra J, Teunis PF, van der Wolf JM (2022) Dose-response relationship of Ralstonia solanacearum and potato in greenhouse and in vitro experiments. Front Plant Sci 13:1074192\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eElshafie HS, Camele I (2021) An overview of metabolic activity, beneficial and pathogenic aspects of Burkholderia spp. Metabolites 11:321\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eErb M, Kliebenstein DJ (2020) Plant secondary metabolites as defenses, regulators, and primary metabolites: the blurred functional trichotomy. Plant Physiol 184:39\u0026ndash;52\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFadiji AE, Babalola OO (2020) Elucidating mechanisms of endophytes used in plant protection and other bioactivities with multifunctional prospects. Front Bioeng Biotechnol 8:467\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFadiji AE, Yadav AN, Santoyo G, Babalola OO (2023) Understanding the plant-microbe interactions in environments exposed to abiotic stresses: An overview. Microbiol Res 271:127368\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFriedman M (2006) Potato glycoalkaloids and metabolites: roles in the plant and in the diet. J Agric Food Chem 54:8655\u0026ndash;8681\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGao C, Montoya L, Xu L, Madera M, Hollingsworth J, Purdom E, Singan V, Vogel J, Hutmacher RB, Dahlberg JA (2020) Fungal community assembly in drought-stressed sorghum shows stochasticity, selection, and universal ecological dynamics. Nat Commun 11:1\u0026ndash;14\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGao M, Xiong C, Gao C, Tsui CK, Wang M-M, Zhou X, Zhang A-M, Cai L (2021) Disease-induced changes in plant microbiome assembly and functional adaptation. Microbiome 9:1\u0026ndash;18\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarc\u0026iacute;a-Guzm\u0026aacute;n G, Heil M (2014) Life histories of hosts and pathogens predict patterns in tropical fungal plant diseases. New Phytol 201:1106\u0026ndash;1120\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGreetatorn T, Boonchuen P, Piromyou P, Songwattana P, Wongdee J, Teamtisong K, Boonkerd N, Sato S, Teaumroong N, Tittabutr P (2025) Differential responses of Bradyrhizobium sp. SUTN9-2 to plant extracts and implications for endophytic interactions within different host plants. Sci Rep 15:3154\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGu Y, Liang W, Li Z, Liu S, Liang S, Lei P, Wang R, Gao N, Li S, Xu Z (2023) The biocontrol agent \u003cem\u003eBacillus velezensis\u003c/em\u003e T-5 changes the soil bacterial community composition by affecting the tomato root exudate profile. Plant Soil 490:669\u0026ndash;680\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuo J, Ning H, Li Y, Xu Q, Shen Q, Ling N, Guo S (2024) Assemblages of rhizospheric and root endospheric mycobiota and their ecological associations with functional traits of rice. MBio 15:e02733\u0026ndash;e02723\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuo Y, Luo H, Wang L, Xu M, Wan Y, Chou M, Shi P, Wei G (2021) Multifunctionality and microbial communities in agricultural soils regulate the dynamics of a soil-borne pathogen. Plant Soil 461:309\u0026ndash;322\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGupta R, Bar MJTHPI, Applications (2020) Plant immunity, priming, and systemic resistance as mechanisms for Trichoderma spp. biocontrol. 81\u0026ndash;110\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGutarra L, Herrera J, Fernandez E, Kreuze J, Lindqvist-Kreuze H (2017) Diversity, pathogenicity, and current occurrence of bacterial wilt bacterium \u003cem\u003eRalstonia solanacearum\u003c/em\u003e in Peru. Front Plant Sci 8:1221\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHacquard S, Martin FM (2024) The chemical language of plant\u0026ndash;microbe\u0026ndash;microbe associations: An introduction to a virtual issue. New Phytologist,\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHaichar FeZ, Marol C, Berge O, Rangel-Castro JI, Prosser JI, Balesdent J, Heulin T, Achouak W (2008) Plant host habitat and root exudates shape soil bacterial community structure. ISME J 2:1221\u0026ndash;1230\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHaldar S, Sengupta S (2015) Plant-microbe cross-talk in the rhizosphere: insight and biotechnological potential. Open Microbiol J 9:1\u0026ndash;7\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHarman GE, Howell CR, Viterbo A, Chet I, Lorito M (2004) Trichoderma species\u0026mdash;opportunistic, avirulent plant symbionts. Nat Rev Microbiol 2:43\u0026ndash;56\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang X, Zeng Z, Chen Z, Tong X, Jiang J, He C, Xiang T (2022) Deciphering the potential of a plant growth promoting endophyte Rhizobium sp. WYJ-E13, and functional annotation of the genes involved in the metabolic pathway. Front Microbiol 13:1035167\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuo C, Lu J, Yin L, Wang P, Cheng WJP, Soil (2024) Coupled of carbon and nitrogen mineralization in rhizosphere soils along a temperate forest altitudinal gradient. Plant Soil 500:197\u0026ndash;211\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJames SA, Bond CJ, Stanley R, Ravella SR, P\u0026eacute;ter G, Dlauchy D, Roberts IN (2016) Apiotrichum terrigenum sp. nov., a soil-associated yeast found in both the UK and mainland Europe. Int J Syst Evol MicroBiol 66:5046\u0026ndash;5050\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJewell KS, Castronovo S, Wick A, Fal\u0026aring;s P, Joss A, Ternes TA (2016) New insights into the transformation of trimethoprim during biological wastewater treatment. Water Res 88:550\u0026ndash;557\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKaur S, Egidi E, Qiu Z, Macdonald CA, Verma JP, Trivedi P, Wang J, Liu H, Singh BK (2022) Synthetic community improves crop performance and alters rhizosphere microbial communities. J Sustainable Agric Environ Ecol 1:118\u0026ndash;131\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eK\u0026otilde;ljalg U, Larsson KH, Abarenkov K, Nilsson RH, Alexander IJ, Eberhardt U, Erland S, H\u0026oslash;iland K, Kj\u0026oslash;ller R, Larsson E (2005) UNITE: a database providing web-based methods for the molecular identification of ectomycorrhizal fungi. New Phytol 166:1063\u0026ndash;1068\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKutty NN, Mishra M (2023) Dynamic distress calls: volatile info chemicals induce and regulate defense responses during herbivory. Front Plant Sci 14:1135000\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLal S, Chiarini L, Tabacchioni S (2016) New Insights in plant-associated Paenibacillus species: biocontrol and plant growth-promoting activity. In: Islam, M., Rahman, M., Pandey, P., Jha, C., Aeron, A. (eds) Bacilli and Agrobiotechnology. Springer, Cham.pp 237\u0026ndash;239 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-319-44409-3_11\u003c/span\u003e\u003cspan address=\"10.1007/978-3-319-44409-3_11\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi F, Jin Z, Wang Z, Liao Y, Yu L, Li X (2022) Host plant selection imprints structure and assembly of fungal community along the soil-root continuum. Msystems 7:e00361\u0026ndash;e00322\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi J, Fan M, Shangguan Z (2020) Research advances in the main ecological functions of root exudates. Chin Bull Bot 55:788\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi J, Wang C, Liang W, Liu S (2021) Rhizosphere microbiome: The emerging barrier in plant-pathogen interactions. Front Microbiol 12:772420\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu C, Cui Y, Li X, Yao M (2021a) microeco: an R package for data mining in microbial community ecology. FEMS Microbiol Ecol 97:fiaa255\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu H, Li J, Carvalhais LC, Percy CD, Verma P, Schenk J, P. M., Singh BK 2021b. Evidence for the plant recruitment of beneficial microbes to suppress soil-borne pathogens. New Phytol 229, 2873\u0026ndash;2885\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLouca S, Parfrey LW, Doebeli M (2016) Decoupling function and taxonomy in the global ocean microbiome. Science 353:1272\u0026ndash;1277\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLuo L, Zhang J, Ye C, Li S, Duan S, Wang Z, Huang H, Liu Y, Deng W, Mei X (2022) Foliar pathogen infection manipulates soil health through root exudate-modified rhizosphere microbiome. Microbiol Spectr 10:e02418\u0026ndash;e02422\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMagalh\u0026atilde;es VC, de Oliveira Barbosa L, Andrade JP, Soares ACF, de Souza JT, Marbach PAS (2017) Burkholderia isolates from a sand dune leaf litter display biocontrol activity against the bole rot disease of \u003cem\u003eAgave sisalana\u003c/em\u003e. Biol Control 112:41\u0026ndash;48\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMannaa M, Seo Y-S (2021) Plants under the attack of allies: moving towards the plant pathobiome paradigm. Plants 10:125\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMeena RS, Vijayakumar V, Yadav GS, Mitran T (2018) Response and interaction of Bradyrhizobium japonicum and arbuscular mycorrhizal fungi in the soybean rhizosphere. Plant Growth Regul 84:207\u0026ndash;223\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMhlongo MI, Piater LA, Madala NE, Labuschagne N, Dubery IA (2018) The chemistry of plant\u0026ndash;microbe interactions in the rhizosphere and the potential for metabolomics to reveal signaling related to defense priming and induced systemic resistance. Front Plant Sci 9:112\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMitter B, Brader G, Pfaffenbichler N, Sessitsch A (2019) Next generation microbiome applications for crop production\u0026mdash;limitations and the need of knowledge-based solutions. Curr Opin Microbiol 49:59\u0026ndash;65\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMorales-Vargas AT, L\u0026oacute;pez-Ram\u0026iacute;rez V, \u0026Aacute;lvarez-Mej\u0026iacute;a C, V\u0026aacute;zquez-Mart\u0026iacute;nez JJM (2024) Endophytic fungi for crops adaptation to abiotic stresses. 12, 1357\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNguyen NH, Song Z, Bates ST, Branco S, Tedersoo L, Menke J, Schilling JS, Kennedy PG (2016) FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol 20:241\u0026ndash;248\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOmomowo IO, Fadiji AE, Omomowo OI (2018) Assessment of bio-efficacy of \u003cem\u003eGlomus versiforme\u003c/em\u003e and \u003cem\u003eTrichoderma harzianum\u003c/em\u003e in inhibiting powdery mildew disease and enhancing the growth of cowpea. Annals Agricultural Sci 63:9\u0026ndash;17\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOzimek E, Hanaka A (2020) Mortierella species as the plant growth-promoting fungi present in the agricultural soils. Agriculture 11:7\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePang Z, Lu Y, Zhou G, Hui F, Xu L, Viau C, Spigelman AF, MacDonald PE, Wishart DS, Li S (2024) MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation. Nucleic Acids Res 52:W398\u0026ndash;W406\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePanth M, Hassler SC, Baysal-Gurel F (2020) Methods for management of soilborne diseases in crop production. Agriculture 10:16\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQiu Z, Verma JP, Liu H, Wang J, Batista BD, Kaur S, de Araujo Pereira AP, Macdonald CA, Trivedi P, Weaver T (2022) Response of the plant core microbiome to Fusarium oxysporum infection and identification of the pathobiome. Environ Microbiol 24:4652\u0026ndash;4669\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQiu Z, Wang J, Delgado-Baquerizo M, Trivedi P, Egidi E, Chen Y-M, Zhang H, Singh BK (2020) Plant microbiomes: do different preservation approaches and primer sets alter our capacity to assess microbial diversity and community composition? Front Plant Sci 11:993\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQuast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Gl\u0026ouml;ckner FO (2012) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41:D590\u0026ndash;D596\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRen G, Wang G, Ma Y (2021) Research progresses on relationship between plant root exudates and soil-borne diseases. Soil 53:229\u0026ndash;235\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRobert CAM, Himmighofen P, McLaughlin S, Cofer TM, Khan SA, Siffert A, Sasse J (2025) Environmental and Biological Drivers of Root Exudation. Annu Rev Plant Biol 76:317\u0026ndash;339\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRoychowdhury R, Mishra S, Anand G, Dalal D, Gupta R, Kumar A, Gupta R (2024) Decoding the molecular mechanism underlying salicylic acid (SA)-mediated plant immunity: an integrated overview from its biosynthesis to the mode of action. Physiol Plant 176:e14399\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSalam U, Ullah S, Tang Z-H, Elateeq AA, Khan Y, Khan J, Khan A, Ali S (2023) Plant metabolomics: an overview of the role of primary and secondary metabolites against different environmental stress factors. Life 13:706\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSegata N, Waldron L, Ballarini A, Narasimhan V, Jousson O, Huttenhower C (2012) Metagenomic microbial community profiling using unique clade-specific marker genes. Nat Methods 9:811\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSiddique MAB, Fateh FS, Saleem H (2020) Black scurf of potato disease prevalence in the markets of federal capital territory, Pakistan. Pakistan J Agricultural Res 33:440\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSingh A, Delgad-Baquerizo M, Egidi E, Guirado E, Leach JE, Liu H, Trivedi P (2023a) 100253. Climate change impacts on plant pathogens, food secutiry and paths forward. Nat Rev Microbiol 21:640\u0026ndash;656\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSingh BK, Hu H-W, Macdonald CA, Xiong C (2025a) Microbiome-facilitated plant nutrient acquisition. Cell Host Microbe 33:869\u0026ndash;881\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSingh BK, Jiang G, Wei Z, S\u0026aacute;ez-Sandino T, Gao M, Liu H, Xiong C (2025b) Plant pathogens, microbiomes, and soil health. Trends Microbiol. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tim.2025.03.013\u003c/span\u003e\u003cspan address=\"10.1016/j.tim.2025.03.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSingh BK, Yan Z-Z, Whittaker M, Vargas R, Abdelfattah A (2023b) Soil microbiomes must be explicitly included in One Health policy. Nat Microbiol 1\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41564-023-01386-y\u003c/span\u003e\u003cspan address=\"10.1038/s41564-023-01386-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStępień Ł, Lalak-Kańczugowska J (2021) Signaling pathways involved in virulence and stress response of plant-pathogenic Fusarium species. Fungal Biology Reviews 35:27\u0026ndash;39\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTahat MM, Sijam K (2010) Ralstoina solanacearum: The bacterial wilt causal agent. Asian J Plant Sci 9:385\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTans-Kersten J, Huang H, Allen C (2001) \u003cem\u003eRalstonia solanacearum\u003c/em\u003e needs motility for invasive virulence on tomato. J Bacteriol 183:3597\u0026ndash;3605\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTao J, Jin J, Lu P, Yu S, Gu M, Wang J, Zhang J, Cao P (2025) Bacterial wilt disease alters the structure and function of fungal communities around plant roots. BMC Plant Biolog 25:39\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTkacz A, Bestion E, Bo Z, Hortala M, Poole PS (2020) Influence of plant fraction, soil, and plant species on microbiota: a multikingdom comparison. MBio 11:e02785\u0026ndash;e02719\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTrivedi P, Leach JE, Tringe SG, Sa T, Singh BK (2020) Plant\u0026ndash;microbiome interactions: from community assembly to plant health. Nat Rev Microbiol 18:607\u0026ndash;621\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWeinberger NV, Cibils-Stewart X, Brien C, Jewell N, Berger B, Cavagnaro TR, Salomon MJ, Mikhael M, Plett JM, Plett KL (2025) Plant phenotyping and root‐associated metabolomics reveal insights into pathogen protection by diverse arbuscular mycorrhizal fungi. \u003cem\u003ePlants, People, Planet\u003c/em\u003e 2025, 1\u0026ndash;15\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWen T, Xie P, Liu H, Liu T, Zhao M, Yang S, Niu G, Hale L, Singh BK, Kowalchuk GA (2023) Tapping the rhizosphere metabolites for the prebiotic control of soil-borne bacterial wilt disease. Nat Commun 14:4497\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWen T, Zhao M, Liu T, Huang Q, Yuan J, Shen Q (2020) High abundance of Ralstonia solanacearum changed tomato rhizosphere microbiome and metabolome. BMC Plant Biol 20:1\u0026ndash;11\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXing Y, Zhang P, Zhang W, Yu C, Luo Z (2024) Continuous cropping of potato changed the metabolic pathway of root exudates to drive rhizosphere microflora. Front Microbiol 14:1318586\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXiong C, Singh BK, He J-Z, Han Y-L, Li P-P, Wan L-H, Meng G-Z, Liu S-Y, Wang J-T, Wu C-F (2021) Plant developmental stage drives the differentiation in ecological role of the maize microbiome. Microbiome 9:1\u0026ndash;15\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYan L, Zhu J, Zhao X, Shi J, Jiang C, Shao D (2019) Beneficial effects of endophytic fungi colonization on plants. Appl Microbiol Biotechnol 103:3327\u0026ndash;3340\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYan M, Wu M, Liu M, Li G, Liu K, Qiu C, Bao Y, Li Z (2024) Comparative analysis on root exudate and rhizosphere soil bacterial assembly between tomatoes and peppers infected by Ralstonia. Chem Biol Technol Agric 11:36\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang C-X, Chen S-J, Hong X-Y, Wang L-Z, Wu H-M, Tang Y-Y, Gao Y-Y, Hao G-F (2025) Plant exudates driven microbiome recruitment and assembly facilitates plant health management. FEMS Microbiol Rev, fuaf008\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang N, Zhang Y, Li J, Li X, Ruan H, Bhople P, Keiblinger K, Mao L, Liu D (2022) Interaction among soil nutrients, plant diversity and hypogeal fungal trophic guild modifies root-associated fungal diversity in coniferous forests of Chinese Southern Himalayas. Plant Soil 481:395\u0026ndash;408\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYaqoob S, Bhatti HN, Sultana B, Shahid M (2020) Prognosticating the potential of Sorghum bicolor root exudates in response to abiotic stress. Pakistan J Agricultural Sci 57:1661\u0026ndash;1668\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhalnina K, Louie KB, Hao Z, Mansoori N, da Rocha UN, Shi S, Cho H, Karaoz U, Loqu\u0026eacute; D, Bowen BP (2018) Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly. Nat Microbiol 3:470\u0026ndash;480\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang J, Liu Y-X, Guo X, Qin Y, Garrido-Oter R, Schulze-Lefert P, Bai Y (2021) High-throughput cultivation and identification of bacteria from the plant root microbiota. Nat Protoc 16:988\u0026ndash;1012\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhong Y, Xun W, Wang X, Tian S, Zhang Y, Li D, Zhou Y, Qin Y, Zhang B, Zhao G (2022) Root-secreted bitter triterpene modulates the rhizosphere microbiota to improve plant fitness. Nat Plants 8:887\u0026ndash;896\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Western Sydney University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Biotic stress, metabolites, plant resilience, pathogen, Ralstonia solanacearum","lastPublishedDoi":"10.21203/rs.3.rs-7161758/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7161758/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePlant microorganisms are an essential component of the host and perform critical functions in plant development and health. Emerging evidence shows that plants use their root exudates to recruit beneficial microbes that protect them against abiotic and biotic stresses, including diseases. However, the metabolic responses of plant under pathogen infection remain underexplored. In this study, using a manipulative experiment, we employed amplicon sequencing and untargeted metabolomics to investigate the response of rhizosphere microbial communities and metabolites of root exudates to potato-wilt disease caused by \u003cem\u003eRalstonia solanacearum\u003c/em\u003e (RS) across two developmental stages (vegetative and tuber bulking). Our results revealed that β-diversity showed distinct shifts in bacterial and fungal communities between healthy and diseased plants. Higher relative abundance of bacterial taxa from genera, \u003cem\u003eBradyrhizobium, Cadidatus, Paenibacillus\u003c/em\u003e and the fungal genus \u003cem\u003eTerramyces\u003c/em\u003e were observed in the rhizosphere of healthy plants. Similarly, \u003cem\u003eBurkholderia\u003c/em\u003e spp and the fungal \u003cem\u003eApiotrichum\u003c/em\u003e spp dominated the rhizosphere of diseased plants across the developmental stages. Further compared to healthy plants, microbial functional potentials and metabolomic profiles of root exudates linked to pathogen resistance were significantly enhanced in diseased plants. Particularly, metabolites from alkaloids, triterpenoids and polyketides were enriched in disease plants and exhibited associations with microbial groups known to influence host immunity, nutrient acquisition, and stress adaptation. We observed that variations in disease index were associated with the identified enriched metabolites. Our integrative analysis provides evidence for multifaceted signalling, sensing between plants, pathogens and beneficial microbiota that may shape plant health status and microbiome assembly under pathogen pressure. These insights not only advance our understanding of crop pathophysiology but also lay the foundation for developing targeted biological strategies or metabolic markers for early disease detection and sustainable crop protection.\u003c/p\u003e","manuscriptTitle":"Responses of root microbiome and metabolome are linked to crop disease severity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-22 03:33:01","doi":"10.21203/rs.3.rs-7161758/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9e4eb966-38b3-40a6-9fd1-b6ecbdab077a","owner":[],"postedDate":"July 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":51780004,"name":"Agroecology"}],"tags":[],"updatedAt":"2025-07-22T03:33:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-22 03:33:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7161758","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7161758","identity":"rs-7161758","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}